LAPORAN KEMAJUAN PENELITIAN DOKTOR BARU DANA ITS 2020

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LAPORAN KEMAJUAN

PENELITIAN DOKTOR BARU

DANA ITS 2020

Modified Ensemble Learning untuk Prediksi Eksistensi Infeksi Virus pada

Isolated Dioxyribo Nucleid Acid (DNA)

Tim Peneliti :

Dr. Berlian Al Kindhi (Dept. Teknik Elektro Otomasi/Fakultas Vokasi) Prof. Dr. Ir. Mauridhi Hery Purnomo (Dept. Teknik Komputer/ Fakultas ELEKTIC)

Joko Susila,ST.,MT. (Dept. Teknik Elektro Otomasi/ Fakultas Vokasi)

DIREKTORAT RISET DAN PENGABDIAN KEPADA MASYARAKAT

INSTITUT TEKNOLOGI SEPULUH NOPEMBER

SURABAYA

2020

Sesuai Surat Perjanjian Pelaksanaan Penelitian No: 853/PKS/ITS/2020

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Daftar Isi

Daftar Isi ........................................................................................................................................................... i

Daftar Tabel .................................................................................................................................................... ii

Daftar Gambar ............................................................................................................................................... iii

Daftar Lampiran .............................................................................................................................................. iv

BAB I RINGKASAN .................................................................................................................................... 13

BAB II HASIL PENELITIAN ....................................................................................................................... 14

2.1.TeoriPenunjang.................................................................................................................................14

2.2PetaJalanPenelitian...........................................................................................................................19

2.3.TahapanPenelitian............................................................................................................................22

2.4.Hasilyangtelahdicapai......................................................................................................................24

2.5.DNACOVID-19...................................................................................................................................25

BAB III STATUS LUARAN......................................................................................................................... 27

BAB V KENDALA PELAKSANAAN PENELITIAN ................................................................................ 28

BAB VI RENCANA TAHAPAN SELANJUTNYA .................................................................................... 29

BAB VII DAFTAR PUSTAKA .................................................................................................................... 30

BAB VIII LAMPIRAN.................................................................................................................................... 1

LAMPIRAN 1 Tabel Daftar Luaran ................................................................................................................ 2

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Daftar Tabel

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Daftar Gambar

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Daftar Lampiran

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BAB I RINGKASAN

Penyakit Tropis adalah penyakit yang hanya terjadi pada daerah tropis dan atau ekivalensi dengan peluang kemunculannya lebih besar terjadi pada daerah tropis (Airlangga, 2012). Bakteri pembawa penyakit tersebut mencakup agen infeksi yang multi resistance dan atau transibility (mudah menular). Hepatitis C Virus (HCV) merupakan salah satu jenis penyakit yang peluang penularannya mayoritas di daerah tropis (penyakit tropis). Saat ini belum ada vaksin yang secara mutlak dapat digunakan untuk mencegah Hepatitis C karena virus ini secara genetik amat variatif (subtype genome) dan memiliki angka mutasi tinggi, sehingga memungkinkan generasi virus yang beraneka ragam. Menurut WHO, angka kematian akibat infeksi HCV cukup tinggi, yaitu mencapai 399 ribu jiwa per tahun. Indonesia merupakan salah satu negara yang memiliki jumlah pasien terinfeksi HCV tertinggi di Asia. Penyakit ini sebagian besar menjangkit di daerah tropis namun tidak menutup kemungkinan terdapat carier agent yang mampu menularkan penyakit hingga ke berbagai benua. Pada penelitian ini, diusulkan sebuah perancangan dataset yang sesuai untuk analisa DNA serta usulan metode ensemble learning yang sesuai. Namun sebelum melakukan proses clustering dan prediksi, perlu dilakukan normalisasi dengan semantic similarity. Data sampel yang digunakan adalah 1000 data isolated DNA yang terdiri dari 500 isolated DNA homo sapiens yang positif terinfeksi HCV dan 500 isolated DNA homo sapiens negatif HCV. 1000 data tersebut dihitung jarak kemiripannya menggunakan metode Edit Levensthein Distance. Hasil dari penghitungan Edit Levensthein Distance kemudian dimasukkan ke dalam matriks sebagai variabel. Matriks tersebut adalah input data pada proses prediksi menggunakan Ensenmble learning.

Pada Penelitian ini kami mengusulkan sebuah pendekatan memprediksi adanya suatu virus dalam DNA manusia dengan Machine Learning. Dari hasil penelitian ini, diharapkan mampu memberikan analisa perubahan genetik DNA khususnya pada DNA yang terinfeksi HCV (sebagai data sampel) dan hasilnya dapat dimanfaatkan oleh dunia kedokteran sebagai evaluasi pembuatan vaksin.

Kata kunci: Prediksi Mutasi, DNA, Ensemble Learning, Machine Learning

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Ringkasan penelitian berisi latar belakang penelitian,tujuan dan tahapan metode penelitian, luaran yang ditargetkan, kata kunci

BAB II HASIL PENELITIAN

2.1. Teori Penunjang

Saat ini belum ada vaksin atau penawar yang dapat mengobati infeksi HCV secara mutlak,

walaupun berbagai penelitian telah dan sedang dilakukan untuk mendapatkan vaksin tersebut.

Penelitian tersebut dilakukan baik dari segi biologi kedokteran maupun bioinformatika. Penelitian

ini menggabungkan analisa uji dari dua keilmuan tersebut dengan menggunakan data isolated DNA

dan teknologi machine learning.

Gambar 1. Maturity level di penelitian bidang DNA khususnya pada DNA HCV

Tingkatan penelitian pada bidang DNA HCV atau yang disebut dengan maturity level dapat

diamati pada Gambar 1, dimana penelitian yang dilakukan ini menuju ke arah vaksin (maturity

level 3). Untuk dapat menemukan vaksin beberapa tahap penelitian pendahuluan dilakukan yaitu

dengan melakukan analisa clustering dan prediksi eksitensi HCV dalam suatu isolated DNA,

dalam arti lain, penelitian ini adalah pendahuluan yang menuju ke arah maturity level 3.

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Tabel 3.State of the art dalam lingkup penelitian yang dikembangkan

Topik No Judul Hasil

DNA HCV

1 Sandra Iurecia et. al., “Epitope-driven DNA vaccine design employing immunoinformatics against B-cell lymphoma: A biotech's challenge” ,2011

Menghasilkan model vaksin kanker berbasis epitope DNA yang dapat memungkinkan membangun plasmid dari beberapa epitope imunogenetik

2 Hemiyanti Emmy, “Biologi Molekul Virus”, Pasca Sarjana Universitas Padjajaran, 2012

Penjelasan mengenai pola mutasi dari molecular virus, tempat hidupnya dan pola perkembang biakannya.

3 Grey Rebecca R., et al., “Evolutionary analysis of hepatitis C virus , 2013

Menganalisis dua urutan HCV subgenomic diperoleh dari individu yang terinfeksi di 1953, yang merupakan bukti genetik tertua infeksi HCV. Metodenya adalah dengan memasangkan keragaman genetik antara dua sekuens sehingga menunjukkan substansial periode penularan HCV sebelum tahun 1950-an, dan masuknya virus tersebut dalam evolusi analisis memberikan perkiraan baru dari nenek moyang HCV di Amerika Serikat. Memperkirakan bahwa saat awal mula munculnya HCV subtipe 1b di Amerika Serikat terjadi sekitar tahun 1901 (1874- 1926), yang berarti perkiraan ini konsisten dengan perkiraan sebelumnya. Namun, analisis ini memberikan hasil CI yang tinggi daripada yang dilaporkan sebelumnya untuk subtipe 1b yang menggunakan dua wilayah subgenomik (1905-1965 dan 1806-1959;). Selain itu hasil penelitian ini mencerminkan informasi meningkat diperoleh dari menggunakan seluruh genom urutan referensi dan dari masuknya dua urutan primer yaitu pada tahun 1953 .

4 Takayakagi Toshiaki, “Modeling chronic hepatitis B or C virus infection during antiviral therapy using an analogy to enzyme kinetics:

Model dasar untuk virus hepatitis B kronis (HBV) atau virus Hepatitis C (HCV) selama terapi memungkinkan kita untuk menganalisis kinetika virus jangka pendek. Namun, model ini tidak berguna untuk menganalisis jangka panjang

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Topik No Judul Hasil Long-term viral dynamics without

rebound and oscillation”(2013)

kinetika virus. Oeh karena itu, pada penelitian ini diusulkan model baru yang diperoleh dengan memperkenalkan Michaelis-Menten kinetika ke dalam model dasar. Model baru dapat menunjukkan kinetika virus jangka panjang tanpa Rebound dan osilasi, tidak seperti model dasar. Nilai parameter K dalam model baru analog dengan Michaelis adalah konstan dan diprediksi menjadi kurang dari sekitar 1.010 / ml.

Infrastrukt ur Sistem Pakar DNA Analisis

1 Shabut et.al., “An

intelligent mobile-

enabledd expert system

for tuberculosis disease in

real time” (2018)

Suatu expert system untuk mendiagnosa penyakit tuberculosis dengan melakukan analisa gejala-gejala secara langsung berbasis aplikasi mobile.

DNA Semantic Similarity

1 Fredonnet Julie, “Dynamic PDMS inking for DNA patterning by soft lithography”(2013)

Pencetakan microcontact (LCP) digunakan sebagai teknik pola untuk menghasilkan DNA microarray sederhana, cepat dan biaya-efektif.

2 Mika Göös,et al., “Search methods for tile sets in patterned DNA self- assembly”(2014)

Pattern self Assembly Tile set Synthesis (PATS), yang muncul dalam teori terstruktur DNA self- assembly, adalah untuk menentukan satu set coloured tiles, mulai dari struktur benih berbatasan, hingga merakit diri untuk pola warna persegi panjang yang diberikan. Tugas mencari minimum ukuran tile set dikenal NP-keras. Penelitian mengeksplorasi beberapa teknik pencarian yang lengkap dan tidak lengkap untuk menemukan minimal tile set dan juga menilai keandalan solusi yang diperoleh sesuai dengan Tile kinetik Assembly Model.

3 Fernau Henning, et al.,, “Pattern matching with variables: A multivariate complexity

Dalam DNA pattern matching terdapat banyak parameter masalah antara lain: jumlah variabel, panjang w, panjang kata-kata menggantikan variabel, jumlah kejadian per variabel,

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Topik No Judul Hasil analysis”(2015) kardinalitas alfabet terminal dan untuk semua

kemungkinan kombinasi dari parameter (dan varian yang dijelaskan sebelumnya), penelitian ini menjawab pertanyaan apakah ada masalah atau tidak pada NP-lengkap jika parameter ini dibatasi oleh konstanta. Hasil dari penelitian menunjukkan bahwa pemberian konstanta akan memudahkan analisis DNA namun dengan adanya konstanta juga akan menurunkan tingkat sensitivitas terhadap mutasi.

Pengelom pokan DNA

1 Yilmas Kaya, Murat Uyar, “A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease”(2013)

Mengusulkan diagnosis penyakit hepatitis menggunakan metode Rough Set dan Extreme Learning Machine (RS-ELM) dalam sebuah kumpulan data diagnosa. Hasil penelitian menunjukkan bahwa model RS-ELM 100% telah cukup sukses dibandingkan dengan metode lainnya dalam literatur

2 Boeka veselva,” Clustering approaches for dealing with multiple DNA microarray datasets”(2014)

Menggabungkan empat algoritma clusterring untuk menangani multiple gene expression matrik pada DNA Microarray. Metode cluestering tersebut adalah dua unsupervised technique berbasis integrasi informasi dan dua supervised technique yaitu menggabungkan Particle Swarm Optimization dan k-means. Hasilnya Pendekatan MapReduce Clusterring melebihi tiga algoritma pengelompokan lainnya. Selain itu, versi FCA-ditingkatkan memungkinkan untuk menganalisis lebih lanjut partisi diproduksi dan untuk mengekstrak wawasan biologis yang berharga dari data.

3 Abolfazl Doostparast Torshizi, “A new cluster validity measure based on general type-2 fuzzy sets:

Meneliti pendekatan baru di bidang General Type-2 Fuzzy Sets (GT2 FS) dan aplikasi yang dikembangkan. Pada penelitian ini telah dianalisis ukuran kesamaan berdasarkan jarak yang

Application in gene expression data clustering”(2014)

melebihi pendekatan yang ada dan mencakup sebagian besar kekurangan penelitian sebelumnya. Setelah pengujian pada beberapa dataset buatan

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Topik No Judul Hasil dengan berbagai jumlah outlier, dengan

menggunakan tiga gen nyata ekspresi dataset dan memverivikasi kualitas terhadap sejenis pendekatan baik secara visual dan komputasi. Percobaan ini terbukti akurasi dan presisi dari metode yang telah dikembangkan.

4 Dios Fransisco.,et al., “DNA clustering and genome complexity”(2014)

Mengelompokkan DNA kompleks berdasarkan sepuluh elemen genome manusia.

5 Jamal Ade, et al., “Scalability of DNA Sequence Database on Low-End Cluster using Hadoop(2014)

Skalabilitas data sequence DNA pada world gen bank untuk di akses dan di kelompokkan menggunakan hadoop. Data diambil dari NJBI kemudian di buat sebuah arsitektur jaringan untuk clustering server data

6 Dzung Dinh Nguyen, “Towards hybrid clustering approach to data classification: Multiple kernels based interval-valued Fuzzy C- Means algorithms”(2015)

Kelemahan dari Fuzzy C-Means adalah pengelompokan dapat melibatkan berbagai fitur masukan menunjukkan dampak yang berbeda pada hasil yang diperoleh. Penelitian ini mengusulkan metode baru dari Fuzzy C-Means yaitu, komposit kernel dibangun dengan memetakan setiap fitur masukan ke ruang kernel individu dan linear menggabungkan kernel ini dengan bobot dioptimalkan dari kernel yang sesuai.

Prediksi DNA

1 Wang Hongfei, et.al., “Evaluation of an artificial neural network to ascertain why there is a high incidence of hepatitis B in the Chinese population after vaccination”(2013)

Menerapkan artificial neural network untuk menganalisa kenapa angka infeksi HBV tinggi setelah vaksin. Hasil dari neural network menunjukkan tidak ada hubungannya antara tingginya infeksi dengan vaksin.

2 Sasitorn Plakumonthon, “Computational prediction of hybridization patterns between hepatitisC viral genome and human microRNAs”(2014)

Penelitian ini mengambil beberapa human RNA (MiRNA) untuk dibandingkan dengan beberapa primer dan di prediksi apakah RNA tersebut ada kemungkinan mengidap HCV (Sasitorn Plakunmonthon, Nattanan Panjaworayan T- Thienprasert, Kritsada Khongnomnana, Yong Poovorawanc, Sunchai Payungporna, 2014)

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Topik No Judul Hasil 3 T. Feng,et.al., “A medical

cost estimation with fuzzy neural network of acute hepatitis patients in emergencyroom”(2015)

Menerapkan FNN (Fuzzy Neural Network) untuk memprediksi biaya seorang pasien hepatitis, dengan menggunakan neuron acak yang diambil berdasarkan pasien hepatitis yang ada sebanyak 110. Hasil penelitian ini menunjukkan bahwa akurasi prediksi total biaya yang dibutuhkan oleh pasien mencapai 90%. (T. Feng, T. S. Li , P. Kuo, 2015).

4 Neelam Goel,et.al., “An improved method for splice site prediction in DNA sequences using support vector machines (2015)

Melakukan prediksi pre-messenger-RNA (pre- mRNA), untuk menentukan manakah splicing yang intron (dibuang) dan exon (bergabung) untuk berbagai tujuan ahli. Mengusulkan perbaikan, dengan menggabungkan dua metode yaitu SVM dan Markov Model (Neelam Goel, Shailendra Singh, Trilok Chand Aseri, 2015 ).

2.2 Peta Jalan Penelitian

Pada Tabel 1. Dapat diamati peta jalan riset, kebaruan dan ringkasan hasil riset yang telah

dilakukan sebelumnya sehingga tergambar riset yang diusulkan telah memiliki model/purwarupa

yang telah memenuhi konsep sebagai produk/teknologi/model. Seluruh penelitian yang disebutkan

dalam matriks tabel telah dipublikasikan baik di jurnal internasional, jurnal nasional maupun

seminar internasional. Kolom matriks yang ditandai dengan warna kuning menunjukkan rencana

penelitian kami tiga tahun ke depan yaitu mengenai DNA HCV.

Tabel 4. Peta jalan penelitian pada lingkup penelitian yang sebidang yang pernah dilakukan

Topik

Penelitian

Capaian

sampai 2011

2012 2013 2014 2015 2016 2017 2018 2019 2020 2020-2028

Pattern

matching and

detection

Analisa voice spectrum pada

pasien laryngectomised

dengan atau tanpa electro

larynx

(*terapan dan industri)

Vaksin

Modelling

untuk

Hepatitis C

Virus

(HCV)

Deteksi kondisi abnormal

pada pankreas beta cell

penyebab diabetes

menggunakan citra iris


Identifikasi malaria pada thick blood

film berbasis genetic programming


CT lung image filtering

berbasis max-tree method

(*terapan)

Analisis

metode

pattern

matching

yang sesuai

untuk DNA

(*terapan)

Modifikasi

hamming

untuk DNA

similarity

(*terapan)

Medical

clustering

Implementasi fungsi

Weighted kernel untuk

klasifikasi hyperspectral

image berbasis support

vector machine

(*terapan)

Klasifikasi

Osteoarthritis

classification

menggunakan self

organizing map

berbasis gabor

kernel dan contrast-

Classification pasien diabetes

retinopathy menggunakan

support vector machine (SVM)

berbasis citra retina digital


Clustering DNA

berdasarkan nilai kedekatan

dengan primer HCV.


Clutering

DNA

berbasis

deep

learning

ntuk

menganalis

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limited adaptive

histogram

equalization

(*terapan dan

industri)

a pola HCV

Analisa klasifikasi lokasi

aktifitas dominan pada otak

berbasis K-means

(*terapan)

Klasifikasi cyst dan

tumor lesion using

Support Vector

Machine

berdasarkan citra

dental panoramic

(*terapan dan

industri)

Prediction

modelling

Implementasi

recurrent

neural network

untuk time

series

forecasting

(*terapan)

Rekonstruksi citra 3D rahang

bawah berdasar fitur 2D pada citra

Xray gigi


EEG Signal Identification based

on root mean square and

average power spectrum by

using backPropagation


Hubungan antara

Electromyography Signal Of

Neck Muscle dan sinyal suara

manusia untuk mengontrol

Electrolarynx

(*terapan)

Prediksi

HCV

berbasis

SVM

(*terapan

dan

industri)

Prediksi

eksistensi

HCV pada

isolated

DNA dari

berbagai

negara di

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seluruh

dunia.

Biomedika

Modelling dan

analisis

Analisa

dosis

vaksin

Hepatitis B

Virus

(HBV)

Prevalensi

HBV

dengan

molekular

karakteristi

k pada

wanita

hamil

Mutasi HBV pada Pre-S1

dan Pre S2

HCV dan

HBV pada

transgender

Analisa genetik Hepatitis A

Virus pada siswa tingkat

SMP

Analisa HCV pada pasien

di RSUD dr. Soetomo pada

sisi genotip

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2.3. Tahapan Penelitian

Metode penelitian yang direncanakan terbagi dalam dua kelompok pekerjaan selama satu

tahun, yaitu :

Gambar 2. Rencana kegiatan riset selama satu tahun

Pembangunan tahap pertama akan berfokus pada klasifikasi kecenderungan sequence

tersebut hasil dari pengenalan pola pada pleriminary penelitian. Tahap terakhir adalah

analisa trend mutasi DNA tersebut menggunakan sistem prediksi sebagai acuan terhadap

pemodelan vaksin.

Yangtelah dikerjakan

Tahap

Tahap kedua

Pengumpulandataisolates DNA

Pengumpulandataisolate homosapiens

learningsystemantara DNAhomosapiensdan

HCV

Pengumpulandataprimer Normalisasidatasethomo sapiens K-NNLearningsystem

Normalisasidatabase Pembangunanmetode adaptivefuzzyuntukklasifikasi

MultiLayerPercepton LearningSystem

Pembanggunansistem pengolahDNAdata

Pembangunanmetode assemblyclustering

Integrasihasil pengelompokandan

prediksi

Perancanganinfrastruktur untukmachinelearning

Rekomendasiprimeryang elgible

Pembelajaranpolaberbasis deeplearning

PrediksieksistensiHCV (virus) padaisolatedDNAdariberbagainegaradidunia

AnalisapolaprimerHCV padaDNAmanusianormal

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Pada tahun pertama penelitian, modul utama yang dibangun adalah sebagai berikut:

1. Modul Primer Modul primer berfungsi untuk mengolah data primer, yang nantinya pada proses

pengolahan data berfungsi sebagai pattern yang dibandingkan. Data primer

beserta tahun ditemukannya akan disimpan di dalam database.

2. Modul Isolate Modul Isolate berfungsi untuk mengolah data isolate. Data isolate DNA tidak

tersimpan pada database melainkan disimpan pada file .txt. Hal ini dikarenakan

untuk mengurangi beban kerja database. Ketika sistem akan melakukan

pengenalan pola, modul ini akan memanggil dan membaca file .txt isolate.

Kemudian modul ini akan memecah sequence DNA sepanjang primer yang akan

dibandingkan.

3. Modul Normalisasi Primer Modul normalisasi primer adalah modul yang berfungsi untuk melakukan

normalisasi pada karakter primer. Pada tahap ini, perlu adanya normalisasi pada

primer agar penghitungan pengenalan pola seimbang.

4. Modul Pengenalan pola Modul utama dari seluruh sistem adalah modul hamming, modul ini memiliki

looping kondisi yang berlapis. Proses pengenalan pola ini terdapat dua input yaitu

sequence DNA sebagai data yang dibandingkan dan primer sebagai patter.

5. Modul Treshold Modul terakhir yang dibutuhkan pada penelitian tahap 1 adalah modul treshold.

Modul ini berfungsi untuk menggabungkan hasil penghitungan modul

pengenalan pola dan modul normalisasi primer. Kemudian dari hasil

penghitungan tersebut akan dianalisa sequence mana yang memenuhi nilai

treshold. Hanya sequence yang memenuhi nilai treshold yang akan disimpan di

database, prosedur penyimpanan tersebut tetap harus menyertakan atribut yang

menempel pada sequence tersebut diantaranya adalah kode primer, kode isolate,

index sequence, tahun primer dan isolate, dan sebagainya.

Pada Hepatitis, pada setiap isolate DNA terdiri dari 9000 hingga 15.000

sequence DNA. Data isolate tersebut akan bertambah terus seiring dengan

bertambahnya pasien yang terinfeksi Hepatitis. Selain itu, dalam satu jenis virus

Hepatitis, jumlah sequence primer Hepatitis juga akan terus bertambah seiring dengan

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terjadinya mutasi dari virus tersebut, setiap mutasi akan dikelompokkan ke dalam

subtype genome. Beberapa metode supervise learning akan diujikan pada tahun

pertama penelitian dengan tujuan untuk menjembatani perbedaan primer yang

berdampak pada tidak berhasilnya suatu primer mendeteksi adanya HCV pada isolated

DNA di seluruh dunia. Hasil dari penelitian tahun pertama adalah pengujian metode

pattern matching yang tepat untuk mengolah data DNA. Tahap kedua penelitian ini

adalah melakukan normalisasi data dan proses clustering. Seperti yang telah dijelaskan

pada bab sebelumnya bahwa sebelum melakukan proses clustering terlebih dahulu data

dinormalisasi dan disiapkan sesuai format pada metode clustering.

Tahapan penelitian meliputi:

- Menghitung nilai kedekatan masing-masing data isolate DNA dengan masing-

masing data primer Hepatitis

- Menghitung nilai kedekatan tahun ditemukan isolate DNA dengan masing-masing

data primer Hepatitis.

- Melakukan perbandingan kedekatan masing-masing node (hasil perhitungan pada

proses sebelumnya)

- Melakukan proses clustering.

- Analisa hasil clustering.

2.4. Hasil yang telah dicapai

Penelitian ini telah berjalan hampir empat tahun dengan hasil yang cukup signifikan,

kami terus mempelajari pola-pola mutasi dari suatu yang mengifeksi pada manusia sehingga

mengubah susunan pola DNA homo sapiens tersebut. Beberapa kemajuan yang telah kami

capai telah kami publikasikan baik di jurnal maupun konferensi internsional, yaitu:

6. Manajaemen big data DNA dengan membandingkan metode string matching

(Kindhi & Arief, 2015)

7. Perancangan inrastruktur bank DNA berbasis cloud server dan database terintegrasi

(Kindhi, Arief, & Purnomo, Prototype Infrastructure Cloud Expert System DNA

Analysis (CESDA) as the Basis of Sustainability DNA Software Improvement in

Indonesia, 2017)

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8. Modifikasi metode hamming untuk pengenalan semantic similairty pada susunan nukleotida DNA yang terinfeksi virus (Kindhi, Hendrawan, Purwitasari, Arief, & Purnomo,

2017)

1. Mengusulkan suatu metode hybrid clustering yang dapat menghasilkan 3

analisa sekaligus dalam satu kali proses clustering DNA (Kindhi, Sardjono,

Purnomo, & Verkerke, 2019)

2. Mengusulkan mengoptimasi metode Support Vector Machine (SVM) untuk

memprediksi eksistensi virus di dalam DNA (Kindhi, Arief, & Purnomo, Optimasi

Support Vector Machine (SVM) untuk memprediksi adanya mutasi pada DNA

Hepatitis C Virus, 2018).

3. Mengusulkan metode Recurrent Neural Network Back Pro Pagation Through Time

(RNN_BPPT) untuk mendeteksi time series pola perubahan mutasi dari suatu virsus

yang melekat pada DNA (Kindhi, Sardjono, & Purnomo, Prediction of DNA

Hepatitis C Virus based on Recurrent Neural Network-Back Propagation Through

Time (RNN-BPTT), 2019)

Pada penelitian ini kami terus memperbaiki hasil yang telah dicapai, dengan menguji

coba metode lain dalam machine learning, dan bila perlu melakukan modifikasi pada

metode tersebut agar dapat menghasilkan analisa yang maksimal.

2.5. DNA COVID-19 Metode prediksi mutasi suatu virus selama ini diteliti dengan pendekatan biologi kedokteran

dengan percobaan di laboratorium dengan menguji larutan PCR ke dalam sampel DNA. Pada penelitian ini kami mengusulkan sebuah pendekatan baru berbasis machine learning melalui analisa pola urutan DNA yang telah tercata dalam file isolated DNA.

Pada penelitian ini, kami melakukan uji coba penerapan machine learning pada data set COVID-19. Data set yang kami gunakan diperoleh dari Bank DNA Dunia yaitu NCBI sebanyak 20 isoalted DNA positif terinfeksi COVID-19, 20 isolated DNA positif terinfeksi MERS, dan 20 islated DNA yang positif terinfeski SARS. Total nukleotida yang dibandingkan adalah kurang lebih 500.000 nukleotida.

27

BAB III STATUS LUARAN Status tercapainya luaran wajib yang dijanjikan dan luaran tambahan (jika ada). Uraian status luaran dapat diamati pada tabel berikut:

No Luaran Judul Status

1 Paper jurnal Q2 Optimization of Machine Learning Algorithms for Predicting Infected COVID19 in Isolated DNA

Publish di IJIIES Japan

2 Paper internasional

konferensi

Ensemle learning for DNA mutation

predictions

Draft siap submit

3 Paten Perangkat cerdas untuk analisis DNA Telah dikoreksi oleh

reviewer ITS dalam

tahap perbaikan dan

resubmit ke ITS

28

BAB V KENDALA PELAKSANAAN PENELITIAN

Kendala selama melaksanakan penelitian adalah terbatasnya data DNA yang positif COVID-19 dari berbagai negara, pada bank DNA dunia sebagian besar data DNA yang telah didaftarkan adalah DNA dari negara China dan Amerika Serikat sehingga menyulitkan proses analisa.

29

BAB VI RENCANA TAHAPAN SELANJUTNYA

Rencana penyelesaian penelitian dan rencana untuk mencapai luaran yang dijanjikan adalah memenuhi luaran tambahan yaitu manuscript pada conference internasional dan menyelesaikan paten yang telah mendapat masukan dari tim reviewer paten di ITS.

30

BAB VII DAFTAR PUSTAKA

Airlangga, U. (2012). Penyakit Tropis Ilmu Ilmiah Dasar. Surabaya: Universitas

Airlangga. WHO. (2017). Global Hepatitis Report 2017. Geneva: Licence: CC BY-NC-SA 3.0

IGO.

Juniastuti et al. (2014). High Rate of Seronegative HCV infection in HIV-Positive

Patients. Biomedical Reports, 2, 79-84.

Bin Liu; Shanyi Wang; Qiwen Dong; Shumin Li; Xuan Liu. (2016). Identification of DNA-

Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble

Learning. IEEE Transactions on NanoBioscience, 15(4), 328-334.

Kindhi, B. A., & Arief, S. T. (2015). Pattern Matching Performance Comparison as Big Data

Analysis Recomendations for Hepatitis C Virus (HCV) Sequence DNA. IEEE

International Conference of Artificial Intelligence and Modelling System (pp. 155-160).

Kinabalu, Malaysia: IEEE.

Kindhi, B. A., Hendrawan, A., Purwitasari, D., Arief, S. T., & Purnomo, M. H. (2017). Distance-

based Pattern Matching of DNA Sequences for Evaluating Primary Mutation. IEEE

International Conference of ICITESE (p. 200). Jogjakarta: IEEE.

Kindhi, B. A., Arief, S. T., & Purnomo, M. H. (2017). Prototype Infrastructure Cloud Expert

System DNA Analysis (CESDA) as the Basis of Sustainability DNA Software

Improvement in Indonesia. IEEE International Conference of European Modelling

Symposium (EMS). Manchester, United Kingdom: IEEE.

Kindhi, B. A., Sardjono, T. A., Purnomo, M., & Verkerke, G. (2019). Hybrid K-Means, Fuzzy

C-Means, and Hierarchical Clustering (KFHC) for DNA Hepatitis C Virus (HCV) Trend

Mutation Analysis. Expert System with Application (ESwA),, 122.

Kindhi, B. A., Sardjono, T. A., & Purnomo, M. H. (2019). Prediction of DNA Hepatitis C Virus

based on Recurrent Neural Network-Back Propagation Through Time (RNN-BPTT). 3rd

IEEE ICAMIMIA. Batu.

Kindhi, B. A., Arief, S. T., & Purnomo, M. H. (2018). Optimasi Support Vector Machine (SVM)

untuk memprediksi adanya mutasi pada DNA Hepatitis C Virus. Journal of JNTETI, 7(3).

1

BAB VIII LAMPIRAN

2

LAMPIRAN 1 Tabel Daftar Luaran Program : Penelitian Doktor Baru Nama Ketua Tim : Berlian Al Kindhi Judul :

1.Artikel Jurnal

No Judul Artikel Nama Jurnal Status Kemajuan*)

1 Optimization of Machine Learning Algorithms for Predicting Infected COVID19 in Isolated DNA

International Journal of Intelligent Engineering Systems (http://www.inass.org/Volume2020.html )

Publish

*) Status kemajuan: Persiapan, submitted, under review, accepted, published

2. Artikel Konferensi

No Judul Artikel Nama Konferensi (Nama Penyelenggara, Tempat,

Tanggal)

Status Kemajuan*)

1 Ensemble learning for DNA mutation prediction

- Siap submit

*) Status kemajuan: Persiapan, submitted, under review, accepted, presented

3. Paten

No Judul Usulan Paten Status Kemajuan 1 Parangkat Cerdas untuk Analisis DNA Sudah di revisi reviewer

paten ITS dan siap resubmit ke ITS

*) Status kemajuan: Persiapan, submitted, under review 4. Buku

No Judul Buku (Rencana) Penerbit Status Kemajuan*)

*) Status kemajuan: Persiapan, under review, published

5. Hasil Lain No Nama Output Detail Output Status Kemajuan*)

3

*) Status kemajuan: cantumkan status kemajuan sesuai kondisi saat ini

6. Disertasi/Tesis/Tugas Akhir/PKM yang dihasilkan

No Nama Mahasiswa NRP Judul Status*)

*) Status kemajuan: cantumkan lulus dan tahun kelulusan atau in progress

Received: May 12, 2020. Revised: June 5, 2020. 423

International Journal of Intelligent Engineering and Systems, Vol.13, No.4, 2020 DOI: 10.22266/ijies2020.0831.37

Optimization of Machine Learning Algorithms for Predicting Infected COVID-

19 in Isolated DNA

Berlian Al Kindhi1*

1Institut Teknologi Sepuluh Nopember, Indonesia

* Corresponding author’s Email: [email protected]

Abstract: The stipulation of the COVID-19 (Corona Virus Disease 2019) as a global pandemic by the WHO

(World Health Organization) made a number of countries lockdown. Countries like Italy, Denmark, China, and

Ireland have taken lockdown steps to prevent this disease from spreading and taking many lives. COVID-19,

SARS (Severe Acute Respiratory Syndrome), and MERS (Middle-East Respiratory Syndrome) are viral

infections in the respiratory tract that can be fatal. SARS first became an epidemic in China in 2002, while MERS

first appeared in the Middle East in 2012. At the end of 2019, a new disease appeared in China called COVID-

19. These three viruses are still in the same family so they have very similar nucleotide sequences. The tested

COVID-19 primer was able to adhere well with a similarity level of more than 70% in all DNA SARS and MERS

isolates tested. To distinguish DNA samples between MERS, SARS, and COVID-19 using the basic local

alignment sequence nucleotide approach alone is not enough. We propose an optimization of machine learning

methods to predict the COVID-19, the optimization method depends on the method we improved. In

Discriminant Analysis, we use Wilks Lamda's approach and change Linear into Diagonal Discriminant Matrix.

In the Decision Tree method, we make optimization by making gain formulation to minimize the entropy value

to get more information on the result. We optimized K-NN with add weighted distance optimization, and in SVM

we try several kernels and optimize the hyperplane with SRM (Structural Risk Minimization) approach to looking

for the best result. Besides that, in preparation for input features, we use Edit Levenshtein Method with the

calculation of the optimum similarity from each DNA sequence. The results of our test, optimization of the

Decision Tree method produces an accuracy of 98.3%, optimization of Discriminant Analysis 98.3%, and

optimization of SVM and KNN 100% respectively. We also find a fact in the DNA Alignment process, when the

primer being compared is 'R', the nucleotides in the COVID-19 sample data are always 'A' and this approach

from the bioinformatic side can be used as analytical material in the medical world.

Keywords: COVID-19, Discriminant analysis, K-NN, Decision Tree, SVM, DNA.

1. Introduction

Since the discovery of a new type of Coronavirus

at the end of 2019 which call COVID-19, the number

of infected patients has increased significantly by

March 2020. US reports the largest number of deaths

worldwide, followed by Italy. This study conducts

trials and analysis of the proximity of MERS,

COVID-19, and SARS in terms of DNA nucleotide

patterns that can be used as decision support in

biomedical research. The incubation period is the

time needed by germs to multiply in a person's body

to cause complaints. In other words, the incubation

period is the time span between the occurrence of

infection and the appearance of symptoms [1].

Although the viruses COVID-19, SARS, and MERS

are from the same family of viruses, namely

coronavirus, these three diseases have different

incubation periods, for SARS disease is 1–14 days

(average 4-5 days). The incubation period for MERS

disease is 2–14 days (average 5 days), while the

incubation period for COVID-19 is 1–14 days, with

an average of 5 days.

These three diseases can cause fever, cough, sore

throat, nasal congestion, weakness, headaches, and

muscle aches. If it gets worse, the symptoms of the



three can resemble pneumonia. The big difference

between these three diseases is that COVID-19 is

rarely accompanied by colds and digestive

complaints, such as bowel movements, nausea, and

vomiting. The spread of coronavirus from animals to

humans is actually very rare, but this is what

happened to COVID-19, SARS, and MERS. Humans

can get the coronavirus through direct contact with

animals infected with this virus. This method of

transmission is called zoonotic transmission [2].

SARS is known to be transmitted from mongoose

to humans and MERS is transmitted from humped

camels. While in COVID-19, there are allegations

that the animal that first transmitted the disease to

humans was a bat. A person can become infected with

the Coronavirus if he inhales a splash of saliva

released by a COVID-19 sufferer when sneezing or

coughing. Not only that, but transmission can also

occur if someone holds an object that has been

contaminated with COVID-19 saliva splashes and

then holds the nose or mouth without washing hands

first. SARS and COVID-19 are known to spread

more easily from human to human than MERS [3].

And when compared with SARS, the transmission of

COVID-19 from human to human is easier and faster.

So far, the death rate from COVID-19 is not higher

than SARS and MERS. The SARS death rate reaches

10%, while MERS reaches 37%. However, the

transmission of COVID-19 which is faster than

SARS and MERS cause the number of sufferers of

this disease to increase sharply in a short time. So far,

there is no proven drug that is effective in dealing

with COVID-19 [4]. Several antiviral drugs, such as

oseltamivir, cloroquine, lopinavir, and ritonavir, have

been tried to be given to COVID-19 patients while

continuing to be studied. Whereas in SARS and

MERS, administration of lopinavir, ritonavir, and the

latest broad-spectrum antiviral drug called

Remdesivir has been proven effective as a treatment.

In patients with Coronavirus infection with severe

symptoms, in addition to antiviral drugs, they also

need to get fluid therapy (infusion), oxygen,

antibiotics, and other medicines according to

symptoms that appear. Patients with COVID-19 also

need to be treated in the hospital so that their

condition can be monitored and not transmit the

infection to others [5].

In this study, we compared the similarity patterns

of the SARS and MERS nucleotide structures with

COVID-19 to determine the similarity of the

nucleotides with the bioinformatic approach. The

data we used consisted of 20 COVID-19 DNA

samples, 20 SARS DNA samples, 20 MERS DNA

samples, and primers from COVID-19. The three

types of DNA samples tested have a short enough

distance or in other words have a high enough

similarity value when compared to the Primary

COVID-19. So if we detect the presence of a

coronavirus simply by matching a DNA sample with

a COVID-19 primer, then all DNA samples, both

SARS and MERS, will be detected as COVID-19.

Apart from biomedical, if it is discussed from the

perspective of bioinformatics, the process of string

similarity alone or the basic sequence alignment is

not enough to prove that the DNA sample includes

Covid-19 because SARS and MERS still have close

kinship values.

Therefore, it is necessary to add a machine

learning method to study the distance pattern of each

DNA sample so that it can be known and predicted

where the DNA infected with COVID-19 really is.

We optimize the four machine learning methods,

namely Decision Tree, Discriminant Analysis, K-NN,

and SVM. The optimization process of each machine

learning method varies according to the need to get

the best prediction results. Good input features will

provide predictive analysis of machine learning with

good results. For the DNA Alignment process we use

the Edit Levenshtein algorithm with the addition of a

DNA sequence normalization filter that meets the

positive minimum limit and has the greatest

similarity to the primers being compared as an input

feature. We describe the optimization process in each

method in Chapter 3, while we present the analysis of

the results and the discussion in Chapter 4, and

Chapter 5 contains conclusions from the results of our

research. The results of the study show that the

optimization of machine learning method is very

helpful in predicting DNA samples by producing

accuracy values above 98% for all methods that have

been optimized, that were not able to be done in the

previous string similarity process.

2. Literature study

DNA alignment is a method for analyzing the

sequence of a DNA sample by aligning the sequence

with another sequence. In bioinformatics, the

nucleotide alignment method can also be said with

the character comparison method. In one isolated file

DNA can consist of tens of thousands of nucleotide

sequences. In large numbers, the process of finding

patterns in a sample will require significant time,

therefore the speed of an algorithm in determining

patterns is an important factor. Research before

comparing the performance of the Brute Force,

Knuth-Morris-Pratt, and Boyer Moore algorithms to

find patterns in isolated DNA [6]. In the process of

finding DNA patterns, there are millions of sequences

that are compared, so the speed and accuracy of an



algorithm in finding these patterns is a major factor.

In addition, the length of the primary characters that

are not always the same can also provide different

distance measurement results, one of the solution

problems is by adding the normalization method to

the Hamming algorithm so that the comparison

process between primers can be balanced [6].

Decision Tree method is often used to determine

a problem with multilevel consideration factors [7].

A condition can be chosen based on the selection of

previous conditions and continues to flow until the

final decision. This method can help provide a

decision on the number of hospital costs to be paid by

a patient by looking at the background factors of the

patient [8]. Decision tree is one of the strong data

mining that can be used to understand the factors that

influence health condition decisions. Decision trees

can be used to design factors in an urban environment

that can affect health outcomes [10]. Previous

research used a decision tree learning algorithm

called classification and regression tree (CART) for

CAD diagnosis as an alternative to the currently

available diagnostic methods [10]. In machine

learning, sometimes a problem occurs because of an

unbalanced data set, this can be overcome by

applying ensemble learning. Decision Tree method

can be used as an initial classification in the ensemble

learning method [11].

Beside decision tree method, machine learning

methods that are also often compared are

discriminant analysis and SVM [13]. Discriminant

Analysis can be applied as a kernel for discrete cross-

models to reduce the loss in some cases on

quantization [13]. Linear Discriminant Analysis

(LDA) can be used to classify patterns, this technique

is often used to detect illness early in the data set

being tested [14]. However, LDA sometimes cannot

provide a good classification if it meets data that are

matrices covariant and unseparated linear [15].

Problems in this LDA model can be overcome with a

new model approach called Lp- and Ls-Norm

Distance Based Robust Linear Discriminant Analysis

(FLDA-Lsp) [16]. Linear Discriminant Analysis is

also able to classify the bent of a cell based on

bispectral invariant features and the results of this

classification can be analyzed in more detail by

combining the SVM method [17]. For speaker

recognition, Discriminant Analysis can be used by

make optimization in Kernel Discriminant Analysis

(KDA) in higher dimension [18]. In addition to a

linear approach, to solve unstructured Covariance

matrices is by applying Vanishing Non-Linear

Discriminant Analysis (VNDA), this method is able

to solve the ratio of trace problems on limited

polynomials data [19].

KNN is one of the supervised machine learning

methods that are able to solve various problems

flexibly [21]. KNN can also be easily combined with

other machine learning methods such as SVM, string

distance, and neural network [21]. The results of the

KNN classification process can increase significantly

if at the time of comparison the pattern is given two

paired criteria [23]. To determine the node on the

KNN sometimes use the average value of the data,

the disadvantage of this method is that it cannot

determine the really good variable [23]. One solution

to this problem is to choose sparse group features as

candidates for relevant classes [24]. KNN algorithm

is also able to recognize patterns in high-resolution

images by calculating the similarity distance around

the pixels being compared [25].

Support Vector Machine (SVM) is a supervised

machine learning algorithm that is able to solve both

classification and regression problems [26]. The way

SVM works are to maximize the Hyperplane limit

(maximum Hyperplane margin) [27]. There are a

number of possible hyperplane choices for a data set,

to get the best results from SVM is to determine the

maximum Hyperplane [28]. Hyperplane with

maximum margins will give better generalization to

the classification method [30]. Hyperplane in SVM is

not always linear, this model can be in the form of a

quadratic curve, or Gaussian in accordance with the

kernel that is applied to the data classification process

[30].

3. Optimization of machine learning

algorithms

3.1 DNA alignment

Sample data from this study totaled 60 isolated

DNA consisting of isolated positive DNA infected

with COVID-19, MERS, and SARS each of them is

20 samples. All data is taken from the world gene

bank [31]. For comparison, we use published Primary

COVID-19 data [32, 33]. In one isolated DNA

complete gene COVID-19, MERS, and SARS,

consisting of 20,000 to 30,000 nucleotide sequences,

this number is far more than the other isolated DNA

in our previous study [6]. All samples will be

compared with each primer, with a total of about

18,000,000 nucleotide comparison processes.

The process of comparing DNA alignment with

COVID-19 primers using the Levenshtein distance

Edit method. Each isolated DNA will be cut into

pieces as long as the number of primary characters

and then compared to the primer, calculated the

distance of its proximity then shifts again to the next

nucleotide. An isolated DNA is said to be positive for



a primary virus or bacterium if the similarity level of

the nucleotide fragment reaches greater than 70%

[34]. In this process, all isolated DNA tested at least

one sequence has a similarity greater than 70% in the

forward primer, so it can be said that all of the

samples are Covid-19. SARS and MERS are indeed

still in one group with Covid-19, which is a

Coronavirus group, so it has a similar pattern.

Therefore, a further predictive analysis process needs

to be carried out.

𝑑𝑖𝑠𝑡𝑎,𝑏(𝑖, 𝑗) =

{

max(𝑖, 𝑗) , 𝑖𝑓 min(𝑖, 𝑗) = 0

min{

𝑑𝑖𝑠𝑡𝑎,𝑏(𝑖, 𝑗) + 1

𝑑𝑖𝑠𝑡𝑎,𝑏(𝑖, 𝑗 − 1) + 1

𝑑𝑖𝑠𝑡𝑎,𝑏(𝑖 − 1, 𝑗 − 1) + 1(𝑎𝑖≠𝑏𝑗)

}

}

(1)

𝑆𝑖𝑚𝑎,𝑏 =𝑑𝑖𝑠𝑡𝑎,𝑏(𝑖,𝑗)

𝑛 × 100% (2)

𝑉𝑎𝑟(𝑥,𝑏) = {𝑆𝑖𝑚𝑎,𝑏 , 𝑖𝑓 max (𝑆𝑖𝑚𝑎,𝑏) ≥ 70

0 , 𝑖𝑓 max (𝑆𝑖𝑚𝑎,𝑏) ≤ 70} (3)

Eq. (1) is an algorithm to calculate the distance

between the sequence of DNA slice (a) to the Primer

(b), while i is the character index of a and j is the

character index of b. Then from the results of distance

calculation, the similarity percentage will be

calculated as in Eq (2). The variable n is the amount

or length of the DNA slice being compared, so the

percentage of similarity is calculated by dividing the

resulting distance value by the number of characters

multiplied by 100%. Eq. (3) is an explanation of how

we fill the value of the variable independence in the

matrix that we build as input features machine

learnings.

We conducted various simulations to change the

data from the comparison results so that it could be

used as an appropriate input feature for machine

learning. From some simulation results, the right

simulation model in our opinion is to use primers as

each input feature. Then every sequence comparison

that produces higher similarity from 70% will be

entered into the application database. From all data

stored in the database, one comparison result that has

the highest similarity value on each isolated DNA

(has the shortest distance) to a primary will be

selected as an input feature. If there is one isolated

DNA that does not have a similarity level greater than

70% in a particular primer, then the dataset will be

written 0. The amount of training data is the amount

of isolated DNA compared to 60 and the number of

input features is eight (the number of primers

compared), for the target output, there are three

classes namely 0 for COVID-19, 1 for SARS, and 2

for MERS.

3.2 Decision tree optimization

The first Machine Learning algorithm that we

tried is the Decision Tree. Decision trees use a

hierarchical structure for supervised learning. The

process of the decision tree starts from the root node

to the leaf node which is done recursively. Where

each branching states a condition that must be met

and at each end of the tree states the class of data.

We use the Entropy concept which is used to

measure "how informative" a node (which is usually

called how good it is). Entropy (S) = 0, if all the

examples in S are in the same class. Entropy (S) = 1,

if the number of examples positive and the number of

negative examples in S is the same. 0 <Entropy (S)

<1, if the number of positive and negative examples

in S is not the same. S is the case dataset and k is the

number of S partitions, while 𝑝𝑗 is the probability

obtained from Sum (Yes / values more than 70%)

divided by Total Cases. k is the number of input

features being selected, and P is the condition of the

input feature. The Entropy algorithm can be analyze

in Eq. (4-5). After getting the entropy value, the

attribute selection is done with the largest

information gain value.

𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑆) = − ∑ 𝑝𝑗𝑙𝑜𝑔2𝑘𝑗=1 𝑝𝑗 (4)

which can be applied to this case study:

𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑆) = −(𝑃𝑐𝑜𝑣19𝑙𝑜𝑔2𝑝𝑐𝑜𝑣19 +

𝑃𝑠𝑎𝑟𝑙𝑜𝑔2𝑝𝑠𝑎𝑟 + 𝑃𝑚𝑒𝑟𝑙𝑜𝑔2𝑝𝑚𝑒𝑟 (5)

So the Gain (A) value in this case study can be

calculated with:

𝐺𝑎𝑖𝑛 (𝐴) = 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑆)

− ∑|𝑆𝑖|

|𝑆|𝑘𝑖=1 × 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 (𝑆𝑖) (6)

In Eq. (6), S is the sample data space used for

training. Variable A is the number of attributes, |Si| is

the number of samples for values V and |S| is the sum

of all sample data, both of which have absolute values.

Whereas Entropy (Si) is entropy for samples that have

a value of i. From the application of the formula (6),

it can be concluded that the greater the information

gain we get, the greater the entropy value that we

delete. Because the main purpose of applying this



gain is to get an entropy value close to 0 or equal to

0.

3.3 Discriminant analysis optimization

The next method that we tested was a

discriminant analysis. In our case study, the dataset

tested will be divided into three classes, so it cannot

use the linear discriminant analysis method. Then we

do the discriminant analysis optimization process by

forming an optimal discriminant function with

several assumptions about the data used. These

assumptions include the data on our independent

variables, the multivariate normal distribution and the

similarity of variance-covariance matrices between

groups. In the preparation of discriminant functions,

there are two methods that can be used, namely

simultaneous estimation and stepwise estimation.

The general model of discriminant analysis is a linear

combination of data that can be observed in Eq. (7).

�⃗⃗� and 𝑥 are two vectors whose distances are

measured using the diagonal discriminant method. To

find out the independent variables that can

discriminate against a group we use Wilks Lambda

method as in Eq. (8).

𝑆𝑗𝑘 = 𝑎 + �⃗⃗� 𝑗 ∙ 𝑥 𝑖𝑘 +⋯+ �⃗⃗� 𝑛 ∙ 𝑥 𝑛𝑘 (7)

To find out which independent variables can be

discriminated against:

λ =det(𝐴)

det (𝐴+𝐵)=

|∑ ∑ (𝑥𝑖𝑗−�̅�𝑖)(𝑥𝑖𝑗−�̅�𝑖)′𝑛𝑖𝑗=1

𝑘𝑖=1 |

|∑ ∑ (𝑥𝑖𝑗−�̅�)(𝑥𝑖𝑗−�̅�)′𝑛𝑖𝑗=1

𝑘𝑖=1 |

(8)

In this case study, because there are three groups,

so the linear model is converted into a diagonal model.

With a diagonal matrix 𝐷𝑖𝑐𝑟 = 𝑑𝑖𝑎𝑔(𝑎1, … , 𝑎2) and

a vector for this dataset become 𝑣𝑒𝑐 = [

𝑥1⋮𝑥𝑛] . So

vector operations can be observed in Eq. (9).

𝐷𝑖𝑐𝑟𝑣𝑒𝑐 = 𝑑𝑖𝑎𝑔(𝑦1, … , 𝑦𝑛) [

𝑥1⋮𝑥𝑛]

= [𝑦1

⋱𝑦𝑛

] [

𝑥1⋮𝑥𝑛] = [

𝑦1𝑥1⋮

𝑦𝑛𝑥𝑛] (9)

In the process of optimization, we tested several

kernel analysis including linear, multiple, and

diagonal. The test results show that the diagonal

discriminant analysis gives the best results compared

to other kernels in this case study.

3.4 K-NN optimization

The K-Nearest Neighbor algorithm uses

Neighborhood Classification as the predicted value

of the new instance value. In this case, the variables

we use are independent variables (variables that are

not related to each other) so it can be said that these

variables are input features. To calculate the distance

between nodes and surrounding neighbors we use the

Euclidean distance algorithm, we add weighted

distance optimization between one node and another

[35]. The kNN optimization algorithm can be

observed in Eqs. (10)-(12), where L is the data set to

be grouped.

𝐿 = {(𝑦𝑖 , 𝑥𝑖), 𝑖 = 1,… , 𝑛𝐿 (10)

𝑑(𝑥, 𝑥(1)) = 𝑚𝑖𝑛𝑖(𝑑𝑖𝑠𝑡𝑎,𝑏(𝑥, 𝑥𝑖))

with distance:

𝑑𝑖𝑠𝑡𝑎,𝑏 = √(𝑥𝑏 − 𝑥𝑎)2 + (𝑦𝑏 − 𝑦𝑎)

2

= (∑ (𝑥𝑖𝑎 − 𝑥𝑗𝑎)2𝑏

𝑎=1 )1

2

node turn into the class by weighted

�̂� = 𝑚𝑎𝑥𝑟(∑ 𝑤(𝐼)𝑘𝑖=1 𝐼(𝑦(𝑖) = 𝑟)) (11)

�̂� is the max value of a node to the neighbor value

compared whether the node has a similarity to the

neighbor. rom our test results analysis, the amount of

K that we determined also determines the results of

the classification. The number of output classes

produced can be influenced by the number of distance

neighbors or the specified number of K. It can be

observed a pattern that by using an odd K, our test

results produce a more precise predictive value, the K

we use in this study is 1.

3.5 SVM optimization

Support Vector Machine (SVM) is a learning

system that uses hypothetical spaces in the form of

linear functions in a high-dimensional feature space,

trained with learning algorithms based on

optimization theory by implementing learning bias

derived from statistical learning theory. To classify

data that cannot be separated linearly the SVM

formula must be modified because no solution will be

found. Therefore, the two bounding fields must be

changed so that they are more flexible (for certain

conditions) by adding the variable 𝑆𝑖 (𝑆𝑖 ≥0, ∀𝑖: 𝑆𝑖 = 0 if 𝑥𝑖 is classified correctly) to be 𝑥𝑖𝑤 +𝑏 ≥ 1 − 𝑆𝑖 for class 1 and 𝑥𝑖𝑤 + 𝑏 ≤ − 1 + 𝑆𝑖 for



class 2. Finding the best separator field by adding the

variable 𝑆𝑖 is often also called the soft margin

hyperplane. In this study we use a Gaussian kernel

that can be optimized as in Eqs. (12)-(13).

𝑘(𝑥𝑖 , 𝑥𝑗) = exp ( −𝛾‖𝑥𝑖, 𝑥𝑗‖)2 (12)

Can be applied for 𝛾 = 0 , if the parameter is

different then 𝛾 =1

(2𝜎2) and the hyperplane

optimization become:

𝑦𝑖(�⃗⃗� ∙ 𝑥1⃗⃗⃗⃗ − 𝑏) ≥ 1, 𝑓𝑜𝑟 𝑖 = 1,… , 𝑛

[1

2∑max (0.1 − 𝑦𝑖(�⃗⃗� ∙ 𝑥1⃗⃗⃗⃗ − 𝑏))

𝑛

𝑖=1

] + 𝛾‖�⃗⃗� ‖2

𝑚𝑖𝑛1

2|𝑤|2 + 𝐶 (∑ 𝑆𝑖

𝑛𝑖=1 ) or

𝑠. 𝑡. 𝑦𝑖(𝑤. 𝑥𝑖 + 𝑏) ≥ 1 − 𝑆𝑖 or

𝑆𝑖 ≥0 (13)

C is the parameter that determines the large

selection and the data value is determined by the user.

This optimization process follows the rules of

Structural Risk Minimization (SRM). SRM principle

is finding a subset of space. The hypothesis is chosen

so that the upper limit is the actual risk by using that

subset minimized. SRM aims to minimize actual risk

by minimizing error in training data. In this study,

minimizing 1

2|𝑤|2 are equivalent to minimizing VC

dimension and minimize 𝐶(∑ 𝑆𝑖𝑛𝑖=1 ) means

minimizing error in training data [36].

4. Result and discussion

In the string similarity process, the results of

matching the character of each primer to each isolated

DNA tested give varying degrees of similarity.

What's interesting about this study is that all isolated

DNA tested both SARS, MERS, and COVID-19 all

yield a similarity percentage of higher than 69% at

least in one of the COVID-19 primers compared, so

it can be said that the sample is positive for COVID-

19. Table 1 shows the number of sequences that have

a higher similarity percentage of 69% in each primer.

It can be observed that the sequence tends to have a

high similarity value in the forward primary, but

some also have a high similarity value in the primary

refers.

Below is a piece of positive DNA COVID-19

accession code LR757996.1 on index 15850, MERS

accession code MG923468.1 on index 1858, SARS

accession code NC_004718 on index 15798. On the

MERS DNA, there is one insert command that is

adding T nucleotides blue characters) to get the

shortest distance.

Primer : GTGARATGGTCATGTGTGGCGG

COVID-19 : GTGAAATGGTCATGTGTGGCGG

MERS : GTGACATTGTCAGGTGTGGGGG

SARS : GTGAGATGGTCATGTGTGGCGG

Through DNA alignment above, it can be

observed that the distance difference lies in the

nucleotide R, where R is one component of RNA that

can be transformed into nucleotides A, T, G, C. From

the observations above, that the changes are not

always specific to certain nucleotides. But from our

deeper observation, from 20 COVID-19 samples, all

of them turned into nucleotide A (Adenine). It can be

concluded that the pattern of COVID-19 tends to be

A, as in some of the alignment examples below:

Primer : GTGARATGGTCATGTGTGGCGG

LC528232.1 : GTGAAATGGTCATGTGTGGCGG

LC528233.1 : GTGAAATGGTCATGTGTGGCGG

LR757995.1 : GTGAAATGGTCATGTGTGGCGG



MN908947.3 : GTGAAATGGTCATGTGTGGCGG



Table 1 describes the number of sequences that

have a percentage similarity of ≥ 70% with respect

to each primer. This amount is cumulative of all

isolated DNA grouped according to the type of virus

that infected it. Indeed, the COVID-19 DNA sample

has the greatest number of similar sequences because

what is tested is the COVID-19 primer. However,

SARS also has a sequence of similarity above 70% in

some primers, and MERS, although only two types of

primers, can still be said to have a high degree of

similarity to primers COVID-19.

In the Decision Tree algorithm, to decide on an

isolated DNA including the type of which virus are

quite difficult because the percentage of similarity in

COVID-19 and SARS is almost the same, therefore

this Decision Tree algorithm needs to add an entropy

approach to measure how informative the value is

given from the measurement results similarity

distance in the previous process, this process also

decide the value of gini index. The optimization

process of Decision Tree algorithm can be observed

in Fig. 1, the result of this system show that maximum

split is tree using maximum deviance reduction

method.



Table 1. The number of DNA Sequence having similarity

level ≥ 70% in each of the COVID-19 primer tested

Primer COV

ID-19

ME

RS

SA

RS

5’-

TGGGGYTTTACRGGTAAC

CT-3’(Forward)

80 0 98

5’-

AACRCGCTTAACAAAGCA

CTC-3’(Reverse)

26 0 0

5’-

TAATCAGACAAGGAACTG

ATTA-3’(Forward)

141 0 151

5’-

CGAAGGTGTGACTTCCAT

G-3’(Reverse)

14 28 0

5′-

GTGARATGGTCATGTGTG

GCGG-3’ (Forward)

90 2 86

5’-

CARATGTTAAASACACTA

TTAGCATA-‘3 (Reverse)

0 0 0

5’-

ACAGGTACGTTAATAGTT

AATAGCGT-3’ (Forward)

122 0 126

5’-

ATATTGCAGCAGTACGCA

CACA-3’ (Reverse)

41 0 1

Figure. 1 Optimization process of decision Tree algorithm

Discriminant analysis algorithms usually use a

linear approach to determine which node belongs in

which class. However, because the class needed in

this study amounted to three, so the linear

discriminant analysis is less precise in solving

problems. We add the Wilks Lambda method to

determine the independence variable used as input

features of discriminant analysis. The test results

using the Optimize Discriminant Analysis algorithm

produce an accuracy rate of more than 98%. The

optimization process of Discriminant Analysis

algorithm can be observed in Fig. 2.

In K-NN algorithm, determining the value of K,

Figure. 2 Optimization process of discriminant analysis

Figure. 3 Optimization process of K-NN algorithm

Figure. 4 Optimization process of SVM algorithm

if the sum of our classifications is even then we better

use even K values, and vice versa if our total

classifications are odd then we better use even K

values because if it is not so, there is a possibility that

we will not be optimal results from testing. In this

study, we use K = 1, which is choosing 1 neighbor

who have high proximity values with the node that

we are comparing. The training result of K-NN can

be observe in Fig. 3.

Similar to the Discriminant Analysis Algorithm,

the SVM algorithm also provides results with low

accuracy in linear SVM. The advantage of SVM, this

method has a kernel that can be adjusted to the needs-



Figure. 5 Confusion matrix results of (the order of images from left to right) Opt. decision tree, Opt. discriminant

analysis, Opt. K-NN, and Opt. SVM

Table 2. Sensitivity, specificity, precision (Positive Predictive Value), and negative predictive value (NPV) values for

each class

Algorithm Class Sensitivity Specificity Precision NPV

Opt. Decision Tree

COVID-19 0.950 1.000 1.000 0.976

SARS 1.000 1.000 1.000 1.000

MERS 1.000 0.975 0.952 1.000

Opt. Discriminant Analysis

COVID-19 0.950 1.000 1.000 0.976

SARS 1.000 0.975 0.952 1.000

MERS 1.000 1.000 1.000 1.000

Opt. K-Nearest Neighbors

COVID-19 1.000 1.000 1.000 1.000

SARS 1.000 1.000 1.000 1.000

MERS 1.000 1.000 1.000 1.000

Opt. Support Vector Machine

COVID-19 1.000 1.000 1.000 1.000

SARS 1.000 1.000 1.000 1.000

MERS 1.000 1.000 1.000 1.000

based on input data or the number of output classes

desired. In the SVM optimization process, we tested

several kernels to produce the best hyperplane. The

optimization process to get the best hyperplane uses

the SRM principle and considers the actual risk factor.

Kernel testing can be observed in Fig. 4.

The validation process in this study uses the

Cross-Validation approach with K as many as 10. In

each of our tested optimization methods, we divided

the data into two groups, 90% for training data and

10% for test data. Then our application will form the

composition of the data randomly 10 times to test its

accuracy. The test results showed that the

optimization of the Decision Tree algorithm and

Discriminant analysis each resulted in 1 data error

prediction. In the Decision Tree, one data that should

be DNA infected with COVID-19 is predicted to be

DNA infected with MERS. Whereas in Discriminant

Analysis, one data which should be DNA infected

with COVID-19 is predicted to be DNA infected with

SARS. Comparison of these data uses the COVID-19

primer, but instead, the error data is found in COVID-

19, while MERS and SARS can be predicted well,

this shows that the pattern on COVID-19 is still

changing more.

In the SVM and K-NN algorithms, each data can

be predicted well and produces 0 prediction errors.

The confusion matrix of the four optimization

algorithms can be observed in Fig. 5. From the

confusion matrix in Fig. 5, the sensitivity, specificity,

precision/Positive Prediction Value (PPV), and

Negative Prediction Value (NPV) values can be

calculated. Sensitivity values are obtained from

correctly predicted data values divided by the amount

of correct data in real conditions.

The specificity value is obtained from dividing

correctly predicted data not the class divided by real

data that is not the class. Then Precision is obtained

from all data that is in the class and correctly

predicted divided by the amount of true data that is

predicted correctly and incorrectly. Calculating the

value of sensitivity, specificity, precision, and NPV

on a multi-class matrix is different from the

calculation of a two-class matrix basically. In this

case, when calculating the sensitivity value for the

COVID-19 class, the MERS and SARS data will be

considered True Negative (TN) data, as well as the

calculations on MERS, and SARS. In Table 2, the

sensitivity value for the COVID-19 class using the

Decision Tree optimization algorithm is 0.95. A

COVID-19 data is predicted to be wrong into MERS

data, which causes the specificity and precision

values in the MERS class to be imperfect. Whereas



Fig. 6. The accuracy value of each class uses decision

tree, discriminant analysis, K-NN, and SVM with

optimization methods

in the Discriminant Analysis Optimization method,

one member of the COVID-19 class is predicted to be

wrong in the SARS class and also results in an

imperfect precision and specificity value. For the K-

NN and SVM Optimization methods, they can

correctly predict data into each class. Fig. 6. is the

accuracy value of each class for the tested methods.

5. Conclusion

The similarity in DNA structure between

COVID-19, MERS, and SARS is one of the obstacles

in predicting samples that are actually infected with

COVID-19. The DNA alignment method with Primer

produces a positive value of COVID-19 in all MERS

and SARS samples. Machine learning methods can

help the prediction process by observing changes in

the pattern of DNA alignment that are included as

input features. The results of predictions show

Optimization of SVM and KNN are able to predict

100% correctly, while optimization of Discriminant

Analysis and Decision Tree produces an accuracy of

98.3%. The prediction error is precisely in the

COVID-19 sample data, even though the Primer

tested was the COVID-19 primer. This shows that the

composition of DNA in COVID-19 samples is still

diverse and there is a possibility that mutations will

continue to occur. In the process of DNA alignment

between COVID-19 Primer and isolated DNA

samples, we analyzed that when tested with certain

primers containing RNA 'R', the sequence in isolated

DNA infected COVID-19 always becomes 'A'

Conflicts of Interest

The authors declare no conflict of interest

Author Contributions

Berlian Al Kindhi in this study contributed to the

entire process of machine learning and data set

processing and writing paper.

Acknowledgments

This research is partially funded by the Institut

Teknologi Sepuluh Nopember for research grants No.

853/PKS/ ITS/2020.

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