Rancang bangun aplikasi, Astrid Tamara, FTI UMN, 2019kc.umn.ac.id/10995/5/HALAMAN_AWAL.pdf · 2020....

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RANCANG BANGUN APLIKASI E-LEARNING

AKSARA JEPANG DENGAN METODE

MNEMONIC DAN PATTERN RECOGNITION

(Studi Kasus: SMA Citra Kasih)

SKRIPSI

Diajukan sebagai salah satu syarat untuk memperoleh gelar

Sarjana Komputer (S. Kom.)

Astrid Tamara

14110110040

PROGRAM STUDI INFORMATIKA

FAKULTAS TEKNIK DAN INFORMATIKA

UNIVERSITAS MULTIMEDIA NUSANTARA

TANGERANG

2019

Rancang bangun aplikasi..., Astrid Tamara, FTI UMN, 2019

iv

KATA PENGANTAR

Puji syukur kepada Tuhan Yang Maha Esa, karena atas rahmat-Nya

penyusunan skripsi dengan judul “Rancang Bangun Aplikasi E-Learning Aksara

Jepang Dengan Metode Mnemonic dan Pattern Recognition (Studi Kasus: SMA

Citra Kasih)” dapat diselesaikan. Skripsi ini disusun sebagai persyaratan kelulusan

pada Program Studi Informatika Fakultas Teknik dan Informatika Universitas

Multimedia Nusantara.

Ucapan terima kasih disampaikan kepada:

1. Dr. Ninok Leksono, selaku rektor Universitas Multimedia Nusantara,

2. Seng Hansun, S.Si., M.Cs., selaku ketua program studi Informatika dan

dosen pembimbing II yang telah memberikan bimbingan selama

pengerjaan skripsi,

3. Andre Rusli, S.Kom., M.Sc., selaku dosen pembimbing I yang telah

memberikan bimbingan selama pengerjaan skripsi dengan sabar,

4. Papi, Mami, Cici Mechele Hwan, dan sepupu Gisela Felicia, yang selalu

memberi dukungan, mendorong untuk terus berprestasi, dan mendoakan

yang terbaik,

5. Keshia Tiffany Wangko, Indah Noviasari, dan Ang Rahma Febryani,

teman seperjuangan yang telah melewati manis pahit perkuliahan sejak

semester 1 di Informatika UMN, yang selalu memberi dukungan moral

yang menenangkan dan menghibur dengan cara yang unik,

6. Julio Cristian Young, S.Kom., M.Kom., yang dengan ringan hati

membantu mencari topik dan menjawab pertanyaan-pertanyaan di saat

kebingungan,


v

7. Farica Perdana Putri, S.Kom., M. Sc., selaku penanggung jawab

Laboratorium Artificial Intelligence C504 yang mengijinkan pemakaian

perangkat untuk keperluan skripsi ini,

8. Sarah Widjaja, S.T., teman dekat yang selalu ada dan menemani sejak

sekolah dasar meskipun jarang bertemu,

9. Shendy Harlim, Janssen, Enrico Nathaniel, Nathania Elvina, Christofer

Derian, Kenny Wantara, Rakadetyo Alif, Ferdinand, Yudha Teguh

Hartanto, Albert Kosasi, Marisa Tri Utami, dan Willy William, teman-

teman yang sangat suportif, saling membantu dalam segala hal, dan

menghibur setiap saat,

10. Teman-teman di Laboratorium Mobile Development B507 dan

Laboratorium Artificial Intelligence C504 yang selalu bersedia

mendengarkan keluh kesah dan memberi masukkan selama pengerjaan

skripsi,

11. Teman-teman lain yang namanya tidak dapat disebutkan satu per satu

yang senantiasa menyemangati dan mendukung.

Tangerang, 28 Januari 2019

Astrid Tamara


vi

RANCANG BANGUN APLIKASI E-LEARNING

AKSARA JEPANG DENGAN METODE

MNEMONIC DAN PATTERN RECOGNITION

(Studi Kasus: SMA Citra Kasih)

ABSTRAK

Negara Jepang populer sebagai tujuan bekerja di masa depan, namun terdapat

kesulitan dalam mempelajari bahasa Jepang. Aksara Jepang berbeda dengan alfabet

yang digunakan secara internasional sehingga sulit dipelajari pelajar internasional.

Pictograph dan keyword mnemonic digunakan dalam penelitian ini sehingga aksara

Jepang dapat divisualisasikan sebagai gambar dan kalimat yang mempermudah

mengingat. Materi pembelajaran aksara Jepang dengan mnemonic dikemas dalam

aplikasi e-learning sehingga pelajar dapat belajar dimana saja dan kapan saja.

Pattern recognition menggunakan algoritma Convolutional Neural Network

diimplementasikan untuk menilai kebenaran penulisan aksara sesuai input

pengguna. Penelitian ini bertujuan untuk merancang dan membangun aplikasi e-

learning aksara Jepang dengan metode mnemonic dan pattern recognition

menggunakan algoritma Convolutional Neural Network dan mengetahui apakah

aplikasi e-learning yang dibangun menghasilkan perbedaan signifikan pada hasil

pembelajaran aksara Jepang bagi pelajar SMA Citra Kasih. Berdasarkan penelitian

yang telah dilakukan, aplikasi e-learning aksara Jepang dengan metode mnemonic

dan pattern recognition menggunakan algoritma Convolutional Neural Network

telah dirancang dan dibangun. Implementasi pattern recognition dengan algoritma

Convolutional Neural Network menghasilkan model dengan akurasi training

99,19%, validasi 100%, dan testing 88,08%. Perbedaan selisih hasil pre-test dan

post-test kelas eksperimen dan kontrol menyimpulkan bahwa tidak adanya

perbedaan signifikan, namun hasil wawancara menyatakan bahwa pelajar SMA

Citra Kasih tertarik menggunakan aplikasi untuk mempelajari bahasa Jepang.

Kata Kunci: aksara Jepang, Convolutional Neural Network, e-learning, mnemonic,

SMA Citra Kasih


vii

DEVELOPMENT OF E-LEARNING APPLICATION FOR

JAPANESE CHARACTER USING MNEMONIC

METHOD AND PATTERN RECOGNITION

(Case Study: SMA Citra Kasih)

ABSTRACT

Japan is popular for future career purpose despite the difficulty to learn Japanese.

Japanese character is different from internationally used alphabet system which

causes difficulty for international students to acquire. Pictograph and keyword

mnemonic are used in this research so Japanese characters can be visualized as

pictures and sentences to aid in memorization. Japanese character learning material

with mnemonic is packaged in an e-learning application so students can learn

anywhere and anytime. Pattern recognition using Convolutional Neural Network

algorithm is implemented to determine the correctness of user’s written input. This

research aims to design and develop Japanese character e-learning application using

mnemonic method and pattern recognition using Convolutional Neural Network

and determine whether developed e-learning application contributes to significant

difference in SMA Citra Kasih students’ Japanese character learning result. Based

on conducted research, Japanese character e-learning application using mnemonic

method and pattern recognition using Convolutional Neural Network has been

designed and developed. Implementation of pattern recognition using

Convolutional Neural Network resulted in a model with accuracy of training

99,19%, validation 100%, and testing 88,08%. While the difference between pre-

test and post-test result of experiment and control class concludes that there is no

significant difference, SMA Citra Kasih students expressed their interest to learn

Japanese with developed e-learning application during the interview.

Keywords: Convolutional Neural Network, e-learning, Japanese character,

mnemonic, SMA Citra Kasih


viii

DAFTAR ISI

PERNYATAAN TIDAK MELAKUKAN PLAGIAT ........................................... ii LEMBAR PENGESAHAN SKRIPSI ................................................................... iii KATA PENGANTAR ........................................................................................... iv ABSTRAK ............................................................................................................. vi ABSTRACT .......................................................................................................... vii DAFTAR ISI ........................................................................................................ viii DAFTAR TABEL .................................................................................................... x DAFTAR GAMBAR ............................................................................................. xi DAFTAR RUMUS .............................................................................................. xiii BAB I PENDAHULUAN ........................................................................................ 1

1.1 Latar Belakang .......................................................................................... 1 1.2 Rumusan Masalah ..................................................................................... 4 1.3 Batasan Masalah ........................................................................................ 4 1.4 Tujuan Penelitian ....................................................................................... 4 1.5 Manfaat Penelitian ..................................................................................... 5 1.6 Sistematika Penulisan ................................................................................ 5

BAB II LANDASAN TEORI .................................................................................. 7 2.1 Aksara Jepang ................................................................................................ 7

2.1.1 Kana「仮名」 ....................................................................................... 7

2.1.2 Kanji「漢字」....................................................................................... 9

2.1.3 Cara Penulisan ..................................................................................... 10 2.1.4 Japanese-Language Proficiency Test (JLPT) ...................................... 11

2.2. Metode Pembelajaran Mnemonic ............................................................... 12 2.2.1 Tipe-tipe Mnemonic ............................................................................ 12 2.3. E-Learning ................................................................................................... 13 2.4 Pattern Recognition ...................................................................................... 16 2.4.1 Convolutional Neural Network (CNN)................................................ 16 2.4.2 Training ................................................................................................ 19 2.5 TensorFlow .................................................................................................. 21 2.6 T-Test ........................................................................................................... 22

BAB III METODOLOGI DAN PERANCANGAN SISTEM ............................... 23 3.1 Metodologi ................................................................................................... 23

3.2 Variabel Penelitian ....................................................................................... 25 3.3 Teknik Pengumpulan Data ........................................................................... 25

3.4 Teknik Pengambilan Sampel ........................................................................ 26

3.5 Perancangan Sistem ..................................................................................... 26 3.5.1 Data Flow Diagram .................................................................................. 26 3.5.2 Flowchart .................................................................................................. 32 3.5.3 Entity Relationship Diagram .................................................................... 40

3.5.4 Struktur Tabel ........................................................................................... 40 3.5.5 Perancangan Tampilan Antarmuka .......................................................... 41

BAB IV IMPLEMENTASI DAN UJI COBA ....................................................... 52 4.1 Spesifikasi Perangkat ................................................................................... 52 4.2 Implementasi ................................................................................................ 53

4.2.1 Implementasi Perancangan Aplikasi E-learning Berbasis Mobile ........... 53 4.2.2 Implementasi Algoritma Convolutional Neural Network (CNN) ............ 62


ix

4.3 Skenario Pengujian ....................................................................................... 64 4.3.1 Skenario Pelatihan Algoritma Convolutional Neural Network ................ 64 4.3.2 Skenario Pengujian Aplikasi .................................................................... 64 4.4 Hasil Pengujian ............................................................................................ 66 4.4.1 Hasil Pelatihan dan Pengujian Algoritma Convolutional Neural

Network ............................................................................................................. 66 4.4.2 Hasil Pengujian Aplikasi ......................................................................... 66 4.5 Evaluasi ........................................................................................................ 69 4.5.1 Hasil Evaluasi Algoritma Convolutional Neural Network ....................... 69 4.5.2 Hasil Evaluasi Pre-Test dan Post-Test ..................................................... 69

BAB V Simpulan dan saran ................................................................................... 72 5.1 Simpulan ...................................................................................................... 72

5.2 Saran ............................................................................................................. 73 DAFTAR PUSTAKA ............................................................................................ 74 LAMPIRAN ........................................................................................................... 78


x

DAFTAR TABEL

Tabel 2.1 Tabel Perbedaan Hiragana, Katakana, dan Kanji ................................... 7 Tabel 2.2 Hiragana dan Urutan Penulisannya ......................................................... 8 Tabel 2.3 Katakana dan Urutan Penulisannya ........................................................ 8 Tabel 2.4 Goresan Lurus ......................................................................................... 9 Tabel 2.5 Goresan Bersiku .................................................................................... 10 Tabel 2.6 Goresan Bersiku Banyak ....................................................................... 10

Tabel 3.1 Desain Penelitian................................................................................... 24 Tabel 3.2 Tabel Characters ................................................................................... 40 Tabel 3.3 Tabel Lists ............................................................................................. 41 Tabel 3.4 Tabel Lists_Characters .......................................................................... 41

Tabel 4.1 Jadwal Testing Aplikasi ........................................................................ 65 Tabel 4.2 Hasil Pre-Test, Post-Test, dan Selisih Kelompok Kontrol ................... 66 Tabel 4.3 Hasil Pre-Test, Post-Test, dan Selisih Kelompok Eksperimen ............. 67


xi

DAFTAR GAMBAR

Gambar 2.1 Contoh Kalimat dalam Bahasa Jepang, Cara Pelafalan, dan Arti dalam

Bahasa Inggris ....................................................................................................... 10 Gambar 2.2 Daftar Kanji JLPT N5 ....................................................................... 11 Gambar 2.3 Contoh Keyword Mnemonics ........................................................... 12 Gambar 2.4 Contoh Pictographs ........................................................................... 13 Gambar 2.5 Arsitektur CNN Pada Umumnya....................................................... 17 Gambar 2.6 Proses Convolution ........................................................................... 18 Gambar 2.7 Average Pooling dan Max Pooling ................................................... 18 Gambar 2.8 Fully Connected Layers .................................................................... 19 Gambar 2.9 Algoritma Adam ................................................................................ 20

Gambar 2.10 Kode Convolutional Layer Menggunakan TensorFlow .................. 21

Gambar 3.1 Context Diagram ............................................................................... 27 Gambar 3.2 Diagram Level 1 ................................................................................ 28 Gambar 3.3 Diagram Level 2 Proses Study Mode ................................................ 29 Gambar 3.4 Diagram Level 2 Proses Quiz Mode ................................................. 30 Gambar 3.5 Diagram Level 2 Proses Klasifikasi dengan Convolutional Neural

Network ................................................................................................................. 31 Gambar 3.6 Diagram Level 2 Proses Manajemen Private List ............................. 32 Gambar 3.7 Flowchart Aplikasi ............................................................................ 33 Gambar 3.8 Flowchart Study Mode ...................................................................... 35 Gambar 3.9 Flowchart Quiz Mode........................................................................ 37 Gambar 3.10 Flowchart Add To Privatelist .......................................................... 38

Gambar 3.11 Flowchart Pattern Recognition dengan Convolutional Neural

Network ................................................................................................................. 39 Gambar 3.12 Entity Relationship Diagram ........................................................... 40 Gambar 3.13 Tampilan Splash Screen .................................................................. 42 Gambar 3.14 Tampilan Study Mode Tab Levels .................................................. 42 Gambar 3.15 Tampilan Study Mode Tab Private ................................................. 43

Gambar 3.16 Tampilan Pemilihan Aksara ............................................................ 44 Gambar 3.17 Tampilan Seleksi Aksara ................................................................. 45 Gambar 3.18 Tampilan Menambahkan Aksara yang Dipilih Ke Private List ...... 45 Gambar 3.19 Tampilan Pembelajaran Aksara Kana ............................................. 46 Gambar 3.20 Tampilan Pembelajaran Aksara Kanji............................................. 47

Gambar 3.21 Tampilan Mode Quiz Tab Levels.................................................... 47 Gambar 3.22 Tampilan Mode Quiz Tab Private List ............................................ 48

Gambar 3.23 Tampilan Quiz dan Hint .................................................................. 49 Gambar 3.24 Tampilan Jawaban Benar dan Salah................................................ 49 Gambar 3.25 Tampilan Nilai Quiz ........................................................................ 50

Gambar 3.26 Tampilan About Tab Japanese dan Tab Credits .............................. 51

Gambar 4.1 Splash Screen .................................................................................... 54

Gambar 4.2 Tampilan Study Mode ....................................................................... 55 Gambar 4.3 Tampilan Pemilihan Aksara .............................................................. 56 Gambar 4.4 Tampilan Seleksi Aksara dan Penambahan ke PrivateList ............... 57 Gambar 4.5 Tampilan Study Detail ...................................................................... 58

Gambar 4.6 Tampilan Quiz Mode ........................................................................ 59 Gambar 4.7 Tampilan Pertanyaan Quiz ................................................................ 60


xii

Gambar 4.8 Tampilan Hint ................................................................................... 60 Gambar 4.9 Tampilan Hasil Quiz ......................................................................... 61 Gambar 4.10 Tampilan About............................................................................... 62 Gambar 4.11 Data Training dan Testing ............................................................... 62 Gambar 4.12 Implementasi CNN menggunakan Tensorflow Keras API ............. 63


xiii

DAFTAR RUMUS

Rumus 2.1 Rumus T-Test ..................................................................................... 22


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