restaurants system recommendation
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Pemodelan Sistem Rekomendasi Restoran berdasarkan Preferensi Selera pada Pengguna
~ Arif Akbarul Huda ~
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Pertumbuhan restoran di indonesia
2007 2008 2009 2010 20110
500
1000
1500
2000
2500
3000
3500
Ilustrasi pertumbuhan restoran di indonesia
Sumber : Statistik Restoran/Rumah Makan (BPS)
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TREN KULINER
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THE OVERLOAD OF INFORMATION
bagi sebagian orang keberlimpahan konten/informasi justru menjadi suatu kesulitan tersendiri terutama dalam mengolah informasi yang
diperlukan (A. Zanda, 2012)
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Perlu alat untuk menyaring informasi
Sistem rekomendasi
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Related work
Nugroho [1] telah berhasil membuat sistem rekomendasi restoran dengan mempertimbangkan kebutuhan kalori penggunanya. 20014
Arief [8] membangun sebuah sistem rekomendasi wisata di
wilayah Yogyakarta dengan pendekatan collaborative-filtering
dan penyaringan informasi berdasarkan lokasi. 2012
Chu [5] memperkenalkan sistem rekomendasi restoran yang dapat mengenali perilaku diet,
pola makan dan kesukaan makanan bersayur pada penggunanya. 2013
Daraghmi [14] memberikan kontrobusi dalam pembuatan sistem rekomendasi restoran dengan
mempertimbangkan agama, budaya, alergi, riwayat kesehatan, dan aktifitas diet
penggunanya 2013
Liu [13] memerkenalkan teknik rekomendasi restoran dengan cara memperhatikan opini dan rating yang
diberikan penggunanya. 2013
2011 2012 2013 2014 2015
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S E L E R A
Rasa, sangat penting bagi seseorang terutama dalam memilih makanan ~Arthur Guyton~
(Textbook of Medical Physiology, page 663)
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Fakta tentang Rasa
✔ 3k-10k senseor pengecap
✔ Rasa => reaksi kimia
✔ lima dasar rasa yaitu manis, asam,
asin, pahit, gurih
Arthur Guython (Textbook of Medical Physiology, page 665)
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Kuantifikasi rasa
Domain Expert ( a chef) Electronic tongue
-VS-
It is not my domain
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Adityo Nugroho
● 2 tahun di Hotel Royal Ambarukmo● 4 bulan di Hotel 101 Yogyakarta● 2 bulan sebagai konsultan menu di
The Real Steak House Batam● selama 3 tahun menekuni usaha
katering-sekarang
The expert….
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F.O.C.U.S
Memodelkan sistem rekomendasi, dengan
kelebihan mampu mengenali preferensi
selera seorang pengguna sehingga dapat
merekomendasikan restoran bersama salah
satu menu makanannya yang sesuai dengan
selera pengguna.
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BATASAN MASALAH
1) Datasheet diberikan berdasar asumsi dari pakar
2) smartphone android.
3) Yogyakarta area.
4)Pengguna tidak memiliki pantangan terhadap
makanan.
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Usulan metode{arsitektur tingkat tinggi}
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Usulan metode{Pemodelan sistem rekomendasi}
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atribut
BitterSalty
SavorySour
SweetSauceSpicyMeat
vegetable
BitterSalty
SavorySour
SweetSauceSpicyMeat
vegetable
Preferensi seleraKarakter dan rasa
Usulan metode{Pemodelan sistem rekomendasi}
Setiap atribut berisi bobot nilai antara 0-1
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atributitem
Soto Ayam Kampung
preferensi pengguna
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57
Usulan metode{Pemodelan sistem rekomendasi}
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atribut
nama makananpreferensi pengguna
Mie Ayam Super Jumbo
Soto Ayam Kampung
Soto Campur
Rica-rica Mentok
Tengkleng Kambing
bitter 0.00 0.00 0.00 0.00 0.00 0.00
sweet 0.50 0.70 0.70 0.40 0.40 0.63
savory 0.40 0.60 0.50 0.40 0.40 0.60
salty 0.20 0.20 0.20 0.10 0.10 0.23
sour 0.00 0.00 0.20 0.00 0.10 0.07
spicy 0.50 0.00 0.40 0.70 0.60 0.43
sauce 0.80 1.00 1.00 0.70 0.60 0.67
meat 0.80 0.80 0.60 1.00 1.00 0.83
vegetable 0.30 0.70 0.70 0.00 0.00 0.57
Makanan mana yang cocok dengan selera pengguna?
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Similarity measurement
● Eudiclane distance● Cosine similarity● Pearson Corellation
pearson corellation memberikan hasil yang lebih bagus dibandingkan cara yang lain terutama dalam situasi dimana data
tidak ternormalisasi dengan baik. Pearson correlation menunjukkan seberapa linear hubungan antara dua buah item
yang diperbandingkan. (T. Segaran,2007)
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Eudiclane Distance
Cosine Similarity
Pearson Correlation
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Usulan metode
atributitem
Soto Ayam Kampung
preferensi pengguna
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57
Pearson Correlation
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Usulan metode
atributitem
x y
bitter 0.00 0.00
sweet 0.70 0.63
savory 0.60 0.60
salty 0.20 0.23
sour 0.00 0.07
spicy 0.00 0.43
sauce 1.00 0.67
meat 0.80 0.83
vegetable 0.70 0.57
Pearson Correlation
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.20
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
bitter
sweetsavory
salty
sour
spicy
sauce
meat
vegetable
Soto Ayam Kampung
pre
fere
nsi
pe
ng
gu
na
r(soto ayam , preferensi pengguna)=0.789
√0.658∗√1.242=0.8727
Pearson Correlation
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rentang nilai korelasi
keterangan pesan
0.80-1.00 tingkat korelasi sangat tinggi
sangat direkomendasikan
0.60-0.79 korelasi tinggi mungkin Anda suka
0.40-0.59 korelasi rendah boleh dicoba
0.20-0.39 korelasi sangat rendah -
0.00-0.19 tidak ada korelasi -
<< -1.00 berkebalikan -
Pengelompokan Nilai Korelasi
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Evaluasi dan hasil
✔ Skenario ekstrim
✔ Skenario reset preferensi
✔ Skenario normal
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atribut
nama makananpreferensipenggunaSoto Pisah Nasi Uduk
Ayam BakarSoto Sapi
bitter 0.00 0.00 0.00 0.00
sweet 0.70 0.70 0.50 0.63
savory 0.50 0.80 0.50 0.60
salty 0.20 0.20 0.30 0.23
sour 0.20 0.00 0.00 0.07
spicy 0.40 0.70 0.20 0.43
sauce 1.00 0.00 1.00 0.67
meat 0.60 1.00 0.90 0.83
vegetable 0.70 0.40 0.60 0.57
Skenario Ekstrim
preferensi sweet=0.7+0.7+0.5
3=0.63
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noitem
nilai korelasi
jarak pinnedrestoran menu
1 Mie Ayam Super Jumbo “AFUI” Mie Ayam Super Jumbo 0.9076 2.8993 ya
2 Soto Sawah Bu Hadi Soto Ayam Kampung 0.8727 1.9495
3 Soto Kudus Pak Dewo Soto Campur 0.8604 0.2433
4 Warung Rica-Rica Mentok Wirobrajan
Rica-rica Mentok 0.7493 1.8250
5 Tengkleng Gajah Tengkleng Kambing 0.7430 4.0269
Skenario Ekstrim
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Skenario Ekstrim
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Memberi feedback pin
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noitem
Nilai korelasi
jarakrestoran menu
1 Soto Kudus Pak Dewo Soto Campur 0.8646 0.2433
2 Soto Sawah Bu Hadi Soto Ayam Kampung
0.8602 1.9495
3 Warung Rica-Rica Mentok Wirobrajan
Rica-rica mentok 0.8069 1.8250
4 Warung Rica-Rica Mentok Wirobrajan
Rica-rica ayam 0.8069 1.8250
5 Tengkleng Gajah Tengkleng Kambing 0.7947 4.0269
Memberi feedback pin
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Reset preferensi
atribut Preferensi Pengguna
bitter 0.00
sweet 0.10
savory 0.10
salty 0.20
sour 0.10
spicy 1.00
sauce 0.00
meat 0.20
vegetable 0.80
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no item Nilai korelasi jarak
restoran menu
1 Spesial Bawang ‘Mas Kobis’
Nasi Telur Terong Penyet
0.8793 1.1406
2 Warung Sego Macan Sego Macan 0.8282 1.2946
3 Lotek Teteg Lempuyangan
Lotek 0.8007 0.7930
4 Lotek Teteg Lempuyangan
Gado-gado 0.7604 0.7930
5 Kupat Tahu Magelang 21
Tahu Kupat 0.7235 2.5803
Reset preferensi
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noitem
nilai korelasi jarakrestoran menu
1 Sambal Bawang Bu Santi
Nasi Ikan Nila Sambal Bawang
0.9156 2.364
2 warung makan ngudi rejeki
Nasi uduk ayam bakar
0.9050 1.1401
3 Kedai Rumah Pohon
Nasi Guendheng 0.8860 0.7781
4 Spesial Bawang “Mas Kobis”
nasi ayam penyet 0.8826 1.1406
5 Warung Sate Samirono
Sate Kambing 0.8825 0.5240
Skenario Normal
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1)Rasa dan Selera dapat direpresentasikan dengan bitter, salty,
sour, savory, sweet, sauce, spicy, meat dan vegetable.
2)Dapat merekomendasikan restoran + makanannya.
3)rentang nilai korelasi dari 0.7 hingga 0.9.
Kesimpulan
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1)Tag, Rating dan Komentar
2)Collaborative Filtering untuk restoran
3)User Generated Content
4)Share
Saran
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