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SISTEM REKOMENDASI DRAMA KOREA MENGGUNAKAN METODE USER-BASED COLLABORATIVE FILTERING William Kristianto 1), Dyah Erny Herwindiati 2), Janson Hendryli3)

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SISTEM REKOMENDASI DRAMA KOREA MENGGUNAKAN METODE USER-BASED COLLABORATIVE FILTERING William Kristianto 1), Dyah Erny Herwindiati 2), Janson Hendryli3) Jurnal Ilmu Komputer dan Sistem Informasi SISTEM REKOMENDASI DRAMA KOREA MENGGUNAKAN METODE USER-BASED COLLABORATIVE FILTERING William Kristianto 1), Dyah Erny Herwindiati 2), Janson Hendryli3) Teknik Informatika, FTI, Universitas Tarumanaraga Jl. Letjen S Parman no 1, Jakarta 11440 Indonesia 1) email : [email protected] 2) email : [email protected] 3) email : [email protected] ABSTRACT serial drama korea yang sesuai dengan profil mereka Korean drama is one of the entertainment that is very masing-masing. popular with the public, be it among children, teenager, Sistem rekomendasi ini, menggunakan metode User adults and parents. There is a huge selection of dramas Based Colaborative Filtering, dimana input dari metode to watch, however, viewers have limited time. Therefore, ini berupa data rating yang diberikan oleh pengguna a recommendation system is used to provide input to untuk daftar drama korea yang tersedia. Data tersebut viewers in choosing Korean drama series that suits their akan digunakan sebagai basis untuk mencari respective profiles. This recommendation system is made rekomendasi drama Korea, yaitu melalui analisa using the User-Based Collaborative Filtering method, kecenderungan atas kemiripan antara satu pengguna where the input of this method is in the form of rating dengan pengguna lainnya. Metode ini menggunakan data provided by the user for a list of available Korean variabel input berupa data rating angka 1 sampai 5 dramas. Based on the results of interviews via video terhadap drama korea yang dipilih untuk mencari calls and questionnaires, this Korean drama application rekomendasi yang sesuai. can provide different recommendation results based on user ratings of Korean dramas. 2. Dasar Teori Key words : Korean Drama, Recommendation 2.1. Sistem Rekomendasi System, User-Based Collaborative Filtering. Sistem rekomendasi dapat didefinisikan sebagai program yang bertujuan untuk memberikan rekomendasi 1. Pendahuluan item (produk atau jasa) yang paling cocok untuk pengguna tertentu (individu atau bisnis). Sistem Di masa era digital seperti saat ini, rekomendasi memprediksi minat pengguna berdasarkan perkembangan teknologi di Indonesia berkembang informasi yang terkait tentang item, pengguna dan dengan pesat. Seiring dengan perkembangan tersebut, interaksi antara item dan pengguna. Sistem rekomendasi informasi yang tersedia semakin banyak, baik mengenai sering kali digunakan untuk mencari suatu prediksi, berita terkini, perdagangan, edukasi, maupun hiburan. seperti buku, musik, film, dan lain-lain. Salah satu topik yang sedang diminati saat ini adalah serial drama korea. Drama korea sendiri merupakan 2.2. Drama Korea salah satu bagian yang sangat disukai masyarakat, baik Drama menurut Kamus Besar Bahasa Indonesia itu kalangan anak-anak, remaja, bahkan kalangan dewasa (KBBI) adalah komposisi syair atau prosa yang maupun orang tua juga menyukai drama Korea. diharapkan dapat menggambarkan kehidupan dan watak Drama Korea banyak diminati karena alur ceritanya melalui tingkah laku (akting) atau dialog yang yang menarik. Selain itu, akting, wajah, dan penampilan dipentaskan. Drama Korea merupakan kisah atau dari para pemain juga merupakan salah satu indikator adegan asal Korea yang menggambarkan suatu konflik yang banyak disukai oleh para penonton . Setiap atau emosi, yang khusus disusun untuk suatu tahunnya, terdapat banyak judul drama korea baru yang pertunjukan teater. bermunculan. Pada tahun 2017, total drama korea yang 2.3. Collaborative Filtering dirilis berjumlah 81 drama, tahun 2018 berjumlah 126 Collaborative Filtering pada sistem drama, dan pada tahun 2019 mencapai 104 drama[1]. rekomendasi adalah metode didasarkan pada interaksi Terdapat banyak sekali pilihan drama untuk ditonton, antara user dan item untuk menghasilkan rekomendasi. akan tetapi, waktu yang dimiliki para penonton terbatas. Interaksi ini disimpan dalam bentuk matirks interaksi Maka dari itu, digunakanlah sistem rekomendasi untuk antara pengguna dan item. memberikan masukkan kepada penonton dalam memilih 1 Jurnal Ilmu Komputer dan Sistem Informasi Collaborative filtering menggunakan database Keterangan: yang diperoleh dari user. Ada dua komponen utama D(x,y) = Nilai distance antara user x dan user y dalam data ini agar dapat membuat prediksi bagi n = Jumlah total item recommender system, yaitu user dan item[2]. Keduanya Xi = Rating user X terhadap item i membentuk rating matrix berupa m user {u1, u2, u3, …, Yi = Rating user Y terhadap item i um} dan daftar n item {i1, i2, i3, …, i4}. Di mana setiap user memberikan penilaiannya pada item berupa rating Dari persamaan (1) dapat diketahui jarak rating dalam skala 1 sampai 5. User item rating matrix dapat antara dua pengguna. Semakin besar nilai Distance, dilihat pada tabel di bawah ini. semakin besar perbedaan rating yang diberikan oleh Tabel 1 Matriks Rating User Item orang pengguna tersebut, dan sebaliknya semakin kecil I1 I2 I3 I4 Im nilai Distance, semakin besar kemiripan rating terhadap U1 1 … 3 … kedua pengguna tersebut . U2 5 4 … … Untuk menghitung nilai kemiripan atau U3 … 5 3 … similaritas, didasarkan pada nilai distance antara U4 … 4 … … pengguna satu dengan pengguna lain. Berikut persamaan (2) untuk menghitung nilai kemiripan atau similaritas Um antar pengguna[4]: Collaborative Filtering diterapkan untuk menghitung prediksi rating pengguna terhadap item yang sebelumnya masih kosong. Terdapat 2 alur atau algoritma utama dalam memprediksi rating sebagai Keterangan: berikut : sim(x,y) = Nilai kemiripan antara user x dan user y • Menghitung nilai kemiripan (similarity) berdasarkan D(x,y) = Nilai distance antara user x dan user y item (Item-Based Collaborative Filtering) atau kemiripan (similarity) berdasarkan user (User-Based Nilai similaritas memiliki rentang 0 sampai Collaborative Filtering). dengan 1 dimana semakin tinggi nilai similaritas berarti • Memprediksi rating dari pengguna terhadap suatu pengguna lain tersebut memiliki kemiripan yang item. semakin tinggi dengan user. 2.4. User-Based Collaborative Filtering 2.6. Prediksi User Based Collaborative Filtering merupakan Perhitungan prediksi digunakan untuk metode yang dilakukan dengan menghitung kesamaan di memprediksi suatu nilai rating yang diberikan oleh user antara pengguna sebagai parameter untuk menghasilkan untuk item tertentu dengan membandingkan rating yang rekomendasi. Sebagai contoh, Anton dan Stanley telah pernah diberikan pengguna pada suatu item dengan nilai menonton film yang sama, dan mereka memberikan nilai kemiripan antara pengguna tersebut dengan pengguna atas film tersebut hampir sama. Pada kondisi dimana lainnya. Salah satu metode perhitungan prediksi rating Stanley belum pernah menonton film 'The Godfather: adalah Weighted Sum. Berikut adalah rumus dari Part II' dan sebaliknya Anton pernah menontonnya, akan Weighted Sum[5]: terdengar logis untuk berpikir jika Anton menyukai film itu maka Stanly juga akan menyukainya. Untuk membuat sistem rekomendasi menggunakan metode user-based ada dua langkah yang harus Keterangan: dilakuakan, yaitu seperti berikut: Predu,i = Prediksi user u terhadap item i • Menghitung Similarity atau nilai kemiripan antara sim(u,v) = Nilai kemiripan antara user u dan user v satu user dengan user lainnya. m = jumlah total user • Menghitung Prediksi Rating Rv,i = rating oleh user v kepada item i 2.5. Distance Based Similarity Distance Based Similarity merupakan metode 3. Hasil Percobaan yang digunakan untuk menghitung nilai similaritas atau kemiripan berdasarkan nilai distance. Pada pendekatan Pengujian data pada metode User-Based ini, dilakukan perhitungan jarak rating antara pengguna Collaborative Filtering menggunakan data rating satu dengan pengguna lain terlebih dahulu. Salah satu pengguna pada drama korea. Jika pengguna memberikan ukuran jarak yang dapat dipakai adalah Euclidean rating bernilai 1 menandakan pengguna tidak menyukai distance. Berikut rumus (1) untuk ruang berdimensi n[3]: drama tersebut dan 5 menandakan pengguna sangat suka dengan drama tersebut. Untuk data rating pengguna terhadap drama korea dapat dilihat pada Tabel 2. 2 Jurnal Ilmu Komputer dan Sistem Informasi Tabel 2 Rating Drama Drama Korea A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA BB CC DD A 4 4 4 - - - 5 - 4 - - 2 - 3 - - - 4 - - 3 4 5 - - - - - - - Agnes 4 4 - - - - - - - - - - - - - - - - - - - - - - - - - - - - Agustino - - - 2 5 - 4 5 5 - 4 - - - - 5 - - - - - - - - - - - - - - Ahmad - - 4 - 4 5 4 5 4 - 4 5 5 4 - - 4 4 - - 5 - 4 - - - - 4 5 - Alda 5 5 5 - - - - - - - - - - - - - - - - - - - - - - - - - - - Aldri 4 5 4 - - - - - - - - - - - - - - - - - - - - - - - - - - - Alfi 4 1 3 1 5 4 5 4 3 5 5 - - 4 3 3 2 4 5 3 4 3 5 5 4 - - 3 4 4 Alya 5 5 5 - - - - - - - - - - - - - - - - - - - - - - - - - - 5 Ama - - 3 - - 5 - - - - 5 - - - - - - - - - - - - - - - - - - 5 Amelia 4 5 5 3 3 4 4 4 5 4 4 4 3 5 4 3 4 3 4 2 4 3 4 3 4 3 2 2 3 4 Angelia - - - - - - 4 - - - 3 - - - - 5 - - - - - - - - - - - - - - Antonius 4 5 3 - - 4 5 - - 5 5 - - 5 - - 5 - - - - - - - - - - - 5 4 Arifah 3 4 4 - 5 4 2 5 4 - 4 - - 4 3 3 - - - - 5 - 3 3 - - - - 3 - Arimbi 3 - - 3 3 5 5 3 5 5 - - - 5 2 5 4 4 5 5 5 - 4 - 5 5 - 5 5 Audita 4 4 5 4 5 - 4 5 4 4 3 - - 5 - 4 - - 5 - 4 - - 4 - - - 5 4 4 Budy 5 4 5 - 5 4 - - - - - - - - - - - - - - - - - - - - - - - - Catherine 5 5 5 - - - - - - - - - - - - - - - - - - - - - - - - - - - Charolina 3 5 5 - 5 5 5 5 5 5 5 - - 5 - 5 5 5 5 - 5 - - 5 - - 5 - - 5 Christina 4 4 4 - 5 - - 4 - - - - - - - - - - - - - - - - - - - - - - Cindy 5 - - - - - - - 4 - - - - - - - - -
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