SISTEM REKOMENDASI LAGU DENGAN METODE

COLLABORATIVE FILTERING YANG MEMANFAATKAN

IMPLICIT FEEDBACK DATASETS

SONG RECOMMENDATION SYSTEM WITH COLLABORATIVE

FILTERING METHOD USING IMPLICIT FEEDBACK DATASETS

Laporan ini disusun untuk memenuhi salah satu syarat menyelesaikan

Pendidikan Diploma Program Studi D3 Teknik Informatika

Jurusan Teknik Komputer dan Informatika

Disusun oleh

Dina Rahmadhani 091511040

Firman Maulana 091511048

Maisa Nurul Agnia 091511055

POLITEKNIK NEGERI BANDUNG

2012

 

 

 

 

 

 

 

 

 

 

 

 

SISTEM REKOMENDASI LAGU DENGAN METODE

COLLABORATIVE FILTERING YANG MEMANFAATKAN

IMPLICIT FEEDBACK DATASETS

SONG RECOMMENDATION SYSTEM WITH COLLABORATIVE

FILTERING METHOD USING IMPLICIT FEEDBACK DATASETS

Laporan ini disusun untuk memenuhi salah satu syarat menyelesaikan

Pendidikan Diploma Program Studi D3 Teknik Informatika

Jurusan Teknik Komputer dan Informatika

Disusun oleh

Dina Rahmadhani 091511040

Firman Maulana 091511048

Maisa Nurul Agnia 091511055

POLITEKNIK NEGERI BANDUNG

2012

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Nama : Dina Rahmadhani

NIM : 091511040

Tempat, Tanggal Lahir : Bandung, 8 April 1991

SD Lulus Tahun : 2003 dari SD Negeri Banjarsari IV Bandung

SLTP Lulus Tahun : 2006 dari SMP Negeri 2 Bandung

SLTA Lulus Tahun : 2009 dari SMA Negeri 2 Bandung

Prestasi yang pernah dicapai : -

 

 

 

 

 

 

 

 

 

 

 

 

Nama : Firman Maulana

NIM : 091511048

Tempat, Tanggal Lahir : Cirebon, 5 Oktober 1990

SD Lulus Tahun : 2002 dari SD Negeri Karang Anom III

SLTP Lulus Tahun : 2005 dari SMP Negeri 6 Cirebon

SLTA Lulus Tahun : 2008 dari SMA Negeri 3 Cirebon

Prestasi yang pernah dicapai : Juara 1 MTQ ( hafalan Al-Qur’an ) SD se-kota Cirebon

Juara 2 basket 3 on 3 SMAN 4 Cirebon

 

 

 

 

 

 

 

 

 

 

 

 

Nama : Maisa Nurul Agnia

NIM : 091511055

Tempat, Tanggal Lahir : Bandung, 13 Agustus 1990

SD Lulus Tahun : 2002 dari SD Negeri I Rajamandala

SLTP Lulus Tahun : 2005 dari SMP Negeri I Cikalong Wetan

SLTA Lulus Tahun : 2009 dari SMK Negeri I Cimahi

Prestasi yang pernah dicapai : -

 

 

 

 

 

 

 

 

 

 

 

 

ABSTRAK

Tugas akhir ini berkaitan dengan pembangunan sistem rekomendasi yang dapat memberikan saran-saran untuk lagu yang sekiranya ingin didengarkan oleh pengguna. Sistem rekomendasi lagu yang akan dibuat hanya memanfaatkan implicit feedback, yaitu berapa kali pengguna tersebut mendengarkan suatu lagu dan penilaian tidak langsung terhadap suatu lagu. Adapun metode rekomendasi yang digunakan pada sistem ini adalah metode Collaborative Filtering

dengan memanfaatkan Neighborhood Model, yaitu metode Cosine Similarity, dan metode pengelompokkan data dengan memanfaatkan algoritma K-Means. Lagu-lagu dari artis dengan tag tertentu dikelompokkan menggunakan algoritma metode cluster, lalu dari setiap cluster yang terbentuk dihitung kedekatan antar lagu-lagu didalamnya, sehingga ketika pengguna mendengarkan suatu lagu, sistem akan merekomendasikan lagu yang mungkin dipilih oleh pengguna, nilai kemiripannya paling tinggi.

Kata Kunci: Sistem Rekomendasi, Lagu, Collaborative Filtering, Neighborhood Model, K-

Means

 

 

 

 

 

 

 

 

 

 

 

 

ABSTRACT

This project is about recommendation system that can provide suggestions for song to be

listened. Song recommendation use implicit feedback, that is how many times users listen to a

song and indirect assessment given to the song. The method used in this system is

Collaborative Filtering using Neighborhood Model, that is Cosine Similarity, and a method of

clustering data by using K-Means algorithm. The tracks of the artist with particular tag is

clustered using that algorithm to form cluster, then from each clusters the closeness is

calculated neighborhood between the track, so that when users listen to a song, the system

will recommend songs that may be selected by the user, that have highest closeness.

Keywords: Recommender System, Song, Collaborative Filtering, Neighborhood Model, K-

Means

 

 

 

 

 

 

 

 

 

 

 

 

i

KATA PENGANTAR

Puji dan syukur kehadirat Tuhan Yang Maha Esa karena berkat rahmat dan hidayah-Nya

penulis dapat menyelesaikan laporan yang berjudul Sistem Rekomendasi Lagu dengan Metode

Collaborative Filtering yang Memanfaatkan Implicit Feedback Datasets.

Adapun tujuan dari pembuatan laporan ini adalah untuk memenuhi syarat menyelesaikan

Pendidikan Diploma III Program Studi Teknik Informatika, Jurusan Teknik Komputer dan

Informatika. Selama penulisan laporan ini penulis mendapat bantuan, bimbingan, dan

pengarahan dari berbagai pihak, maka dari itu penulis menyampaikan rasa hormat dan terima

kasih kepada :

1. Bapak Jonner Hutahaean, BSET. selaku Dosen Pembimbing I.

2. Bapak Dewa Gede Parta, BSCS. selaku Dosen Pembimbing II.

3. Bapak Ade Chandra Nugraha, S.Si., M.T. selaku Ketua Jurusan Teknik Komputer

Politeknik Negeri Bandung.

4. Teman – teman serta pihak-pihak yang telah memberikan dukungan baik moriil maupun

materil

5. Orang tua dan seluruh keluarga yang telah memberikan dukungan baik moriil maupun

materil.

Semoga segala bantuan yang telah diberikan kepada penulis mendapat balasan yang lebih dari

Allah SWT. Penulis berharap semoga laporan ini dapat bermanfaat bagi para khalayak

sekalian.

Bandung, Juli 2012

Kelompok Tugas Akhir 205

 

 

 

 

 

 

 

 

 

 

 

 

ii

DAFTAR ISI

KATA PENGANTAR ................................................................................................................. i DAFTAR ISI .............................................................................................................................. ii DAFTAR GAMBAR ................................................................................................................ iv DAFTAR TABEL ..................................................................................................................... vi DAFTAR ISTILAH ................................................................................................................. viii DAFTAR SINGKATAN ........................................................................................................... ix DAFTAR SIMBOL .................................................................................................................... x BAB I PENDAHULUAN .......................................................................................................... 1

1.1 Latar Belakang ........................................................................................................ 1 1.2 Rumusan Masalah ................................................................................................... 3 1.3 Tujuan Sistem ......................................................................................................... 4 1.4 Batasan Masalah ..................................................................................................... 4 1.5 Metode Pengerjaan .................................................................................................. 4

BAB II KAJIAN PUSTAKA ..................................................................................................... 6 2.1 Sistem Rekomendasi ............................................................................................... 6

2.1.1 Neighborhood Models................................................................................. 8 2.1.2 Association Rule Mining .......................................................................... 11

2.2 Perangkat Pendukung ............................................................................................ 13 2.2.1 Pemodelan UML ( Unified Model Language ) ......................................... 13

BAB III ANALISIS .................................................................................................................. 15 3.1 Analisa Sistem Rekomendasi Musik Last.fm ....................................................... 15

3.1.1 Use Case Model Dari Current System ..................................................... 16 3.1.2 Definisi Aktor ........................................................................................... 18 3.1.3 Use case Specification .............................................................................. 18

3.2 Analisa Lagu ......................................................................................................... 29 3.3 Analisa Data .......................................................................................................... 31 3.4 Analisa Metode Rekomendasi .............................................................................. 35 3.5 Spesifikasi Kebutuhan Fungsional ........................................................................ 50 3.6 Business Rule ........................................................................................................ 50

BAB IV PERANCANGAN...................................................................................................... 51 4.1 Tujuan & Batasan Perancangan ............................................................................ 51

4.2 Arsitektur Sistem................................................................................................... 51 4.3 Model Perilaku Sistem .......................................................................................... 55

4.3.1 Use Case Diagram ..................................................................................... 55 4.3.2 Definisi Aktor ........................................................................................... 56 4.3.3 Use Case Specification.............................................................................. 56

4.4 Perancangan Data .................................................................................................. 62 4.4.1 Kamus Data ............................................................................................... 64

4.4.2 Conceptual Data Model ............................................................................ 69 4.4.3 Physical Data Model ................................................................................. 69

4.5 Perancangan antar package ................................................................................... 71 4.6 Perancangan Interaksi Class .................................................................................. 73 4.7 Logika Proses ........................................................................................................ 79

4.8 Rancangan Tampilan........................................................................................... 130

 

 

 

 

 

 

 

 

 

 

 

 

iii

BAB V IMPLEMENTASI ..................................................................................................... 135 5.1 Batasan Implementasi ......................................................................................... 135 5.2 Infrastruktur Sistem............................................................................................. 135 5.3 Struktur Komponen Sistem ................................................................................. 137 5.4 Implementasi User Interface ............................................................................... 139 5.5 Requirement yang diimplementasikan ................................................................ 141 5.6 Pengujian ............................................................................................................. 142

BAB VI PENUTUP ................................................................................................................ 151 6.1 Kesimpulan ......................................................................................................... 151 6.2 Saran.................................................................................................................... 152

DAFTAR PUSTAKA ............................................................................................................. 154

 

 

 

 

 

 

 

 

 

 

 

 

iv

DAFTAR GAMBAR

Gambar 1 Vektor ....................................................................................................................... 9 Gambar 2 K-means dengan dua group ................................................................................... 10 Gambar 3 Flow chart k-means................................................................................................ 11 Gambar 4 Ilustrasi Pola Asosiasi ............................................................................................ 12 Gambar 5 Last.fm ................................................................................................................... 16 Gambar 6 Use case model sistem rekomendasi musik last.fm ............................................... 17 Gambar 7 Last.fm radio untuk pencarian lagu ....................................................................... 23

Gambar 8 Last.fm radio untuk pemutaran lagu ...................................................................... 24 Gambar 9 Chart lagu yang didengarkan ................................................................................. 24 Gambar 10 Pemutaran lagu pada media player ...................................................................... 25 Gambar 11 Audioscrobller ..................................................................................................... 26 Gambar 12 Charts lagu yang didengarkan ............................................................................. 26 Gambar 13 Rekomendasi data artist ....................................................................................... 27 Gambar 14 Rekomendasi data lagu ........................................................................................ 27 Gambar 15 Hasil Rekomendasi data Lagu yang berkaitan dengan lagu lain ......................... 27 Gambar 16 Skema relasi database last.fm .............................................................................. 31 Gambar 17 ER – diagram database last.fm ............................................................................ 32 Gambar 18 Top track pengguna last.fm ................................................................................. 33 Gambar 19 K-Means Clustering Iterasi – 1 ............................................................................ 39

Gambar 20 K-Means Clustering Iterasi – 2 ............................................................................ 42 Gambar 21 K-Means Clustering Iterasi - 2 ............................................................................ 43 Gambar 22 Clustering pada SPSS .......................................................................................... 44 Gambar 23 Arsitektur sistem .................................................................................................. 53 Gambar 24 Use Case Diagram Sistem Rekomendasi Lagu ................................................... 55 Gambar 25 ER-diagram sistem rekomendasi lagu ................................................................. 63 Gambar 26 Conceptual data model sistem rekomendasi lagu ................................................ 69 Gambar 27 Physical data model sistem rekomendasi lagu..................................................... 70 Gambar 28 package diagram sistem rekomendasi lagu.......................................................... 71 Gambar 29 Class diagram packagae model ........................................................................... 72 Gambar 30 Class diagram package controller ........................................................................ 73

Gambar 31 Hubungan antar objek pengolahan rekomendasi "cluster & cosine similarity scenario" ................................................................................................................................... 74 Gambar 32 Hubungan antar objek pengolahan rekomendasi "cluster & cosine similarity scenario" lanjutan ..................................................................................................................... 75 Gambar 33 Hubungan antar objek perekomendasian lagu kepada pengguna ........................ 76

Gambar 34 Interaksi class untuk usecase registrasi ............................................................... 77 Gambar 35 Interaksi class untuk usecase mendengarkan lagu ............................................... 78

Gambar 36 Interaksi class untuk usecase Authentikasi .......................................................... 79 Gambar 37 Rancangan tampilan login page ......................................................................... 130 Gambar 38 Alert message login gagal .................................................................................. 131

Gambar 39 Rancangan tampilan halaman registrasi ............................................................ 132 Gambar 40 Message registrasi berhasil ................................................................................ 132

Gambar 41 Rancangan tampilan home page ........................................................................ 133

Gambar 42 Infrastruktur Sistem ........................................................................................... 136

 

 

 

 

 

 

 

 

 

 

 

 

v

Gambar 43 Struktur komponen sistem ................................................................................. 137 Gambar 44 Form login ......................................................................................................... 140 Gambar 45 Form registrasi ................................................................................................... 140 Gambar 46 Home page .......................................................................................................... 141 Gambar 47 Hasil pengujian 1 K-F-1 .................................................................................... 146 Gambar 48 Hasil pengujian 2 K-F-1 .................................................................................... 146

 

 

 

 

 

 

 

 

 

 

 

 

vi

DAFTAR TABEL

Tabel 1 World internet usage and population ........................................................................... 1 Tabel 2 Hybridization method ................................................................................................... 7 Tabel 3 Perbandingan metode rekomendasi hybrid ................................................................. 7 Tabel 4 Definisi aktor .............................................................................................................. 18 Tabel 5 Usecase specification menambah data artis ke playlist .............................................. 18 Tabel 6 Usecase specification memberikan rating atau love .................................................. 18

Tabel 7 Usecase specification memberikan tag ....................................................................... 19 Tabel 8 Usecase specification mendengarkan lagu ................................................................. 20 Tabel 9 Usecase specification menentukan artis favorit ......................................................... 21 Tabel 10 Usecase specification mencari data lagu tertentu ..................................................... 22 Tabel 11 Kesimpulan dari analisa sistem rekomendasi musik Last.fm ................................... 28 Tabel 12 Saran perancangan ( analisa sistem rekomendasi musik last.fm )............................ 28 Tabel 13 Analisa lagu .............................................................................................................. 29 Tabel 14 Kesimpulan analisa lagu ........................................................................................... 30 Tabel 15 Evaluasi Data ............................................................................................................ 34 Tabel 16 Saran perancangan ( analisa lagu & data ) ............................................................... 35 Tabel 17 Data implicit feedback last.fm .................................................................................. 36 Tabel 18 Cosine similarity ....................................................................................................... 36

Tabel 19 Tabel kemiripan 5 sampel data implicit feedback .................................................... 37 Tabel 20 K-means clustering ................................................................................................... 38 Tabel 21 Tabel data lagu untuk k-means clustering ................................................................ 39 Tabel 22 Jarak setiap objek ke centroid .................................................................................. 40 Tabel 23 Pengelompokan objek ke dalam cluster ................................................................... 41 Tabel 24 Jarak objek ke centroid ............................................................................................. 42 Tabel 25 Pengelompokan objek ke dalam cluster ................................................................... 42 Tabel 26 Hasil Pengelompokkan Lagu ke dalam cluster ........................................................ 43 Tabel 27 Association rule mining ............................................................................................ 45 Tabel 28 Tabel binari association rule mining ........................................................................ 46

Tabel 29 Nilai asosiasi terhadap lagu yang akan direkomendasikan ...................................... 48

Tabel 30 Kesimpulan metode rekomendasi ............................................................................ 49

Tabel 31 Saran perancangan ( analisa metode rekomendasi ) ................................................. 49 Tabel 32 Kebutuhan fungsional ............................................................................................... 50 Tabel 33 Business Rule ........................................................................................................... 50

Tabel 34 Keterangan Aktifitas .................................................................................................. 54 Tabel 35 Definisi aktor ............................................................................................................. 56 Tabel 36 UC-01 melakukan registrasi ..................................................................................... 56 Tabel 37 UC-02 mendengarkan lagu ....................................................................................... 57 Tabel 38 UC-03 mendapatkan rekomendasi ........................................................................... 59

Tabel 39 UC-05 membuat jadwal untuk update cluster (jarak & sudut) ................................ 60 Tabel 40 Entity artist ............................................................................................................... 64 Tabel 41 Entity Tag ................................................................................................................. 65

Tabel 42 Entity Track .............................................................................................................. 65 Tabel 43 Entity User ................................................................................................................ 67

 

 

 

 

 

 

 

 

 

 

 

 

vii

Tabel 44 Entity Cluster ............................................................................................................ 68 Tabel 45 Logika Proses updateClusterController .................................................................... 79 Tabel 46 Logika Proses predictionController ........................................................................ 103 Tabel 47 Logika Proses trackClusterModel .......................................................................... 108 Tabel 48 Logika Proses clusterModel ................................................................................... 115 Tabel 49 Logika Proses artistTagModel ................................................................................ 117 Tabel 50 Logika Proses tagModel ......................................................................................... 118 Tabel 51 Logika Proses trackModel ...................................................................................... 119 Tabel 52 Logika Proses songSimilarityModel ...................................................................... 120 Tabel 53 Logika Proses trackUserModel .............................................................................. 122 Tabel 54 Logika Proses userModel ....................................................................................... 128

Tabel 55 Rancangan tampilan UI.01 .................................................................................... 130 Tabel 56 Rancangan Tampilan UI.02 .................................................................................... 131 Tabel 57 Tabel 4. 26 Rancangan Tampilan UI.03 ................................................................. 133 Tabel 58 Keterangan dari infrastruktur sistem ...................................................................... 136 Tabel 59 Keterangan dari struktur komponen ....................................................................... 138 Tabel 60 Daftar requirement yang diimplementasikan ......................................................... 141 Tabel 61 Pengujian sistem rekomendasi berdasarkan data masukan .................................... 143 Tabel 62 Data masukan untuk pengujian K-F-1 .................................................................... 144 Tabel 63 Keluaran yang diharapkan ( cluster 1 )................................................................... 144 Tabel 64 Keluaran yang diharapkan ( cluster 2 )................................................................... 144 Tabel 65 Keluaran yang diharapkan ( cluster 3 )................................................................... 145 Tabel 66 Keluaran yang diharapkan ( cluster 4 )................................................................... 145

Tabel 67 Data pengujian untuk K-F-2 ................................................................................... 147 Tabel 68 Hasil Responden 1 .................................................................................................. 148 Tabel 69 Hasil Responden 2 .................................................................................................. 148 Tabel 70 Hasil Responden 3 .................................................................................................. 149

 

 

 

 

 

 

 

 

 

 

 

 

viii

DAFTAR ISTILAH

Correlation

Hubungan antar 2 pengguna terdaftar atau hubungan antar 2 lagu. Dihitung dengan menggunakan formula yang memanfaatkan nilai rating

Cosine Similarity Nilai kemiripan antara dua buah vector yang dilihat dari sudut kosinus yang terbentuk diantara keduanya.

Clustering Suatu alat untuk analisa data, yang memecahkan permasalahan mengenai penggolongan.

Tag Kata atau frase yang membantu mengkategorikan topik

 

 

 

 

 

 

 

 

 

 

 

 

ix

DAFTAR SINGKATAN

Singkatan Deskripsi

CF Collaborative filtering

CN Content-Based

ER Entity Relationship

KB Knowledge-Based

KSR Kesimpulan Sistem Rekomendasi

SP Saran Perancangan

SQL Structured Query Language

UCA Use Case analisis

UI User Interface

 

 

 

 

 

 

 

 

 

 

 

 

x

DAFTAR SIMBOL

Simbol Nama Penggunaan Keterangan

Activity Activity diagram Elemen yang menggambarkan kegiatan.

Action Activity diagram Elemen yang menggambarkan aksi dari suatu kegiatan.

Initial State Activity diagram,

state diagram

Elemen yang memperlihatkan dimana aliran aktivitas berawal.

Final State Activity diagram Elemen yang memperlihatkan dimana aliran aktivitas berakhir.

Decision Activity Diagram,

state diagram

Elemen yang menggambarkan suatu kondisi.

Class Class diagram Merepresentasikan suatu objek yang menggambarkan struktur dan perilaku sistem.

package Package diagram Merepresentasikan suatu paket-paket class.

act Use Case Model

Activ ity3

act Use Case M...

Action1

act Use Cas...

ActivityInitial

act Use Ca...

ActivityFinal

act Us...

act Use Case Model

Class1

act Use Case Model

Package1

 

 

 

 

 

 

 

 

 

 

 

 

xi

DAFTAR PUSTAKA

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Group, M. M., 2012. INTERNET USAGE STATISTICS. [Online]

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York: Cambridge University Press.

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xiii

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