MUSIC INTEREST RECOMMENDATION ON FACEBOOK USING SELF ORGANIZING MAP.
LIST OF REVIEWERS
Prof. Dr. dr. Darwin Amir Sp.S(K). (Neurology, Faculty of Medical, Andalas University,
Padang, Indonesia).
Dhany Arifianto, Dr. Eng. (Dept. Engineering Physics, Sepuluh Nopember Institute of
Technology, Surabaya, Indonesia)
Dr.-Ing. Azwirman Gusrialdi (Dept. Electrical Engineering and Computer Science,
University of Central Florida, USA)
Yoshinao Hoshi, Dr. Eng. (Tokyo University of Science, Japan)
Yose Fachmi Buys, D.Eng. (Polymer Research Center, Texchem Polymers Sdn Bhd,
Penang, Malaysia)
Nopriadi Hermani, Dr. Eng. (Dept. Engineering Physics, Gadjah Mada University,
Indonesia)
Farid Triawan, ST, M. Eng., Dr. Eng. (Tokyo Institute of Technology, Japan).
Chastine Fatichah, ST, MT. Dr. Eng.
(Faculty of Information Engineering, Sepuluh
Nopember Institute of Technology, Surabaya, Indonesia)
Fithra Faisal Hastiadi, Ph.D (Faculty of Economics, University of Indonesia, Indonesia).
Aris Aryanto, ST, MT. (Tokyo Institute of Technology, Japan).
Donna Maretta Ariestanti, Apt., M.Sc. (Tokyo Institute of Technology, Japan).
Radon Dhelika, B.Eng, M.Eng. (Tokyo Institute of Technology, Japan)
Martin Leonard Tangel, S. Kom., M.Eng. (Tokyo Institute of Technology)
Arya Adriansyah, M. Sc. (Eindhoven University of Technology, the Netherlands)
I Made Andi Arsana (Australian National Centre for Ocean Resources and Security
(ANCORS), University of Wollongong, Australia, Geodesy and Geomatics,
Gadjah Mada University, Indonesia)
Indriani Noor Hapsari, S.T., MT. (Information System Laboratory, College of Electrical
and Information, Bandung Institute of Technology, Indonesia).
Prasetyo Andy Wicaksono, ST, M.T (LayangLayang Mobile, Indonesia).
Fitriani, ST, M. Eng. (Penang, Malaysia).
Setia Pramana (Sekolah Tinggi Ilmu Statistik Jakarta, Indonesia & Medical
Epidemiology and Biostatistics Department, Karolinska Institute Stockholm
Sweden.)
Janet Pomares Betancourt, M.Sc. (Tokyo Institute of Technology, Japan).
Nur Ahmadi, ST. (Tokyo Institute of Technology, Japan).
BOARD OF EDITORS
Sri Hastuty, ST., MT., M. Eng., Dr. Eng. (National Institute for Materials Science,
Tsukuba, Japan).
Ihsan Iswaldi, Dr. (Granada University, Spain).
Dian Andriany, Amd. (Turkey)
ADVISORS (THE FOUNDERS OF ACIKITA)
Associate Prof. Prihardi Kahar (Meisei University, Japan)
Jumiarti Agus, S. Si., M. Si., Dr. Eng. (Tokyo Institute of Technology, Japan).
Prof. Fasli Jalal (Andalas University)
LIST OF CONTENT
Page
Preface
i
Oral Presentation
Group I : Medicine, Health, and Pharmaceutical, Chemistry, Sensor, and Material
Engineering
Code
Authors
Title
OP-001
OP-002
Lia Aprilia and
Ratno Nuryadi
Ratno Nuryadi
OP-003
James Sibarani
OP-004
B. A. Budiman,
K. Takahashi, K.
Inaba, K.
Kishimoto
OP-005
Nuring Tyas
Wicaksono and
Yu Chun Chiang
OP-006
OP-007
Nurlienda
Hasanah, Nia
Novita Wirawan,
Harijanto, and
Nanik Setijowati
Tris Dewi
Indraswati, Adang
Suwandi Ahmad,
Irman Idris, and
Adrian Venema
STUDY OF THE CHANGE IN RESONANCE
FREQUENCY AND SENSITIVITY FOR
MICROCANTILEVER SENSOR
MATHEMATICAL MODEL OF
SPRING-DAMPER SYSTEM FOR
MICROCANTILEVER-BASED BIOSENSOR
APPLICATION
BIODEGRADABLE AMPHIPHILIC
COPOLYMERS PREPARED BY REVERSIBLE
ADDITION-FRAGMENTATION TRANSFER
AGENT (RAFT) POLYMERIZATION
TECHNIQUE FOR PHOTODYNAMIC
THERAPY
EVALUATION OF INTERFACIAL
PROPERTIES QUALITY BASED ON STRAIN
DISTRIBUTION CONTOUR IN MATRIX
COMPOSITE
PLATINUM NANOPARTICLES
PREPARATION ON THIOLIZED
MULTI-WALLED CARBON NANOTUBES
FOR ELECTROCATALYSTS
VEGETABLES CORRELATE MOST WITH
BLOOD PRESSURE AMONG OUTPATIENT
AT DINOYO COMMUNITY HEATLH
CENTER IN MALANG, INDONESIA
SENSING ELEMENT DESIGN OF
MEMS-BASED TRANSLATIONAL
VIBRATORY Z-AXIS GYROSCOPE USING
DESIGNER MODULE IN COVENTORWARE
1
12
22
34
49
63
76
OP-008
Adiar Ersti
Mardisiwi, Yusuf
Ariyanto, Candra
Irawan, Fajar
Dzikri
Harwiansyah,
Rahma Sakinah,
Ridho Prawiro,
and Vincentius
Totok Noerwasito
OP-009
A. Wikarta and
C.K. Chao
OP-010
Dito Anurogo and
Taruna Ikrar
OP-011
Dito Anurogo
OP-012
Dito Anurogo
Nur Akmalia
Hidayati, Made
OP-013 Puspasari
Widhiastuty, and
Fida Madayanti
Warganegara
Group II : Energy
Code
Authors
98
THE EFFECT OF TWO-LEVEL HIERARCHIES
FOR STRENGTHENING GECKO’S FEET
(G-FEET) JOINT SYSTEM WITH
COMPOSITION RATIO OF 50:50 AND
DESIGN AS INDEPENDENT VARIABLE
SOLUTIONS OF A CRACK INTERACTING
WITH A TRI-MATERIAL UNDER REMOTE
UNIFORM SHEAR LOAD
PHARMACOGENETICS AND
PHARMACOGENOMICS: THE ART OF
EPILEPSY MANAGEMENT
THE SCIENCE OF “TINDIHAN”
PHENOMENON
110
THE ALICE IN WONDERLAND SYNDROME
149
OP-016
PURIFICATION OF RECOMBINANT LIPASE
FROM LOCAL ISOLATE
TITLE
Bayu Prabowo,
Kentaro Umeki,
Kunio Yoshikawa
Ayub Torry
Satriyo Kusumo
and Handojo
138
158
UTILIZATION OF CO2 FOR RENEWABLE
ENERGY PRODUCTION THROUGH
BIOMASS GASIFICATION
DESIGN SYSTEM CONTROL FOR RADIO
BATTERY FUNCTION CHECKING
OP-015 Syahril Ardi and
Alfan Subiantoro USING PROGRAMMABLE LOGIC
CONTROLLER
Group III : Education, Economics, Law, and Marketing,
Telecommunication, Computer Information Science, Management
Code
Authors
TITLE
OP-014
117
STUDY OF MARITIME BOUNDARY
REGULATION BETWEEN
INDONESIA-MALAYSIA IN THE
169
185
195
Leksono
OP-017
Rizanna
Rosemary
OP-018
Asri Wijayanti
OP-019
Rudy, Eka
Miranda, and Eli
Suryani
OP-020
Yohannes
Kurniawan
OP-021
Indrajani
OP-022
Gusti Ayu Vida
Mastrika Giri,
Kadek Cahya
Dewi, and Agus
Muliantara
OP-023
Aini Farmania,
Shieh-Liang
Chen, Ananda
Fortunisa
OP-024
OP-025
OP-026
Rismanda
Dewanti, Kadek
Cahya Dewi,
Agus Muliantara
Ahmad
Firmansyah, Yudi
Satria
Gondokaryono
Deshinta Arrova
Dewi
FRAMEWORK TO DEFEND SOVEREIGNTY
OF REPUBLIC OF INDONESIA
A CONTENT ANALYSIS OF TOBACCO
ADVERTISING AND PROMOTION FOR
INDONESIAN TOBACCO BRANDS ON
YOUTUBE
PROMOTE LABOR EDUCATION THROUGH
INTERNATIONAL COOPERATION IN THE
FIELD OF APPRENTICESHIP
BUSINESS INTELLIGENCE MODEL
DEVELOPMENT FOR MAXIMIZE
MARKETING PROCESS AT HIGHER
EDUCATION
KNOWLEDGE MANAGEMENT FOR
SCHOOL ACADEMIC OPERATION
SERVICES: PERCEPTIONS OF
APPLICATION AND BENEFITS
BUILD AN ENTERPRISE DATA
WAREHOUSE TO IMPROVE THE QUALITY
OF HOSPITAL
MUSIC INTEREST RECOMMENDATION ON
FACEBOOK USING SELF ORGANIZING
MAP
ANALYSIS COMPARISON OF FINANCIAL
PERFORMANCE BY USING VARIOUS
FINANCIAL RATIOS AMONG
COMMERCIAL BANKS IN INDONESIA,
MALAYSIA AND SINGAPORE
MUSIC CLASSIFICATION BASED ON
GENRE USING BACKPROPAGATION AND
SOCIAL TAGGING IN WEB MUSIC
DATABASE
ENTERPRISE ARCHITECTURE PLANNING
ON BADAN PEMERIKSA KEUANGAN
REPUBLIK INDONESIA
INVESTIGATION OF A POTENTIAL
BLENDED LEARNING MODEL FOR
TEACHING AND LEARNING COMPUTER
20
7
228
238
255
270
277
291
306
316
332
OP-027
Rudiman and
Zamhar Ismail
OP-028
Haviluddin,
Rayner Alfred,
and Patricia
Anthony
OP-029
OP-030
OP-031
OP-032
OP-033
OP-034
OP-035
PROGRAMMING COURSES IN A PRIVATE
HIGHER LEARNING INSTITUTION IN
MALAYSIA
ACTION RESEARCH :
DEVELOPING A KNOWLEDGE PORTAL
WITH SOCIAL MEDIA
UTILIZATION OF COBIT FRAMEWORK
WITHIN IT GOVERNANCE: A STUDY
LITERATURE
THE DEVELOPMENT OF INFORMATION
SYSTEM FOR WEB-BASED ONLINE
ACCREDITATION FOR VOCATIONAL
SCHOOL TO IMPROVE ASSESOR’S WORK,
Inayatulloh
PARTICIPANTS OF ACCREDITATION AND
INTEGRATED ACCREDITATION
INFORMATION STORAGE
EFFORTS OF INDONESIA AND MALAYSIA
Irham Rizani and
IN TOUCH EDUCATION FOR CHILDREN OF
Wan Shawaluddin
INDONESIAN MIGRANT WORKERS IN
Wan Hassan
SABAH
Bustanul Arifin
MICROGEM THERMAL CONTROL
ASPECTS ‘PHASE A – PHASE D ANALYSIS’
Rini Sovia, Retno DIAGNOSE INTELLIGENCY LEVEL BY
Devita, and Etri
EXPERT SYSTEM USING WAP
Suhelmidawati
PROGRAMMING
ANALYSIS OF ENVIRONMENTAL IMPACT
DOCUMENTATION SUBSURFACE
Angy Sonia
JAKARTA CITY TO BALANCE MOBILITY
AND URBANIZATION OF SOCIETY
IN A BIG CITY
VIEWING ADS IN THE CULTURAL
PRODUCT USE OF THE DECISIONS, HOW
Angy Sonia
THE MEDIA SHAPPING OPINION? AND
HOW THE MEDIA AFFECT THE
INFORMATION SOCIETY
Angy Sonia
DEVELOPING EFFECTIVENESS ANALYSIS
INFORMATION PACKAGE FOR FOOD AND
HEALTH THROUGH INFORMATION
352
369
386
392
402
414
422
423
424
Poster Presentation
Group I : Material Engineering
Code
Authors
Makhyan Jibril A,
Laili Fitri N, Sri
PP-001
Ratna W, Lidia
M, Fetreo N, and
Rasjad Indra
Group II : Energy
Code
Authors
Title
IMMUNIZATION WITH KEYHOLE LIMPET
HEMOCYANIN CONJUGATED WITH
ADVANCED GLYCATION END PRODUCT
PREVENT DIABETIC COMPLICATION IN
MICE
425
Title
Group III : Education, social sciences, Economics, Computer Information
Science
Code
Authors
Title
PP-002
Tri Pudjadi and
Harisno
PP-003
Evaristus Didik
Madyatmadja and
Albert V Dian
Sano
PP-004
PP-005
PP-006
LEARNING OBJECTS USING POWER POINT 439
ANIMATION TO INCREASE LEARNING
PROCESS IN SCHOOL. CASE STUDY AT
SMP YASPIA, SMK BINA MANAJEMEN
AND MTsN 7 JAKARTA TIMUR
451
DECISION SUPPORT SYSTEM FOR
PHYSICALLY TOOLS ALLOCATION
ASSESSMENT OF IT GOVERNANCE USING
Siti Elda Hiererra,
COBIT 4.1 FRAMEWORK METHODOLOGY:
Johan Muliadi
CASE STUDY UNIVERSITY IS
Kerta, and
DEVELOPMENT IN IT DIRECTORATE
Noerlina
BINUS UNIVERSITY
Rosalina
THE EVALUATION OF RESIDENTIAL
Kumalawati,
DEVELOPMENT BASED ON LAHAR RISK
Rijanta, Junun
ANALYSIS IN KALI PUTIH SUB
Sartohadi, and
WATERSHED,
Rimawan
MAGELANG CENTRAL JAVA, INDONESIA
Pradiptyo
Rosalina
THE MAPPING OF LAHAR FLOOD RISK
Kumalawati,
ABOUT RESIDENTIAL
IN SALAM REGENCY, MAGELANG,
Seftiawan S.
CENTRAL JAVA
Rijal, Rijanta,
468
485
492
Junun Sartohadi,
and Rimawan
Pradiptyo
PP-007
Agus Hamdi
PEMBANGUNAN MODEL APLIKASI
INVENTORY DAN PEMBAYARAN PADA
KOPERASI DAN UKM
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Jakarta, August 26-28, 2012
OP-022
MUSIC INTEREST RECOMMENDATION ON FACEBOOK
APPLICATION USING SELF ORGANIZING MAP
Gusti Ayu Vida Mastrika Giri1, Kadek Cahya Dewi2, Agus Muliantara3
1,2,3
Department of Computer Science, Faculty of Mathematics and Natural Sciences,
Udayana University, Indonesia
1
g.a.vida.mastrika@cs.unud.ac.id
2
cahya.dewi@cs.unud.ac.id
3
muliantara@cs.unud.ac.id
ABSTRACT
The rapid development of the diversity of music and technology caused the music
listeners to need recommendations of the types of music which were identical with their
music interest from the social media on the internet which was easily accessed such as
Facebook. The recommendations of music interest could not only be obtained from the
same genre of music but also from the similar music features that used to give
recommendations. This research implement the application of Music Information by
Computer Science (MICS) Recommendations on Facebook. MICS Recommendations
could be used to recommend music interest using Self Organizing Map algorithm
accompined by the analysis of relatedness between audio similarities with music genre.
The music features that used in this research were key, mode, loudness, energy, and
tempo. The dataset that used in this paper are 280 songs from 14 different genres. The
dataset were divided into 196 as the initial dataset and 86 dataset for testing. In this
research, the genres with the biggest score of relatedness were Acoustic and Jazz; the
score was 43.33%. The genres with the lowest score of relatedness were Electronic and
Rock; the score was 6.67%.
Keywords: Self Organizing Map, Music Recommendation, Facebook Music Interest,
Facebook Music Page, Music Information Retrieval.
1. Introduction
Music is a collection of tones that are assembled into a harmony in a regular
rhythm and tempo. Today, there are many artists who express their creativity through
music, which is cause the increasing of music quantity and diversity.
The rapid development of the music diversity and technology caused the music
listeners need more music or artist recommendations which were identical with their
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Jakarta, August 26-28, 2012
music interest from the social media on the internet which was easily accessed such as
Facebook. According to search new music recommendations, music listeners can find
Facebook Pages of various artists and find information about their music, latest news,
and events. But opening artists’ Facebook Pages one by one takes a long time, so that
music listeners need a new way to get recommendations that is more efficient.
The recommendations of music interest could not only be obtained from the
same genre of music but also from the similar music features that can be used to give
recommendations. Research about music recommendations and audio similarity has
done by some researchers, such as Dewi and Putri which was recommending music
according to audio similarity using rhythm feature and K-Nearest Neighbor method
[1]
.
Pampalk, Flexer, and Widmer searched for music similarity using the combination of
spectral similarity and fluctuation patterns [2].
In this research, music interest recommendations were determined using Self
Organizing Map algorithm which was implemented in Facebook application,
accompanied by analysis of relatedness between audio similarities with music genre.
Name of the application is Music Information by Computer Science (MICS)
Recommendations. Music collection that used was obtained from us.7digital.com and
www.last.fm. Music features that used in this research were key, mode, loudness,
energy, and tempo which were obtained using Echo Nest API. The music listeners’
music interest were obtained from four Facebook Music Pages that they like and music
recommendations given are five Music Pages from different artists for each music
interest.
2. Material and Method
a.
Music Theory
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Jakarta, August 26-28, 2012
Music consists of music features such as mode, harmony, tempo, rhythm, and
dynamic (loudness) [3]. There are also other music features, such as energy, key, timbre,
pitch, and many others. In music, genre is simply means type or class of music. Music
genre gives expectation how the music will sound, how long the music is, and how the
listeners should behave. In the Mozart era, there were five main genres. The genres are
symphony, string quartet, sonata, concerto, and opera
[4]
. In the modern music era,
music can be divided into some genre, such as acoustic, blues, classical, country,
electronic, emo, hip hop, jazz, metal, pop, R&B, reggae, rock, and soul.
b.
Dataset Collection
Echo Nest is a website that provides platform for music application developers,
which can be accessed through www.echonest.com. Music features dataset were
obtained using Echo Nest API. Echo Nest provides Application Programming Interface
(API) that can be used by developer for building music web or making play list
arrangements. Echo Nest API that used in this research is Echo Nest API version 4.2.
Echo Nest used signal processing and machine learning for extracting all
features in music. Output produced by music feature analysis done by Echo Nest consist
of complete music description, global structure and attributes such as key, loudness,
time signature, tempo, beats, sections, and harmony.
Echo Nest API will give access to music application developers to access and
interact with the music data that are stored in Echo Nest using methods that will give
responds in the form of JSON or XML. Method that is used in this research is Song API
Method. Here is an example of Song API usage in showing information from the song
“Just Dance”, from Lady Gaga:
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Jakarta, August 26-28, 2012
http://developer.echonest.com/api/v4/song/search?api_key=96GANOIWHEX5ZI6LL&
format=xml&results=1&artist=lady%gaga&title=just%20dance&bucket=id:7digitalUS&bucket=audio_summary
By using Song API Method, user will get output in XML that consist of song
information about the song “Just Dance”. Output of the Song API Method is shown in
Figure 1. The output consists of music descriptions, such as song title and artist, and
also global structure, such as key, mode, and time signature.
Figure 1. Song API Method Output
c.
Self Organizing Map
Self Organizing Map (SOM) network use unsupervised learning method that
maps data from any dimension into two dimensional maps. In its basic form it produces
a similarity graph of input data [5]. SOM network consist of two layers, which is named
input layer and output layer. Every neuron in input layer is connected with every neuron
in output layer. Every neuron in output layer is representing cluster from inputs that are
given.
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SOM algorithm has steps as follows:
step 0 initialize weights wij
set topological neighborhood parameters
set learning rate parameter
step 1 while stopping condition is false do step 2-8
step 2 for each input vector x, do step 3-5
step 3 for each j, compute:
√∑
step 4 find index j such that d(j) is a minimum
step 5 for all units j within a specified neighborhood of j and for all i:
Wij(new) = Wij(old) + α (Xi – Wij(new))
step 6 update learning rate
step 7 reduce radius of topological neighborhood at specific time
step 8 test stopping condition [6].
Test stopping condition is true if the difference between weight change in
iteration to the next iteration is less than 0.0001
[7]
. The constant 0.0001 is used in this
experiment and it produced the best result of the experiment. This means that the
network has become convergent, so it can be stopped.
d.
Facebook Applications
Facebook is a social network where the users can make a personal profile with
photos, personal information, and self interest list. Facebook Applications are small
programs that run inside Facebook. Only Facebook users can use Facebook
Applications. Facebook Platform and Facebook Query Language are used for
developing Facebook applications. Some parts from high class Facebook Platform
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Jakarta, August 26-28, 2012
components are Graph API, Authentication, Social Plugins, and Open Graph Protocol.
Facebook Query Language (FQL) offers features and language elements similar to
Structured Query Language (SQL) which give access for Facebook Applications to
directly query Facebook’s internal data.
e.
Clustering Method
Variables that used in this research were weight value that connects input layer
and output layer and learning rate value in clustering process. Research dataset were
280 songs that consist of 20 songs for each genre. Music genre used in this research
were acoustic, blues, classical, country, electronic, emo, hip hop, jazz, metal, pop, R&B,
reggae, rock, and soul. Music features used were key, mode, loudness, energy, and
tempo. Key, mode, loudness, energy, and tempo are chosen because they are the main
music features that provided by Echo Nest and they also can distinguish music better
than other features that are provided, such as time signature and duration.
Music features dataset should be in the same range according to make them have
the same impact in clustering process, therefore dataset normalization is needed. The
normalization done with equation
SOM network used for dataset clustering consist of five input neurons that
represent five music features and fourteen output neurons that represent music genre
used in this research. SOM network for clustering can be seen in Figure 2.
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Jakarta, August 26-28, 2012
Y2
W15
W11 W12
Input Layer
X1
W21
Y3
W25
W22
X2
W31
W33
….
Y1
….
Output Layer
W35
Y14
W14 1
W14 2 W14 5
X5
Figure 2 Self Organizing Map Network Structure
A total of 196 datasets used as initial data for SOM algorithm were processed in
SOM network to determine the optimal weights (wij) that connects input neurons (xi)
and output neurons (yi).
f.
Classification and Recommendation Method
A total of 84 testing data were classified into clusters that produced by clustering
process using SOM. Steps for classification and determining recommendations are as
follows:
1.
Search for user’s music interest name in their Facebook profile, and then search the
artist’s song feature in the dataset.
2.
Calculate Euclidean Distance from every music interest with optimal SOM weight.
3.
Determine index of the smallest Euclidean Distance. The index is representing the
cluster number.
4.
Recommendation given was determined by artists’ music with the most similar
music features with input features. The similarity was measured using Euclidean
Distance, so that the smaller the Euclidean Distance, the more similar the music.
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g.
System Testing Analysis Method and Result Analysis Method
Testing the results of clustering was done by finding the value of cohesion and
separation. Following equations is used to get the value of cohesion and separation:
∑
(
[8]
)
System display testing was done by checking the Music Interest Display and
Music Page Recommendations Display. System display was done by finding the value
of precision and recall, using following equations:
|
|
|
|
|
|
|
|[9]
Besides finding the value of precision and recall, the system display is also
checked by using F-Measure. F-Measure (or F-Score) is the harmonic mean of precision
and recall and is calculated as
[10]
It will have a high value only when both precision and recall have high values, and can
be seen as a way to find the best compromise between precision and recall [10].
Recommendations result was analyzed by finding the relatedness score between
music page recommendations given with music genre of user’s music interest. So that
genre with the greater and the least relatedness score can be determined. Relatedness
score was calculated using the following equation:
With RS = relatedness score, x = the total of recommendations that have the same genre
with the music interest, and y = the total of recommendations.
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3. Result and Discussions
In this research, eight experiments were conducted using different weights and
learning rates. The first weight matrix was the average value of each feature from each
genre, while the second weight matrix was random value weights. List of experiments
that have been done is shown in Table 1.
Table 1 List of Experiments
Experiment
Weight
1
2
3
4
5
6
7
8
1st
1st
1st
1st
2nd
2nd
2nd
2nd
Learning
Rate
0,5
0,5
0,3
0,3
0,5
0,5
0,3
0,3
Learning Rate
Reduction
0,9995
0,999
0,9995
0,999
0,9995
0,999
0,9995
0,999
Total
Iteration
15.192
7.595
13.553
6.775
15.192
7.595
13.553
6.775
Every experiment has its own optimal weights produced by SOM algorithm. The
result of optimal weights from experiments with the same learning rate, such as
experiment number 1 and number 5 is the same, while experiments with different
learning rate, such as experiment number 1 and number 2 is different.
Result of optimal weights from every experiment showed that in experiments
conducted with different learning rate but same initial weight, SOM will produce
different optimal weights. In experiments conducted with different initial weights but
same learning rate, SOM will produce the same optimal weight.
Clustering result was analyzed using cohesion and separation value.
Experiments with the smallest average value of cohesion and bigger average value of
separation are experiment number 4 and number 8. The average value of cohesion and
separation from each experiment is shown in Table 2.
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Table 2 The Average Value of Cohesion and Separation from Each Experiment
Experiment
1
2
3
4
5
6
7
8
Cohesion
0.236493
0.237867
0.228352
0.228224
0.236493
0.237867
0.228352
0.228224
Separation
0.883213
0.885589
0.902854
0.902857
0.883213
0.885589
0.902854
0.902857
Relatedness score obtained from calculation using experiment that has the
smallest cohesion average value and bigger separation average value. The genres with
the highest relatedness score were acoustic and jazz; the score was 43.33%. The genres
with the lowest score of relatedness were electronic and rock; the score was 6.67%.
The bigger the relatedness score that a genre had indicates that most of the
recommendations will have the same genre as the music interest. The smaller the
relatedness score that a genre had indicates that the recommendations given will be
from various genres. Score of relatedness in every genre is shown in Table 3.
Table 3 Relatedness Score in Every Genre
Genre
Relatedness
Score
Acoustic 43.33%
Blues
16.67%
Classical 26.67%
Country
10.00%
Electronic 6.67%
Emo
23.33%
Hip Hop
16.67%
Jazz
43.33%
Metal
40.00%
Pop
16.67%
R&B
13.33%
Reggae
13.33%
Rock
6.67%
Soul
13.33%
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
System display testing was done by checking the Music Interest Display and
Music Page Recommendations Display, which is shown in Figure 3.
Figure 3 MICS Recommendations Display
The testing was done by 21 MICS Recommendations users with different artist and
different genres for each user. In order to do the test, users allowed the MICS
Recommendations application to access their favorite music, and then MICS
Recommendations will give artist recommendations according to the clustering result
and display the artist’s music page on the application.
According to the test result, the value of precision and recall for Music Interest
Display
were
0.9167
and
0.6471.
Precision
value
indicates
that
MICS
Recommendations can obtain 91.67% relevant music interest data from users’ profiles.
Recall value indicates that MICS Recommendations can obtain 64.71% relevant music
page from Facebook database.
The value of precision and recall for Music Page Recommendations Display
were 0.9019 and 0.5679. Precision value indicates that MICS Recommendations can
obtain 90.19% relevant music page recommendations from total recommendations.
Recall value indicates that MICS Recommendations can obtain 56.79% relevant music
page from Facebook database.
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
The recall value is still low because in Facebook, there are many artist music
pages that have the same name. For Example, artist System of A Down have five
different Facebook Music Page named “System of A Down”, this caused the recall have
lower value. To improve the recall value, the application could retrieve more Facebook
Music Page from the database, but it will make the user confused because there are
many Music Pages with the same artist name.
The F-Measure value for Music Page Recommendations Display was 0.7585 and
the F-Measure value for Music Page Recommendations Display was 0.6968. The FMeasures that are above 0.5 indicate that the MICS Recommendation can retrieve and
display more correct Music Page for the users.
4. Conclusions
1.
Self Organizing Map algorithm has successfully used to group the music features
dataset by key, mode, loudness, energy, and tempo to be used in the MICS
Recommendations
application.
MICS
Recommendations
application
has
successfully built in Facebook social network with the value of precision, recall,
and F-Measure for Music Interest Display were 0.9167, 0.6471, and 0.7585 and the
value of precision, recall, and F-Measure for Music Page Recommendations
Display were 0.9019, 0.5679, and 0.6968.
2.
Relatedness score analysis result showed that there is relatedness between music
features similarity (key, mode, loudness, energy, and tempo) with music genre.
Every genre has a different relatedness score. In this research, the genres with the
highest relatedness score were acoustic and jazz; the score was 43.33%, while the
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
genres with the lowest score of relatedness were electronic and rock; the score was
6.67%.
3.
In this research, in experiments conducted with different initial weights but same
learning rate, SOM will produce the same optimal weight and same clusters. So that
only the determination of initial learning rate and learning rate reduction can
change the clustering result.
Acknowledgement and References
[1]
Dewi, K. C., and Putri, L. A., Music Recommendation Based on Audio Similarity
Using K-Nearest Neighbour., Proceeding of The 1st ACIKITA International
Conference of Science and Technology., -, 124, (2011).
[2]
Pampalk, E., Flexer, A., & Widmer, G., Improvements of Audio-Based Music
Similarity and Genre Classification., In Proceedings of the 6th International
Conference on Music Information Retrieval, -, -, (2005).
[3]
Meyers, O., A Mood-Based Music Classification and Exploration System, Master
of Science in Media Arts and Sciences, Massachusetts Institute Of Technology,
United States, (2007)
[4] Wright, Craig,Listening to Music, Sue Gleason,Schirmer, 20 Channel Center
Street, Boston, MA 022010, USA,2011, 6th Edition, 188-200.
[5]
Kohonen, Teuvo, Self-Organizing Maps, Huang, T.S. Kohonen, Teuvo,
Schroeder, M.R, Springer-Verlag Berlin Heidelberg New York, 2001, 3rd Edition,
106-176.
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
[6]
Mehrad, J, and S Koleini, Using SOM Neural Network In Text Information
Retrieval, Iranian Journal of Information Science and Technology, Volume 5, 5364, (2007).
[7]
Xiao, Xiang, E.R. Dow, E Eherhart, Z.B. Miled, and R.J. Oppelt, Gene Clustering
Using Self-Organizing Maps and Particle Swarm Optimization, IPDPS ’03
Proceedings 0f the 17th International Symposium on Parallel and Distributed
Processing, -, -, (2003).
[8]
Tan, Pang Ning, Michael Steinbach, and Vipin Kumar, Introduction to Data
Mining, -,Pearson Addison Wesley, 2006, -,537-568.
[9]
Kadyanan, I Gusti Agung Gede Arya, Perolehan Citra Berbasis Konten pada
Aplikasi Penginderaan Jarak Jauh, Indonesia University, -, 2011, -, -.
[10] Guillet, Fabrice and Hamilton, Howard J., Quality Measures in Data Mining, -,
Springer, 2007, -, 141.
290
Prof. Dr. dr. Darwin Amir Sp.S(K). (Neurology, Faculty of Medical, Andalas University,
Padang, Indonesia).
Dhany Arifianto, Dr. Eng. (Dept. Engineering Physics, Sepuluh Nopember Institute of
Technology, Surabaya, Indonesia)
Dr.-Ing. Azwirman Gusrialdi (Dept. Electrical Engineering and Computer Science,
University of Central Florida, USA)
Yoshinao Hoshi, Dr. Eng. (Tokyo University of Science, Japan)
Yose Fachmi Buys, D.Eng. (Polymer Research Center, Texchem Polymers Sdn Bhd,
Penang, Malaysia)
Nopriadi Hermani, Dr. Eng. (Dept. Engineering Physics, Gadjah Mada University,
Indonesia)
Farid Triawan, ST, M. Eng., Dr. Eng. (Tokyo Institute of Technology, Japan).
Chastine Fatichah, ST, MT. Dr. Eng.
(Faculty of Information Engineering, Sepuluh
Nopember Institute of Technology, Surabaya, Indonesia)
Fithra Faisal Hastiadi, Ph.D (Faculty of Economics, University of Indonesia, Indonesia).
Aris Aryanto, ST, MT. (Tokyo Institute of Technology, Japan).
Donna Maretta Ariestanti, Apt., M.Sc. (Tokyo Institute of Technology, Japan).
Radon Dhelika, B.Eng, M.Eng. (Tokyo Institute of Technology, Japan)
Martin Leonard Tangel, S. Kom., M.Eng. (Tokyo Institute of Technology)
Arya Adriansyah, M. Sc. (Eindhoven University of Technology, the Netherlands)
I Made Andi Arsana (Australian National Centre for Ocean Resources and Security
(ANCORS), University of Wollongong, Australia, Geodesy and Geomatics,
Gadjah Mada University, Indonesia)
Indriani Noor Hapsari, S.T., MT. (Information System Laboratory, College of Electrical
and Information, Bandung Institute of Technology, Indonesia).
Prasetyo Andy Wicaksono, ST, M.T (LayangLayang Mobile, Indonesia).
Fitriani, ST, M. Eng. (Penang, Malaysia).
Setia Pramana (Sekolah Tinggi Ilmu Statistik Jakarta, Indonesia & Medical
Epidemiology and Biostatistics Department, Karolinska Institute Stockholm
Sweden.)
Janet Pomares Betancourt, M.Sc. (Tokyo Institute of Technology, Japan).
Nur Ahmadi, ST. (Tokyo Institute of Technology, Japan).
BOARD OF EDITORS
Sri Hastuty, ST., MT., M. Eng., Dr. Eng. (National Institute for Materials Science,
Tsukuba, Japan).
Ihsan Iswaldi, Dr. (Granada University, Spain).
Dian Andriany, Amd. (Turkey)
ADVISORS (THE FOUNDERS OF ACIKITA)
Associate Prof. Prihardi Kahar (Meisei University, Japan)
Jumiarti Agus, S. Si., M. Si., Dr. Eng. (Tokyo Institute of Technology, Japan).
Prof. Fasli Jalal (Andalas University)
LIST OF CONTENT
Page
Preface
i
Oral Presentation
Group I : Medicine, Health, and Pharmaceutical, Chemistry, Sensor, and Material
Engineering
Code
Authors
Title
OP-001
OP-002
Lia Aprilia and
Ratno Nuryadi
Ratno Nuryadi
OP-003
James Sibarani
OP-004
B. A. Budiman,
K. Takahashi, K.
Inaba, K.
Kishimoto
OP-005
Nuring Tyas
Wicaksono and
Yu Chun Chiang
OP-006
OP-007
Nurlienda
Hasanah, Nia
Novita Wirawan,
Harijanto, and
Nanik Setijowati
Tris Dewi
Indraswati, Adang
Suwandi Ahmad,
Irman Idris, and
Adrian Venema
STUDY OF THE CHANGE IN RESONANCE
FREQUENCY AND SENSITIVITY FOR
MICROCANTILEVER SENSOR
MATHEMATICAL MODEL OF
SPRING-DAMPER SYSTEM FOR
MICROCANTILEVER-BASED BIOSENSOR
APPLICATION
BIODEGRADABLE AMPHIPHILIC
COPOLYMERS PREPARED BY REVERSIBLE
ADDITION-FRAGMENTATION TRANSFER
AGENT (RAFT) POLYMERIZATION
TECHNIQUE FOR PHOTODYNAMIC
THERAPY
EVALUATION OF INTERFACIAL
PROPERTIES QUALITY BASED ON STRAIN
DISTRIBUTION CONTOUR IN MATRIX
COMPOSITE
PLATINUM NANOPARTICLES
PREPARATION ON THIOLIZED
MULTI-WALLED CARBON NANOTUBES
FOR ELECTROCATALYSTS
VEGETABLES CORRELATE MOST WITH
BLOOD PRESSURE AMONG OUTPATIENT
AT DINOYO COMMUNITY HEATLH
CENTER IN MALANG, INDONESIA
SENSING ELEMENT DESIGN OF
MEMS-BASED TRANSLATIONAL
VIBRATORY Z-AXIS GYROSCOPE USING
DESIGNER MODULE IN COVENTORWARE
1
12
22
34
49
63
76
OP-008
Adiar Ersti
Mardisiwi, Yusuf
Ariyanto, Candra
Irawan, Fajar
Dzikri
Harwiansyah,
Rahma Sakinah,
Ridho Prawiro,
and Vincentius
Totok Noerwasito
OP-009
A. Wikarta and
C.K. Chao
OP-010
Dito Anurogo and
Taruna Ikrar
OP-011
Dito Anurogo
OP-012
Dito Anurogo
Nur Akmalia
Hidayati, Made
OP-013 Puspasari
Widhiastuty, and
Fida Madayanti
Warganegara
Group II : Energy
Code
Authors
98
THE EFFECT OF TWO-LEVEL HIERARCHIES
FOR STRENGTHENING GECKO’S FEET
(G-FEET) JOINT SYSTEM WITH
COMPOSITION RATIO OF 50:50 AND
DESIGN AS INDEPENDENT VARIABLE
SOLUTIONS OF A CRACK INTERACTING
WITH A TRI-MATERIAL UNDER REMOTE
UNIFORM SHEAR LOAD
PHARMACOGENETICS AND
PHARMACOGENOMICS: THE ART OF
EPILEPSY MANAGEMENT
THE SCIENCE OF “TINDIHAN”
PHENOMENON
110
THE ALICE IN WONDERLAND SYNDROME
149
OP-016
PURIFICATION OF RECOMBINANT LIPASE
FROM LOCAL ISOLATE
TITLE
Bayu Prabowo,
Kentaro Umeki,
Kunio Yoshikawa
Ayub Torry
Satriyo Kusumo
and Handojo
138
158
UTILIZATION OF CO2 FOR RENEWABLE
ENERGY PRODUCTION THROUGH
BIOMASS GASIFICATION
DESIGN SYSTEM CONTROL FOR RADIO
BATTERY FUNCTION CHECKING
OP-015 Syahril Ardi and
Alfan Subiantoro USING PROGRAMMABLE LOGIC
CONTROLLER
Group III : Education, Economics, Law, and Marketing,
Telecommunication, Computer Information Science, Management
Code
Authors
TITLE
OP-014
117
STUDY OF MARITIME BOUNDARY
REGULATION BETWEEN
INDONESIA-MALAYSIA IN THE
169
185
195
Leksono
OP-017
Rizanna
Rosemary
OP-018
Asri Wijayanti
OP-019
Rudy, Eka
Miranda, and Eli
Suryani
OP-020
Yohannes
Kurniawan
OP-021
Indrajani
OP-022
Gusti Ayu Vida
Mastrika Giri,
Kadek Cahya
Dewi, and Agus
Muliantara
OP-023
Aini Farmania,
Shieh-Liang
Chen, Ananda
Fortunisa
OP-024
OP-025
OP-026
Rismanda
Dewanti, Kadek
Cahya Dewi,
Agus Muliantara
Ahmad
Firmansyah, Yudi
Satria
Gondokaryono
Deshinta Arrova
Dewi
FRAMEWORK TO DEFEND SOVEREIGNTY
OF REPUBLIC OF INDONESIA
A CONTENT ANALYSIS OF TOBACCO
ADVERTISING AND PROMOTION FOR
INDONESIAN TOBACCO BRANDS ON
YOUTUBE
PROMOTE LABOR EDUCATION THROUGH
INTERNATIONAL COOPERATION IN THE
FIELD OF APPRENTICESHIP
BUSINESS INTELLIGENCE MODEL
DEVELOPMENT FOR MAXIMIZE
MARKETING PROCESS AT HIGHER
EDUCATION
KNOWLEDGE MANAGEMENT FOR
SCHOOL ACADEMIC OPERATION
SERVICES: PERCEPTIONS OF
APPLICATION AND BENEFITS
BUILD AN ENTERPRISE DATA
WAREHOUSE TO IMPROVE THE QUALITY
OF HOSPITAL
MUSIC INTEREST RECOMMENDATION ON
FACEBOOK USING SELF ORGANIZING
MAP
ANALYSIS COMPARISON OF FINANCIAL
PERFORMANCE BY USING VARIOUS
FINANCIAL RATIOS AMONG
COMMERCIAL BANKS IN INDONESIA,
MALAYSIA AND SINGAPORE
MUSIC CLASSIFICATION BASED ON
GENRE USING BACKPROPAGATION AND
SOCIAL TAGGING IN WEB MUSIC
DATABASE
ENTERPRISE ARCHITECTURE PLANNING
ON BADAN PEMERIKSA KEUANGAN
REPUBLIK INDONESIA
INVESTIGATION OF A POTENTIAL
BLENDED LEARNING MODEL FOR
TEACHING AND LEARNING COMPUTER
20
7
228
238
255
270
277
291
306
316
332
OP-027
Rudiman and
Zamhar Ismail
OP-028
Haviluddin,
Rayner Alfred,
and Patricia
Anthony
OP-029
OP-030
OP-031
OP-032
OP-033
OP-034
OP-035
PROGRAMMING COURSES IN A PRIVATE
HIGHER LEARNING INSTITUTION IN
MALAYSIA
ACTION RESEARCH :
DEVELOPING A KNOWLEDGE PORTAL
WITH SOCIAL MEDIA
UTILIZATION OF COBIT FRAMEWORK
WITHIN IT GOVERNANCE: A STUDY
LITERATURE
THE DEVELOPMENT OF INFORMATION
SYSTEM FOR WEB-BASED ONLINE
ACCREDITATION FOR VOCATIONAL
SCHOOL TO IMPROVE ASSESOR’S WORK,
Inayatulloh
PARTICIPANTS OF ACCREDITATION AND
INTEGRATED ACCREDITATION
INFORMATION STORAGE
EFFORTS OF INDONESIA AND MALAYSIA
Irham Rizani and
IN TOUCH EDUCATION FOR CHILDREN OF
Wan Shawaluddin
INDONESIAN MIGRANT WORKERS IN
Wan Hassan
SABAH
Bustanul Arifin
MICROGEM THERMAL CONTROL
ASPECTS ‘PHASE A – PHASE D ANALYSIS’
Rini Sovia, Retno DIAGNOSE INTELLIGENCY LEVEL BY
Devita, and Etri
EXPERT SYSTEM USING WAP
Suhelmidawati
PROGRAMMING
ANALYSIS OF ENVIRONMENTAL IMPACT
DOCUMENTATION SUBSURFACE
Angy Sonia
JAKARTA CITY TO BALANCE MOBILITY
AND URBANIZATION OF SOCIETY
IN A BIG CITY
VIEWING ADS IN THE CULTURAL
PRODUCT USE OF THE DECISIONS, HOW
Angy Sonia
THE MEDIA SHAPPING OPINION? AND
HOW THE MEDIA AFFECT THE
INFORMATION SOCIETY
Angy Sonia
DEVELOPING EFFECTIVENESS ANALYSIS
INFORMATION PACKAGE FOR FOOD AND
HEALTH THROUGH INFORMATION
352
369
386
392
402
414
422
423
424
Poster Presentation
Group I : Material Engineering
Code
Authors
Makhyan Jibril A,
Laili Fitri N, Sri
PP-001
Ratna W, Lidia
M, Fetreo N, and
Rasjad Indra
Group II : Energy
Code
Authors
Title
IMMUNIZATION WITH KEYHOLE LIMPET
HEMOCYANIN CONJUGATED WITH
ADVANCED GLYCATION END PRODUCT
PREVENT DIABETIC COMPLICATION IN
MICE
425
Title
Group III : Education, social sciences, Economics, Computer Information
Science
Code
Authors
Title
PP-002
Tri Pudjadi and
Harisno
PP-003
Evaristus Didik
Madyatmadja and
Albert V Dian
Sano
PP-004
PP-005
PP-006
LEARNING OBJECTS USING POWER POINT 439
ANIMATION TO INCREASE LEARNING
PROCESS IN SCHOOL. CASE STUDY AT
SMP YASPIA, SMK BINA MANAJEMEN
AND MTsN 7 JAKARTA TIMUR
451
DECISION SUPPORT SYSTEM FOR
PHYSICALLY TOOLS ALLOCATION
ASSESSMENT OF IT GOVERNANCE USING
Siti Elda Hiererra,
COBIT 4.1 FRAMEWORK METHODOLOGY:
Johan Muliadi
CASE STUDY UNIVERSITY IS
Kerta, and
DEVELOPMENT IN IT DIRECTORATE
Noerlina
BINUS UNIVERSITY
Rosalina
THE EVALUATION OF RESIDENTIAL
Kumalawati,
DEVELOPMENT BASED ON LAHAR RISK
Rijanta, Junun
ANALYSIS IN KALI PUTIH SUB
Sartohadi, and
WATERSHED,
Rimawan
MAGELANG CENTRAL JAVA, INDONESIA
Pradiptyo
Rosalina
THE MAPPING OF LAHAR FLOOD RISK
Kumalawati,
ABOUT RESIDENTIAL
IN SALAM REGENCY, MAGELANG,
Seftiawan S.
CENTRAL JAVA
Rijal, Rijanta,
468
485
492
Junun Sartohadi,
and Rimawan
Pradiptyo
PP-007
Agus Hamdi
PEMBANGUNAN MODEL APLIKASI
INVENTORY DAN PEMBAYARAN PADA
KOPERASI DAN UKM
510
The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
OP-022
MUSIC INTEREST RECOMMENDATION ON FACEBOOK
APPLICATION USING SELF ORGANIZING MAP
Gusti Ayu Vida Mastrika Giri1, Kadek Cahya Dewi2, Agus Muliantara3
1,2,3
Department of Computer Science, Faculty of Mathematics and Natural Sciences,
Udayana University, Indonesia
1
g.a.vida.mastrika@cs.unud.ac.id
2
cahya.dewi@cs.unud.ac.id
3
muliantara@cs.unud.ac.id
ABSTRACT
The rapid development of the diversity of music and technology caused the music
listeners to need recommendations of the types of music which were identical with their
music interest from the social media on the internet which was easily accessed such as
Facebook. The recommendations of music interest could not only be obtained from the
same genre of music but also from the similar music features that used to give
recommendations. This research implement the application of Music Information by
Computer Science (MICS) Recommendations on Facebook. MICS Recommendations
could be used to recommend music interest using Self Organizing Map algorithm
accompined by the analysis of relatedness between audio similarities with music genre.
The music features that used in this research were key, mode, loudness, energy, and
tempo. The dataset that used in this paper are 280 songs from 14 different genres. The
dataset were divided into 196 as the initial dataset and 86 dataset for testing. In this
research, the genres with the biggest score of relatedness were Acoustic and Jazz; the
score was 43.33%. The genres with the lowest score of relatedness were Electronic and
Rock; the score was 6.67%.
Keywords: Self Organizing Map, Music Recommendation, Facebook Music Interest,
Facebook Music Page, Music Information Retrieval.
1. Introduction
Music is a collection of tones that are assembled into a harmony in a regular
rhythm and tempo. Today, there are many artists who express their creativity through
music, which is cause the increasing of music quantity and diversity.
The rapid development of the music diversity and technology caused the music
listeners need more music or artist recommendations which were identical with their
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Jakarta, August 26-28, 2012
music interest from the social media on the internet which was easily accessed such as
Facebook. According to search new music recommendations, music listeners can find
Facebook Pages of various artists and find information about their music, latest news,
and events. But opening artists’ Facebook Pages one by one takes a long time, so that
music listeners need a new way to get recommendations that is more efficient.
The recommendations of music interest could not only be obtained from the
same genre of music but also from the similar music features that can be used to give
recommendations. Research about music recommendations and audio similarity has
done by some researchers, such as Dewi and Putri which was recommending music
according to audio similarity using rhythm feature and K-Nearest Neighbor method
[1]
.
Pampalk, Flexer, and Widmer searched for music similarity using the combination of
spectral similarity and fluctuation patterns [2].
In this research, music interest recommendations were determined using Self
Organizing Map algorithm which was implemented in Facebook application,
accompanied by analysis of relatedness between audio similarities with music genre.
Name of the application is Music Information by Computer Science (MICS)
Recommendations. Music collection that used was obtained from us.7digital.com and
www.last.fm. Music features that used in this research were key, mode, loudness,
energy, and tempo which were obtained using Echo Nest API. The music listeners’
music interest were obtained from four Facebook Music Pages that they like and music
recommendations given are five Music Pages from different artists for each music
interest.
2. Material and Method
a.
Music Theory
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Jakarta, August 26-28, 2012
Music consists of music features such as mode, harmony, tempo, rhythm, and
dynamic (loudness) [3]. There are also other music features, such as energy, key, timbre,
pitch, and many others. In music, genre is simply means type or class of music. Music
genre gives expectation how the music will sound, how long the music is, and how the
listeners should behave. In the Mozart era, there were five main genres. The genres are
symphony, string quartet, sonata, concerto, and opera
[4]
. In the modern music era,
music can be divided into some genre, such as acoustic, blues, classical, country,
electronic, emo, hip hop, jazz, metal, pop, R&B, reggae, rock, and soul.
b.
Dataset Collection
Echo Nest is a website that provides platform for music application developers,
which can be accessed through www.echonest.com. Music features dataset were
obtained using Echo Nest API. Echo Nest provides Application Programming Interface
(API) that can be used by developer for building music web or making play list
arrangements. Echo Nest API that used in this research is Echo Nest API version 4.2.
Echo Nest used signal processing and machine learning for extracting all
features in music. Output produced by music feature analysis done by Echo Nest consist
of complete music description, global structure and attributes such as key, loudness,
time signature, tempo, beats, sections, and harmony.
Echo Nest API will give access to music application developers to access and
interact with the music data that are stored in Echo Nest using methods that will give
responds in the form of JSON or XML. Method that is used in this research is Song API
Method. Here is an example of Song API usage in showing information from the song
“Just Dance”, from Lady Gaga:
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Jakarta, August 26-28, 2012
http://developer.echonest.com/api/v4/song/search?api_key=96GANOIWHEX5ZI6LL&
format=xml&results=1&artist=lady%gaga&title=just%20dance&bucket=id:7digitalUS&bucket=audio_summary
By using Song API Method, user will get output in XML that consist of song
information about the song “Just Dance”. Output of the Song API Method is shown in
Figure 1. The output consists of music descriptions, such as song title and artist, and
also global structure, such as key, mode, and time signature.
Figure 1. Song API Method Output
c.
Self Organizing Map
Self Organizing Map (SOM) network use unsupervised learning method that
maps data from any dimension into two dimensional maps. In its basic form it produces
a similarity graph of input data [5]. SOM network consist of two layers, which is named
input layer and output layer. Every neuron in input layer is connected with every neuron
in output layer. Every neuron in output layer is representing cluster from inputs that are
given.
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Jakarta, August 26-28, 2012
SOM algorithm has steps as follows:
step 0 initialize weights wij
set topological neighborhood parameters
set learning rate parameter
step 1 while stopping condition is false do step 2-8
step 2 for each input vector x, do step 3-5
step 3 for each j, compute:
√∑
step 4 find index j such that d(j) is a minimum
step 5 for all units j within a specified neighborhood of j and for all i:
Wij(new) = Wij(old) + α (Xi – Wij(new))
step 6 update learning rate
step 7 reduce radius of topological neighborhood at specific time
step 8 test stopping condition [6].
Test stopping condition is true if the difference between weight change in
iteration to the next iteration is less than 0.0001
[7]
. The constant 0.0001 is used in this
experiment and it produced the best result of the experiment. This means that the
network has become convergent, so it can be stopped.
d.
Facebook Applications
Facebook is a social network where the users can make a personal profile with
photos, personal information, and self interest list. Facebook Applications are small
programs that run inside Facebook. Only Facebook users can use Facebook
Applications. Facebook Platform and Facebook Query Language are used for
developing Facebook applications. Some parts from high class Facebook Platform
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Jakarta, August 26-28, 2012
components are Graph API, Authentication, Social Plugins, and Open Graph Protocol.
Facebook Query Language (FQL) offers features and language elements similar to
Structured Query Language (SQL) which give access for Facebook Applications to
directly query Facebook’s internal data.
e.
Clustering Method
Variables that used in this research were weight value that connects input layer
and output layer and learning rate value in clustering process. Research dataset were
280 songs that consist of 20 songs for each genre. Music genre used in this research
were acoustic, blues, classical, country, electronic, emo, hip hop, jazz, metal, pop, R&B,
reggae, rock, and soul. Music features used were key, mode, loudness, energy, and
tempo. Key, mode, loudness, energy, and tempo are chosen because they are the main
music features that provided by Echo Nest and they also can distinguish music better
than other features that are provided, such as time signature and duration.
Music features dataset should be in the same range according to make them have
the same impact in clustering process, therefore dataset normalization is needed. The
normalization done with equation
SOM network used for dataset clustering consist of five input neurons that
represent five music features and fourteen output neurons that represent music genre
used in this research. SOM network for clustering can be seen in Figure 2.
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Jakarta, August 26-28, 2012
Y2
W15
W11 W12
Input Layer
X1
W21
Y3
W25
W22
X2
W31
W33
….
Y1
….
Output Layer
W35
Y14
W14 1
W14 2 W14 5
X5
Figure 2 Self Organizing Map Network Structure
A total of 196 datasets used as initial data for SOM algorithm were processed in
SOM network to determine the optimal weights (wij) that connects input neurons (xi)
and output neurons (yi).
f.
Classification and Recommendation Method
A total of 84 testing data were classified into clusters that produced by clustering
process using SOM. Steps for classification and determining recommendations are as
follows:
1.
Search for user’s music interest name in their Facebook profile, and then search the
artist’s song feature in the dataset.
2.
Calculate Euclidean Distance from every music interest with optimal SOM weight.
3.
Determine index of the smallest Euclidean Distance. The index is representing the
cluster number.
4.
Recommendation given was determined by artists’ music with the most similar
music features with input features. The similarity was measured using Euclidean
Distance, so that the smaller the Euclidean Distance, the more similar the music.
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g.
System Testing Analysis Method and Result Analysis Method
Testing the results of clustering was done by finding the value of cohesion and
separation. Following equations is used to get the value of cohesion and separation:
∑
(
[8]
)
System display testing was done by checking the Music Interest Display and
Music Page Recommendations Display. System display was done by finding the value
of precision and recall, using following equations:
|
|
|
|
|
|
|
|[9]
Besides finding the value of precision and recall, the system display is also
checked by using F-Measure. F-Measure (or F-Score) is the harmonic mean of precision
and recall and is calculated as
[10]
It will have a high value only when both precision and recall have high values, and can
be seen as a way to find the best compromise between precision and recall [10].
Recommendations result was analyzed by finding the relatedness score between
music page recommendations given with music genre of user’s music interest. So that
genre with the greater and the least relatedness score can be determined. Relatedness
score was calculated using the following equation:
With RS = relatedness score, x = the total of recommendations that have the same genre
with the music interest, and y = the total of recommendations.
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3. Result and Discussions
In this research, eight experiments were conducted using different weights and
learning rates. The first weight matrix was the average value of each feature from each
genre, while the second weight matrix was random value weights. List of experiments
that have been done is shown in Table 1.
Table 1 List of Experiments
Experiment
Weight
1
2
3
4
5
6
7
8
1st
1st
1st
1st
2nd
2nd
2nd
2nd
Learning
Rate
0,5
0,5
0,3
0,3
0,5
0,5
0,3
0,3
Learning Rate
Reduction
0,9995
0,999
0,9995
0,999
0,9995
0,999
0,9995
0,999
Total
Iteration
15.192
7.595
13.553
6.775
15.192
7.595
13.553
6.775
Every experiment has its own optimal weights produced by SOM algorithm. The
result of optimal weights from experiments with the same learning rate, such as
experiment number 1 and number 5 is the same, while experiments with different
learning rate, such as experiment number 1 and number 2 is different.
Result of optimal weights from every experiment showed that in experiments
conducted with different learning rate but same initial weight, SOM will produce
different optimal weights. In experiments conducted with different initial weights but
same learning rate, SOM will produce the same optimal weight.
Clustering result was analyzed using cohesion and separation value.
Experiments with the smallest average value of cohesion and bigger average value of
separation are experiment number 4 and number 8. The average value of cohesion and
separation from each experiment is shown in Table 2.
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
Table 2 The Average Value of Cohesion and Separation from Each Experiment
Experiment
1
2
3
4
5
6
7
8
Cohesion
0.236493
0.237867
0.228352
0.228224
0.236493
0.237867
0.228352
0.228224
Separation
0.883213
0.885589
0.902854
0.902857
0.883213
0.885589
0.902854
0.902857
Relatedness score obtained from calculation using experiment that has the
smallest cohesion average value and bigger separation average value. The genres with
the highest relatedness score were acoustic and jazz; the score was 43.33%. The genres
with the lowest score of relatedness were electronic and rock; the score was 6.67%.
The bigger the relatedness score that a genre had indicates that most of the
recommendations will have the same genre as the music interest. The smaller the
relatedness score that a genre had indicates that the recommendations given will be
from various genres. Score of relatedness in every genre is shown in Table 3.
Table 3 Relatedness Score in Every Genre
Genre
Relatedness
Score
Acoustic 43.33%
Blues
16.67%
Classical 26.67%
Country
10.00%
Electronic 6.67%
Emo
23.33%
Hip Hop
16.67%
Jazz
43.33%
Metal
40.00%
Pop
16.67%
R&B
13.33%
Reggae
13.33%
Rock
6.67%
Soul
13.33%
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System display testing was done by checking the Music Interest Display and
Music Page Recommendations Display, which is shown in Figure 3.
Figure 3 MICS Recommendations Display
The testing was done by 21 MICS Recommendations users with different artist and
different genres for each user. In order to do the test, users allowed the MICS
Recommendations application to access their favorite music, and then MICS
Recommendations will give artist recommendations according to the clustering result
and display the artist’s music page on the application.
According to the test result, the value of precision and recall for Music Interest
Display
were
0.9167
and
0.6471.
Precision
value
indicates
that
MICS
Recommendations can obtain 91.67% relevant music interest data from users’ profiles.
Recall value indicates that MICS Recommendations can obtain 64.71% relevant music
page from Facebook database.
The value of precision and recall for Music Page Recommendations Display
were 0.9019 and 0.5679. Precision value indicates that MICS Recommendations can
obtain 90.19% relevant music page recommendations from total recommendations.
Recall value indicates that MICS Recommendations can obtain 56.79% relevant music
page from Facebook database.
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The recall value is still low because in Facebook, there are many artist music
pages that have the same name. For Example, artist System of A Down have five
different Facebook Music Page named “System of A Down”, this caused the recall have
lower value. To improve the recall value, the application could retrieve more Facebook
Music Page from the database, but it will make the user confused because there are
many Music Pages with the same artist name.
The F-Measure value for Music Page Recommendations Display was 0.7585 and
the F-Measure value for Music Page Recommendations Display was 0.6968. The FMeasures that are above 0.5 indicate that the MICS Recommendation can retrieve and
display more correct Music Page for the users.
4. Conclusions
1.
Self Organizing Map algorithm has successfully used to group the music features
dataset by key, mode, loudness, energy, and tempo to be used in the MICS
Recommendations
application.
MICS
Recommendations
application
has
successfully built in Facebook social network with the value of precision, recall,
and F-Measure for Music Interest Display were 0.9167, 0.6471, and 0.7585 and the
value of precision, recall, and F-Measure for Music Page Recommendations
Display were 0.9019, 0.5679, and 0.6968.
2.
Relatedness score analysis result showed that there is relatedness between music
features similarity (key, mode, loudness, energy, and tempo) with music genre.
Every genre has a different relatedness score. In this research, the genres with the
highest relatedness score were acoustic and jazz; the score was 43.33%, while the
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The 2nd ACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
genres with the lowest score of relatedness were electronic and rock; the score was
6.67%.
3.
In this research, in experiments conducted with different initial weights but same
learning rate, SOM will produce the same optimal weight and same clusters. So that
only the determination of initial learning rate and learning rate reduction can
change the clustering result.
Acknowledgement and References
[1]
Dewi, K. C., and Putri, L. A., Music Recommendation Based on Audio Similarity
Using K-Nearest Neighbour., Proceeding of The 1st ACIKITA International
Conference of Science and Technology., -, 124, (2011).
[2]
Pampalk, E., Flexer, A., & Widmer, G., Improvements of Audio-Based Music
Similarity and Genre Classification., In Proceedings of the 6th International
Conference on Music Information Retrieval, -, -, (2005).
[3]
Meyers, O., A Mood-Based Music Classification and Exploration System, Master
of Science in Media Arts and Sciences, Massachusetts Institute Of Technology,
United States, (2007)
[4] Wright, Craig,Listening to Music, Sue Gleason,Schirmer, 20 Channel Center
Street, Boston, MA 022010, USA,2011, 6th Edition, 188-200.
[5]
Kohonen, Teuvo, Self-Organizing Maps, Huang, T.S. Kohonen, Teuvo,
Schroeder, M.R, Springer-Verlag Berlin Heidelberg New York, 2001, 3rd Edition,
106-176.
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Jakarta, August 26-28, 2012
[6]
Mehrad, J, and S Koleini, Using SOM Neural Network In Text Information
Retrieval, Iranian Journal of Information Science and Technology, Volume 5, 5364, (2007).
[7]
Xiao, Xiang, E.R. Dow, E Eherhart, Z.B. Miled, and R.J. Oppelt, Gene Clustering
Using Self-Organizing Maps and Particle Swarm Optimization, IPDPS ’03
Proceedings 0f the 17th International Symposium on Parallel and Distributed
Processing, -, -, (2003).
[8]
Tan, Pang Ning, Michael Steinbach, and Vipin Kumar, Introduction to Data
Mining, -,Pearson Addison Wesley, 2006, -,537-568.
[9]
Kadyanan, I Gusti Agung Gede Arya, Perolehan Citra Berbasis Konten pada
Aplikasi Penginderaan Jarak Jauh, Indonesia University, -, 2011, -, -.
[10] Guillet, Fabrice and Hamilton, Howard J., Quality Measures in Data Mining, -,
Springer, 2007, -, 141.
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