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)
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)
LIST OF CONTENT
Page
Preface i
Oral Presentation
Group I : Medicine, Health, and Pharmaceutical, Chemistry, Sensor, and Material Engineering
Code Authors Title
OP-001 Lia Aprilia and Ratno Nuryadi
STUDY OF THE CHANGE IN RESONANCE FREQUENCY AND SENSITIVITY FOR MICROCANTILEVER SENSOR
1
OP-002 Ratno Nuryadi
MATHEMATICAL MODEL OF SPRING-DAMPER SYSTEM FOR
MICROCANTILEVER-BASED BIOSENSOR APPLICATION
12
OP-003 James Sibarani
BIODEGRADABLE AMPHIPHILIC
COPOLYMERS PREPARED BY REVERSIBLE ADDITION-FRAGMENTATION TRANSFER
PROPERTIES QUALITY BASED ON STRAIN DISTRIBUTION CONTOUR IN MATRIX COMPOSITE
34
OP-005 Nuring Tyas Wicaksono and Yu Chun Chiang
VEGETABLES CORRELATE MOST WITH BLOOD PRESSURE AMONG OUTPATIENT AT DINOYO COMMUNITY HEATLH
CENTER IN MALANG, INDONESIA
63
OP-007
Tris Dewi
Indraswati, Adang Suwandi Ahmad, Irman Idris, and Adrian Venema
SENSING ELEMENT DESIGN OF MEMS-BASED TRANSLATIONAL
VIBRATORY Z-AXIS GYROSCOPE USING DESIGNER MODULE IN COVENTORWARE
OP-008
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
98
OP-009 A. Wikarta and C.K. Chao
SOLUTIONS OF A CRACK INTERACTING WITH A TRI-MATERIAL UNDER REMOTE UNIFORM SHEAR LOAD
110
OP-010 Dito Anurogo and Taruna Ikrar
PHARMACOGENETICS AND
PHARMACOGENOMICS: THE ART OF EPILEPSY MANAGEMENT
117
OP-011 Dito Anurogo THE SCIENCE OF “TINDIHAN” PHENOMENON 138 OP-012 Dito Anurogo THE ALICE IN WONDERLAND SYNDROME 149
OP-013
PURIFICATION OF RECOMBINANT LIPASE FROM LOCAL ISOLATE
158
Group II : Energy
Code Authors TITLE
OP-014 Bayu Prabowo, Kentaro Umeki, Kunio Yoshikawa
UTILIZATION OF CO2 FOR RENEWABLE ENERGY PRODUCTION THROUGH BIOMASS GASIFICATION
169
OP-015 Syahril Ardi and Alfan Subiantoro
DESIGN SYSTEM CONTROL FOR RADIO BATTERY FUNCTION CHECKING
USING PROGRAMMABLE LOGIC CONTROLLER
185
Group III : Education, Economics, Law, and Marketing,
Telecommunication, Computer Information Science, Management
Code Authors TITLE
OP-016 Ayub Torry Satriyo Kusumo and Handojo
STUDY OF MARITIME BOUNDARY REGULATION BETWEEN
INDONESIA-MALAYSIA IN THE
Leksono FRAMEWORK TO DEFEND SOVEREIGNTY OF REPUBLIC OF INDONESIA
OP-017 Rizanna Rosemary
A CONTENT ANALYSIS OF TOBACCO ADVERTISING AND PROMOTION FOR INDONESIAN TOBACCO BRANDS ON YOUTUBE
20 7
OP-018 Asri Wijayanti PROMOTE LABOR EDUCATION THROUGH INTERNATIONAL COOPERATION IN THE FIELD OF APPRENTICESHIP MARKETING PROCESS AT HIGHER EDUCATION
OP-021 Indrajani 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 ON BADAN PEMERIKSA KEUANGAN REPUBLIK INDONESIA
316
OP-026 Deshinta Arrova Dewi INVESTIGATION OF A POTENTIAL BLENDED LEARNING MODEL FOR TEACHING AND LEARNING COMPUTER
PROGRAMMING COURSES IN A PRIVATE HIGHER LEARNING INSTITUTION IN MALAYSIA
OP-027 Rudiman and Zamhar Ismail
ACTION RESEARCH :
DEVELOPING A KNOWLEDGE PORTAL WITH SOCIAL MEDIA
UTILIZATION OF COBIT FRAMEWORK WITHIN IT GOVERNANCE: A STUDY LITERATURE
369
OP-029 Inayatulloh
THE DEVELOPMENT OF INFORMATION SYSTEM FOR WEB-BASED ONLINE ACCREDITATION FOR VOCATIONAL SCHOOL TO IMPROVE ASSESOR’S WORK, PARTICIPANTS OF ACCREDITATION AND INTEGRATED ACCREDITATION
INFORMATION STORAGE
386
OP-030 Irham Rizani and Wan Shawaluddin Wan Hassan
EFFORTS OF INDONESIA AND MALAYSIA IN TOUCH EDUCATION FOR CHILDREN OF INDONESIAN MIGRANT WORKERS IN SABAH
392
OP-031 Bustanul Arifin MICROGEM THERMAL CONTROL
ASPECTS ‘PHASE A – PHASE D ANALYSIS’ 402 OP-032 Rini Sovia, Retno Devita, and Etri
Suhelmidawati
DIAGNOSE INTELLIGENCY LEVEL BY EXPERT SYSTEM USING WAP
PROGRAMMING
414
OP-033 Angy Sonia
ANALYSIS OF ENVIRONMENTAL IMPACT DOCUMENTATION SUBSURFACE
JAKARTA CITY TO BALANCE MOBILITY AND URBANIZATION OF SOCIETY
IN A BIG CITY
422
OP-034 Angy Sonia
VIEWING ADS IN THE CULTURAL
PRODUCT USE OF THE DECISIONS, HOW THE MEDIA SHAPPING OPINION? AND HOW THE MEDIA AFFECT THE
INFORMATION SOCIETY
423
OP-035 Angy Sonia DEVELOPING EFFECTIVENESS ANALYSIS INFORMATION PACKAGE FOR FOOD AND HEALTH THROUGH INFORMATION
Poster Presentation
Group I : Material Engineering
Code Authors Title
IMMUNIZATION WITH KEYHOLE LIMPET HEMOCYANIN CONJUGATED WITH ADVANCED GLYCATION END PRODUCT PREVENT DIABETIC COMPLICATION IN MICE
425
Group II : Energy
Code Authors Title
-
Group III : Education, social sciences, Economics, Computer Information Science
Code Authors Title
PP-002 Tri Pudjadi and Harisno
LEARNING OBJECTS USING POWER POINT ANIMATION TO INCREASE LEARNING PROCESS IN SCHOOL. CASE STUDY AT SMP YASPIA, SMK BINA MANAJEMEN AND MTsN 7 JAKARTA TIMUR
439
DECISION SUPPORT SYSTEM FOR PHYSICALLY TOOLS ALLOCATION
ASSESSMENT OF IT GOVERNANCE USING COBIT 4.1 FRAMEWORK METHODOLOGY: CASE STUDY UNIVERSITY IS
DEVELOPMENT IN IT DIRECTORATE BINUS UNIVERSITY
THE EVALUATION OF RESIDENTIAL DEVELOPMENT BASED ON LAHAR RISK ANALYSIS IN KALI PUTIH SUB
WATERSHED,
MAGELANG CENTRAL JAVA, INDONESIA
485
THE MAPPING OF LAHAR FLOOD RISK
ABOUT RESIDENTIAL IN SALAM REGENCY, MAGELANG,
CENTRAL JAVA
Junun Sartohadi, and Rimawan Pradiptyo
PP-007 Agus Hamdi PEMBANGUNAN MODEL APLIKASI INVENTORY DAN PEMBAYARAN PADA KOPERASI DAN UKM
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
277
OP-022
MUSIC INTEREST RECOMMENDATION ON FACEBOOK APPLICATION USING SELF ORGANIZING MAP
Gusti Ayu Vida Mastrika Giri1, Kadek Cahya Dewi2, Agus Muliantara3
1,2,3Department of Computer Science, Faculty of Mathematics and Natural Sciences,
Udayana University, Indonesia
1g.a.vida.mastrika@cs.unud.ac.id
2cahya.dewi@cs.unud.ac.id
3muliantara@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 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
278
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
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
279
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.
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
280
http://developer.echonest.com/api/v4/song/search?api_key=96GANOIWHEX5ZI6LL& format=xml&results=1&artist=lady%gaga&title=just%20dance&bucket=id:7digital-US&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
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
281
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
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
282
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
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
283
X1 X2 X5
Y1 Y2 Y3 Y14
Input Layer Output Layer
W11 W12 W15
W21 W22 W25 W31 W33 W35 W14 1W14 2 W14 5
…. ….
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.
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
284
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:
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
285
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 Learning
Rate Learning Rate Reduction Iteration Total
1 1st 0,5 0,9995 15.192
2 1st 0,5 0,999 7.595
3 1st 0,3 0,9995 13.553
4 1st 0,3 0,999 6.775
5 2nd 0,5 0,9995 15.192
6 2nd 0,5 0,999 7.595
7 2nd 0,3 0,9995 13.553
8 2nd 0,3 0,999 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.
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
286
Table 2 The Average Value of Cohesion and Separation from Each Experiment
Experiment Cohesion Separation 1 0.236493 0.883213 2 0.237867 0.885589 3 0.228352 0.902854 4 0.228224 0.902857 5 0.236493 0.883213 6 0.237867 0.885589 7 0.228352 0.902854 8 0.228224 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
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
287
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 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
288
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 Measure value for Music Page Recommendations Display was 0.6968. The F-Measures 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.
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
289
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,
The 2
ndACIKITA International Conference on Science and Technology (AICST)
Jakarta, August 26-28, 2012
290
[6] Mehrad, J, and S Koleini, Using SOM Neural Network In Text Information Retrieval, Iranian Journal of Information Science and Technology, Volume 5, 53-64, (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, -, -.
