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All Journal Perfecting a Video Game with Game Metrics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bina Insani ICT Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INOVTEK Polbeng - Seri Informatika Technomedia Journal Indonesian Journal of Computer Science El-Mujtama: Jurnal Pengabdian Masyarakat
iwan Syarif
Politeknik Elektronika Negeri Surabaya
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : Perfecting a Video Game with Game Metrics

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SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance Iwan Syarif; Adam Prugel-Bennett; Gary Wills
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.3956

Abstract

Machine Learning algorithms have been widely used to solve various kinds of data classification problems. Classification problem especially for high dimensional datasets have attracted many researchers in order to find efficient approaches to address them. However, the classification problem has become very complicated and computationally expensive, especially when the number of possible different combinations of variables is so high. Support Vector Machine (SVM) has been proven to perform much better when dealing with high dimensional datasets and numerical features. Although SVM works well with default value, the performance of SVM can be improved significantly using parameter optimization. We applied two methods which are Grid Search and Genetic Algorithm (GA) to optimize the SVM parameters. Our experiment showed that SVM parameter optimization using grid search always finds near optimal parameter combination within the given ranges. However, grid search was very slow; therefore it was very reliable only in low dimensional datasets with few parameters. SVM parameter optimization using GA can be used to solve the problem of grid search. GA has proven to be more stable than grid search. Based on average running time on 9 datasets, GA was almost 16 times faster than grid search. Futhermore, the GA’s results were slighlty better than the grid search in 8 of 9 datasets.
Co-Authors Adam Prugel-Bennett Agung Muliawan Agus Indra Gunawan Ahmad Syauqi Ahsan Aidil Saputra Kirsan Ali Ridho Barakbah Amalia Wirdatul Hidayah Andhik Ampuh Yunanto Arna Fariza Assodiky, Hilmy Ayu Ahadi Ningrum Bagas Dewangkara Binti Kholifah Daisy Rahmania Syarif Desy Intan Permatasari Deyana Kusuma Wardani Dian Neipa Purnamasari Dimas Bagus Santoso Dona Wahyudi Edi Satriyanto Entin Martiana Kusumaningtyas Fabyan Kindarya Fachrul Rozie Fahrudin, Tresna Maulana Ferry Astika S Ferry Astika Saputra Fitri Setyorini Gary Wills Grezio Arifiyan Primajaya Hasan Basri Hilmy Assodiky Hisyam, Masfu Idris Winarno Irsal Shabirin M Udin Harun Al Rasyid, M Udin Harun Masfu Hisyam Maulana, Yufri Isnaini Rochmat Mufid, Mohammad Robihul Muhammad Fajrul Falah Muhlis Tahir Mustika Kurnia Mayangsari Nadila Wirdatul Hidayah Nana Ramadijanti Nur Sakinah Nur Ulima Rusmayani Rabiatul Adawiyah Reesa Akbar Rengga Asmara Rengga Asmara Renovita Edelani Rosmaliati, Rosmaliati Rulisiana Widodo S, Ferry Astika Selvia Ferdiana Kusuma Stritusta Sukaridhoto Sumarsono, Irwan Tessy Badriyah, Tessy Tresna Maulana Fahrudin Umi Sa'adah Utomo, Agus Priyo Willy Sandhika Yufri Isnaini Rochmat Maulana