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All Journal TEKNIK INFORMATIKA Seminar Nasional Aplikasi Teknologi Informasi (SNATI) JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Prosiding Seminar Nasional Teknoka JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JUKI : Jurnal Komputer dan Informatika TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Sisfo: Jurnal Ilmiah Sistem Informasi JIKA Journal Of Artificial Intelligence And Software Engineering Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Solusi Bersama : Jurnal Pengabdian Dan Kesejahteraan Masyarakat Indonesian Journal of Innovation Multidisipliner Research
Iis Afrianty
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Evaluation of Information Gain Feature Selection on Support Vector Machines Performance in Craniometric Sex Classification Sonya Aulia Febriana; Iis Afrianty; Jasril Jasril; Eka Pandu Cynthia
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

Sex classification is a fundamental stage in forensic anthropology because it provides the basis for constructing an individual's biological profile during the identification process. Although Support Vector Machine (SVM) has demonstrated high performance for craniometric sex classification, previous studies have primarily focused on classification performance using the complete feature set, with limited evaluation of the trade-off between feature reduction and predictive performance. Therefore, this study aims to evaluate the influence of Information Gain feature selection on SVM performance, identify the optimal combination of Information Gain threshold and SVM kernel configuration, and analyse the trade-off between feature reduction and classification performance. The proposed approach was evaluated using the William W. Howells Craniometric Dataset consisting of 2,524 skull samples. After removing identification attributes, 82 predictor features were used for classification. The research process included data preprocessing, label transformation, Z-score normalization, Information Gain feature selection using threshold values of 0.01, 0.05, and 0.1, followed by classification using SVM with Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid kernels. Model performance was evaluated using 10-fold cross-validation based on Accuracy, Precision, Recall, and F1-score. The baseline SVM model without feature selection achieved an accuracy of 89.59% using 82 features. Experimental results show that Information Gain effectively reduced feature dimensionality while maintaining competitive classification performance. The best feature-selection result was obtained using an Information Gain threshold of 0.01, which retained 68 features. The optimal configuration used the RBF kernel with C = 1 and gamma='auto', achieving an Accuracy of 89.42%, Precision of 90.73%, Recall of 89.69%, and F1-score of 90.17%. Compared with the baseline model, the Information Gain-based model reduced the number of features by 17.07% while producing an accuracy difference of only 0.17 percentage points. These findings indicate that Information Gain primarily contributes to feature reduction and model simplification rather than improving classification accuracy. The results demonstrate that a moderate reduction in craniometric features can maintain classification performance close to the baseline model and highlight the trade-off between feature reduction and predictive performance in computer-assisted forensic sex classification.
Co-Authors Agung Surya Maulana Alwaliyanto Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Alwis Nazir Aqshol Al Fahrozi Arif Mudi Priyatno Boni Iqbal Darmila Dewi Nasien Dinyah Fithara efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Ekawati Saputri Ekawati Saputri, Saputri Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Elvia Budianita Erni Rouza, Erni Evodius Nasus Fadhilah Syafria Faizah Husniah Fakhri Fakhri Febi Yanto Felian Nabila Fioni Nikmatul Fajar Fitra Lestari Fitri Insani Fitri Insani Fratiwi Rahayu grace tedy tulak Gusti, Siska Kurnia Habibollah Haron Ihlal Hanafi Harahap Ikhsanul Hamdi Irfan Jamal Matondang Ismail marzuki Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Karina Julita Lestari Handayani M. Aufaa Rahman Mawadda Warohma Mentari Aulia Putri Muhammad Fikry Muhammad Fikry Muhammad Habib Nazlis Muhammad Irsyad Nabilla Alya Firana Nada Tsawaabul Khair Nazruddin Safaat H Negara, Benny Sukma Novriyanto Novriyanto Nurafni Syahfitri Nurul Fatiara Purnama, Nilam Puspa Melani Almahmuda Qurrata A'yuni Rahayu Anggraini Rahmad Abdillah Rahmad Kurniawan Rahmat Al Hafiz Ramadhani Herfin Ravi Umbara Reski Mai Candra Restu Kharrisa Andini Riyan Wibowo Saputra Robby Azhar Sahrul Poalahi Salu Said Nurfan Hidayad Tillah Salmiyati Salmiyati Sephia Pratista Sonya Aulia Febriana SURYA ADITYA GD Surya Agustian Suswantia Andriani Suwanto Sanjaya Syaiful Azhar Ummy Agustina Putri Yusra Yusra