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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informatika Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Nasional Komputasi dan Teknologi Informasi Krea-TIF: Jurnal Teknik Informatika JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Informatics and Data Science
Gusti, Siska Kurnia
Universitas Islam Negeri Sultan Syarif Kasim Riau
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Journal : Bulletin of Informatics and Data Science

 1 
Implementation of Feature Selection Information Gain in Support Vector Machine Method for Stroke Disease Classification Fitri, Anisa; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.116

Abstract

Stroke is a disease with a high mortality and disability rate that requires early detection. However, the main challenge in the classification process of this disease is data imbalance and the large number of irrelevant features in the dataset. This study proposes a combination of Support Vector Machine (SVM) method with Information Gain feature selection technique and data balancing using Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. The dataset used consists of 5,110 data with 10 variables and 1 label. Feature selection was performed with three threshold values (0.04; 0.01; and 0.0005), while SVM classification was tested on three different kernels: Linear, RBF, and Polynomial. Model evaluation was performed using Confusion Matrix and training and test data sharing using k-fold cross validation with k=10. The best results were obtained on the RBF kernel with Cost=100 and Gamma=5 parameters at an Information Gain threshold of 0.0005, with accuracy reaching 90.51%. These results show that the combination of techniques used aims to determine the variables that most affect SVM classification in detecting stroke disease
Implementation of XGBoost Ensemble and Support Vector Machine For Gender Classification of Skull Bones Ramadhani, Astrid; Afrianty, Iis; Budianita, Elvia; Gusti, Siska Kurnia
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.115

Abstract

Sex identification based on skull bones is an important step in forensic anthropology, especially in cases where unidentified human skeletons are found. Conventional methods such as DNA analysis are often used, but have limitations, especially when the bones are damaged, charred or decayed, making the analysis process difficult. This research applies XGBoost ensemble and Support Vector Machine for sex classification on skull bones. The purpose of this research is to handle complex data with many features and unbalanced data using the XGBoost ensemble method and Support Vector Machine (SVM). The data used consisted of 2,524 samples with 82 measurement features. Model performance was evaluated using accuracy, precision, recall, and F1 score metrics. The results showed that the combination of XGBoost and SVM methods, especially with the RBF kernel, was able to achieve accuracy of up to 91.52%. This finding proves that machine learning-based approaches can be an effective and reliable solution in supporting the forensic identification process
Diabetes Classification using Gain Ratio Feature Selection in Support Vector Machine Method Al Rasyid, Nabila; Afrianty, Iis; Budianita, Elvia; Kurnia Gusti, Siska
Bulletin of Informatics and Data Science Vol 4, No 1 (2025): May 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i1.114

Abstract

Diabetes is a major cause of many chronic diseases such as visual impairment, stroke and kidney failure. Early detection especially in groups that have a high risk of developing diabetes needs to be done to prevent problems that have a wide impact. Indonesia is ranked seventh in the world with a prevalence of 10.7% of the total number of people with diabetes. This research aims to determine the attributes in the diabetes dataset that most affect the classification and apply the Support Vector Machine method for diabetes classification. For the determination process, Gain Ratio feature selection technique is applied. The dataset used consists of 768 data with 8 attributes. In this classification process, 3 SVM kernels (Linear, Polynomial, and RBF) are used with three possible data divisions using the ratio (70:30; 80:20; 90:10). Before applying feature selection, there were 8 attributes used and achieved the highest accuracy of 94.81% at a ratio of 80:20 using the RBF kernel with a combination of two parameters namely C = 100, Gamma = 3 and C = 100, Gamma = Scale.  Feature selection parameters in the form of thresholds used include 0.02; 0.03; and 0.05. After applying feature selection, the attribute that produces the highest accuracy uses 6 attributes. The highest accuracy after applying feature selection reached 95.45% at a threshold of 0.02 with a ratio of 80:20 using the RBF kernel with parameters C = 100 and Gamma = Scale. The results showed that there was an increase in accuracy after applying feature selection
Co-Authors Abdul Wahid Abdullah Abdullah Abdullah, Said Noor Abdussalam Al Masykur Adi Mustofa Al Rasyid, Nabila Alfaiza, Raihan Zia Alfin Hernandes Alwaliyanto Alwis Nazir Alwis Nazir Alwis Nazir Amelia, Felina Anggi Vasella Azhima, Mohd Baehaqi Beni Basuki Cut Lira Kabaatun Nisa Destri Putri Yani Devi Julisca Sari Dina Septiawati efni humairah Eka Pandu Cynthia Eka Pandu Cynthia Elin Haerani Elin Haerani Elin Haerani Elin Haerani Elvia Budianita Erni Rouza, Erni Fadhilah Syafria Faska, Ridho Mahardika Febi Yanto Fitri Insani Fitri Insani Fitri Wulandari Fitri, Anisa Gusti, Gogor Putra Hafi Puja Hamwar, Syahbudin Iis Afrianty Iis Afrianty Iqbal Salim Thalib Irsyad (Scopus ID: 57204261647), Muhammad Iwan Iskandar Jasril Jasril Jasril Jasril Khair, Nada Tsawaabul Kurniansyah, Juliandi Lestari Handayani M Wandi Dwi Wirawan Maemonah, Maemonah Morina Lisa Pura Muhammad Affandes Muhammad Fauzan Muhammad Irsyad Muhammad Khairy Dzaky Muhammad Rifaldo Al Magribi Nazir, Alwis Norhiza, Fitra Lestari Novriyanto Novriyanto Nurul Ikhsan Okfalisa Okfalisa Pizaini Pizaini Prima Yohana Rahmah Miya Juwita Raja Indra Ramoza Ramadhani, Astrid Risfi Ayu Sandika Robbi Nanda Robby Azhar Sardi, Hajra Satria Bumartaduri Sayyid Muhammad Habib Siti Ramadhani Siti Ramadhani Siti Ramadhani Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Syafria, Fadhillah Syaputra, Muhammad Dwiky Umam, Isnaini Hadiyul Vusuvangat, Imam Wulandari, Fitri Yayuk Wulandari Yelfi Yelfi Yola, Melfa Yusra Yusra, - Yusra, Yusra