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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA SITEKIN: Jurnal Sains, Teknologi dan Industri Prosiding Semnastek Scientific Journal of Informatics Sistemasi: Jurnal Sistem Informasi Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri Journal of Economic, Bussines and Accounting (COSTING) INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science bit-Tech Zonasi: Jurnal Sistem Informasi INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal UNITEK Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT Jurnal Komtika (Komputasi dan Informatika)
Elvia Budianita
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 Halim Adzhima, Fauzan Afriyanti, Iis Agnesti, Syafira Agung Syaiful Rahman Agustina, Auliyah Aji Pangestu Adek Akbar, Lionita Asa Akhyar, Amany Al Rasyid, Nabila Alfaiza, Raihan Zia Alfarabi.B, Alif Alwis Nazir Alwis Nazir Alwis Nazir Amalia Hanifah Artya Ammar Muhammad Anggi Pranata Aprilia, Tasya Aprima, Muhammad Dzaky Arif Pratama Budiman Ayu Sri Wahyuni Azhima, Mohd Baehaqi Boni Iqbal buhfi arides hanyodi Chely Aulia Misrun Citra Wulandari Damayanti, Elok Desra Rizki Riyandi Dicky Abimanyu Dinyah Fithara Dodi Efendi doli fancius silalahi Eka Pandu Cynthia Eka Pandu Cynthia Eka Pandu Cynthia Eka Suryani Indra Septiawati Elin Haerani Elin Haerani Elin Haerani Elin Haerani Ellin Haerani Fadhilah Syafria Faska, Ridho Mahardika Fatma Hayati Fauzan Adzim Febi Yanto Fikri Utri Amri Fikry Utri Amri Fitri Astuti Fitri Insani Fitri Insani Fitri Insani Fitri Insani Fitri, Anisa Fratiwi Rahayu Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Guswanti, Widya Habibi Al Rasyid Harpizon Habibi, M. Ilham Hara Novina Putri Hariansyah, Jul Ibnu Afdhal Ichsan Permana Putra Ihda Syurfi Ihlal Hanafi Harahap Iis Afrianty Iis Afrianty Ikhsanul Hamdi Ilham Habibi Hasibuan Indah Wulandari Isra Almahsa, Muhammad Iwan Iskandar Iwan Iskandar Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril jasril jasril jasril Jeki Dwi Arisandi Khair, Nada Tsawaabul Lestari Handayani Lestari Handayani Lili Rahmawati lis Afrianty Liza Afrianti Lola Oktavia M Fikry M Ikhsan Maulana M ridwan Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Matondang, Irfan Jamal Mawadda Warohma Mazdavilaya, T Kaisyarendika Megawati Megawati Meiky Surya Cahyana Mhd. Kadarman Mohd. Ridho Zarkasih Rahim Muhammad Affandes Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Fikry Muhammad Hafiz Muhammad Irsyad Muhammad Rizky Ramadhan Mulyati, Sabar Mulyono, Makmur Musa Irfan Mustasaruddin Mustasaruddin Nabyl Alfahrez Ramadhan Amril Nanda Sepriadi Nazir, Alwis Nazruddin Safaat H Neni Sari Putri Juana Novi Yanti Novi Yanti Novriyanto Novriyanto Nur Iza Nuradha Liza Utami Nurafni Syahfitri Nurfadilah, Nova Siska Okfalisa Okfalisa Pasiolo, Lugas Permata, Rizkiya Indah Pizaini Pizaini Putri, Widya Maulida Rahmad Abdillah Rahmad Kurniawan Ramadani, Repi Ramadhan, Aweldri Ramadhani, Astrid Ramadhani, Siti Reni Susanti Reski Mai Candra Reski Mai Candra Rinaldi Syarfianto Robby Azhar Roni Salambue Rusnedy, Hidayati Said Nurfan Hidayad Tillah Saktioto Saktioto Sephia Pratista Silfia Silfia Siti Sri Rahayu Surya Agustian Suwanto Sanjaya Syahputra, Armadani Trisia Intan Berliana Ulti Desi Arni, Ulti Desi Ummy Agustina Putri Wang, Shir Li Widodo Prijodiprodjo Wiranti, Lusi Diah Yeni Fariati Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra Yusra, Yusra Zabihullah, Fayat Zulastri, Zulastri Zulkarnain Zulkarnain