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All Journal Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Prosiding Semnastek Panrita Abdi - Jurnal Pengabdian pada Masyarakat Jurnal Akuntansi Jurnal Komputasi Jurnal Teknoinfo JURIKOM (Jurnal Riset Komputer) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Telematics and Information Technology (TELEFORTECH) Journal of Engineering and Information Technology for Community Service Takuana: Jurnal Pendidikan, Sains, dan Humaniora Jurnal Ilmiah Edutic : Pendidikan dan Informatika Journal of Artificial Intelligence and Technology Information Jurnal Medika: Medika Jurnal Komputasi
Damayanti, Damayanti
Universitas Teknokrat Indonesia
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : building of informatics technology and science

 1 
Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest Erland Adhe Sharendra; Tri Widodo; Damayanti Damayanti; Okma Arnilia
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9818

Abstract

Indonesia is a country with a high level of disaster vulnerability, requiring effective methods to accurately classify disaster risk levels. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in disaster risk classification. The dataset used consists of disaster event data from 2019–2024, including disaster type, region, number of victims, and population density. Disaster risk levels were classified into three categories, namely low, medium, and high, based on the total impact calculated from the number of victims. The proposed method includes data preprocessing, normalization, and train-test data splitting. The results show that both models achieved high performance, where Random Forest obtained an accuracy of 95.66% and SVM achieved 95.28%, with ROC-AUC values of 0.9823 and 0.9769, respectively. Random Forest demonstrated slightly better performance with an accuracy difference of 0.38% and more consistent prediction results. The high performance indicates that the models were able to recognize the main patterns within the dataset, although the results were also influenced by the characteristics of the data used. Overall, Random Forest is more suitable for disaster risk classification on data with complex characteristics.
Perbandingan Algoritma Random Forest, XGBoost dan SVM Pada Klasifikasi Penyakit Demam Berdarah Dengue (DBD) Vionando Wira Mada; Damayanti Damayanti
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8591

Abstract

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of the Aedes aegypti mosquito. This disease remains a serious health problem in Indonesia because it can cause severe complications and even death if not treated immediately. This study aims to classify the severity of DHF based on patient clinical data using three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM). The dataset used consists of 5,000 patient data that includes various vital parameters and laboratory test results such as age, gender, hemoglobin level, white blood cell count (WBC), leukocyte count (Differential Count), red blood cell parameters (RBC Panel), platelet count, and Platelet Distribution Width (PDW). The research stages include data cleaning, handling missing values, coding categorical variables, data normalization, and dividing the dataset into 80% training data and 20% test data. Evaluation is carried out using accuracy, precision, sensitivity (recall), and F1 score metrics. The results showed that the XGBoost algorithm performed best with an accuracy of 87.72%, followed by Random Forest (86.21%) and SVM (84.78%). Based on these findings, XGBoost was deemed most effective in classifying dengue fever. Further research is recommended to use a larger dataset and perform hyperparameter optimization to improve the accuracy and reliability of the resulting model.
Komparasi Algoritma Extreme Gradient Boosting, Support Vector Machine, dan Random Forest Klasifikasi Penyakit Jantung Fadil Arrosyid Fayyadh; Damayanti Damayanti
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9833

Abstract

Heart disease is one of the leading causes of death worldwide and requires early detection to reduce the risk of fatality. This study aims to analyze and compare the performance of three machine learning algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost, in heart disease classification. The dataset was obtained from the Kaggle platform and consists of variables such as BMI, smoking habits, physical activity, general health conditions, and other health-related attributes, with HeartDisease as the target variable. The research stages include data preprocessing, categorical data encoding, data normalization, and data splitting using the 80:20 train-test split method. The results show that SVM and XGBoost achieved an accuracy of 0.90, while Random Forest achieved 0.89. Based on other evaluation metrics, XGBoost demonstrated the best performance with a precision of 0.88, recall of 0.90, and F1-score of 0.88. Feature importance analysis also revealed that several health factors significantly influence the risk of heart disease. The contribution of this study lies in comparing the performance of machine learning algorithms for heart disease classification and identifying influential health factors related to heart disease risk. The findings are expected to serve as a reference for developing decision support systems to assist early heart disease detection more accurately and efficiently.
Co-Authors ., Rusliyawati aan setiawan Ade Dwi Putra Ade Dwi Putra Ade Putra Adi Sucipto, Adi Ady Candra Nugroho Agung Tri Prastowo Ahmad Ari Aldino Aldi Bagus Prasetyo Alfiah Alfiah Algho Niyu Behainksa Amdi Rizal Ana Loria Andi Nurkholis Andre Wijaya Anton Mahendra Anton Mahendra Arfinia Rahma Ari Sulistyawati Arief Budiman Arina Salsabila Arnilia, Okma Ayesha Ziky Iftikhor Ayu Amalia Ayu Febriani Bahtiar Ayu Febriyani Bachtiar Ayu Ningtiara Berlintina Permatasari Chris Mario Clarisa Armanda Fernandis Creani Handayani Dani Saputra Dedi Darwis Desi Oktarina Desti Ambar Wati Devita Tri Yulianti Dhea Annisa Fitri Dian Novita Dian Widiarti Dimas Elang Setyoko Dyah Ayu Megawaty Erland Adhe Sharendra Erliyan Redi Susanto Erliyan Redi Susanto Erliyan Redy Susanto Fadil Arrosyid Fayyadh Fakihah Farhah Faridaashri Fitrah Amalia Fitrah Amalia Rahmadianti Gamaliel Jones Gerardo Hutapea Habib Rizky A.Siregar Habibah Ainun Mardiyah Heni Sulistiani Ida Nurwanti Imam Ahmad Joni Alfian Jupriyadi, Jupriyadi Leny Meilisa M Ghufroni An'ars M Ghufroni An’ars M. Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An’ars M. Tova M.Ghufroni An'ars Mahendra Dewantoro Maulana Aziz Assuja Miswanto Miswanto Mohamad Ghazi Rio Muhammad Al Kahfi Masputra Muhammad Alfarizi Muhammad Bakri Muhammad Fitratullah Muhammad Yusuf Hernandez Nadia Aftirah Nirwana Hendrastuty Novia Agus Mila Pahlewi, Anita Diah Pangestu Agung Pasha, Donaya Prabowo Prabowo Priliyani Hana Primawan Adrian Sitinja Putri Lestari Rifki Yudiantara Rifli Yanto Rohmat Indra Borman Rubiyah Rubiyah S. Samsugi Samuel Hutagalung Sefrizal Nata Prawira Setiawansyah Setiawansyah Siti Hotijah suaidah suaidah Tiara Azizul Andika Tri Widodo Tri Widodo Vionando Wira Mada Wahyudi, Agung Deni Warnars, Harco Leslie Hendric Spits Wisnu Mukti Dwiyudho Putro Wiwid Ayu Nisa Wulandia Wulandia Lestari Yeni Anggraini Yohanes Aji Pratama Yuri Rahmanto Zakaria Sani Assubhi Zurfah Muzrifah