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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengabdian UntukMu NegeRI Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTEK: Informatika dan Teknologi Informasi Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Journal of Software Engineering and Information System (SEIS) Teknomatika: Jurnal Informatika dan Komputer Indonesian Journal of Innovation Multidisipliner Research
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Universitas Muhammadiyah Riau
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Deteksi Serangan Dalam Ekosistem Iot Melalui Analisis Multi-Class Dengan Model Xgboost Dan Penerapan Teknik Imbalance Ratio Pada Dataset IoTID20 Amien, Januar Al; Sunanto, Sunanto; Rangkuti, Muhammad Al-Ikhsan; Soni, Soni
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9861

Abstract

This research focuses on attack detection in the Internet of Things (IoT) ecosystem using the XGBoost algorithm and the Imbalance Ratio technique on the IoTID20 dataset. The main goal is to overcome the problem of data imbalance that is common in IDS datasets and improve accuracy in classifying attack types. The methodology used includes data preprocessing, feature selection, and applying the Imbalance Ratio technique to handle class imbalance in the IoTID20 dataset. Next, the XGBoost model is implemented with the scale_pos_weight parameter to handle the class imbalance problem. This model is trained on training data and evaluated using metrics such as accuracy, precision, recall, and F1-score. The research results show that the combination of the XGBoost algorithm and the Imbalance Ratio technique is able to overcome data imbalance problems effectively. The resulting model achieved an accuracy rate of 99.32%, precision 99.32%, recall 99.32%, and F1-score 99.32% in classifying attack types on the IoTID20 dataset. These results demonstrate excellent capabilities in detecting attacks and distinguishing between normal and anomalous traffic in the IoT ecosystem. This research contributes to improving IoT network security by applying an effective Machine Learning approach to accurately detect attacks, while also addressing data imbalance problems that often occur in IDS datasets.
A comprehensive evaluation of multiclass imbalance techniques with ensemble models in IoT environments Januar Al Amien; Hadhrami Ab Ghani; Nurul Izrin Md Saleh; Soni Soni; Yulia Fatma; Regiolina Hayami
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 3: June 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i3.25887

Abstract

The internet of things (IoT) has revolutionized connectivity and introduced significant security challenges. In this context, intrusion detection systems (IDS) play a crucial role in detecting attacks in IoT environments. Bot-IoT datasets often face class imbalance issues, with the attack class having significantly more samples than the normal class. Addressing this imbalance is essential to enhance IDS performance. The study evaluates various techniques, including imbalance ratio techniques we call imbalance ratio formula (IRF) for controlling imbalance data, while also testing IRF to compare it with oversampling techniques like synthetic minority oversampling technique (SMOTE) and adaptive synthetic sampling (ADASYN). This research also incorporates the extreme gradient boosting (XGBoost) ensemble model approach to improve IDS performance in dealing with multiclass imbalance issues in Bot-IoT datasets. Through in-depth analysis, we identify the strengths and weaknesses of each method. This study aims to guide researchers and practitioners working on IDS in high-risk IoT environments. The proposed IRF, when integrated with the XGBoost algorithm has been demonstrated to achieve comparable accuracy of 99.9993% while reducing the training time to be on average at least two times faster than those achieved by the other state-of-the-art ensemble methods.
Systematic Literature Review Perbandingan Algoritma Tree-Based Dan Boosting Dalam Klasifikasi Diabetes Mellitus Alidin M; Soni Soni
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 3 (2026): Juli - September
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i3.2010

Abstract

Diabetes Mellitus (DM) merupakan penyakit metabolik kronis dengan prevalensi global yang terus meningkat, sehingga deteksi dini menjadi penting untuk mencegah komplikasi. Penelitian ini menyajikan Systematic Literature Review (SLR) yang membandingkan kinerja algoritma Tree-Based dan Boosting dalam klasifikasi Diabetes Mellitus. Sebanyak 51 artikel yang diterbitkan pada rentang tahun 2020-2025 dipilih melalui proses pencarian basis data, kriteria inklusi-eksklusi, dan quality assessment. Algoritma Tree-Based yang ditinjau meliputi Decision Tree, Random Forest, dan Extra Trees, sedangkan algoritma Boosting meliputi XGBoost, LightGBM, Gradient Boosting, AdaBoost, dan CatBoost. Karena studi yang dianalisis menggunakan dataset, preprocessing, skema validasi, dan metrik yang berbeda, sintesis dilakukan secara deskriptif dan bukan sebagai meta-analisis. Hasil review menunjukkan bahwa Random Forest lebih sering melaporkan performa tinggi dan stabil, sedangkan XGBoost serta algoritma Boosting lainnya tetap kompetitif, terutama ketika dikombinasikan dengan preprocessing, resampling, dan hyperparameter tuning. Teknik resampling seperti SMOTE-ENN umumnya membantu meningkatkan performa pada dataset tidak seimbang. Penggunaan Explainable Artificial Intelligence (XAI), khususnya SHAP, mendukung interpretabilitas model dengan mengidentifikasi fitur klinis penting seperti glukosa, BMI, usia, dan tekanan darah. Review ini memberikan sintesis yang lebih hati-hati sebagai dasar pemilihan algoritma dan strategi preprocessing pada sistem prediksi diabetes.  
Systematic Literature Review Perbandingan Algoritma Tree-Based dan Boosting dalam Klasifikasi Diabetes Mellitus Alidin M; Soni Soni
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12934

Abstract

Diabetes Mellitus (DM) merupakan penyakit metabolik kronis dengan prevalensi global yang terus meningkat, sehingga deteksi dini menjadi penting untuk mencegah komplikasi. Penelitian ini menyajikan Systematic Literature Review (SLR) yang membandingkan kinerja algoritma Tree-Based dan Boosting dalam klasifikasi Diabetes Mellitus. Sebanyak 51 artikel yang diterbitkan pada rentang tahun 2020-2025 dipilih melalui proses pencarian basis data, kriteria inklusi-eksklusi, dan quality assessment. Algoritma Tree-Based yang ditinjau meliputi Decision Tree, Random Forest, dan Extra Trees, sedangkan algoritma Boosting meliputi XGBoost, LightGBM, Gradient Boosting, AdaBoost, dan CatBoost. Karena studi yang dianalisis menggunakan dataset, preprocessing, skema validasi, dan metrik yang berbeda, sintesis dilakukan secara deskriptif dan bukan sebagai meta-analisis. Hasil review menunjukkan bahwa Random Forest lebih sering melaporkan performa tinggi dan stabil, sedangkan XGBoost serta algoritma Boosting lainnya tetap kompetitif, terutama ketika dikombinasikan dengan preprocessing, resampling, dan hyperparameter tuning. Teknik resampling seperti SMOTE-ENN umumnya membantu meningkatkan performa pada dataset tidak seimbang. Penggunaan Explainable Artificial Intelligence (XAI), khususnya SHAP, mendukung interpretabilitas model dengan mengidentifikasi fitur klinis penting seperti glukosa, BMI, usia, dan tekanan darah. Review ini memberikan sintesis yang lebih hati-hati sebagai dasar pemilihan algoritma dan strategi preprocessing pada sistem prediksi diabetes.
Co-Authors Ab Ghani, Hadhrami Agusriadi, - Al Amien, Januar Alidin M Alris Gusnanda Aminullah, Rabiah Aminuyati Amran, Hasanatul Fu'adah Anam, M Khairul Ananda Fitria Andesa, Khusaeri ANDRIANSYAH Arkan, M Alif Baidarus Bambang Sugiantoro Bayu Anugerah Putra Br Bangun, Elsi Titasari Daud, Kauthar Mohd Deprizon, Deprizon Desti Mualfah Deyola Shifana Diah Angraina Fitri Diah Angraini Putri Dian Utami Didik Sudyana Edi Ismanto Eka Putra Eka Ramadhan Evans Fuad Fakhira Frisya Ramadhani Falda Dimantara Fatma, Yulia Febby Apri Wenando Fitri Handayani Fitri, Nurkhairi Fitria Aini, Fitria Fransiskus Zoromi, Fransiskus Gunawan, Rahmad Hadhrami Ab Ghani Hadi Nasbey Hafid, Afdhil Hanum Salsabila Hari Sepdian Harun Mukhtar Hasanuddin Hasanuddin Hayami, Regiolina Hendri, Yusriadi Herianto Herianto Hul Hasanah, Sifa Ilham Firdaus Irzi Gunawan Januar Al Amien Januar Al Amien Jihan Aulia Kultum, Fi Ardhi Laksono Trisnantoro Lisman, Muhammad Mas’yuri, Dhina Nurriska Md Saleh, Nurul Izrin Miftakhul Jannah Mikdad Amseno Mohamad, Mohd Saberi Mohd Daud, Kauthar Muhammad Fajri Jamil Muhammad Hamadi Muzahaffar, Fatih Al Nengsih, Rafni Yulia Nurul Izrin Md Saleh Prastiwi, Adila Pramudiah Putra, Reza Tanujiwa Rahmad Firdaus Rahmad Firdaus Rahmaddeni Rahmaddeni Ramadhanti, Nurul Randra Aguslan Pratama Rangkuti, Muhammad Al-Ikhsan Remli, Muhammad Akmal Reny Medikawati Taufik Ricinur Ricinur Rico Apriandika Ridhollah, Farhan Rinaldi Rinaldi Rizki Anwar Rizki, Yoze Rizky Rahman Salam Septiana Srinandini Sofhia Mohnica Sunanto Sunanto Sy, Yandiko Saputra Torkis Nasution Unik, Mitra Vanama, Melsa Wan Salihin Wong, Khairul Nizar Syazwan Yogi Alfinaldo Yoze Rizki Yudi Prayudi Yulia Fatma Yulia Fatma Yusril Ibrahim