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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA JURTEKSI INTEK: Informatika dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal PkM (Pengabdian kepada Masyarakat) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Jurnal Pengabdian Kepada Masyarakat Ekonomi, Pendidikan, dan Sosial Humaniora
Hayami, Regiolina
Universitas Muhammadiyah Riau
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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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.
Analisis Kinerja Model Machine Learning untuk Memprediksi Data Deret Waktu: Sebuah Tinjaun Irsan Hanafiah; Regiolina Hayami
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.v5i2.11037

Abstract

Penelitian ini bertujuan untuk menganalisis kinerja model Machine Learning dan Deep Learning dalam memprediksi data deret waktu (time series) melalui pendekatan Systematic Literature Review (SLR). Kajian dilakukan terhadap 27 artikel ilmiah yang dipublikasikan pada periode 2020–2026 dan diperoleh dari berbagai basis data bereputasi, seperti Scopus, IEEE Xplore, ScienceDirect, MDPI, dan Google Scholar. Penelitian ini difokuskan pada identifikasi algoritma yang paling dominan digunakan, perbandingan kinerja antara model tunggal dan model hybrid, serta faktor-faktor yang memengaruhi peningkatan performa prediksi. Hasil kajian menunjukkan bahwa algoritma Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), dan Extreme Gradient Boosting (XGBoost) merupakan metode yang paling banyak digunakan karena mampu menangani pola temporal yang kompleks dan dinamis. LSTM unggul dalam menangkap ketergantungan jangka panjang, GRU menawarkan efisiensi komputasi yang lebih baik dengan tingkat akurasi yang sebanding, sedangkan XGBoost memiliki stabilitas tinggi dan kemampuan yang baik dalam mengatasi overfitting. Selain itu, model hybrid seperti CNN-LSTM, ARIMA-LSTM, Wavelet-GRU, dan Autoencoder-CNN-LSTM secara konsisten menunjukkan kinerja yang lebih unggul dibandingkan model tunggal berdasarkan metrik evaluasi RMSE, MAE, MAPE, dan SMAPE. Faktor-faktor seperti rekayasa fitur, pra-pemrosesan data, reduksi noise, optimasi hiperparameter, serta strategi ensemble dan hybrid terbukti berperan penting dalam meningkatkan akurasi prediksi. Dengan demikian, pendekatan hybrid dinilai lebih efektif dan andal dalam menangani karakteristik data deret waktu yang kompleks, nonlinier, volatil, dan mengandung ketidakpastian tinggi pada berbagai bidang aplikasi modern.
Co-Authors Aditya Quantano Surbakti Agung Jefrianto Anggrawan Al Amien, Januar Aldi, M Tri Alfando, Alfando Almer Kharisma Aminuyati Ananda Fitria Arif Budiman Armen Salim Ayodya Putri Chan, Ridzky Daud, Kauthar Mohd Denin Nur Ichsan Deyola Shifana Dhini Septhya Eka Putra Elvina Rahmadani Evans Fuad Falda Dimantara Fatma, Yulia Fatma, Yulia Febby Apri Wenando Finanta Okmayura Fitri Handayani Fitri, Nurkhairi Fitriani, Aisyah Gunawan, Rahmad Hadhrami Ab Ghani Hafid, Afdhil Harun Mukhtar Hasanuddin Hasanuddin Herlandy, Pratama Benny Irfan Oktaviandi Irsan Hanafiah Irzi Gunawan Januar Al Amien Januar Al Amien Ken Rio Agizki Kharisma Rahayu Muhammad Hamadi Munanda, Rizka Nurul Izrin Md Saleh Okamisar Okta Tri Antoni Prastiwi, Adila Pramudiah Rahmad Firdaus Rahmad Firdaus Rahmaddeni Rahmaddeni Rahmawilda, Rahmawilda Riki Harianto Risnal Diansyah, Risnal Royan Choiro Yahya Salsabila Rabbani Sandy Eka Nuggroho Sofhia Mohnica Soni Suryanti, Anggi Aprilia Taufiq, Reny Medikawati Unik, Mitra Vindi Fitria Vitriani Yoze Rizki Yuda Irawan Yulia Fatma Yulia Fatma Yusoff, Nooraini