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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Media Penelitian Pendidikan : Jurnal Penelitian dalam Bidang Pendidikan dan Pengajaran JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal CoreIT Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Pengabdian Pada Masyarakat IT JOURNAL RESEARCH AND DEVELOPMENT Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Pertanian Agros MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI International Journal of Community Service Learning Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Ensiklopedia of Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Al-Khidmat : Jurnal Ilmiah Pengabdian Kepada Masyarakat Jurnal Agrium Zonasi: Jurnal Sistem Informasi Best Journal (Biology Education, Sains and Technology) Jurnal Teknik Informatika (JUTIF) Community Empowerment Jurnal Cahaya Mandalika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Margin Eco : Jurnal Ekonomi dan Perkembangan Bisnis Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan J-COSCIS : Journal of Computer Science Community Service Jurnal Ilmiah Fokus Ekonomi, Manajemen, Bisnis & Akuntansi (EMBA) CONSEN: Indonesian Journal of Community Services and Engagement International Journal of Educational Research Excellence (IJERE) Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat JURNAL KARYA ILMIAH MULTIDISIPLIN Jurnal Agro Fabrica Blantika : Multidisciplinary Journal JIPITI: Jurnal Pengabdian kepada Masyarakat INOVTEK Polbeng - Seri Informatika Jurnal Komtika (Komputasi dan Informatika)
Guntoro, Guntoro
Universitas Lancang Kuning
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Improving Imbalanced Data Handling in Intrusion Detection Systems using SMOTE with an Extended Kalman Filter Guntoro, Guntoro; Omar, Mohd. Nizam; Mohsin, Mohamad Farhan Mohamad
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1687

Abstract

Class imbalance is a major hurdle when building intrusion detection systems (IDS). Most network traffic is normal, while certain types of attacks are very rare. This uneven distribution makes it hard for machine learning models to perform well—they often focus on the common traffic and miss the less frequent but critical attacks, like Remote to Local (R2L) and User to Root (U2R). To tackle this problem, this study proposes an improved oversampling method called SMOTE-EKF. It combines the Synthetic Minority Oversampling Technique (SMOTE) with the Extended Kalman Filter (EKF). By treating the creation of synthetic data as a nonlinear estimation problem, the EKF helps refine the generated samples, making them more accurate and reducing noise or overly broad boundaries. The method was tested on the NSL-KDD dataset using a Random Forest classifier, with performance evaluated through metrics like Accuracy, Precision, Recall, F1-score, G-Mean, and AUC-ROC, along with runtime analysis and cross-validation. The results show that SMOTE-EKF outperforms the baseline approaches, achieving impressive scores: 99.70% accuracy, 98.33% precision, 98.38% recall, 98.35% F1-score, a G-Mean of 98.29%, and an AUC-ROC of 0.993. Importantly, it also improves detection of rare attacks, with F1-scores of 96.76% for R2L and 93.65% for U2R. The SMOTE-EKF model proves to be more balanced in detecting all attack classes, without succumbing to overfitting. This study also suggests that incorporating predictive methods into the oversampling process can serve as a valuable strategy for improving the performance of machine learning-based intrusion detection systems.
Optimizing Random Forest for IoT Cyberattack Detection using SMOTE: A Study on CIC-IoT2023 Dataset Guntoro, Guntoro; Lisnawita, Lisnawita; Costaner, Loneli
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 1 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v25i1.5382

Abstract

The growing number of Internet of Things devices has led to an increased risk of complex and diverse cyberattacks. However, a significant challenge in this domain is the imbalanced class distribution in most Internet of Things datasets, cautilizing classification algorithms to be biased towards the majority class, hindering effective threat detection. This study addresses this issue by leveraging the Random Forest algorithm optimised by the Synthetic Minority Oversampling Technique. This research aims to develop an effective model for detecting cyberattacks in Internet of Things environments by resolving class imbalance issues inside of the CIC-IoT2023 dataset. The methodology involves several stages, comprising data preprocessing and applying Synthetic Minority Oversampling Technique for data balancing. The balanced dataset was then used to train a Random Forest model, by its performance evaluated utilizing accuracy, precision, recall, F1-score, and Cohen's Kappa metrics. The results demonstrate the model's effectiveness, achieving an accuracy of 99.01%, an F1-score of 98.96%, and a Cohen's Kappa of 98.92%. This marks a notable improvement in performance, particularly in detecting minority classes, compared to the model trained devoid of Synthetic Minority Oversampling Technique, that struggled to identify several less common attack types. The outcomes suggest that combining Random Forest by Synthetic Minority Oversampling Technique can significantly enhance the development of intrusion detection systems by improving detection accuracy for all 33 attack types and reducing the risks associated by undetected threats. In conclusion, this study advances Internet of Things cybersecurity by presenting an effective and efficient method for addressing data imbalance in attack detection. Future research should focus on evaluating the model's robustness utilizing more complex datasets and enhancing its performance for real-time deployment on resource-constrained Internet of Things Devices.
Studi Literatur Penyakit Jamur Akar Putih pada Tanaman Karet: Penyebab, Dampak, dan Pengendalian Putri, Riska Adelina; Tarigan, Yudi Prananta; Riki, Herkulanus; Adriano, Mika Fauzan; Guntoro, Guntoro
Jurnal Pendidikan Tambusai Vol. 10 No. 1 (2026)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v10i1.37707

Abstract

Penyakit Jamur Akar Putih (JAP) merupakan salah satu penyakit utama pada tanaman karet yang disebabkan oleh jamur patogen Rigidoporus microporus. Penyakit ini menyerang sistem perakaran sehingga mengganggu penyerapan air dan unsur hara, yang pada akhirnya menyebabkan penurunan produktivitas bahkan kematian tanaman. Studi literatur ini bertujuan untuk mengkaji penyebab, dampak, serta metode pengendalian penyakit Jamur Akar Putih pada tanaman karet. Hasil kajian menunjukkan bahwa penyebaran penyakit dipengaruhi oleh kondisi lingkungan, keberadaan sumber inokulum di dalam tanah, serta praktik budidaya yang kurang tepat. Dampak yang ditimbulkan meliputi penurunan hasil lateks secara signifikan dan kerugian ekonomi bagi petani. Upaya pengendalian dapat dilakukan melalui pendekatan terpadu, meliputi penggunaan bahan tanam tahan, sanitasi lahan, pengendalian hayati menggunakan agen antagonis, serta aplikasi fungisida secara tepat. Dengan penerapan strategi pengendalian yang efektif dan berkelanjutan, diharapkan penyebaran penyakit ini dapat diminimalkan sehingga produktivitas tanaman karet tetap terjaga.
Empowering Vocational School Students Through Digital Security Training to Prevent Cyber Threats: A Case Study at SMKN 7 Pekanbaru : Pemberdayaan Siswa SMK Melalui Pelatihan Keamanan Digital untuk Mencegah Ancaman Siber: Studi Kasus di SMKN 7 Pekanbaru Guntoro, Guntoro; Lisnawita , Lisnawita; Monika, Winda; Costaner, Loneli
CONSEN: Indonesian Journal of Community Services and Engagement Vol. 6 No. 1 (2026): Consen: Indonesian Journal of Community Services and Engagement
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/consen.v6i1.2326

Abstract

Digital devices now form the backbone of nearly every classroom, yet that convenience comes tangled with new cybersecurity peril. Students in vocational tracks sit at the crossroads: they click through learning modules all day but rarely receive targeted instruction on how to keep themselves safe online. Without that practical know-how, the hallways of a single school can quietly accumulate risks like data leaks, identity theft, and rogue software. In response, the present study piloted a campus-based workshop designed to meet learners exactly where they are. Courses were delivered at SMKN 7 Pekanbaru, involving thirty trade students who volunteered despite their busy schedules. Lectures spoke in plain language; hands-on exercises replayed incidents pulled from local news; quick-fire quizzes and spirited group debates stitched it all together. Student mastery was quantified by side-by-side snapshots taken before and after the event, measured against five essential security benchmarks. The opening average sat at a modest 18.7 out of 25; the closing number soared to 24.4. A paired t-test for the twenty-nine complete sets of data returned t(29) = 13.25, p < 0.0001, clearly ruling out chance. Glance at the run charts and the upward drift is obvious: every learner moved forward, and the room buzzed with confidence that had been absent hours earlier. Recent research confirms that focused, brief cybersecurity workshops can significantly boost learners grasp of online threats and the defensive habits they employ. Because the instructional framework proved practical, other institutions are well-positioned to adopt it and thereby reduce the cyber vulnerabilities that affect campus communities.
Exploring Research and Service Information System Usability by Heuristic Evaluation as a Compelement of System Usability Scale Guntoro; Lisnawita; Loneli Costaner
Jurnal Penelitian Pendidikan IPA Vol 9 No 12 (2023): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i12.5571

Abstract

The Research and Community Service Information System of LPPM University Lancang Kuning is a web-based application designed to facilitate research and community service activities at the university. This study incorporated two methodologies: a descriptive approach with qualitative analysis for heuristic data collection, and the System Usability Scale (SUS) method, employing quantitative analysis. The research process included stages of problem analysis, literature review, data collection, data analysis, and formulating recommendations based on the findings and discussions. The heuristic evaluation, the first method applied, revealed that aspects H1, H3, and H4 scored 1 when rounded, indicating these were merely cosmetic issues not requiring immediate attention unless spare time was available. Conversely, aspects H2, H5, H6, H7, H8, H9, and H10 scored 2 when rounded, categorizing them as minor usability issues needing resolution, albeit with low priority, to prevent potential user difficulties. Recommendations for these seven heuristic aspects scoring 2 encompassed improvements in system information clarity, feedback processes, image utilization, color selection, grammar quality, and writing consistency. The second method, the SUS, indicated that most users demonstrated adequate skills in terms of learnability, efficiency, memorability, error management, and overall satisfaction with their system usage experience.
Co-Authors Abdullah Abdullah Adriano, Mika Fauzan Ahmad Zamsuri, Ahmad Alfarasy, Febrizal Ali, Helmiyati Abdullah Anto Ariyanto Antonius Fernando AYU RAHMAWATI Bakar, Juhaida Abu Bayu Febriadi, Bayu Bimby, Novia Putri Budia Misri Budianto Hamuddin Budiastuti, Susanti Costaner, Loneli Costaner David Setiawan David Setiawan, David Djunaedi Djunaedi Elfrida Ratnawati Fadhillah, Resty Fenty Widya Fitria, Poppy Hamzah Eteruddin Hamzah Hamzah Handoko, Habib Hari Gunawan Herni Utami Rahmawati Hidayat Hidayat Hutabarat, Charles Parmonangan idel waldelmi, idel Ikhwan, Ferdy Iqbal Bukhori Istiatin, Istiatin Jeni Wardi Johar, Olivia Anggie Khaira, Ulfa Lasri Nijal Latifa Siswati Lisnawita , Lisnawita Lisnawita Lisnawita Lisnawita Lisnawita Loneli Costaner Lubis, Ahmad Fahmi Alhafiz Maisarah Maisarah Maisarah, Maisarah Makhrani Sari Ginting Mariza Devega Maulina, Viny Meilano, Dimas Mhd. Arief Hasan, Mhd. Arief Mohsin, Mohamad Farhan Mohamad Monika, Winda Monika Muhamad Sadar, Muhamad Muhammad Fikri Muhammad Iqbal Muhammad Yusuf Dibisono Mulyara, Budi Musfawati Mustakim Mustakim Nurhamin Nurhamin Nurholidan Siregar Nurholidan Siregar Nurul Hasanah Omar, Mohd. Nizam Ovie, Ingrid Pandu Pratama Putra, Pandu Pratama Putri, Riska Adelina Rahmad Dian Rahmad Syah Putra Rasli, Ruziana Mohamad Ratu Mutiara Siregar Riki, Herkulanus Rina Maharany Ririn Sari Wati Rizky Octa Putri Charin Roosmawati, Febriana SANTOSO SANTOSO Sapiri, Muhtar Saputra, Septian Tri Sari, Makhrani Sasi Utami Simorangkir, Jansihar Sinaga, Anisyah Sri Utaminingsih Sudarwati Sudarwati Suhardi Suhardi Sunaryanto, Hadi Sutejo Sutejo Tarigan, Yudi Prananta Taufik, Kemal Tohir, Kurnainy Wagino Wenni Syafitri Wenny Syafitri Wibisono, Moh Arief Aryo Wisard Widsli Kalengkongan Yuhelmi Yuhelmi Yusuf Dibisono, Mhd zamzami Zamzami, Zamzami Zulham Effendi