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All Journal Journal of Applied Engineering and Technological Science (JAETS) Jurnal Pendidikan dan Teknologi Indonesia
Samuri, Suzani Mohamad
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Pengembangan Game Edukasi Logika dengan Sistem Pembelajaran Adaptif Berbasis Reinforcement Learning Agus, Rahmadi; Hidayah, Rizqi Elmuna; Samuri, Suzani Mohamad
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 10 (2025): JPTI - Oktober 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1378

Abstract

Media pembelajaran untuk anak usia dini seringkali bersifat statis dan gagal mengakomodasi perbedaan kecepatan belajar individual, sehingga berpotensi menurunkan motivasi dan keterlibatan anak. Untuk mengatasi masalah tersebut, penelitian ini bertujuan merancang dan mengimplementasikan sebuah game edukasi logika adaptif berbasis Reinforcement Learning (RL). Metode pengembangan sistem mencakup tiga komponen utama: antarmuka ramah anak, modul learning analytics untuk perekaman interaksi, dan mesin adaptif  yang secara dinamis menyesuaikan tingkat kesulitan soal. Efektivitas sistem diuji melalui uji coba pada 20 anak PAUD berusia 4–6 tahun. Hasil penelitian menunjukkan bahwa sistem adaptif berhasil meningkatkan kinerja belajar secara signifikan, dibuktikan dengan peningkatan rata-rata akurasi jawaban dari 62% menjadi 84%, penurunan waktu pengerjaan per soal dari 45 menjadi 28 detik, serta menurunnya frekuensi penggunaan bantuan yang mengindikasikan tumbuhnya kemandirian. Kontribusi utama penelitian ini adalah pembuktian bahwa penerapan Reinforcement Learning mampu menciptakan lingkungan belajar yang personal dan efektif, sekaligus menjaga motivasi tinggi (85% anak menunjukkan minat lebih) pada jenjang pendidikan anak usia dini. Dengan demikian, sistem ini menawarkan inovasi sebagai solusi atas keterbatasan media pembelajaran konvensional.
Enhancing Intrusion Detection System Performance Using Reinforcement Learning : A Fairness-Aware Comparative Study on NSL-KDD and CICIDS2017 Arta, Yudhi; Samuri, Suzani Mohamad; Syafitri, Nesi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

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Abstract

Conventional Intrusion Detection Systems (IDS) often fail to generalize in dynamic network environments, facing challenges with evolving attack patterns and class imbalance. This study aims to evaluate and compare the effectiveness of three Reinforcement Learning (RL) paradigms to enhance IDS adaptability and accuracy against these challenges. This research employs a comparative experimental design, implementing Q-Learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO). These algorithms were systematically evaluated using the NSL-KDD and CICIDS2017 benchmark datasets to represent both legacy and modern network traffic. A fairness-aware evaluation framework was applied, prioritizing the Matthews Correlation Coefficient (MCC) as a primary metric alongside accuracy to ensure robust performance assessment against skewed class distributions. Experimental results demonstrate that PPO significantly outperforms value-based algorithms such as Q-Learning and DQN. On the high-dimensional CICIDS2017 dataset, PPO achieved the highest detection accuracy (96.3%) and MCC (0.913). Confusion matrix analyses confirmed PPO’s capability to simultaneously minimize false positives and false negatives. Conversely, Q-Learning exhibited poor generalization on complex data, while DQN showed improved performance due to deep value approximation but remained less stable than PPO. These findings imply that policy-gradient methods like PPO are superior for real-world IDS deployments where scalability, adaptability, and low error rates are critical. Theoretically, the results suggest that stochastic policy optimization handles complex, continuous state spaces more effectively than traditional value-estimation approaches. This study contributes a rigorous head-to-head comparative analysis of RL algorithms across multiple standard datasets using fairness-aware metrics. It bridges the research gap found in previous studies that often evaluated algorithms in isolation or relied on accuracy metrics that can be misleading in imbalanced security contexts.
Co-Authors Agus, Rahmadi Rizqi Elmuna Hidayah Syafitri, Nesi Yudhi Arta, Yudhi