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All Journal E-Link: Jurnal Teknik Elektro dan Informatika Cyber Security and Network Management
Munji Hanafi
Institut Teknologi dan Bisnis Semarang
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy

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PERANCANGAN DAN IMPLEMENTASI RUANG PINTAR BERBASIS ANDROID SENSOR PIR DAN PENGELOLAAN DATA PADA MIKROKONTROLER ARDUINO Supriyanto; Munji Hanafi; Siska Ayu Widiana; Durand Fernandito Freddy Setlight; Sry Dhina Pohan
E-Link: Jurnal Teknik Elektro dan Informatika Vol. 20 No. 2: Oktober 2025
Publisher : Universitas Muhammadiyah Gresik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30587/e-link.v20i2.10914

Abstract

Tingginya kasus pencurian, khususnya di Kota Semarang dan insiden di laboratorium komputer SMK Askhabul Kahfi, menunjukkan kelemahan signifikan pada sistem keamanan manual, yang juga gagal membatasi akses oleh pihak yang tidak berkepentingan. Penelitian ini bertujuan untuk merancang dan mengimplementasikan "Sistem Keamanan Pada Ruangan Menggunakan Android Dan Sensor PIR Dengan Database User Berbasis Arduino" untuk menciptakan sistem keamanan yang otomatis, mampu membatasi hak akses, dan menyimpan database aktivitas pengguna. Sistem ini menggunakan Arduino Uno R3 sebagai controller utama, yang terintegrasi dengan Solenoid Door Lock (kunci elektrik) yang dikendalikan melalui aplikasi Android ("SKP Security Room") via Bluetooth setelah pengguna memindai QR Code pada kartu karyawan. Selain itu, sistem dilengkapi Sensor PIR untuk mendeteksi penyusup dan mengaktifkan alarm. Hasil pengujian menunjukkan bahwa sistem keseluruhan bekerja dengan tingkat keberhasilan mencapai 83,33% keberhasilan. Database sistem juga berhasil merekam, menyimpan, membatasi, dan mengelola user secara multiuser, membuktikan bahwa sistem keamanan berbasis Arduino ini dapat berfungsi dengan baik untuk menggantikan keamanan manual di ruang laboratorium komputer tersebut.
Design and Evaluation of an Adaptive Intrusion Detection Framework for IoT Edge Networks Using Hybrid Machine Learning and Deep Reinforcement Learning Techniques Victor Marudut Mulia Siregar; Munji Hanafi
Cyber Security and Network Management Vol. 1 No. 1 (2026): February: Cyber Security and Network Management
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/cybernet.v1i1.8

Abstract

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.
Co-Authors Durand Fernandito Freddy Setlight Pohan, Sry Dhina Siska Ayu Widiana Supriyanto Victor Marudut Mulia Siregar