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Putri, Fahra Pebiana
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Perancangan dan Implementasi Sistem Logging Jaringan Berbasis Website dengan Fitur Multi-User Real-Time dan Deteksi Zai, Tri Sapta Warman; Kiswanto, Dedy; Putri, Fahra Pebiana; Valentino, Bob
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 6 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i6.9915

Abstract

Abstrak - Perkembangan teknologi informasi menimbulkan tantangan dalam deteksi anomali log secara real-time. Penelitian ini mengembangkan NetLog, sistem monitoring log dengan pendekatan hybrid yang menggabungkan deep learning dan deteksi berbasis aturan. Sistem menggunakan Autoencoder untuk mempelajari pola log normal dan rule-based detector sebagai fallback. Arsitektur terdiri dari backend FastAPI, frontend React/Next.js, dan modul anomaly detection. Hasil implementasi menunjukkan sistem berhasil mendeteksi 60% serangan simulasi dengan precision 100% dan recall 20%. Evaluasi komprehensif menunjukkan ROC AUC 82% dan PR AUC 86.7%, mengindikasikan kemampuan model yang baik dalam membedakan log normal dan log anomali. Dashboard real-time menampilkan log dengan latensi di bawah 2 detik. Kesimpulannya, pendekatan hybrid pada NetLog terbukti efektif memperluas cakupan deteksi anomali dibandingkan metode tunggal, meskipun masih diperlukan peningkatan sensitivitas deteksi.Kata kunci: Deteksi Anomali; Autoencoder; Deep Learning; Monitoring Log; Sistem Real-time; Abstract - The rapid advancement of information technology poses new challenges in real-time log anomaly detection. This study develops NetLog, a log monitoring system based on a hybrid approach that combines deep learning with rule-based detection. The system employs an Autoencoder to learn normal log patterns and a rule-based detector as a fallback mechanism. The architecture consists of a FastAPI backend, React/Next.js frontend, and an anomaly detection module. The implementation results show that the system successfully detected 60% of simulated attacks with 100% precision and 20% recall. A comprehensive evaluation demonstrates ROC AUC of 82% and PR AUC of 86.7%, indicating a strong ability of the model to distinguish between normal and anomalous logs. The real-time dashboard displays log data with latency below 2 seconds. In conclusion, the hybrid approach of NetLog effectively broadens anomaly detection coverage compared to single-method systems, although improvements in sensitivity are still required.Keywords: Anomaly Detection; Autoencoder; Deep Learning; Log Monitoring; Real-time System;
Implementasi Sistem Keamanan Laci Pintar: Integrasi Kendali Akses Berbasis Web dan Klasifikasi Anomali Getaran Menggunakan Machine Learning Drilanang, Mhd Ilyasyah; Kiswanto, Dedy; Nababan, Sirus Daniel Haholongan; Putri, Fahra Pebiana
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10317

Abstract

Abstrak - Penelitian ini mengimplementasikan sistem keamanan laci pintar berbasis IoT dan Edge Machine Learning untuk mengatasi kelemahan kunci konvensional dan tingginya false alarm pada sensor getar biasa. Tujuan utama penelitian adalah mengembangkan sistem klasifikasi anomali getaran secara real-time menggunakan algoritma Rule-Based Decision Tree pada mikrokontroler ESP32. Sistem mengintegrasikan sensor MPU-6050, sensor LDR, dan solenoid lock yang dikendalikan melalui dashboard web. Metode klasifikasi menerapkan logika multi-threshold dengan parameter empiris Threshold Force 4.5 m/s² dan Counter Persistence 5 siklus. Berdasarkan pengujian terhadap 30 sampel uji data getaran, sistem menghasilkan akurasi sebesar 100% dalam membedakan pola "senggolan" dan "congkelan". Mekanisme counter terbukti menurunkan tingkat false alarm hingga 0% dibandingkan metode threshold tunggal. Selain itu, sistem menunjukkan responsivitas tinggi dengan rata-rata latensi notifikasi ke dashboard sebesar 2 detik. Penelitian ini berkontribusi pada pengembangan solusi keamanan fisik yang responsif, akurat, dan minim alarm palsu.Kata kunci :  Laci Pintar; Pembelajaran Mesin Edge; Deteksi Anomali Getaran; Keamanan IoT; ESP32; Abstract - This research implements a smart drawer security system based on IoT and Edge Machine Learning to overcome the weaknesses of conventional locks and the high rate of false alarms on ordinary vibration sensors. The main objective of the research is to develop a real-time vibration anomaly classification system using the Rule-Based Decision Tree algorithm on an ESP32 microcontroller. The system integrates an MPU-6050 sensor, an LDR sensor, and a solenoid lock controlled through a web dashboard. The classification method applies multi-threshold logic with empirical parameters of Threshold Force 4.5 m/s² and Counter Persistence 5 cycles. Based on testing on 30 vibration data test samples, the system produces 100% accuracy in distinguishing "nudge" and "pry" patterns. The counter mechanism is proven to reduce the false alarm rate to 0% compared to the single threshold method. In addition, the system shows high responsiveness with an average notification latency to the dashboard of 2 seconds. This research contributes to the development of physical security solutions that are responsive, accurate, and have minimal false alarms.Keywords: Smart Drawer;, Edge Machine Learning; Vibration Anomaly Detection; IoT Security; ESP32;
Co-Authors Dedy Kiswanto Drilanang, Mhd Ilyasyah Nababan, Sirus Daniel Haholongan Valentino, Bob Zai, Tri Sapta Warman