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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Building of Informatics, Technology and Science
Nyoman Karna, Nyoman
Institut Teknologi Bandung
Computer Science & IT Electrical & Electronics Engineering

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Sistem Monitoring dan Kontrol Aeroponik Menuju Smart Greenbox untuk Tanaman Selada berbasis IoT Karna, Nyoman; Naufal, Rangga; Raniprima, Sevierda; Putra, I Kadek Andrean Pramana; Rahyuni, Dewa Ayu Putu; Parti, I Ketut
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3125

Abstract

Horticultural commodities are agriculture that has a lot of demand in the market. Based on this, a planting system with the aeroponic method was made with an IoT-based monitoring and control system so that its growth is maintained. A system with complex planning is needed to simplify human life. Then a remote monitoring and control system was designed with IoT technology in the aeroponic method. The way this tool works is to send sensor data from the Node MCU via the internet to the cloud and the data is stored in real-time in the Firebase, the data is sent to the Android platform so that the data can be read by the user and the data is sent to a Google spreadsheet automatically which will later be analyzed. Will update data every 15 minutes. In this study, calibration of the DHT11 sensor with HTC Digital obtained an accuracy of 95.5% humidity and 97% temperature, the LDR sensor with LUX meter obtained an accuracy rate of 75.163%, pH sensor with pH meter 97.33%, ultrasonic sensor and ruler. get 100% accuracy, the bandwidth used is 20 Mbps. The network quality test is delayed, with 3 different test times, busy hours (19.00 - 23.00 WIB), empty hours (01.00 - 03.00 WIB), normal hours (12.00 - 14.00 WIB). From network testing, the minimum delay is 0.255 seconds, and the maximum is 0.291 seconds. The results of testing tools during seeding, lettuce plants can grow well.
Enhancing Network Security Through Real-Time Threat Detection with Intrusion Prevention System (Case Study on Web Attack) Rahmawati, Tia; Karna, Nyoman; Shin, Soo Young; Putra, Made Adi Paramartha
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30380

Abstract

Cyberattacks on government websites in Indonesia have been steadily increasing, with over 109 million incidents recorded in 2023 by the National Cyber Security Operations Center (BSSN). A Netcraft survey revealed that more than one billion websites globally face similar threats, highlighting the urgent need for improved security measures, especially given infrastructure limitations and inadequate security implementations. Approximately 51% of Micro, Small, and Medium Enterprises in Indonesia reported experiencing web attacks, with 95% stating that these attacks severely disrupted their operations. This study implements a Suricata-based Intrusion Prevention System (IPS) to protect web servers from attacks such as SQL Injection, XSS, and command injection. Suricata monitors network traffic and blocks threats in real time. Detection logs in JSON format are managed through Filebeat, processed by Logstash, stored in Elasticsearch, and visualized using Kibana. The key contribution of this research lies in designing a comprehensive set of rules and integrating all components into a single Docker container, streamlining the deployment process. Testing confirmed that the designed rules effectively detect and block attack payloads by leveraging a rule structure in suricata and nfqueue capable of identifying all suspicious traffic. The novelty of this research lies in deploying a fully operational real-time security system on low-resource computers, demonstrating effective threat management under constrained conditions.
Optimizing Machine Learning-Based Network Intrusion Detection System with Oversampling, Feature Selection and Extraction Shiddiq, Rama Wijaya; Karna, Nyoman; Irawati, Indrarini Dyah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i2.30675

Abstract

Network security is a global challenge that requires intelligent and efficient solutions. Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS) have been proven to enhance accuracy in detecting cyberattacks. However, the main challenges in implementing ML-based IDS are dataset imbalance and large dataset size. This research addresses these challenges by applying oversampling techniques to balance the dataset, feature selection using random forest to identify the most relevant features, and feature extraction using Principal Component Analysis (PCA) to further reduce the selected important features. Additionally, K-fold cross-validation is used to test the features to minimize bias and ensure the model does not suffer from overfitting, while Optuna is implemented to automatically optimize model parameters for maximum accuracy. Since IDS performance deteriorates with high-dimensional features, the combination of methods used is evaluated based on feature selection applied to the model using datasets wtih 45 features selected from UNSW-NB15, 78 features from CIC-IDS-2017, and 80 features from CIC-IDS-2018 using various ML algorithms. The results demonstrate that the combination technique with feature selection, along with maximum optimization for each model significantly improves performance on large and imbalanced datasets reaching 99% accuracy compared to conventional methods in network traffic analysis.
Co-Authors Indrarini Dyah Irawati Naufal, Rangga Parti, I Ketut Putra, I Kadek Andrean Pramana Putra, Made Adi Paramartha Rahmawati, Tia Rahyuni, Dewa Ayu Putu Sevierda Raniprima Shiddiq, Rama Wijaya Shin, Soo Young