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Journal of Embedded Systems, Security and Intelligent Systems
Published by Universitas Negeri Makassar
ISSN : 2745925X     EISSN : 2722273X     DOI : -
Core Subject : Science,
Computer Science & IT
The Journal of Embedded System Security and Intelligent System (JESSI), ISSN/e-ISSN 2745-925X/2722-273X covers all topics of technology in the field of embedded system, computer and network security, and intelligence system as well as innovative and productive ideas related to emerging technology and computer engineering, including but not limited to : Network Security System Security Information Security Social Network & Digital Security Cyber Crime Machine Learning Decision Support System Intelligent System Fuzzy System Evolutionary Computating Internet of Thing Micro & Nano Technology Sensor Network Renewable Energy Wearable Devices Embedded Robotics Microcontroller
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 216 Documents
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Internet of Things (Iot)-based Overcurrent Protection and Detection Device for Household Eletrical Safety Muliadi; Muhammad Mahdinul Bahar; Aulia Sabril; Muhammad Riska
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.11715

Abstract

The increasing risk of electrical fires caused by overcurrent, overheating cables, and the lack of early monitoring mechanisms in conventional household electrical protection systems has become a significant safety concern, as most protection devices only disconnect power without providing real-time monitoring or early warnings. This study aims to develop an Internet of Things (IoT)-based electrical protection system capable of detecting abnormal electrical conditions and providing early warning notifications. The system uses an ESP32 microcontroller integrated with several sensors, including the PZEM-004T sensor to monitor electrical parameters, the DS18B20 sensor to detect cable temperature, the MQ-2 sensor for smoke detection, and a flame sensor for fire detection. The research methodology includes system design, hardware and software integration, and performance testing through sensor functionality tests and response time measurements. The testing results show that the PZEM-004T sensor measured voltage in the range of 212.50–213.60 V and current around 1.95–1.96 A during testing. The DS18B20 sensor recorded temperatures between 27.81°C and 28.94°C with an average response time of 12.05 seconds when the threshold was exceeded. The MQ-2 sensor achieved a 100% success rate in detecting smoke, while the flame sensor detected fire in approximately 85% of the trials, with response times of 1.4 seconds and 1.7 seconds before triggering automatic power disconnection via a relay. The system also transmitted monitoring data and warning notifications through a web application and Telegram using cloud-based IoT communication, demonstrating its potential to improve household electrical safety through integrated monitoring, automatic protection, and early warning notifications.
Integrating Interface Design and Usability Evaluation in a Web-Based Zakat and Sadaqah Application Khasanah, Fata Nidaul
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2603

Abstract

Purpose – This study aims to design a user-centered interface for a web-based E-Zakat application to support the digital management of zakat and sadaqah by Mosque Prosperity Councils in the Industry 5.0 era. Design/methods/approach – The research adopts a user-centered design approach in developing the application interface. A usability evaluation was conducted using the Heuristic Evaluation method to assess the effectiveness, efficiency, and user experience of the designed interface. Findings – The results show that 80% of the interface design elements did not present any usability issues. However, two indicators were identified as cosmetic problems, indicating minor improvements are needed in future iterations. Research implications/limitations – This study is limited to interface design and usability evaluation using heuristic methods, without involving extensive user testing. Future research can expand by incorporating empirical user testing and system implementation to validate real-world effectiveness. Originality/value – This study contributes to the digital transformation of mosque financial management by providing a user-centered interface design specifically for E-Zakat systems, enhancing transparency, efficiency, and accessibility in managing zakat and sadaqah.
IoT-Based Smart Energy Management System for Air-Conditioning Energy Efficiency in Mosques Syaiful Amri; Rahmat Fajrul; Johny Custer; Murdani; Azizul
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2604

Abstract

Purpose – This study aims to develop and evaluate an Internet of Things (IoT)-based Smart Energy Management System (SEMS) to improve air-conditioning energy efficiency in mosque buildings. Design/methods/approach – The proposed system utilizes an infrared (IR) transmitter to replicate native air-conditioner remote signals, allowing implementation without modifying existing electrical installations. The system was implemented and tested at Taj-Al’ulum Mosque, Politeknik Negeri Bengkalis, under four operating modes: Default Manual, IoT Manual, Automatic, and Hybrid. Performance evaluation was conducted by comparing energy consumption across these modes and validating results through simulation and field testing. Findings – The results indicate that the Automatic mode achieved the highest energy savings, reducing energy consumption by 23.7% compared to the baseline, while also demonstrating the most stable operational performance. The Hybrid and IoT Manual modes also contributed to energy savings, although their effectiveness was influenced by user intervention and variations in mosque activities. Model validation showed strong agreement between simulation and real-world implementation, confirming system reliability. Research implications/limitations – This study is limited to a single case study location and focuses primarily on air-conditioning systems. Future research could expand to multiple buildings, integrate additional energy loads such as lighting and audio systems, and explore long-term performance under varying environmental and occupancy conditions. Originality/value – This study offers a practical, non-invasive, and replicable IoT-based energy management solution for mosque buildings, contributing to the development of smart energy management systems for smart mosques, particularly in environments with intermittent occupancy patterns.
Advancements in Brain Tumor Classification: Leveraging Mobilnet-V2 and Densenet121 For High-Precision Prediction Yuliawan, Kristia
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11471

Abstract

Purpose – This study aims to compare the performance of MobileNet-V2 and DenseNet121 in classifying brain tumor types from MRI images under identical preprocessing, partitioning, and training conditions. Design/methods/approach – The study used a Kaggle-based dataset consisting of 3,264 MRI images, divided into 88% training data and 12% testing data. Both models were implemented using transfer learning and fine-tuning. Preprocessing included image resizing, normalization, and data augmentation through rotation, flipping, and zooming. The models were trained using the Adam optimizer, a learning rate of 0.0001, batch size of 32, and early stopping. Performance was evaluated using confusion matrix analysis, precision, recall, and F1-score. Findings – The results show that MobileNet-V2 achieved better overall performance than DenseNet121 in brain tumor classification. MobileNet-V2 produced more stable classification results and higher evaluation scores across most tumor classes, particularly in glioma and pituitary tumor prediction. In contrast, DenseNet121 showed a greater tendency to overfit, although both models performed well in identifying non-tumor images. Research implications/limitations – The study is limited by the relatively small dataset size, the use of a single dataset source, and the absence of external validation, which may affect generalizability. Originality/value – This study provides a direct comparative analysis of MobileNet-V2 and DenseNet121 for four-class brain tumor classification and highlights MobileNet-V2 as a more efficient and reliable model for this task.
A Multi-Branch EfficientNet-U-Net Hybrid Framework for Segmentation of Oyster Mushrooms in Cultivation Media Husain, Nursuci Putri; Kadir, Muh Ichwan
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2607

Abstract

Purpose – This study aims to develop a semantic segmentation model for oyster mushrooms in cultivation media to support automated monitoring, growth analysis, and yield estimation in smart farming systems. Design/methods/approach – A Multi-Branch EfficientNet-U-Net hybrid architecture was proposed, using EfficientNet-B0 as the encoder and a multi-branch fusion strategy to integrate multi-scale features from three encoder levels. The dataset consisted of 150 manually annotated oyster mushroom images collected from two cultivation sites under varying illumination, mushroom cluster density, and background texture. The model was evaluated using Intersection over Union (IoU) and Dice Coefficient metrics on training, validation, and testing subsets. Findings – Experimental results show that the proposed model achieved high segmentation performance, with a median IoU of approximately 0.90 and a Dice coefficient of 0.93. Compared with the baseline U-Net, the proposed architecture produced cleaner segmentation boundaries and more consistent detection of mushroom regions under complex environmental conditions. Research implications/limitations – This study is limited by the relatively small dataset and evaluation on images from only two cultivation sites. Further studies should involve larger and more diverse datasets to assess robustness across broader cultivation environments. Originality/value – This study offers an effective semantic segmentation framework that combines EfficientNet-B0 encoding and multi-branch multi-scale feature fusion to improve oyster mushroom segmentation accuracy in real cultivation settings, with potential application in smart agriculture monitoring systems.
Performance Evaluation of a Cowrie-Based Honeypot Integrated with a Suricata Intrusion Prevention System in a School Wireless Network Putri, Aurellia Maharani; Tahir, Muhlis
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.2605

Abstract

Purpose – This study aims to implement and evaluate the integration of the Cowrie honeypot model with a Suricata-based Intrusion Prevention System (IPS) as a proactive security solution for school wireless networks. Design/methods/approach – The research method includes a literature review, needs analysis, security topology design, system implementation, and testing using port scanning and SSH brute force attack scenarios. The Cowrie honeypot is utilized to detect and log attack activities, while Suricata functions as an IPS to perform automatic real-time blocking. Findings – The results show that without a security system, both detection and blocking rates were 0%. After implementing Cowrie, the detection rate increased to 100%, although no blocking capability was present. The integration of Cowrie and Suricata IPS achieved 100% detection and 100% blocking rates for port scanning attacks (10 test packets) with an average response time of 0.8 seconds. In the SSH brute force test, the system successfully blocked attacks after 5 out of 10 login attempts, with an average response time of 0.9 seconds. Research implications/limitations – The evaluation was conducted under controlled experimental conditions using a limited number of attack simulations (10 scanning packets and 10 brute force attempts). Therefore, the results may not fully represent performance in large-scale or real-world network environments. Future studies should involve more extensive testing with diverse attack patterns and higher traffic volumes. Originality/value – This study contributes a practical integration of honeypot and IPS technologies to enhance wireless network security in school environments, providing a proactive and automated approach to both detect and mitigate cyberattacks in real time.
Semar AquaConnect: A Closed-Loop IoT System for Real-Time Monitoring and Autonomous Control in Catfish (Pangasianodon hypophthalmus) Larvae Rearing Rafif Zainun Ridhwan; Basino; ⁠Berbudi Wibowo; Rahmad Surya Hadi Saputra; ⁠I Ketut Daging; Yusuf Syam; Akhmad Syarifudin; Ade Hermawan
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11782

Abstract

Purpose – This study aims to develop and validate Semar AquaConnect, a closed-loop Internet of Things (IoT) system for real-time monitoring and automated environmental control in catfish (Pangasianodon hypophthalmus) larval rearing tanks. Design/methods/approach – The system integrates an ESP32-based controller with sensors for temperature, pH, dissolved oxygen, total dissolved solids, and water level, as well as relay-based control of pumps, an aerator, and an immersion heater. A web-based dashboard and Telegram Bot were also developed for remote monitoring, data visualization, and emergency notifications. System performance was evaluated through sensor accuracy testing and repeated simulated extreme-condition scenarios (n=5). Findings – The results showed low mean differences of 0.12°C for temperature, 0.33 for pH, 0.08 mg/L for dissolved oxygen, and 5.10 ppm for total dissolved solids. In simulated scenarios, the system achieved a 100% relay activation success rate, including responses to critical pH fluctuations and temperature drops. Mean response latency ranged from 0.85 ± 0.10 s to 1.42 ± 0.18 s. Research implications/limitations – This study is limited to technical validation and has not yet included live biological trials to measure actual larval survival improvement. Originality/value – The study presents an integrated smart aquaculture platform combining multi-parameter sensing, autonomous actuator control, and dual-interface remote monitoring for catfish larval rearing.
Measurement of Gauge Block Surface Structure Using 12-Lamp Photometric Stereo Method Syahwir, Irawati Dewi; Setiawan, Irwan; Khairah, Inayah Dwi
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.11769

Abstract

Purpose – This study aims to develop and evaluate a non-contact method for measuring surface roughness of gauge blocks using an enhanced photometric stereo approach with multiple lighting sources, as an alternative to conventional coordinate measuring machine (CMM) methods that may damage the surface. Design/methods/approach – The research employs a photometric stereo method using 12 different lighting sources arranged at 55° intervals. The captured images are reconstructed using a photometric stereo algorithm to generate a three-dimensional surface model. Surface roughness is determined by measuring peak and valley heights and calculating their average. The objects tested are gauge blocks of class 0 and class 1, compared against standard blocks based on ISO/R 468-1966. Findings – The results show that gauge blocks of class 0 and class 1 have roughness values close to the N7 standard group, with an average roughness of 1.6 μm. The reconstruction results indicate an average error of 0.11933 μm for N7 blocks and -0.0718 μm for N8 blocks. The photometric stereo method demonstrates lower error compared to conventional contact-based measurements. Research implications/limitations – This method provides a promising non-contact alternative for surface roughness measurement and calibration. However, the study is limited to specific lighting configurations and gauge block samples, and further research is needed to validate its application across different materials and surface conditions. Originality/value – This study introduces an improved photometric stereo configuration using 12 lighting sources, enhancing reconstruction accuracy and offering a safer and more precise alternative for surface roughness measurement, particularly for calibration processes regulated by the National Standardisation Agency.
E-Presence and Monitoring System Based on Face Image Recognition with Local Binnary Pattern Histogram (LBPH) Algorithm Nurhikma; Abdul Wahid; Jumadi M Parenreng
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.556

Abstract

Abstract : There are still many attendance systems that are done manually, and some still use paper as an attendance tool. So it is not effective because the data from attendance is easy to manipulate. The purpose of this study is to create attendance by using web-based facial recognition so as to produce e-presence. The accuracy rate of facial sensitivity obtained by this researcher is 90%. This researcher uses the LBPH method to determine the level of accuracy of various lighting conditions. Researchers analyzed two samples, each of which had a different level of accuracy. For the error rate in recognizing faces as unknown (FRR), all are 100% with a duration of 1 minute each. The conclusion of this study is to use more dataset facial images and a bright level of brightness to make it easier to recognize face
Perancangan Smart Trash Limbah Rumah Makan Untuk Pemenuhan Pakan Maggot Berbasis IOT Muhammad Syafaat; Fizar Syafaat
Journal of Embedded Systems, Security and Intelligent Systems Vol 4, No 2 (2023): November 2023
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v4i2.595

Abstract

The increase in population in urban areas causes food waste production to continue to increase. In 2020, Indonesia has entered an emergency signal for food waste production. Losses from unmanaged food waste reached Rp. 1,011,743,415 annually. The negative impact of food waste which is dominated by organic type waste can cause environmental pollution because it produces dangerous biogas. Most food waste comes from restaurants resulting from leftover food from visitors and leftover ingredients from kitchens that are not managed properly by restaurant owners. In developing alternative feed innovations for several livestock, it was argued that BSF maggot is a good source of natural nutrition for livestock, then the feed from maggot itself comes from organic ingredients in its breeding, but maggot breeders still have difficulty meeting feed from maggot because it only relies on organic feed from household waste around the farm, so the potential for reducing food waste which is dominated by organic waste from restaurants can be utilized by maggot breeders. This study designed a smart trash technology or smart trash bin that can sort organic and non-organic waste based on the internet of things using the Long Range (LoRa) module which will later send data on the availability of organic waste to maggot breeders via a mobile application. The test results from this study show that the entire smart trash system functions properly through blackbox testing, besides that an analysis of data transmission is carried out with the overall Quality of Service results being recommended as good or satisfactory.

Page 16 of 22 | Total Record : 216

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