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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 134 Documents
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Fire Detection and Room Firefighting System Based on IoT Using C4.5 Decision Tree Algorithm Ismayanti, Rika; Triadi, Fara; Jaya, Arsan Kumala; Irawan, Ade
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.v6i4.10685

Abstract

Early fire detection is a critical requirement in indoor safety systems, where delays of only a few seconds can escalate into severe damage and casualties. Conventional devices often rely on single-sensor thresholds, which are highly susceptible to false alarms and unstable performance in dynamic indoor environments. This study develops an Internet of Things (IoT)-based multi-sensor fire detection and autonomous firefighting system integrated with a C4.5 decision tree classifier for real-time hazard recognition and short-term risk prediction. The prototype combines DHT22 temperature, MQ-135 gas, infrared flame, and ultrasonic water-level sensors with an ESP32 microcontroller, servo-controlled nozzle, and pump-based water spraying, all connected to an Android–Firebase platform for remote monitoring. A multivariate time-series dataset of 200 sensor sequences was preprocessed using a five-step sliding-window model and evaluated through 1,000 repeated hold-out trials. The C4.5 classifier achieved a mean accuracy of 84.9%, with peak values exceeding 90%, and clearly separated Safe, Alert, and Danger states, with smoke concentration emerging as the dominant predictor. Experimental tests in a 60 × 40 × 30 cm chamber produced 1–2 s reaction times, eight successful extinguishing events, and four failures attributable to mechanical belt detachment rather than model errors. These findings indicate that interpretable decision-tree models, when combined with IoT sensing and autonomous actuation, can provide a low-cost framework for real-time fire warning and automatic suppression. Future work should address mechanical robustness, extended deployment, and multi-room scalability
Performance Comparison of Svm and Naïve Bayes For Indonesian-Language Sentiment Analysis On Free Fire Reviews Using Tf-Idf And Smote Wahid, Yokogeri Abdullah; Sanatang; Andayani, Dyah Darma
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.v6i4.10818

Abstract

The popularity of online games continues to increase, including Free Fire, which has gained more than one billion downloads and millions of user reviews on the Google Play Store. However, the variation and inconsistency of user comments make manual sentiment evaluation difficult. This study aims to compare the performance of Support Vector Machine (SVM) and Naïve Bayes in classifying user review sentiment on the Free Fire game. A total of 535 Indonesian-language reviews were collected using web scraping and processed through text cleaning, case folding, normalization, stopword removal, and stemming. Sentiment labels were assigned manually based on review content. The dataset was divided into training and testing using a 70:30 ratio, and feature extraction used Term Frequency–Inverse Document Frequency (TF-IDF). Two scenarios were implemented: a baseline without class balancing and a scenario using Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Results show that SVM outperforms Naïve Bayes in both scenarios. In the baseline, SVM achieved 89.81% accuracy, while Naïve Bayes obtained 82.80%. After SMOTE, SVM improved to 91.08% accuracy and Naïve Bayes to 89.17%. These findings indicate that SVM, especially with SMOTE, provides a more effective and balanced performance for sentiment classification on Free Fire reviews. The study contributes to providing a more accurate understanding of user perception and strengthening model development for sentiment analysis on digital game applications.
Energy Audit Integrated with Fuzzy Neural Network Predictive Maintenance for Central Chillers Saragih, Budiman R; Aldi Cahya Muhammad
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.v6i4.10857

Abstract

Because central chiller systems significantly affect electricity usage in office buildings, predictive maintenance and energy audits would be important to increase efficiency. This research analyzes the data from the thermodynamic audit and the central chillers and the monthly electricity usage to assess the energy performance of the East Jakarta Mayor's Office Building A for the years 2023-2024. Based on the Building A, in 2024, the total estimated electrical consumption will be 2,019,550 kWh. This results in total energy use intensity of 106.9 kWh/m²/year. Based on the estimated data, the HVAC systems use more than half of the total electrical consumption. The simulations show, for the data provided, the energy efficiency measures have a saving potential of approximately 728,847 kWh/year which equals 36.1% on a total consumption of 2,019,550 which would also save 36.2% of 1.26 billion/year. The total energy use intensity would be reduced to 90.5 kWh/m²/year, with the emission reduction of approximately 604.9 tCO₂e/year. Based on the consumed data and the paired t test on the 12 sampled data the results would show, with p value < 0.001, a 97,438 kWh/month average reduction in electrical consumption in the 2023-2024 years, which shows a correlation in the expected data with operational and standard fix measures. The Fuzzy Neural Network is, and can be used with other data to show other measures of predictive maintenance rather than the conventional audit based measures used.
Implementation of Automated Intelligent Irrigation and Fertilization System Based on the Internet of Things for Home Wine Hobbyists Azra, Muh. Azfa; Parenreng, Jumadi M.; Lamada, Mustari S; Wahid, M. Syahid Nur; Wahid, Abdul
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.v6i4.11207

Abstract

The agricultural sector faces serious challenges due to climate change and rising global food needs, which demand more efficient implementation of water and plant nutrient management. Viticulture, particularly on an urban scale, requires precise irrigation and fertilization arrangements because grapes are very sensitive to water availability and nutrient concentrations at certain growth phases. This research aims to design and implement a prototype of an Internet of Things (IoT)-based Smart Nutrition Irrigation System for home wine hobbyists using the Research and Development (R&D) method. The developed system is able to monitor environmental and soil conditions in real-time and control irrigation and fertilization automatically based on predetermined parameters. The results of the performance evaluation showed that the system had an average response time of ±1–3 seconds from the time the sensor data was received until the actuator was activated. TDS sensors are able to detect changes in the concentration of fertilizer solutions in the range of 0–1200 ppm, thus supporting the quality control of nutrient solutions. The results of functional tests show that the system successfully activates irrigation automatically when soil moisture is below the 75% threshold and runs scheduled fertilization consistently. In addition, the system is able to send real-time monitoring and actuator status notifications through an online application with a 100% message success rate during testing. Overall, the system has been shown to improve the efficiency of water and nutrient management in urban grape cultivation and has the potential to be further developed on a larger scale and applied to precision agriculture

Page 14 of 14 | Total Record : 134

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