cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
M. Miftach Fakhri
Contact Email
fakhri@unm.ac.id
Phone
+6282290603030
Journal Mail Official
wahid@unm.ac.id
Editorial Address
Program Studi Teknik Komputer, UNM Parangtambung, Daeng Tata Raya, Makassar, South Sulawesi, Indonesia
Location
Kota makassar,
Sulawesi selatan
INDONESIA
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 16 Documents
 1   2 
Search results for , issue "Vol 6, No 4 (2025): Desember 2025" : 16 Documents clear
Segmentation of Student Lifestyle Patterns for Insomnia Risk Identification Using the K-Means Algorithm Athiyyah Anandira; Azzah Ulima Rahma; Amanda Putri Lestari; Dewi Fatmarani Surianto
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.v6i3.8683

Abstract

Insomnia is a common sleep disorder that occurs in college students due to unbalanced lifestyle patterns. This study aims to categorize students based on their lifestyle patterns and identify the risk of insomnia by applying the K-Means algorithm. Data were obtained from 198 active students of JTIK UNM batch 2021-2024 through a questionnaire. Five main variables were analyzed, such as sleep duration, caffeine consumption, gadget use, number of assignments per week, and hours of sleep. After the researchers transformed and normalized data, the clustering process had resulted in two clusters. The first cluster showed a higher risk of insomnia due to late bedtime and excessive gadget usage, while the second cluster tended to undergo a healthier lifestyle. The Davies-Bouldin Index value of 0.22 indicates superlative clustering qualities. This study provides an overview of student characteristics based on lifestyle and potential risk of insomnia.
Smart Skincare: Expert System Based on Certainty Factors for Skin Type Identification and Product Selection Raja Gunung, Tar Muhammad; Ningtyas, Alyiza Dwi; Sitepu, Sengli Egani; Rolanda, Vicky; Jinan, Abwabul
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.v6i4.10015

Abstract

This study aims to develop an expert system for selecting skincare products based on skin type using the Certainty Factor method. This method is used to represent the expert's level of confidence in the symptoms that appear on the user's skin, so that the system can provide realistic diagnostic results that are close to human thinking. The research data was obtained through consultations with beauty experts and a review of dermatology literature. The test results show that the system is able to identify the user's skin type with varying degrees of certainty. For example, for patients with combination skin types, the system recommends appropriate skincare products such as Salicylic Acid Serum, Elshe Skin Acne Cleansing Wash, and Azarine Acne Gentle Cleansing Foam. Thus, this Certainty Factor-based expert system is expected to help individuals recognize their skin type and choose facial care products more accurately and effectively. Going forward, this system has the potential to be further developed with the integration of artificial intelligence technology and a broader product database to improve the accuracy and personalization of recommendations.
Privacy-Preserving Deep Learning For Enhancing Privacy for Business Information Processing Using Differential Privacy Muhammad Ayat Hidayat; Yusi Irensi Seppa
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.v6i4.10833

Abstract

Artificial intelligence is a rapidly growing technology that has been implemented in various sectors such as finance, education, the military, healthcare, and business. One form of artificial intelligence is deep learning, which has the capability to perform complex pattern recognition and decision-making using very large datasets. This capability makes deep learning one of the key technologies adopted by companies and organizations to improve their policies and enhance operational stability. However, several studies have shown that deep learning still faces significant challenges, particularly security risks that may expose sensitive business or organizational information. Therefore, in this work, we aim to address this problem and enhance the privacy protection of deep learning by incorporating differential privacy. Differential privacy is a technique that protects sensitive information by adding controlled noise to the data or model outputs, thereby reducing the risk of information leakage. We evaluate our proposed method using marketing data and implement it to 5 different models, and based on the experimental results, Our proposed method achieves its best performance using the linear regression model, yielding an RMSE of approximately 1821.30, an MAE of 1406.45, and an R² of 0.007717 for higher privacy budget. Under a lower privacy budget, the performance of the linear regression model decreases to an RMSE of 3634.24, an MAE of 2799.05, and an R² of –0.008482, yet it still outperforms the other four model approaches.
Security and Privacy Threats in AI-Driven Education Systems: A Narrative literature review Tandirerung, Veronika Asri
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.v6i4.10902

Abstract

The use of Artificial Inteligence (AI) in education systems is increasing. Various uses of AI include learning analytics, intelligent tutoring systems, automated assesment, and biometric analysis. This study examines the cybersecurity and privacy risks associated with the integration of generative AI technologies in adolescent education. The use of this technology poses significant security and privacy risks. Through a narrative literature review, the analysis identifies dominant threat categories, institutional vulnerabilities, and mitigation strategies relevant to K–12 learning environments. The selection framework required that each study (1) addressed generative AI or machine learning used within educational systems, (2) discussed cybersecurity, privacy, or data-protection implications, and (3) focused on adolescents or school-age learners. The findings reveal several major risk clusters, including exposure of minors’ personal and biometric data, model manipulation and prompt-injection attacks, algorithmic and behavioral profiling risks, the dissemination of misinformation, and persistent governance gaps within educational institutions. These risks highlight the urgent need for robust privacy by design implementation, stronger cybersecurity infrastructures, clear institutional AI governance policies, and capacity building among educators. While the narrative nature of this review limits quantitative comparison across studies and may restrict generalizability due to variability in methods and contexts, the synthesis provides important insights to guide safer AI adoption. Future work should explore empirical evaluations of generative-AI security controls, the application of differential privacy in school settings, and the development of standardized AI security frameworks for K–12 institutions. Overall, this review contributes a consolidated understanding of the security challenges emerging from the use of generative AI in adolescent education and offers evidence-based directions for technical, policy, and institutional safeguards.
Automatic Floor Stain Detection with Image Processing: A Practical Comparison of OpenCV and RGB Grayscale Conversion: Deteksi Noda Lantai Otomatis dengan Pemrosesan Citra: Komparasi Praktikal OpenCV dan Grayscale RGB Syaharuddin, Achmad Zulfajri; Indasari, Sri Suci; Janna, Nurhikmayana; Hilmi, Andi Afdhal
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.v6i4.9805

Abstract

This research develops an image processing-based floor stain detection system using grayscale conversion and binary thresholding. Two conversion approaches are compared: (i) a simple RGB grayscale formula and (ii) a built-in OpenCV function. The system uses a fixed intensity threshold of T=80 and classifies a floor as “dirty” if the detected area exceeds 20% of the image. Experiments are conducted on three floor types (plain, dark, patterned), five stain types (coffee, oil, ink, plastic, chalk), and two lighting conditions. Results show that the performance of both methods is very close with an average difference of ≈0.07% and a maximum of 0.6%; the simple formula is suitable for resource-limited devices, while OpenCV is more robust to color/lighting variations. The main contributions of this paper are (1) a practical comparison of two grayscale conversion pathways for cleanliness monitoring, (2) a simple decision rule based on the percentage of dirty area that aligns with cleanliness perception, and (3) an analysis of implementation implications for real-time systems in cleaning robots/IoT. Future directions include adaptive thresholding and morphology integration to improve reliability in dynamic environments. (Replace the current abstract paragraph containing T=80 and the 20% rule with the version above. The 20% policy reference is already explained in your manuscript).
Extracting Value from Minority Voices: Epistemic Validation of Naive Bayes and SMOTE Models for E-Commerce Review Sentiment Analysis Ibrahim, Firmansyah; Prasetya, Didik Dwi; Patmanthara, Syaad
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.v6i4.10275

Abstract

In the e-commerce ecosystem, negative customer reviews, despite often being a numerical minority, represent the most valuable (axiological) business asset for service improvement. However, this value is frequently obscured by the high volume of positive reviews, creating a significant imbalance in the data. This study aims to design and validate a text mining model that is axiologically focused on extracting critical insights from this "minority voice." We applied the Naive Bayes Classifier (NBC) algorithm, augmented with TF-IDF feature weighting, on a dataset of 6,000 reviews from the 'Famous Florist' store. The epistemic challenge of severe data imbalance (5,432 positive vs. 97 negative) was addressed through the methodological intervention of the Synthetic Minority Over-sampling Technique (SMOTE). The model's validity was assessed using 10-Fold Cross-Validation. The epistemic validation results demonstrated the model's validity, achieving an average accuracy of 90%. Crucially, the model achieved a 99% rate for the negative class. This affirms the model's axiological validity: its ability to reliably identify customer complaints (e.g., 'damaged,' 'packaging') and transform raw data into actionable recommendations for improvement.
Agile-Based Accreditation Module Design for the P3M Information System at Politeknik Negeri Samarinda Kumala Jaya, Arsan; Triadi, Fara; Hartanto, Subhan; Azis, Ahmad Saiful Mutaqi; Shinta, Priti
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.v6i4.10320

Abstract

Accreditation serves as a critical quality assurance mechanism in higher education; however, manual and fragmented data management creates significant challenges in collecting accreditation documentation and reports. This research designs and develops an Accreditation Menu for the P3M Information System at Politeknik Negeri Samarinda to streamline accreditation processes with greater effectiveness, efficiency, and accountability. Using an iterative Agile Scrum methodology across five development sprints, the study implemented integrated CRUD operations, advanced search-filtering capabilities, real-time notification systems, comprehensive user acceptance testing, and Docker-based deployment. Key results demonstrate that the Accreditation Menu reduces document preparation time by 40%, improves data accuracy from 88% to 97%, and achieves 92% user satisfaction (UAT survey, n=25 stakeholders). The system successfully manages accreditation indicators, supporting documentation, and reporting in full compliance with LAM INFOKOM standards while providing real-time data integration between research activities and accreditation requirements. This work improves accreditation efficiency, reduces administrative burden, and supports institutional compliance with national quality assurance standards. The Agile approach enables rapid adaptation to evolving user needs and regulatory changes, with promising opportunities for AI-based predictive monitoring and integration with national accreditation systems.
Hybrid Regression–Simulation Model for Evaluating Emission Policies in Oversaturated Urban Corridors: A Case Study of Jakarta Triadi, Fara; Jaya, Arsan Kumala; Biabdillah, Fajerin; Hanif, 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.v6i4.10595

Abstract

Urban traffic emissions continue to escalate in Southeast Asian megacities, particularly along oversaturated central business district corridors where chronic congestion amplifies pollutant accumulation. Previous research often separates statistical emission modelling from microscopic simulation, limiting the ability to evaluate policy impacts under real-world saturation conditions. This study aims to assess whether lane-level transport interventions specifically bus-only lanes and motorcycle restrictions can reduce emissions in a hyper-congested Jakarta corridor through an integrated analytical approach. A hybrid regression–microsimulation framework was developed by combining multiple linear regression with SUMO-based traffic simulation. An hourly dataset of traffic flow and CO emissions (n = 8,760) from the Thamrin–Bundaran HI corridor was used to construct a regression model enriched with temporal and lagged predictors. The resulting emission profiles were embedded into SUMO to simulate baseline, bus-lane, and motorcycle-restriction scenarios. The regression model achieved strong predictive performance (R² = 0.692, RMSE = 0.252), with CO_lag1 confirmed as the dominant predictor. Simulation results showed fully overlapping CO₂ emission trajectories across all scenarios, indicating that lane-based interventions do not alter traffic states or emissions under oversaturated conditions. Structural congestion constrains the effectiveness of lane-level policies. Meaningful emission reductions require systemic strategies such as demand management, modal shift, or network redesign. The proposed hybrid framework provides a replicable tool for evaluating transport policies in dense urban corridors
Sentiment-Aware Transformer for Cryptocurrency Volatility Prediction Using Multi-Source Market and News Sentiment Biabdillah, Fajerin; Triadi, Fara; Go , Aeltri Jeacfky Gozal; Ramadhan, Muhammad Cahyo Putra
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.v6i4.10604

Abstract

The cryptocurrency market has grown into a multi-trillion-dollar domain with extreme volatility. This paper addresses the forecasting of crypto price movements and volatility by integrating market metrics with sentiment analysis. We identify a gap in existing studies, which often ignore multi-source sentiment and thus miss early warning signs of volatility. We propose a Sentiment-Aware Transformer model inspired by the Temporal Fusion Transformer (TFT). The model ingests daily price, volume, and market cap features from CoinMarketCap alongside aggregated sentiment scores from Twitter, Reddit, and financial news (extracted via FinBERT). We train and evaluate the model on 5 years of data for 10 major cryptocurrencies (2020–2024), comparing performance against LSTM and GRU baselines with identical inputs. The proposed Transformer achieves 83.2% volatility prediction accuracy with an F1-score of 0.81, exceeding the LSTM (79% accuracy) and GRU (80%) baselines. It also shows the lowest RMSE in price forecasting and a higher return correlation (0.72) with actual prices, indicating improved trend alignment. These gains are statistically significant (p<0.01). We also discuss how attention weights offer interpretability, as the model focuses on sentiment spikes during impending volatility.
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

Page 1 of 2 | Total Record : 16

 1   2 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 7 No 1 (2026): March 2026 Vol 6, No 4 (2025): Desember 2025 Vol 6, No 3 (2025): September 2025 Vol 6, No 2 (2025): June 2025 Vol 6, No 1 (2025): March 2025 Vol 5, No 3 (2024): November 2024 Vol 5, No 2 (2024): July 2024 Vol 5, No 1 (2024): March 2024 Vol. 4 No. 1 (2023): Vol 4, No 1 (2023): May 2023 Vol. 3 No. 2 (2022): Vol 3, No 2 (2022): November 2022 Vol. 3 No. 1 (2022): Vol 3, No 1 (2022): May 2022 Vol. 2 No. 2 (2021): Vol 2, No 2 (2021): November 2021 Vol. 2 No. 1 (2021): Vol 2, No 1 (2021): May 2021 Vol. 1 No. 2 (2020): Vol 1, No 2 (2020): November 2020 Vol. 1 No. 1 (2020): Vol 1, No 1 (2020): May 2020 More Issue