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M. Miftach Fakhri
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Program Studi Teknik Komputer, UNM Parangtambung, Daeng Tata Raya, Makassar, South Sulawesi, Indonesia
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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 16 Documents
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Search results for , issue "Vol 6, No 1 (2025): March 2025" : 16 Documents clear
Development of A Web-Based Application Measuring Student and Lecturer Satisfaction for Educational Service Wardani, Ayu Tri Wardani; Alifya NFH
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.6492

Abstract

The development of student and lecturer satisfaction survey applications for web-based education services aims to improve the efficiency of feedback data collection and support data-based decision making. This research uses Extreme Programming (XP) method which involves planning, designing, coding, and testing stages. In the planning stage, user needs are identified to ensure the designed features are in line with the application's objectives. Designing is done using the Class Responsibility Collaborator (CRC) Cards approach to ensure the system design is structured and easy to develop. The coding stage uses PHP and MySQL to produce applications that meet the functional needs of the system, while the testing stage is carried out using the blackbox testing method to ensure each module works according to specifications. The results show that this application is able to improve the efficiency of the survey process, provide an easily accessible web-based platform, and produce structured survey data. The implementation of this application is expected to contribute to improving the quality of education services through valid and real-time survey data analysis. Keywords: Satisfaction Survey, Extreme Programming, PHP, MySQL, Blackbox Testing.
Development of a Web-Based Information System for Food Availability and Production at the Makassar City Food Security Agency Ismail, Hijir; Andayani, Dyah Darma; Kaswar, A.Baso
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.6824

Abstract

This research is motivated by issues related to the limitations of information and data management in the Field of Food Availability and Production at the Makassar City Food Security Agency. To address these issues, the research focuses on developing a web-based information system aimed at improving the efficiency of data and information management in this sector. Additionally, the study seeks to evaluate the system’s performance by applying the ISO/IEC 25010 standard. The research adopts a research and development (R&D) approach, utilizing the Agile development model, which consists of six stages: requirements, design, development, testing, review, and implementation. The system is tested based on the ISO/IEC 25010 standard, assessing eight key aspects: functionality suitability, usability, reliability, compatibility, performance efficiency, maintainability, portability, and security. The testing outcomes confirm that the developed system aligns with the ISO/IEC 25010 standard. Consequently, the web-based information system for the Field of Food Availability and Production at the Makassar City Food Security Agency demonstrated feasibility for implementation.
Development of AI and IoT Based Microcontroller Simulator to Improve 4C Skills in learning Wahyudi; Ahmad Risal; Muhammad Romario Basirung
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7481

Abstract

The development of digital technology demands mastery of 21st century skills, particularly the 4Cs (Critical Thinking, Creativity, Collaboration, Communication). However, conventional microcontroller learning is often limited to technical aspects without training these skills. This research aims to develop a microcontroller simulator based on AI (Artificial Intelligence) and IoT (Internet of Things) as an interactive media to improve 4C skills in digital technology learning. The research method uses a Research and Development (R&D) approach with the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation). The simulator is designed with machine learning integration for learning difficulty adaptation and IoT for cloud-based project simulation. Validation was conducted through expert tests (pedagogy, AI, and embedded systems experts) and field trials on engineering students with questionnaire instruments, observations, and 4C skills tests. The results of the developed Simulator are proven to improve the understanding of microcontroller concepts while training 4C skills, with the following results: 1) Critical Thinking: Participants were able to analyse problems 25% faster through AI-based case simulation. 2) Creativity: There was a 30% increase in the variety of IoT project solutions generated. and 3) Collaboration & Communication: Teamwork effectiveness improved based on the collaboration rubric assessment. The implications of this research can be applied in technical education curriculum, vocational training, or STEM/STEAM development.
Optimizing Convolution Operation Using Winograd Minimal Filtering Transformation Dary Mochamad Rifqie; Muh. Ma’ruf Idris; Nur Azizah Eka Budiarti
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7655

Abstract

Convolutional Neural Networks (CNNs) have achieved significant success in the field of computer vision; however, their high computational complexity poses challenges for deployment in real-time applications. This study explores the application of Winograd-based convolution algorithms, specifically F (2,3) and F (4,3), as a means to accelerate CNN inference. Using the VGG-16 architecture as a benchmark, we evaluate the performance of these algorithms in terms of execution time and computational accuracy. Experimental results demonstrate that Winograd F (2,3) reduces runtime by an average of 59.62%, while Winograd F (4,3) achieves a 39.81% reduction compared to standard convolution. Accuracy is assessed using single-precision 32-bit floating-point arithmetic, with results showing that Winograd F (2,3) achieves the lowest maximum element error in six out of nine convolutional layers. These findings indicate that Winograd-based methods offer an efficient alternative to conventional CNN computations, particularly in performance-constrained environments.
A Hybrid Framework for Plagiarism Detection: Integrating Token-Based Similarity with Density-Based Clustering Fajar B, Muhammad; Lestary, Fitriyanty Dwi; Surianto, Dewi Fatmarani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7664

Abstract

Plagiarism detection in academic assignments remains a critical challenge in maintaining academic integrity in higher education. This study proposes an automated method to detect content similarity between student assignment documents by combining Jaccard Similarity and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. The process begins with the collection of student assignment files in digital format, followed by text extraction to form a set-based representation of each document. Jaccard Similarity is then used to compute the degree of similarity between every document pair, and the resulting similarity matrix is transformed into a distance matrix as input for DBSCAN. Experiments conducted on 23 documents yielded 253 unique document pairs. The results demonstrate that the method successfully identified pairs with high similarity scores—such as 0.9114 and 0.7226—which were visually confirmed through a heatmap and effectively grouped into clusters by DBSCAN. Parameter settings of eps = 0.3 and min_samples = 1 proved optimal for distinguishing original documents from those exhibiting substantial content overlap. This approach is not only accurate and efficient, but also eliminates the need for predefined cluster numbers, making it suitable for deployment in automated plagiarism detection systems for academic texts.
Digital Marketing Analytics: Enhancing Marketing Management through IT Nurani; Abd. Rajab; Muhammad Ikbal; Sri Prilmayanti Awaluddin
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7687

Abstract

This study aims to analyze the impact of Digital Marketing Analytics (DMA) and information technology (IT) capability on marketing management effectiveness. A mixed methods approach with a Sequential Explanatory design was employed, beginning with a quantitative survey of 100 digital marketing practitioners across various industries in Indonesia, followed by in-depth interviews with seven key informants. Linear regression analysis revealed that DMA (β = 0.462, p < 0.001) and IT capability (β = 0.387, p < 0.001) jointly contribute significantly (R² = 0.581) to marketing effectiveness. Qualitative findings support that companies integrating analytics into their marketing processes tend to be more responsive, efficient, and adaptive to market dynamics. Successful DMA implementation is influenced by human resource readiness, leadership that promotes a data-driven culture, and adequate IT infrastructure. On the other hand, MSMEs face challenges related to digital literacy and limited access to affordable analytics systems. The study underscores the importance of investing in information systems, employee training, and cross-functional integration to drive meaningful digital transformation in marketing strategy.
Effectiveness of Digital Intervention on Improving Mental Health Literacy and Health Service Utilization Hidayat, Akmal; Nur Aeni Rahman; Zahrotul Ainil Mahfudhah Umar; Nurfaisa Riono; Abd Majid
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7700

Abstract

The purpose of this study is to assess how well the "MindWell" information system facilitates the assessment and tracking of users' mental health. "MindWell" is an online resource created to give consumers quick and simple access to mental health assessments, educational materials, and expert advice. Using cutting-edge web technology, this system offers real-time screening services, ensures user data confidentiality and privacy, and includes features like mental health education, support service referrals, and monitoring. The system's capacity to offer pertinent information and assist users in understanding their mental health disorders was examined, along with input from beta users and a series of trial runs. The findings demonstrated that "MindWell" was successful in raising users' awareness of mental health issues, assisting in the early detection of mental health issues, and giving them access to helpful resources. It is envisaged that the deployment of this system will aid in the pursuit of more widespread and long-lasting improvements in mental health.
Thermoelectric Generator Demonstration On Stove As Alternative Energy Sudarmanto Jayanegara; Muhammad Wiranda; Kamaluddin
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7703

Abstract

Thermoelectric generators are power plants that utilize the Seebeck effect, a phenomenon that produces electric current when there is a temperature difference in a conductor or semiconductor. In practice, thermoelectric generators are often used to utilize waste heat from various systems. One significant heat source is a stove, which can produce temperatures up to 80ºC. By utilizing a thermoelectric generator, the heat accumulated on the stove wall can be converted into electrical energy. To support this conversion, an effective cooling system is needed so that the temperature difference between the two sides of the thermoelectric module is maintained. This study aims to explore the potential of electrical energy generated by a thermoelectric generator (TEG) module as an alternative energy source through heat from the stove wall with variations in flame settings. The cooling system used consists of an aluminum heat sink and a fan, which works to maintain the temperature difference on the cold side of the thermoelectric at around 12ºC. The test results show that the performance of the thermoelectric generator has quite promising potential as an alternative energy source. This can be seen from the increase in efficiency obtained from each flame variation. In large flames, the maximum measured efficiency value reaches 0.76%, while in small flames it reaches 0.47%. Therefore, the application of the Seebeck effect principle shows very good potential for the development of alternative energy in the future.
The Effect of K-NN Algorithm Practice Chatbot Tutors on the Speed and Accuracy of Solving AI Problems in Vocational Schools Andi Asfar; Andi Winda Purnamasari; Angriani Nur; Aprilia; Arnis; Ashabul Kahfi
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/wrd2bn63

Abstract

Learning Artificial Intelligence (AI) concepts at the vocational high school level often presents challenges, particularly in understanding procedural algorithms such as K-Nearest Neighbor (K-NN). Students frequently experience difficulties in calculating distances, selecting the optimal value of k, and determining classification results accurately and efficiently. This study investigates the effect of a chatbot tutor designed for K-NN practice on students’ speed and accuracy in solving AI problems. A quasi-experimental design with a pretest–posttest control group model was employed. Sixty students from a vocational high school majoring in Software Engineering were divided into an experimental group receiving chatbot-assisted practice and a control group receiving conventional instruction. Data were collected through validated K-NN problem-solving tests and task completion time measurements. Statistical analysis using paired and independent sample t-tests revealed that the experimental group demonstrated significantly faster completion times and higher accuracy scores compared to the control group (p < 0.05). The effect size indicated a moderate to high practical impact. The findings suggest that chatbot-based tutoring can enhance both efficiency and precision in learning K-NN algorithms. This study contributes empirical evidence supporting the integration of chatbot tutors into Artificial Intelligence instruction in vocational education to improve problem-solving performance.
Classification of Students' Emotions from Facial Expressions Using CNN to Support Adaptive Learning Akmal Hidayat; Hera Ariska; Iren Kirana; Asmiyah Auliatna; Dian Sri Yuninda; Elvira Nur
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/b1rcm003

Abstract

The integration of affective aspects into adaptive learning systems remains limited, as most educational technologies primarily rely on cognitive performance indicators. However, students’ emotional conditions significantly influence engagement, motivation, and learning outcomes. This study aims to develop and evaluate a Convolutional Neural Network (CNN) model for classifying students’ emotions based on facial expressions to support adaptive learning environments. A quantitative experimental approach was employed. Facial expression image data were preprocessed through face detection, resizing, normalization, and data augmentation before being trained using a CNN architecture with the Adam optimizer and categorical cross-entropy loss function. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the proposed CNN model achieved an overall accuracy of 90% with an average F1-score of 0.88 across four emotion categories (Happy, Sad, Neutral, and Angry). The confusion matrix indicates that most predictions fall within the correct classification range, although minor misclassifications occurred between low-intensity Sad and Neutral expressions. The stability of training and validation loss curves demonstrates good generalization ability without significant overfitting. These findings indicate that CNN-based facial emotion classification can serve as a reliable component in adaptive learning systems by providing real-time affective feedback. The study contributes to the development of artificial intelligence applications in education by integrating emotional recognition into adaptive instructional design

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