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Electronic Integrated Computer Algorithm Journal
Published by Yayasan Asmin Intelektual Berkah
ISSN : -     EISSN : 30310350     DOI : https://doi.org/10.62123/enigma.v1i1.10
Core Subject : Science, Social, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Social Sciences
ENIGMA : Electronic Integrated Computer Algorithm Journal is open to researchers and experts in the fields of computer science, information engineering and information systems. This journal is a forum for researchers and experts to present the results of research related to the fields of computer science, informatics engineering and information systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 6 Documents
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Search results for , issue "Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025" : 6 Documents clear
Agile-Based Application Architecture Design for Billet Management in Industrial Manufacturing Ramadhani, Rafian; Hizbullah, Fauzi; Auliya Rahman, Ilham; Ahyar Harizillah, M.; Noorachmad Muttaqin, Alif; Saidi Lubis, Fahdi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.96

Abstract

This study presents the planning and iterative development of an enterprise application architecture for the Billet Stacker Rail system in an aluminum manufacturing environment. The system is designed to enhance the management of billet logistics, including receiving, inspection, stacking, and transfer processes. Using the Agile methodology, particularly the Scrum framework, the development team collaborated closely with operational stakeholders to capture requirements and validate functionality through a series of Sprints. The process included modeling workflows, designing class and entity diagrams, and creating interactive user interface mockups. The system architecture was developed incrementally to support modularity, traceability, and real-time data recording. Each component from billet tracking to user management was prototyped and refined based on continuous feedback. The Agile approach facilitated rapid adjustments to changing requirements, reduced development risk, and supported a user-centered design process. The result is a robust and scalable application blueprint that aligns with the industrial environment’s needs for efficiency, reliability, and transparency in billet management operations.
Heart Attack Risk Prediction Using Machine Learning: A Comparative Study of Decision Tree and K-Nearest Neighbors Hizbullah, Fauzi; Noorachmad Muttaqin, Alif; Andiharsa Sih Setiarto, Rahardian; Aulia Hakim, Rizki; Abdulmana, Sahidan
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.98

Abstract

Heart disease, particularly heart attacks, is a leading cause of death worldwide, highlighting the importance of early detection and risk prediction. This study develops and evaluates machine learning models to predict heart attack risk using seven health-related attributes: age, marital status, gender, body weight category, cholesterol level, participation in stress management training, and stress level. The dataset, processed with the Orange Data Mining platform, was divided into training (66%) and testing (34%) sets. Two supervised algorithms, Decision Tree and K-Nearest Neighbors (K-NN), were implemented without extensive hyperparameter tuning. Model performance was evaluated using accuracy, precision, recall, and F1 score. The Decision Tree achieved the best results with 84.78% accuracy, 88.52% precision, 79.41% recall, and 83.72% F1 score, indicating its effectiveness in identifying at-risk individuals. Key predictors included age, stress level, and cholesterol, aligning with established medical findings. While the results are promising, limitations include a small dataset and limited algorithm scope. Future research should expand the dataset, include additional clinical features, and explore advanced algorithms to improve accuracy and reduce false negatives, enhancing applicability in preventive healthcare.
Application of Region of Interest (ROI) in Student Attendance Detection System in Classroom Faizi, Setyo Fahmi Noor; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.107

Abstract

Efficient classroom management is a crucial requirement in academic environments such as the Faculty of Computer Science and Information Technology to increase productivity. This study aims to design and evaluate a real-time presence detection and counting system by implementing the Region of Interest (ROI) method to improve computational efficiency and accuracy. This methodology involves the use of a Logitech C270 HD webcam, with a static ROI set at 90% of the central video frame to focus the analysis. Person detection and counting are performed using a combination of Histogram of Oriented Gradients (HOG) for the body and Haar Cascade for the face. Time series reasoning with a minimum duration of 60 seconds and a grace period of 5 seconds is implemented to validate presence and stabilize the room status, with system performance evaluated using Precision and Recall metrics. The results show that the system successfully displays the status and number of people in the room very well, but the evaluation shows a Recall value of 1.00, which means the system detects every actual human presence. However, this system has significant accuracy issues, indicated by a low Precision of 0.04 and a high number of False Positives of 710. In conclusion, although the ROI application successfully improves the computational load and the temporal logic stabilizes the output, the HOG and Haar Cascade models are inadequate to handle visual noise in the ROI, resulting in low Precision and indicating the need for more sophisticated detection models.
Implementation of Machine Learning For Indonesian Sign Language Recognition Using Convolutional Neural Network Model Salamah, Umi; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.108

Abstract

Sign language is the primary means of communication for people with hearing impairments. However, the public's limited understanding of Indonesian Sign Language (BISINDO) remains a communication barrier. This study implemented machine learning with a Convolutional Neural Network (CNN) model to automatically recognize BISINDO gestures. The dataset consists of 2,600 manually captured hand images representing the letters A–Z. The training process was carried out through data pre-processing, image augmentation, and CNN parameter optimization. Test results showed that the system was able to recognize BISINDO letters with high accuracy and could combine letters into simple words such as "HAI", "SAYA", and "UMI" in real-time. This study demonstrates that CNN is effective in supporting a computer-based sign language translation system, thus becoming an inclusive communication solution for people with hearing impairments.
Implementation of a Drowsiness Detection System in Four-Wheel Vehicle Drivers Using OpenCv Ma’ajid, Farhan Riqi; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.109

Abstract

Drowsiness while driving is one of the triggers of traffic accidents. This study proposes a non-invasive and economical computer vision-based real-time drowsiness detection system. The system combines Eye Aspect Ratio (EAR) to assess eye openness, Convolutional Neural Network (CNN) for open/closed eye classification, and MediaPipe FaceMesh for stable facial landmark extraction. The dataset is taken from Kaggle (Open and Closed classes, totaling 1,452 images) and processed through grayscale conversion, normalization, 64×64 pixel resizing, and augmentation. Drowsiness detection is triggered when EAR <0.25 and CNN classifies both eyes as closed for ±2 consecutive seconds; visual/audio alarms are automatically activated. Test results on 218 images show excellent performance with only 1 misclassification (≈99.5% accuracy), with no false alarms for the open eye class. The system is implemented as a Flask-based web application for easy cross-device access. These findings demonstrate an efficient visual approach that is feasible to be integrated as a driving safety feature.
Design of A Web-Based Online Disposition Information System Case Study: Medan City Police Dewi, Hartika Sari; Zakir, Ahmad; Abdullah, Arif
Electronic Integrated Computer Algorithm Journal Vol. 3 No. 1 (2025): VOLUME 3, NO 1: OCTOBER 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v3i1.117

Abstract

In the digital era, efficiency in managing information is crucial, especially within the police force. The Medan City Police require an information system that can support a fast and integrated mail disposition process. This study designed and built a web-based Online Disposition Information System using the Waterfall method. The system design includes a responsive interface and an appropriate database structure. Testing used the Black Box method to ensure the accuracy of input and output. This application was developed with PHP and MySQL. The results show that the system facilitates Medan City Police personnel in receiving, processing, and tracking incoming mail efficiently.

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