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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 40 Documents
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Comparative Study Towards Energy Efficiency in Wireless Sensor Networks Using Asynchronous Duty Cycle Putri, Indah Pratiwi; Marcelina, Dona; Cahyani, Septa
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i2.57

Abstract

Energy efficiency is a critical determinant in the design and operation of Wireless Sensor Networks (WSNs), as sensor nodes are typically powered by constrained battery resources. Asynchronous duty cycle mechanisms have emerged as a viable strategy to optimize energy consumption while preserving network functionality. This research presents a comparative analysis of multiple energy-efficient Medium Access Control (MAC) protocols, including Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy-Efficient Sensor Routing (EESR), B-MAC, L-MAC, WiseMAC, and hybrid approaches such as TDMA-CSMA. Performance metrics such as energy efficiency, latency, throughput, and packet delivery ratio (PDR) are evaluated under varying network conditions. The findings indicate that AI-driven protocols, particularly those incorporating Artificial Neural Networks (ANN), significantly outperform conventional methodologies by enhancing cluster head selection, distributing energy load effectively, and extending network lifetime. Hybrid ADC emerges as the most robust solution, demonstrating an optimal trade-off between energy efficiency and network reliability across dynamic traffic scenarios. Furthermore, This research highlights the implications of integrating adaptive duty cycling with intelligent network optimization, underscoring its potential to enhance WSN sustainability. The results provide a comprehensive framework for refining MAC protocol architectures, offering actionable insights for optimizing next-generation WSN deployments.
Transformation of Pesantren Education in the Digital Era: AI Innovation and Adaptation for Technology-Based Learning Lestari, Tutik; Rahmayana, Audia; Agustiana, Fina
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i2.58

Abstract

Pesantren as a traditional Islamic educational institution faces challenges in navigating the digital era. Artificial Intelligence (AI) offers significant opportunities to enhance learning effectiveness, administrative systems, and educational management in pesantren. This article examines how AI can be adapted in pesantren education, covering implementation, benefits, and challenges. Using a qualitative approach and literature review, this study finds that AI can support curriculum management, personalize learning, and improve access to broader educational resources. However, AI adaptation also faces obstacles such as infrastructure limitations, human resources, and ethical considerations in applying technology within the pesantren environment. Therefore, an appropriate AI implementation strategy must be designed to align with pesantren values without eliminating its traditional characteristics.
Modification of K-Nearest Neighbor Method with Normalized Euclidean Distance for Classification of Local Berastagi Orange Quality Siregar, Ananda Afifah; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i2.60

Abstract

Local Indonesian fruit is one example of Indonesia's natural wealth, one of which is the local Berastagi orange. Oranges are rich in vitamin C which is good for body health. Oranges tend to have a sour, fresh, and sweet taste. The vitamin C contained in oranges is 97.3 milligrams or equivalent to 163% of the nutritional adequacy rate. Not only Vitamin C, oranges also contain vitamin B6, antioxidants and fiber. Therefore, it is highly recommended to consume oranges every day because oranges can facilitate digestion, reduce the risk of diabetes, maintain healthy skin, and also maintain endurance. This study aims to apply the Classification and assessment of the quality of local oranges using the K-Nearest Neighbor (KNN) method modified with Normalized Euclidean distance to classify the quality of local Berastagi oranges based on the color of the fruit image. The research dataset was taken from 100 images of local Berastagi oranges, where the 100 images were divided into 2, namely, good oranges and bad oranges. The classification process for local Berastagi oranges uses the matlab application.
Quality Classification of Air Quality in Medan Industrial Area Using Naïve Bayes Method Zhafirah, Zhahrah; Al-Khowarizmi
Electronic Integrated Computer Algorithm Journal Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/enigma.v2i2.61

Abstract

Advances in information technology have affected various aspects of life, including efforts to monitor air quality. Clean air is a basic human need, but technological developments and increased industry and the number of motorized vehicles have caused a decline in air quality. Air pollution has various negative impacts, including health problems and global warming. To help the community and government in monitoring air quality, this study implements a data mining method with a classification technique using the Naïve Bayes Algorithm. This method was chosen because of its effective ability to predict air quality based on historical data. This study uses data from the Air Pollution Standard Index (ISPU) parameters to build a classification model that can separate air quality categories, such as Good, Moderate, Unhealthy, Very Unhealthy, and Hazardous. The results of the study are expected to provide accurate information to the public about air quality in KIM, as well as assist the government in efforts to control air pollution.
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.

Page 3 of 4 | Total Record : 40

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