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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
 1 
Search results for , issue "Vol. 2 No. 2 (2025): VOLUME 2, NO 2: APRIL 2025" : 6 Documents clear
Conceptual Review Forward RSM Design Approach: Integrasi Lean Six Sigma, Sprint dan Extreme Programming Muhammad Dwi Hary Sandy; Lubis, Muharman
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.v2i2.54

Abstract

The Recognize, Scrutinize, and Materialize (RSM) Design Approach has evolved into an important framework for addressing diverse challenges in design and innovation across a variety of industries. by combining methodologies such as Lean Six Sigma, Sprint and Extreme Programming, creating customized variants to meet specific domain needs. This study highlights the application of RSM in various case studies, including education, healthcare, digital ecosystems, and business process optimization. Key results show reduced development timelines, increased user engagement, and increased process efficiency. Additionally, the integration of sustainability metrics into the Materialization phase ensures environmentally responsible design. This research underscores the adaptability and relevance of RSM in addressing modern challenges, providing a foundation for future advances in user-cantered design and iterative methodologies.
AI and ML Integration Using Collaborative Filtering in Movie Recommendations Fitria Widianingsih; Ledi Diniyatullah
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.v2i2.55

Abstract

This study aims to integrate Artificial Intelligence (AI) and Machine Learning (ML) technologies with Collaborative Filtering (CF) to build a more accurate and personalized movie recommendation system. This system uses the Singular Value Decomposition (SVD) algorithm to reduce the dimensionality of data and generate rating predictions for users of movies they have not watched. This study implements a dataset from MovieLens to test the effectiveness of the model in providing recommendations. The experimental results show that the system successfully predicts user ratings with fairly high accuracy, reflected in the average Root Mean Square Error (RMSE) value of 0.85 for the five users tested. Although these results show good performance, challenges such as cold start problems and data sparsity are still major obstacles in producing more optimal recommendations. Therefore, this study also proposes the use of hybrid filtering, deep learning, and the use of external data to improve prediction accuracy and overcome these limitations.
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.

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