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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 387 Documents
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Analysis of Household Electricity Consumption Patterns Using K-Nearest Neighbor (KNN) Method Cut Susan Octiva; Sultan Hady; Dedy Irwan; T. Irfan Fajri; Novrini Hasti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3877

Abstract

The increasing demand for electricity in the household sector poses significant challenges to energy efficiency initiatives and environmental conservation efforts. Examining electricity usage patterns offers a pathway to uncover key determinants that influence consumption levels while formulating more effective strategies for energy management. This study attempts to evaluate electricity consumption patterns in the household sector using the K-Nearest Neighbor (KNN) algorithm. This approach is used to categorize consumption data based on attribute similarities among household units. The findings are expected to encourage more rational electricity usage practices, thereby reducing energy inefficiencies and strengthening efforts to conserve natural resources. Furthermore, the analysis aims to provide actionable insights for households to adopt sustainable habits and for policymakers to design targeted interventions that address peak demand periods and promote the use of energy-efficient technologies. By identifying specific behavioral and technological factors that contribute to high consumption, the results can serve as a basis for tailored programs aimed at minimizing waste and promoting long-term environmental management.
Design and Development of a Web-based Online Store Application for Yudistira Jaya Stationery Shop Muhammad Abdul Aziz Abyan; Untung Surapati
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3887

Abstract

The accelerated progress of information technology has fundamentally reshaped the landscape of the trade sector, with notable impacts on micro, small, and medium enterprises (MSMEs). This research aims to design and develop a web-based online sales platform tailored for ATK Yudistira Jaya, a stationery and photocopy service provider, to optimize operational efficiency and broaden market accessibility. The development process employs the Waterfall methodology, a systematic approach that ensures structured progression through distinct phases of system creation. The technological foundation of the platform is built using the Laravel framework for backend operations, paired with MySQL as the database management system to handle data storage and retrieval effectively. Findings from the system evaluation indicate that the developed platform adequately addresses the functional requirements of both administrators and end-users. Key features integrated into the system include a comprehensive product catalog for easy browsing, a streamlined order placement mechanism, secure payment integration through a trusted gateway, and robust order management tools to track and process transactions. The successful implementation of this digital solution is anticipated to empower MSMEs like ATK Yudistira Jaya by equipping them with the necessary tools to navigate the challenges of a digital marketplace, ultimately enhancing their competitive edge through strategic adoption of technology.
Development of Applications with Artificial Intelligence: Expert Perspectives and Recommendations Julien Florkin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3888

Abstract

Artificial intelligence (AI) applications are accelerating significantly, supported by three pillars: core technologies, cost efficiency, and strategic direction. A comparative analysis reveals critical contributions from three technologies: (1) Machine Learning (ML) enhances user engagement by 35% through personalized recommendation systems on e-commerce platforms; (2) Natural Language Processing (NLP) reduces customer service operational costs by 47% via intelligent chatbots in the banking sector; and (3) predictive analytics improves cardiovascular disease diagnosis accuracy by 27% based on multicenter clinical data. Estimated AI application development costs range from $50,000 to $250,000, depending on algorithm complexity and computational infrastructure requirements. Future AI development will be shaped by two trends: (1) Edge AI, which reduces data processing latency by 60% through local computation, and (2) Explainable AI (XAI), which enhances algorithm transparency to comply with GDPR and ISO/IEC 23894 regulations. The study underscores that successful AI implementation requires multidisciplinary integration among data scientists, software engineers, and business stakeholders. Strategic recommendations include allocating 15–20% of R&D budgets for continuous learning, establishing an AI ethics committee aligned with OECD principles, and adopting an agile development model for market responsiveness
Adopting SOLID Principles in Android Application Development: A Case Study and Best Practices Nimisha Hake; Laxmipriya Heena Dip
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3889

Abstract

Developing and maintaining large-scale applications has become a daunting task with the rapid evolution of the Android ecosystem. This research examines the application of SOLID (Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion) principles in contemporary Android development. By the case study of Meta and an analysis of the application in top tech companies, the present research shares how SOLID principles can achieve better product quality, maintainability, and a positive outcome between your team. The study is based on a mixed-methodology, including qualitative and quantitative, analyzing the source code of 25 enterprise-grade Android applications, in-depth interviews with 50 senior professionals from top-tier technology companies, and code-metrics data for 24 months. We implemented it in Kotlin, taking advantage of the modern Android Jetpack ecosystem. The results of the study demonstrate dramatic increases in all aspects of software development. These include 45% reduction in technical debt, 89% increase in test coverage and 30% reduction in bug rate. A qualitative analysis indicates that teams report increased ease of code maintenance and ramp up of new team members. The research also highlights some of the barriers to applying SOLID: high learning curve, challenges convincing team members to adopt SOLID mindset. Our research contributes (1) a SOLID implementation framework for Android, empirically validated in four case studies. It also includes (2) metrics and tools for measuring adherence to SOLID principles, and (3) recommendations for resolving issues encountered during the implementation of these principles. These results have significant practical implications for mobile software industry practitioners and researchers
Designing an Early Detection System for Agricultural Land to Reduce the Risk of Crop Failure Based on Information Technology Edy Atthoillah; Nadia Ayu Safitri; Wishal Azharyan Al Hisyam; Muhammad Sibyan Nafil Ilmi; Asbi Solihin; Dafit Ari Prasetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3919

Abstract

Crop failures in Indonesia still occur frequently and become a source of problems due to the reduction of food supplies for the community. One of the causes of crop failure is the decline in soil quality due to nutrient content, which is rarely detected by farmers. However, the land quality analysis process that has been carried out so far still tends to take a long time and incur high costs. Therefore, it is necessary to create technology that is expected to be able to detect land quality directly, quickly, and easily. PKM- KC Soil Nutrient Monitoring is designed by creating hardware that can analyze moisture, pH, temperature, and essential macro nutrients, namely nitrogen, phosphorus, and potassium. Additionally, software that can process data and produce results in the form of land quality, land improvement recommendations, and suggested crop commodities. This Soil Nutrient Monitoring tool has been tested and calibrated with an accuracy level of 95%. This tool successfully processes data from hardware in the form of temperature, pH, humidity, and NPK sent via a Bluetooth Low Energy network to software that produces outputs in the form of land quality, land improvement recommendations, and suggested crop commodities
Application of Machine Learning in Computer Networks: Techniques, Datasets, and Applications for Performance and Security Optimization Memed Saputra; Fegie Yoanti Wattimena; Davy Jonathan
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.3989

Abstract

This study designs and tests a network security system based on a combined Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) framework. In this study, distributed processing and reinforcement learning methods in combination with differential privacy are introduced into the proposed system to enhance attack detection and network management. The evaluation results show significant improvements; 97.3% detection accuracy, 34% more efficient bandwidth utilization and 45% less latency than the previous system. The 16-node linear scalability of the distributed architecture has a throughput of 1.2 million packets per second. It is defended against adversarial attacks by maintaining accuracy above 92% and provides a total energy saving of 38% using dynamic batch processing. Three months of testing in an operational environment detected 99.2% of 1,247 threats, including 23 new attack types, with an average detection time of 1.8 seconds. Sensitivity analysis was performed to preserve the privacy of sensitive data while maintaining network performance. The results show that the hybrid solution is reliable, scalable and secure for today's network management.
Sales Data Clustering Using the K-Means Algorithm to Determine Retail Product Needs Riwan Irosucipto Manarung; Edy Widodo; Anggi Muhammad Rifai
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 1 (2025): APRIL 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i1.4090

Abstract

Sales data is a systematic record of transactional behavior with goods or services distributed over time boundaries and furnishes primary key business metrics for evaluating and planning. Using the K-Means clustering algorithm, this research segments retail product demand by differences attributes to identify demand patterns. The iterative process of clustering ended at the fifth cycle after the division of objects in each cluster stabilized, which can serve as a sign that we arrive at an optimal solution. Results showed that the first cluster located at a centroid 94, 6 contains 100 data items belonging in a primary set and similarly fifth cluster (same centroid) had also same number of products. The automated approach of Collaboratory also differs from the manual method where there are not pre-defined cluster initial values in our preliminary setup. Despite this procedural difference, there is a remarkable concision in the results which demonstrates the strength of the method when implemented using different ingrained constructions. These results offer some refined results on product classification, which is essential to solve the problem that retail ranks may vary during inventory management and sales optimization.
Development of a Financial Prediction System Based on Machine Learning: A Case Study on Financial Data Management Using Time Series Analysis Davy Jonathan; Memed Saputra
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5052

Abstract

Due to intense volatility, complex nonlinear dynamics, and scant historical data, predicting financial prices in emerging markets is extremely difficult. This paper presents a hybrid ARIMA+LSTM model for stock price forecasting in the Indonesian market and tests it. The model effectively combines traditional econometric techniques with advanced deep learning methodologies. Walk-forward validation on over five years of data from various Indonesian stocks (BBRI, ALFMART, UNVR, BSIM) is applied. The hybrid model achieves a Root Mean Square Error of 112.54, Mean Absolute Percentage Error of 2.21%, and Directional Accuracy of 68.9% for one-day ahead predictions. This performance exceeds that of pure ARIMA by 22.5% and is statistically significant (p < 0.001, Cohen's d = 1.18). The model consistently shows good results over many prediction horizons (1, 5, and 10 days) and several Indonesian stocks from different sectors with a standard deviation of only 8.3 during the test period. A cloud-based deployment architecture is planned to reach about 1,500 predictions per second at a latency of 45ms which will be suitable for real-time institutional trading systems. Sensitivity analysis reveals optimal hyperparameters (60-day window; between 50 to 25 LSTM units with a dropout rate of 0.2) as well as confirming strong performance across parameter variations. SHAP analysis plus attention visualization results show that the model keeps interpretability even though deep learning is complicated; recent prices (lag-1 and lag-2) hold about 70% of the prediction variance. This work validates hybrid ARIMA+LSTM modeling in an emerging market like Indonesia through rigorous walk-forward validation methodology and practical insights into generating actionable trading signals with a win rate of 68.9% which supports portfolio management integrated within risk frameworks as well as limitations that include dependency on historical data, exclusion of transaction costs, and single asset focus yet significantly contributes methodological rigor and empirical validation to machine learning literature in financial forecasting specifically regarding emerging markets
Implementation of N8N Platform for IoT Sensor Monitoring: Real-time Analysis in Smart Farming Legito Legito; Fitriyani Fitriyani; Ferdy Firmansyah
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5064

Abstract

Smart farming has some limitations regarding the management of streaming data from IoT sensors. This is necessary to support real-time decision-making in areas with less infrastructure. This paper discusses the practical use of the N8N platform as a low-code/no-code workflow automation tool for monitoring IoT sensors in smart farming. A mixed-method approach was used, with a prototype design based on Research and Development. The system was built using IoT-A architecture, which includes the perception layer (soil moisture, temperature, humidity, pH, NPK, and ultrasonic sensors on ESP32), network layer (MQTT and HTTP), processing layer (N8N workflow for ingestion, validation, transformation, and decision logic), and application layer (dashboard and alerts). Testing was done in a controlled environment for 72 hours with scenarios such as normal operation, high load, network disruption sensor failure, and scalability up to 20 nodes. Results showed an average response time of 150–300 ms, throughput of up to 500 data points per minute end-to-end latency below 450 ms availability greater than 99% and processing accuracy between 98.7% and 99.2%. The system detected failures accurately and restored operations within an average of 45 seconds. These results proved that N8N can improve the efficiency and reliability of real-time monitoring as an adaptive solution for tropical agriculture in Indonesia. It also suggested long-term field trials together with AI integration for predictive forecasting to enhance scalability and practical adoption.
Optimization of Tesseract OCR for Automatic Text Extraction on Indonesian ID Cards (KTP) Through Image Quality Enhancement Using Preprocessing Techniques Gilang Ramadhan; Dadang Iskandar Mulyana; Sopan Adrianto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5183

Abstract

Tesseract OCR ranks among the most widely adopted open-source tools for text extraction. Nevertheless, processing documents with degraded image quality—including blurry e-KTPs, low-contrast specimens, or those affected by uneven lighting—presents substantial challenges. We conducted experimental research to generate empirical data supporting the development of text detection systems for e-KTPs operating under non-ideal conditions. Our methodology involved testing 10 e-KTP images, each containing 15 text attributes, yielding 150 evaluated data points. Image preprocessing proceeded sequentially through grayscale conversion, denoising, contrast enhancement (CLAHE), and thresholding to improve image clarity prior to Tesseract OCR processing. We evaluated accuracy using confusion matrix analysis, emphasizing True Positive (TP), False Positive (FP), and False Negative (FN) metrics. Results demonstrate that preprocessing stages substantially improved text readability. Baseline OCR accuracy of 39.55% increased incrementally: +22.68% following grayscale conversion, +47.70% after denoising, +60.99% post-CLAHE application, and +19.62% after thresholding, culminating in 64.97% accuracy upon completing all preprocessing stages. Average TP values rose from 4 to 8 out of 15 attributes per image, while precision remained stable at 100% (FP = 0). Despite variable CLAHE performance across samples, preprocessing stages proved essential for OCR systems operating under degraded image conditions. Our work introduces a novel preprocessing pipeline tailored specifically to Indonesian e-KTP characteristics, providing quantitative benchmarks and systematic analysis that can inform the development of more adaptive digitalization and verification systems for population documents under real-world field conditions

Page 36 of 39 | Total Record : 387

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