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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 465 Documents
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Comparative Performance Analysis of Integrated Monitoring Engine for Electric Energy Transaction Data Gateway Infrastructure to Accelerate SLA Incident Resolution Pipit Suryandani; Yuli Kurnia Ningsih; R. Deiny Mardian
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7151

Abstract

PLN Icon Plus operates the Energy Transaction Data Gateway as the sole intermediary between banking partners and the national P2PST core server — an architecture where monitoring failure carries direct consequences for millions of daily transactions. Prior to this study, the monitoring ecosystem operated across three isolated platforms: Huawei iMaster NCE-Fabric for network telemetry, Zabbix for server resource metrics, and Elastic Stack (ELK) for application log management, with no automated correlation between them. This study developed an integrated monitoring system on the Grafana platform that unifies these heterogeneous data sources into a Single Pane of Glass dashboard. The architecture employs NTP-calibrated timestamp alignment and data normalization to ensure cross-platform event correlation accuracy at sub-100 millisecond precision. A unified alerting system was deployed via Telegram Bot API using multi-condition severity thresholding, requiring confirmed cross-layer correlation before notification dispatch to prevent alert fatigue. Comparative performance validation against the pre-implementation siloed condition — based on 69 documented production incidents from January to March 2026 — confirmed a 63.6% reduction in overall Mean Time to Repair (MTTR) and a 79.2% reduction in network incident MTTR specifically. SLA availability improved from 99.71% to 99.94%, surpassing the 99.9% contractual target. The primary contribution is a cross-layer data correlation model that measurably compresses the fault identification phase within national energy transaction infrastructure, validated through both statistical analysis and a structured questionnaire survey across 56 respondents.
Integrating Systematic Literature Review and Longitudinal Analysis for Employee Satisfaction Evaluation: A Hospitality Industry Case Study I Made Sudana; Endah Sudarmilah; Yusuf Sulistyo Nugroho
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7154

Abstract

The success of service quality in organizations is heavily influenced by employee satisfaction. This research proposes an integrated framework combining a Systematic Literature Review (SLR) with a longitudinal analysis of Employee Satisfaction Survey (ESS) data at Zest Parang Raja Solo to evaluate determinants of job satisfaction over the 2022–2025 period. Following the PRISMA 2020 protocol across 35 selected studies, seven key variables were identified, with leadership emerging as the most dominant factor (74.29%) in global literature. Empirical validation through longitudinal analysis of a qualified total population (N=70 respondents over four years, adhering to strict organizational tenure SOPs) reveals a degrading satisfaction trend, decreasing from 91.99% in 2022 to 85.66% in 2025. This decline was primarily driven by a significant divergence in compensation and workplace facilities, with the "Intention to Stay" indicator dropping to a critical 68.8% in the final year. Drawing on Herzberg's Two-Factor Theory, the empirical evidence underscores a psychological threshold: motivator factors, such as high-performing leadership (scoring 96.3%), cannot fully mitigate the decline in satisfaction if hygiene factors, specifically salary competitiveness, are not adequately addressed. The contribution of this study lies in providing a theoretical validation framework that enhances evidence-based HR analytics for strategic decision-making in the hospitality industry.
Web Attack Detection for SQLi and XSS Using Ensemble Learning Based on Character-Level N-Gram Features Yaya Suharya; Mohammad Bayu Anggara
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7193

Abstract

SQL Injection (SQLi) and Cross-Site Scripting (XSS) remain severe threats to web application security, particularly as attackers employ increasingly sophisticated obfuscation techniques to bypass conventional detection systems. This research constructs a machine learning framework using ensemble learning — specifically combining Random Forest and XGBoost — integrated with character-level n-gram feature extraction. The methodology involved rigorous data curation of a large-scale dataset, refining 156,636 raw samples into 151,783 unique entries to ensure high-quality training data. By extracting 10,000 character-level n-gram features, the model captures the intricate structural patterns of complex and obfuscated payloads. Experimental results show consistent and measurable performance: the proposed ensemble model achieved an overall accuracy of 99.67%. Stability was confirmed through a 5-fold cross-validation process, yielding a mean accuracy of 99.64% and a standard deviation of 0.0003. These findings are reinforced by ROC AUC scores of 1.0000 for XSS and 0.9999 for SQLi, indicating near-perfect discriminative capability. The combination of character-level representation and ensemble learning produces a precise and resilient solution for safeguarding modern web environments against dynamic and evolving cyber threats.
Developing a Web-Based Basketball Court Booking Application (Baszone) Using Design Thinking Anak Agung Adi Wiryya Putra; I Gde Suwastika Pande Liemena
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.3424

Abstract

This study designs Baszone, a web-based basketball court booking application developed through design thinking methodology. As basketball participation increases, manual reservation processes create significant obstacles for users and administrators. The research employed interviews with field administrators at DC Basketball in Denpasar, Bali, to identify operational challenges and user needs. Design thinking was implemented across five stages: empathize, define, ideate, prototype, and test. During empathize, interviews revealed booking difficulties including time-consuming manual processes and schedule conflicts. The define stage established user personas representing target audience characteristics. Ideate produced storyboards visualizing application workflows. Prototype development utilized Figma to create high-fidelity interface designs. Testing involved potential users evaluating the application through System Usability Scale (SUS) methodology. Results demonstrate that Baszone significantly enhances booking efficiency and accessibility. The application provides real-time field availability, streamlined reservation processes, and transparent payment management. SUS testing yielded a score of 92, indicating excellent usability performance. Respondents reported satisfaction with operational simplicity, optimal functionality, and comfortable interaction patterns. Baszone successfully addresses manual booking limitations while meeting user expectations for modern sports facility management systems.
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), Indonesia

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), Indonesia

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.
Classification Optimization of Aedes albopictus and Culex quinquefasciatus Mosquito Larvae Using Vision Transformer Method Abdullah Al Faruq; 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), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5120

Abstract

Mosquito-transmitted diseases like Dengue Hemorrhagic Fever and Filariasis pose serious health threats throughout tropical regions, particularly in Indonesia. Quick and accurate identification of mosquito larvae plays a crucial role in disease prevention, especially for Aedes albopictus and Culex quinquefasciatus species that act as main disease carriers. Manual identification methods using microscopes or visual guides often struggle with time constraints, accuracy issues, and dependence on trained specialists. Our research focuses on improving the classification of Aedes albopictus and Culex quinquefasciatus mosquito larvae using Vision Transformer (ViT) technology, a deep learning method that has shown strong results in image recognition tasks. We applied the Vision Transformer model to classify mosquito larvae from microscopic field images. The study also tested how different factors impact model performance, such as image clarity, lighting conditions, and image resolution. Our findings show that using Vision Transformer in classification systems produced excellent results, achieving 98.00% accuracy in recall, precision, and F1-score measurements. The research reveals that Vision Transformer methods deliver better accuracy than traditional approaches like Convolutional Neural Networks and can be adapted into working systems for technology and healthcare sectors.
Design of BPJS Patient Referral Information System Based on Python Tkinter at Mulia Medika Clinic Piyyawati Dewi; Yuyun Yunengsih; Falaah Abdussalaam
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5168

Abstract

Digital transformation is still an ongoing process in health service delivery to improve operational performance and service quality. However, BPJS Kesehatan patient referrals are still trapped in administrative bottlenecks. At Mulia Medika Clinic, staff used to handle BPJS patient referrals manually, so that records were prone to errors and delays in obtaining information. We designed, implemented, and tested a desktop-based BPJS referral information system using Python and Tkinter for clinic operations. The development process followed the Waterfall methodology, which consisted of requirements analysis, system architecture design using Context Diagrams, Data Flow Diagrams, Entity-Relationship Diagrams as well as Flowcharts followed by implementation and black-box testing validation. The system will manage patient records, referral processing as well as user administration. Automated features include generating referral letters and producing reports. Testing has proven that this system is accurate and efficient—the manual workload has reduced, data traceability has improved, and continuity in the referral service has been maintained. Results prove operational readiness for clinic deployment to enhance administrative efficiency and precision of the referral data. Currently, it runs standalone without real-time database synchronization; hence workflow integration cannot be achieved. Future versions should have direct connections with both clinic management and BPJS databases to allow seamless data exchange without manual synchronization
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), Indonesia

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
IoT-Based Prototype System for Automated Measurement of Human Height, Body Weight, and Temperature Using Sensors Christian P. Colombini; Frencis M. Sarimole
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5202

Abstract

This study presents the development of an IoT-based prototype for automatic measurement of body height, weight, and temperature using Arduino Uno microcontroller. The research responds to operational challenges observed at Puskesmas Cengkareng Timur, where conventional manual measurement tools contribute to process inefficiencies and frequent data recording errors during patient examinations. The prototype integrates three sensor components: HC-SR04 ultrasonic sensor for height measurement, Load Cell with HX711 amplifier module for weight detection, and MLX90614 infrared sensor for contactless temperature monitoring. Sensor outputs are processed through Arduino Uno and displayed via I2C LCD interface. Performance evaluation through laboratory calibration and field trials indicates measurement accuracy within 0.15% error margin across all parameters. Implementation of this device at the health center demonstrates potential for improving service delivery efficiency and reducing patient queue duration during preliminary health screening

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