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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 535 Documents
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Prediction of Five Elements Imbalance and Acupuncture Point Recommendations Using Health-LLM Agent Method for Symptom Diagnosis Based on Traditional Chinese Medicine (TCM) Theory at Acumastery Clinic Iwan Muttaqin; Arya Adhyaksa Waskita; Choirul Basir
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.v5i3.5775

Abstract

Traditional Chinese Medicine (TCM) is a medical system that has been historically proven effective in diagnosing and managing various symptoms through the concepts of the Five Element imbalance, Yin-Yang, and acupuncture points. In the era of artificial intelligence, the utilization of Large Language Models (LLMs) specifically designed for the healthcare domain, referred to as Health-LLM Agents (AI-based health agents powered by LLMs), holds great potential in supporting TCM practices with greater efficiency and precision. This study aims to design and evaluate the performance of a Health-LLM Agent in predicting imbalances among the Five Elements (Wood, Fire, Earth, Metal, Water) based on patient symptoms, while also recommending appropriate acupuncture points for therapy. The methodology involves fine-tuning an LLM model with prompt engineering tailored to TCM terminology and principles, along with integrating symptom data in semi-structured text format. Evaluation is conducted using expert validation and classification metrics such as diagnostic accuracy, relevance of acupuncture point recommendations, and result interpretability. The findings indicate that the Health-LLM Agent achieves an 81% accuracy in predicting Five Element imbalances and receives 92% positive validation from TCM practitioners regarding acupuncture point recommendations. These results demonstrate that the Health-LLM Agent can serve as a promising tool to support the digitalization and personalization of TCM diagnosis through AI-based systems
Risk Analysis of Autonomous Vehicle Accidents Using Bayesian Simulation with Statistical and Visual Data Yesy Simanjuntak; Rani Indah Sari; Peter Tymoty Hutabarat; Suvriadi Panggabean
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.v5i3.5781

Abstract

Autonomous vehicles (AVs) are an emerging innovation in intelligent transportation systems, yet traffic accidents remain a critical concern due to environmental uncertainty and sensor limitations. This study aims to analyze collision risk levels in autonomous vehicles using a Bayesian Convolutional Neural Network (Bayesian CNN) integrated with the Monte Carlo Dropout (MC Dropout) technique. The model was trained on 11,000 visual datasets from the Central Bureau of Statistics (BPS) and synthetic data representing diverse road conditions. The Bayesian inference framework enables dynamic and adaptive risk prediction by continuously updating posterior probabilities based on sensor input changes. Simulation experiments were conducted using a Python-based interactive interface (pygame) to visualize vehicle movements and real-time collision probabilities. Results show that 48% of test scenarios were classified as very low risk (0–10%), 28% as low (11–30%), 16% as medium (31–60%), and 8% as high (61–80%). The model achieved a reduction in loss value from 0.43 to 0.08 and maintained 76% of simulations within low and very low risk categories, confirming system stability and reliable convergence. The findings demonstrate that the Bayesian CNN model effectively captures uncertainty and provides adaptive, probabilistic predictions, supporting safer and more intelligent autonomous vehicle operations.
Implementation of Flutter and Firebase in Bamboo Craft Digitalization Application Fajar Jati; Suyud Widiono
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.v5i3.5936

Abstract

: The bamboo craft industry in Brajan Hamlet, Sleman faces significant operational constraints due to continued reliance on manual recording systems, resulting in inefficiency, data duplication, and reporting difficulties. This study develops a web-based digitalization application using Flutter and Firebase to enhance business management efficiency in bamboo craft enterprises. The Research and Development (R&D) method was employed through the Waterfall model across four stages: requirements analysis, system design, implementation, and testing. Data collection involved structured interviews with 6 bamboo craft business operators, 4-week field observations, and literature review. The developed application integrates product management, inventory, transactions, customer relations, and sales reporting features through Backend-as-a-Service (BaaS) architecture utilizing Firebase Authentication, Cloud Firestore, and Firebase Storage. Black Box Testing results demonstrated a 96% functional success rate with an average response time of 1.2 seconds for CRUD operations. User Acceptance Testing with 6 respondents yielded a satisfaction score of 4.3/5 and revealed a 65% reduction in transaction recording time compared to manual methods. However, evaluation identified critical weaknesses in automatic stock synchronization post-transaction, necessitating Firebase Cloud Functions or Firestore Triggers implementation to ensure real-time data consistency. This study offers practical solutions through integrated digitalization for local craft MSMEs while academically demonstrating the effectiveness of Flutter-Firebase integration in developing web-based business management applications, with recognized limitations in business process automation requiring further development.
Design and Development of IoT-Based Mobile Application for Heart Rate and Body Temperature Monitoring Den Bintang Restu Satria Shandra; Anita Fira Waluyo
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.v5i3.5945

Abstract

The rapid development of Internet of Things (IoT) technology has influenced many fields, especially healthcare delivery systems. This paper will analyze the effect of IoT-based mobile applications on the quality of healthcare services through real-time patient monitoring. The application developed in this study was used to monitor vital signs heart rate and body temperature using sensors attached to mobile devices. The research methodology includes system architecture design and implementation with MAX30102, DS18B20, and MLX90614 sensors where the ESP32 microcontroller acts as the main integration platform. The application development was done using Android Studio and Flutter frameworks. User testing showed significant improvements in the speed at which critical conditions are detected among patients and also in the time taken by healthcare providers to respond to such situations. User satisfaction ratings indicated high acceptance levels, thus proving a large potential market for digital healthcare. Results from this study also proved that IoT-mobile application integration can uplift standards in healthcare services while providing a practical solution for modern-day medical practice.
Modeling the Reputation of Digital Banks Based on Public Opinion Using a Text Mining Approach with TF-IDF and the Support Vector Machine (SVM) Algorithm Muhamad Ihsan Ashari; Afif Efendi; Dimas Eko Prasetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.6956

Abstract

This study addressed the growing importance of reputation management in digital banking, where public opinion expressed on social media significantly influences customer trust and business sustainability. The objective of this research was to model the reputation of a digital bank based on public sentiment using a text mining approach. The study employed the CRISP-DM methodology, including data collection, preprocessing, modeling, and evaluation. A total of 1,897 Twitter comments related to the "Jenius" digital banking application were collected from 2023 to 2025. The data underwent preprocessing stages such as case folding, cleansing, tokenizing, normalization, stopword removal, negation handling, and stemming. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification was conducted using Support Vector Machine (SVM). The performance of SVM was compared with Naïve Bayes and K-Nearest Neighbors (KNN). The results showed that SVM achieved the best performance with an accuracy of 81.58%, outperforming Naïve Bayes (70.26%) and KNN (55.00%). Furthermore, sentiment distribution indicated that positive sentiment dominated public opinion, reflecting a generally favorable perception of the digital bank. In conclusion, the combination of TF-IDF and SVM proved effective for sentiment classification and can be utilized to model digital bank reputation, providing valuable insights for improving service quality and customer satisfaction.
Evaluation of Access by KAI Service Quality Using the E-SERVQUAL Method Henoch Juli Christanto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7172

Abstract

Digitalization has transformed public transportation services by encouraging service providers to deliver faster, more accessible, and customer-oriented mobile applications. This study examines the electronic service quality of the Access by KAI application using the E-SERVQUAL method. The objective is to identify gaps between user expectations and perceived service performance across seven dimensions: efficiency, fulfillment, system availability, privacy, responsiveness, compensation, and contact. A descriptive quantitative approach was applied by distributing questionnaires to 100 respondents with experience using the application. Respondents were selected through purposive sampling, and each questionnaire item was measured using a five-point Likert scale for expectation and perception scores. The research instrument underwent validity and reliability testing before the data were analyzed. The results show that all E-SERVQUAL dimensions had negative gap values, indicating that perceived service quality did not fully meet user expectations. The overall expectation score was 4.61, whereas the overall perception score was 3.67, resulting in an average gap of −0.94. The largest gap was found in compensation (−1.28), followed by system availability (−1.15), responsiveness (−1.14), and contact (−1.02). These findings indicate that refund handling, ticket cancellation, failed transaction resolution, application stability, complaint response, and access to customer support are the main areas requiring improvement. The results provide practical guidance for improving digital railway ticketing services and supporting user satisfaction in Indonesia’s transportation sector.
Classification of Anemia Severity Using Random Forest and SHAP Analysis on Complete Blood Count Data Tira Julia Indah Sari; Andreas Perdana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.7199

Abstract

Anemia remains a major global public health challenge, requiring accurate severity classification to support early diagnosis and clinical decision-making, particularly in resource-limited settings. This study aimed to develop an accurate and interpretable multiclass model for classifying anemia severity according to the 2011 WHO hemoglobin thresholds. A Random Forest model combined with SHAP (SHapley Additive exPlanations) analysis was developed and evaluated using a Complete Blood Count (CBC) dataset comprising 364 samples categorized into four classes: Normal, Mild Anemia, Moderate Anemia, and Severe Anemia. Data preprocessing included Interquartile Range (IQR) capping, label encoding, and StandardScaler standardization. The model was optimized through Grid Search Cross-Validation using 432 hyperparameter combinations and five-fold Stratified K-Fold validation. On the test set, the model achieved an accuracy of 95.89%, macro-precision of 96.61%, macro-recall of 97.14%, and macro-F1-score of 96.85%. The Moderate and Severe Anemia classes achieved F1-scores of 100%, although the result for Severe Anemia should be interpreted cautiously because of the limited number of test samples. Mild Anemia was the most challenging class, particularly for samples near the hemoglobin classification thresholds. SHAP analysis identified HGB, PCV, and RBC as the most influential features. However, the SHAP results should be interpreted in light of the correlations among these variables and the use of HGB as the basis for WHO severity labeling. The findings indicate that Random Forest combined with SHAP can accurately reproduce anemia severity classifications based on the applied labeling criteria while providing interpretable predictions. Further external and clinical validation is required before the model can be considered for use in a clinical decision support system.
Digital Forensic Analysis of Signature Images Using Error Level Analysis, Image Hashing, and Support Vector Machine Within the DFRWS Framework Amelia Yahya; Taswanda Taryo; Kahfi Heryandi Suradiradja
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7224

Abstract

The increasing use of digital documents in administrative and legal activities has expanded the use of image-based signatures for authentication and verification. However, signature images are vulnerable to manipulation using image-editing software, potentially resulting in document forgery and disputes over authenticity. This study examined the use of Error Level Analysis (ELA), perceptual hashing (pHash), and the Gray Level Co-occurrence Matrix (GLCM) to detect manipulation in signature images. It also evaluated the performance of a Support Vector Machine (SVM) in classifying genuine and forged signatures within the Digital Forensic Research Workshop (DFRWS) framework. The dataset comprised 720 signature images obtained from the Starter Handwritten Signatures Dataset. The research process involved image preprocessing, feature extraction, model training, and performance evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The model achieved an accuracy of 80.56% on previously unseen test data. The developed system also produced visual analysis outputs and generated digital investigation reports based on the DFRWS framework. These results indicate that the combination of ELA, pHash, GLCM, and SVM can support a structured digital forensic process for distinguishing between genuine and forged signature images.
Web-Based Madrasah E-Report Information System at Madrasah Ibtida'iyah Salafiyah Syafi'iyah Putra Abdullah Amjad Bawazin; Achmad Baijuri; Firman Santoso
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7238

Abstract

Before this study, Madrasah Ibtida’iyah Salafiyah Syafi’iyah (MISS) Putra managed student assessment data in Microsoft Excel and prepared report cards using the mail merge feature in Microsoft Word. This workflow distributed academic records across multiple files, increasing the risk of data inconsistencies, data-entry errors, duplication, and delays in report card preparation. This study aimed to design and implement a web-based e-report information system that centralizes academic data management and supports grade processing, report card generation, and access to academic information at MISS Putra. The system was developed using the Waterfall model, comprising requirements analysis, system design, implementation, testing, deployment, and maintenance. The application was built with the Laravel framework, PHP, and MySQL. Functional testing was conducted using the black-box method on five core functions: user login, student data management, grade entry, report card generation, and report card printing. The test results showed that all five functions produced the expected outputs. The implemented system centralizes academic records, reduces repeated data entry, and supports more practical access to academic information for administrators, teachers, homeroom teachers, madrasah leaders, and student guardians.
Development of a Web-Based Information System for Community Aspirations and Complaints in Bungatan Village, Bungatan District, Situbondo Regency Muhammad Faidhurrahman Wahid; Achmad Baijuri; Firman Santoso
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.7241

Abstract

Web-based information systems can support the management of public services by providing more structured access to information and reports. This study aims to design and implement a web-based Community Aspiration and Complaint Information System in Bungatan Village, Bungatan District, Situbondo Regency. The existing process for submitting aspirations and complaints was still conducted manually through face-to-face communication or messaging applications, resulting in less structured data management and limited transparency in report monitoring. Data were collected through observation, interviews, documentation, and literature studies. System development followed the Waterfall method, consisting of requirements analysis, system design, implementation, testing, deployment, and maintenance. The system was developed using the CodeIgniter 4 framework with PHP and MySQL. The results show that the system enables the community to submit aspirations and complaints online and monitor report statuses through the website. Black-box testing was conducted on six main features: registration, login, complaint submission, aspiration submission, complaint management, and report status monitoring. All tested features functioned as expected based on the defined test scenarios. The system also provides centralized data storage and structured report management, supporting the administration of community aspirations and complaints in Bungatan Village.

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