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Muhammad Wali
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muhammadwali@amikindonesia.ac.id
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+6285277777449
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ijsecs@lembagakita.org
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Jl. Teuku Nyak Arief No. 7b 23112, Kota Banda Aceh, Banda Aceh, Provinsi Aceh
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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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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.

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