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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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Web-Based Inpatient Medical Record Review Information System Design Using the Agile Method: Implementation at RSUD Bandung City Mely Fahmalia; 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)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5441

Abstract

Inpatient medical record review is a major activity in ensuring the quality, completeness, and consistency of hospital healthcare documentation. Besides ensuring compliance with documentation standards, the review process also ensures that clinical services are accurately recorded for reference in future medical, legal, and administrative matters. This study aims to design a web-based information system that will facilitate structured, efficient, and integrated review workflows between medical record officers and other related units. The development of the system uses the Agile method which prioritizes flexibility and fast adaptation to user feedback so that improvements can be made iteratively. The prototype created can manage data input on reviews, generate summary results as well as reduce human errors usually found in manual entries. The hospital currently uses Google Spreadsheets and Google Forms which are tools with too many columns and prone to errors hence making tracking time and data inefficient. Usability testing using the System Usability Scale (SUS) obtained an average score of 68.13 which is categorized as “acceptable” usability indicating that the system is easy to use and feasible for real implementation. This system is then a basic solution for workflow simplification, validation accuracy improvement, and increasing documentation reliability of medical records in hospital environments.
Development of a Proximity-Based Pet Adoption Website Application Muhamad Akbar Susanto; Pratyaksa Ocsa Nugraha Saian
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.v5i3.5443

Abstract

This study examines the development of a web-based application designed to facilitate pet adoption through practical, structured, and location-based mechanisms. The primary problem addressed is the absence of digital platforms capable of integrating adoption processes more efficiently than traditional methods. The application employs Java Spring Boot for backend architecture, ReactJS for frontend interface, and MongoDB for data management. The Haversine formula with a 50 km radius was implemented to display animals based on proximity to users. Algorithm implementation results demonstrate that Haversine effectively calculates distances and presents animals according to nearest locations, thereby enhancing search accuracy and relevance for prospective adopters. Core functionality includes a WebSocket-based real-time chat system enabling direct communication between prospective adopters and owners without page reloading. System development followed the Waterfall model encompassing requirements analysis, design, implementation, and testing phases. Evaluation through User Acceptance Testing (UAT) using a 1-4 Likert scale yielded satisfaction rates of 82% among prospective adopters and 84.5% among pet owners, both categorized as "highly satisfied." These findings validate that the application serves as a more efficient and structured alternative, with potential for further development to support expanding user bases in the future.
Implementation of AI-Based Natural Language Processing (NLP) for Automatic Meeting Minutes and Summarization Using Voice-to-Text on Mobile Applications Muhammad Umar Hamidi Yusuf; Dadang Iskandar Mulyana
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.v5i3.5459

Abstract

Manual recording of the meeting minutes is often ineffective and has a high error rate. It makes access to needed information not fast. This research tries to solve these problems by designing and developing an end-to-end mobile application called Notulen.AI, which combines voice-to-text and Natural Language Processing (NLP) to automatically generate meeting minutes and summaries. The application was developed using the Flutter framework, with the Google Gemini API mainly used as the Automatic Speech Recognition (ASR) service and for NLP analysis. The research methodology consists of system design, module implementation, and testing to see performance and effectiveness. Testing on ASR modules gets good results with an average Word Error Rate (WER) of 6.75%. The NLP module also works well in extracting important information with ROUGE-1 scoring at 0.78 and F1-Score at 0.85. Effectiveness testing involving five respondents showed that this application could reduce minute-taking time by up to 70% and got a System Usability Scale (SUS) score of 85.5, which indicates high user acceptance. This research therefore proves that the integration of ASR and NLP on a mobile platform can be an efficient solution to enhance documentation accuracy in meetings.
Integrating Zero Trust Architecture with Blockchain Technology to Maintain Data Security in the Cloud T. Irfan Fajri; Handry Eldo; Cut Susan Octiva; Dikky Suryadi; Muhammad Lukman Hakim
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.v5i3.5481

Abstract

Data security concerns have increasingly become a challenge to cloud computing services due to rising incidents of cyberattacks, identity theft, and data manipulation. The perimeter-based security model is ineffective because of vulnerabilities in authentication and access control, thus necessitating an adaptive layered approach. This paper presents attempts to merge Zero Trust Architecture (ZTA) with Blockchain technology as one possible way to ensure confidentiality, integrity, and availability of data in cloud environments. Research methodology comprises a detailed review of related literature, system architecture analysis, and simulation of the conceptual merger using encryption protocols and smart contracts. Results revealed that ZTA significantly reduces the opportunities for unauthorized access through multi-layered verification and least privilege principles while Blockchain provides a decentralized transparent immutable method for recording transactions on data. The hybrid will enhance security substantially against breaches from external attackers and insiders with an already established verifiable audit trail. This paper concludes that such a merger could create a stronger model—one that is more measurable—and sustainable for securing today's cloud infrastructure.
Application of Random Forest Method in Predicting Chronic Obstructive Pulmonary Disease (COPD) Muhhamad Fatkhurridlo Mahendra; Nur Aeni Widiyastuti; Sarwido Sarwido
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.v5i3.5551

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is one of the major global health problems and remains among the leading causes of death worldwide. Early detection plays a crucial role in preventing disease progression; however, conventional diagnostic methods such as spirometry and CT scans often require high costs, long processing time, and specialized expertise. This study aims to apply the Random Forest algorithm, one of the machine learning methods, to predict COPD based on clinical and lifestyle data. The dataset was obtained from Kaggle, consisting of attributes including age, gender, smoking status, type of occupation, sleep habits, exercise activity, insurance ownership, and history of comorbidities. The research stages include data preprocessing, train-test splitting (80:20), and model evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The Random Forest model achieved an accuracy below 90% (approximately 87%), reflecting realistic performance in medical prediction while avoiding overfitting. The results indicate that Random Forest can serve as a reliable method for COPD detection and holds potential to be developed as the foundation of a Clinical Decision Support System (CDSS). This study contributes to the growing body of literature on the application of machine learning in healthcare, while also offering a faster, cost-effective, and scalable alternative for diagnosis.
Stunting Prediction in Toddlers Using the K-Nearest Neighbor (KNN) Method Based on a Web Application at Batealit Community Health Center, Jepara Lisa Falichatul Ibriza; Gentur Wahyu Nyipto Wibowo; Teguh Tamrin
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.v5i3.5553

Abstract

Stunting is still a nutritional problem that exists in Indonesia and it needs immediate intervention in Jepara Regency. At the primary healthcare level, Batealit Public Health Center uses manual anthropometric recording for toddlers' growth assessment. This method can be prone to human recording errors and operational delays which hinder prompt clinical decision-making. To improve this condition, this study develops a web-based system for predicting stunting based on the K-Nearest Neighbor (KNN) algorithm. The research method was applied research with system development using the Waterfall model by processing main variables such as age, weight, and height. We tested the algorithm intensively by trying different neighbor values (k) to obtain the maximum value for accuracy, precision, and recall. From experiments, the KNN algorithm is best at k=3 with a 95.23% accuracy rate; this configuration is better compared to larger k values since they increase misclassification rates on normal and stunted categories. By porting this logic into a web interface, detection moves from being a manual task to an automated one occurring in real-time thus application becomes an essential part of decision support enabling health workers to bypass administrative delays and find stunting much faster more accurately within Batealit service area.
Implementation of the Hybrid ARIMA-LSTM Model for Gold Price Prediction Based on Yahoo Finance Data
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.v5i3.5560

Abstract

This paper presents a hybrid ARIMA–LSTM model to forecast daily gold price using historical data from Yahoo Finance. Gold price is highly volatile due to macroeconomic, geopolitical, and monetary factors, making accurate forecasting difficult and increasing uncertainty in investment decisions. In this study, ARIMA is used for modeling linear patterns in the time series data, while an LSTM network captures the nonlinear relationships and temporal dynamics that are not captured by statistical models. The dataset consists of daily observations of gold prices between June 2022 and June 2025. The analysis involves cleaning and normalizing the data, splitting it into training and testing subsets, estimating ARIMA parameters, extracting residuals, and forecasting these residuals with LSTM. Performance evaluation is carried out through MAE, RMSE, and MAPE metrics. The hybrid framework compares favorably against standalone ARIMA and LSTM models in terms of all three metrics used for assessment. Empirical results show that the hybrid ARIMA–LSTM model produces lower forecasting errors than the individual models on all evaluation metrics. These findings validate that combining statistical time series modeling with neural sequence learning increases predictive reliability in volatile commodity markets. The proposed framework can be considered methodologically sound for gold price forecasting and subsequently may enhance informed decision-making within financial analysis as well as investment practice.
Super Encryption Cryptography for Land Certificate Data Security: A Case Study of the Jayapura City Land Agency Ray Setiawan Panyuwa; Suharyadi Universitas Kristen Satya Wacana
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.v5i3.5652

Abstract

This study introduces a lightweight, reproducible super-encryption framework designed to protect land certificate owner data at the Jayapura City Land Office. The system combines two classical algorithms—Rail Fence Cipher for transposition and Vigenère Cipher for substitution—through a structured, layered encryption pipeline implemented in Python. Testing was conducted on 50 simulated certificate owner names (10–15 characters each) under controlled conditions (Intel i5, 8 GB RAM, Windows 10). Black-box validation demonstrated 100% decryption accuracy with sub-10 ms total processing time per record. Robustness assessments revealed an average Shannon entropy increase of 41.6% and an avalanche rate of 47.8%, indicating enhanced ciphertext randomness. Results confirm that strategically layering classical ciphers delivers reliable confidentiality and integrity for small-scale, non-transactional datasets characteristic of land administration offices operating under resource constraints. The research offers a transparent, replicable model for securing identity fields and demonstrates the practical viability of super-encryption as a computationally efficient cryptographic solution for local government digital systems
Analysis of Internet of Things Based Smart Home Systems for Electricity Consumption Efficiency
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.v5i3.5668

Abstract

The IoT technology has opened up new horizons in household energy management through smart home systems. Smart home systems are based on the integration of electronic appliances with sensors and actuators, which provide automated and remote control of domestic devices. This article assesses IoT-based smart home systems as a tool for enhancing electricity consumption efficiency in residential domains. The research uses a literature-based approach complemented by prototype development using current sensors, motion sensors, and internet-connected microcontroller modules to collect real-time data about the usage of electrical energy to recognize the patterns of energy consumption among household appliances. A comparative analysis between normal operating conditions and those enabled by smart home automation is carried out. Results show that IoT-based smart homes lower electricity consumption by controlling device operation according to real usage conditions such as turning off idle devices, adjusting lighting levels based on human presence, and allowing remote control of appliances. These results prove that IoT-based smart home systems can be effectively used for reducing household electricity demand in compliance with energy sustainability efforts within digitally connected residential environments.
Identification of Key Factors in Children's Toy Product Marketing Strategy through Entropy and Gain Analysis Siti Aliyah; Efani Desi; Mas Ayoe Elhias Nst; Enni Maisaroh; Fitri Pranita Nasution
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.v5i3.5690

Abstract

This study aims to analyze the factors influencing product sales success using the C4.5 algorithm data mining method implemented through the WEKA application. The research data consists of 65 instances with six main attributes, namely introduction, durability, price, size, quality, and description. The testing process is carried out using the 10-fold cross validation method to obtain an accurate classification model. The analysis results show that the Price attribute has the highest information gain value ±0.764, so it is designated as the root of the decision tree. Low prices supported by long product durability proved to be the most dominant combination in increasing sales. Conversely, high prices tended to decrease sales levels even though supported by good quality. The resulting classification model has an accuracy of 83.07%, with 54 data correctly classified out of a total of 65 data. These calculation results indicate that consumers are more sensitive to price than quality, so a marketing strategy that emphasizes competitive pricing with guaranteed product durability is the most effective approach to increase purchasing interest. This research is expected to contribute to business decision making, especially in determining product sales strategies in a competitive market.

Page 38 of 39 | Total Record : 387

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