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Jurnal ICT : Information and Communication Technologies
Published by Marq & Cha Institute
ISSN : 20867867     EISSN : 28089170     DOI : https://doi.org/10.35335/jict
Core Subject : Economy, Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering
Jurnal ICT : Information and Communication Technologies (p-ISSN: 2086-7867) is a scientific journal and open access journal published by Pusat Penelitian Teknoligi, Marqcha Institute, Indonesia. Jurnal JICT covers the field of Informatics, Computer Science, Information Technology and Communication.It was firstly published in 2010 for a printed version. The aims of Jurnal JICT are to disseminate research results and to improve the productivity of scientific publications. Jurnal JICT is published two times a year (April and October).
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 81 Documents
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Diet Recommendation System for Kidney Disease Patients Using Collaborative Filtering Sitompul, Bintang Cimdy Lorenza; Puspasari, Ratih
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.265

Abstract

Chronic kidney disease (CKD) remains a major global health challenge, particularly in Indonesia, where limited awareness and inadequate dietary management contribute to the progression of renal complications. Patients often face difficulties in selecting foods that meet both medical and nutritional requirements, underscoring the need for intelligent dietary guidance. This study aims to develop a personalized dietary recommendation system for kidney failure patients using a hybrid approach that combines content-based and collaborative filtering techniques. The model was designed to analyze patients’ food preferences, nutritional composition, and health conditions to generate appropriate dietary recommendations. The system’s performance was evaluated using cosine similarity and predictive accuracy metrics, including RMSE, precision, recall, and F1-score. The results show that the proposed model achieved an accuracy of 83%, precision of 75%, recall of 100%, and F1-score of 86%, demonstrating its effectiveness in identifying dietary similarities and preferences among patients with comparable clinical profiles. Furthermore, by integrating nutritional content data such as sodium, potassium, and protein levels, the system successfully provided clinically safe and personalized recommendations aligned with renal dietary guidelines. These findings highlight the potential of artificial intelligence–based recommendation systems to support dietitians in improving the accuracy and efficiency of nutritional counseling, thereby promoting patient adherence and enhancing the quality of kidney disease management in hospital settings.
Comparison of the Waspas Method with the OCRA Method in Determining Web-Based Aid Recipients Randy, Muhammad Randy; Lestari, Silvia
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.266

Abstract

The determination of scholarship aid recipients at Pangeran Antasari High School has long relied on a manual and subjective process, leading to inefficiencies, inaccuracies, and potential biases in decision-making. To address this issue, this research aims to develop and compare the effectiveness of two multi-criteria decision-making (MCDM) methods—the Weighted Aggregated Sum Product Assessment (WASPAS) and the Operational Competitiveness Rating Analysis (OCRA)—within a web-based Decision Support System (DSS) for determining scholarship recipients. The study applies both methods using nine evaluation criteria, including socioeconomic and academic factors, to rank eligible students objectively. The results reveal that the WASPAS method produces more consistent, stable, and transparent outcomes, with scores that decline gradually and proportionally across alternatives, while the OCRA method demonstrates higher sensitivity to minor data variations, resulting in less stable rankings. Consequently, WASPAS proves to be more suitable for decision contexts that prioritize fairness, stability, and comprehensive evaluation of multiple criteria. The implementation of this method in an automated DSS enhances the objectivity, efficiency, and accountability of scholarship distribution processes. The study’s findings contribute to the advancement of decision-support frameworks in educational institutions and provide a methodological reference for future applications of MCDM models in resource allocation and policy decision-making.
Application of Book Clustering Based on Borrowing Frequency Using K-Means at PBD Aviation Vocational School Nazli, Nurul; Wahyuni, Linda
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.267

Abstract

The rapid advancement of science and technology has enabled the application of data mining techniques in various domains, including education and library management. At PBD Aviation Vocational School, managing the library collection presents a major challenge due to the large number of students and the diverse frequency of book borrowing, leading to inefficiencies in procurement and maintenance. This study aims to develop a data-driven system for clustering books based on borrowing frequency using the K-Means algorithm to optimize collection management. The research was conducted using a quantitative approach, where library borrowing data were processed through the K-Means clustering method integrated into a web-based system. The algorithm classified books into three categories: frequently borrowed, quite frequently borrowed, and rarely borrowed. The results showed that the implementation of the K-Means algorithm effectively identified borrowing patterns, enabling the library to make more accurate decisions regarding book procurement, maintenance, and collection renewal. Furthermore, the web-based interface facilitated faster access and real-time visualization of borrowing trends, improving operational efficiency and data-driven decision-making. The findings highlight the importance of integrating data mining methods in educational library systems to enhance resource utilization and service quality, supporting evidence-based management in the digital transformation era.
Design and Construction of Employee Attendance Using a Facial Recognition System at PT. Astra Daihatsu Krakatau Syahputra, Ihsan; Syahputri, Nita
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.268

Abstract

The rapid advancement of information technology has transformed organizational operations, including human resource management systems, where accuracy and efficiency in employee attendance recording are crucial for maintaining discipline and supporting performance evaluation. However, traditional attendance systems such as manual recording or fingerprint scanning remain limited, especially for employees who are working remotely or traveling, leading to data inconsistencies and delays. This research aims to design and implement a machine learning–based facial recognition attendance system to improve the flexibility, accuracy, and reliability of attendance processes at PT Astra Daihatsu Krakatau. The study employs the Unified Modeling Language (UML) approach for system design and utilizes facial recognition algorithms to automate attendance verification through biometric analysis. The resulting system comprises several key modules—login, main menu, employee data, attendance statistics, and history tracking—providing real-time and integrated attendance monitoring accessible from various devices. The findings indicate that the system effectively addresses the inefficiencies of conventional methods by enabling accurate biometric verification, minimizing fraud, and supporting remote attendance logging. This innovation enhances organizational transparency, operational efficiency, and adaptability in line with digital transformation initiatives. The implication of this study highlights the strategic role of artificial intelligence in modernizing workforce management and optimizing administrative processes within industrial environments.
Implementation of the Least Square Method in Website-Based Product Sales Prediction Alwi, Alfa Rizki; Harahap, Charles Bronson
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.270

Abstract

Sales play a crucial role in determining the continuity and growth of a company. PT. Medan Bajaindo, which operates in the steel product sales sector, requires a system capable of accurately predicting future sales to support decision-making and inventory planning. Currently, sales data analysis is still conducted manually, which is inefficient and prone to errors in forecasting. This research aims to design and develop a web-based sales prediction system by applying the Least Square method. The Least Square method is chosen because it can predict data trends based on historical sales patterns with relatively low error rates. The system is developed using web-based programming languages and supported by a database for storing sales data. The testing results show that the developed system can automatically calculate predictions and present sales trend information more quickly, accurately, and structurally compared to the manual method. With this system, the company’s management can more easily plan sales strategies and stock procurement in a timely manner.
Application of the Moving Average Method in Forecasting Beverage Product Sales at Teman Cerita Cafe Matondang, Jamil Azhari; Indriani, Ulfah
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.271

Abstract

In the current digital era, the rapid growth of the culinary industry, particularly in the beverage sector, has created intense competition among small and medium enterprises. Café Kopi Teman Cerita in Medan is one of the businesses affected by fluctuating beverage sales, often experiencing challenges in predicting demand and managing inventory effectively due to its manual data processing system. This study aims to develop a beverage sales forecasting model using the Moving Average method to assist the café in optimizing raw material procurement and improving operational efficiency. The research was conducted by collecting historical sales data for various beverage products and applying the Moving Average technique to predict future sales trends. The evaluation results show that the forecasting model achieved satisfactory accuracy, with a Mean Absolute Error (MAE) of 10.17, Root Mean Squared Error (RMSE) of 12.26, and Mean Absolute Percentage Error (MAPE) of 20.59. These findings indicate that the Moving Average method can effectively capture sales fluctuations and produce reliable predictions for short-term demand planning. The implementation of this forecasting system is expected to help the café in making data-driven decisions, minimizing inventory discrepancies, and improving customer satisfaction. Moreover, the study provides insights into how simple yet effective analytical tools can support digital transformation and competitiveness in small-scale beverage businesses.
Implementation of the IPA Method in Patient Satisfaction Services at the Dr. Komang Makes Indonesian Navy Hospital, Website-Based Purwopeni, Listyowati; Indriani, Ulfah
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.272

Abstract

Hospitals play a crucial role in providing high-quality healthcare services, particularly for inpatients who require intensive and continuous care. However, gaps often exist between patient expectations and perceived service performance, leading to dissatisfaction and inefficiencies in hospital operations. This study aims to evaluate the quality of inpatient services at Dr. Komang Makes Indonesian Navy Hospital using the Importance Performance Analysis (IPA) method to identify priority areas for improvement. The research employed both library and field studies, including observations, interviews, and questionnaires distributed to 100 inpatients. Data were analyzed by calculating performance and expectation scores across five service quality dimensions—reliability, assurance, responsiveness, empathy, and tangibility—to determine satisfaction levels. The findings revealed an average satisfaction index of 1.01, indicating that overall inpatient services are satisfactory, particularly in the assurance, empathy, and tangible dimensions. However, the reliability and responsiveness dimensions showed notable performance gaps, especially concerning doctor visit schedules, clarity of procedures, and response to patient complaints. These results highlight the need for targeted interventions, such as enhancing digital integration, improving communication systems, and increasing staff responsiveness. The implementation of continuous IPA-based evaluations is recommended to support systematic quality improvement and strengthen patient trust. This study contributes to the development of a patient-centered, technology-driven service model that enhances healthcare quality and organizational performance at Dr. Komang Makes Indonesian Navy Hospital.
Application of the C4.5 Algorithm Method to Identify Factors Affecting Package Delivery Delays at the Post Office Siahaan, Muhammad Taufik; Doni, Rahmad
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.273

Abstract

In the modern logistics landscape, timely package delivery has become a critical determinant of service quality and customer satisfaction. However, frequent delivery delays at the Post Office continue to pose operational challenges, largely due to multifactorial causes such as weather, distance, and scheduling inefficiencies. This study aims to identify and analyze the dominant factors influencing delivery delays using the C4.5 decision tree algorithm, a robust data mining method capable of handling categorical and continuous variables while generating interpretable decision rules. The research utilized historical delivery data from the Post Office, encompassing attributes such as weather conditions, delivery distance, order time, and package type. The analysis revealed that weather conditions had the highest information gain (0.0282430), indicating their dominant impact on delivery performance, followed by distance and package characteristics. The model successfully generated 112 decision rules that enable managers to predict and mitigate potential delays. The findings highlight the effectiveness of the C4.5 algorithm in uncovering complex patterns within operational data and its potential to support data-driven decision-making in logistics management. The implementation of this model can significantly enhance delivery reliability, operational efficiency, and customer trust, representing a strategic advancement toward digital transformation in postal services.
Implementation of the CLCG Method in the Implementation of Final Semester Exams Ramadhan, Fahri; Kurniawan, Helmi
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.295

Abstract

The final semester examination is one of the essential instruments in measuring students’ competency achievement in schools. However, conventional exam implementation often faces challenges such as high operational costs, time-consuming correction processes, and the potential for cheating due to identical questions among students. This study aims to design and develop an Android-based examination application by implementing the Couple Linear Congruential Generator (CLCG) method as a question randomization mechanism. CLCG was chosen because it can generate random numbers with a more uniform distribution and longer period compared to the conventional Linear Congruential Generator (LCG), ensuring that each student receives a different set of questions while maintaining the same level of difficulty. The research was conducted at SMK Harapan Mekar 1 Medan using a prototype-based system development approach. The results show that the developed Android-based exam application runs effectively, successfully randomizes questions using the CLCG method, and facilitates teachers in managing the question bank and accelerating the grading process. Therefore, the application of the CLCG method in an Android-based digital exam system proves to improve efficiency, objectivity, and security in the implementation of final semester examinations in schools.
Application of the Trend Moment Method in Product Sales Forecasting at TB. Mitra Baru Tito, Prans; Azhar, Asbon Hendra
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.296

Abstract

Advances in information technology have made it easier for companies to manage sales data and conduct forecasting to support decision-making. TB. Mitra Baru, a trading company, often faces challenges in predicting future product sales, which impacts stock availability and business planning. This study aims to design and implement a website-based sales forecasting system using the Trend Moment method, a time series method that utilizes a trendline equation based on the average value over time and sales volume. Sales data is used as input to generate predictions for the next period in a systematic and structured manner. Test results indicate that the Trend Moment method is capable of providing sales estimates with a relatively low error rate, thus providing a basis for consideration in inventory management and sales strategies. With this website-based system, TB. Mitra Baru can easily access actual sales reports and forecast results at any time in real time, thus supporting operational effectiveness and more informed decision-making.

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