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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 83 Documents
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Data Mining Using Multiple Linear Regression Method for Stock Prediction Sembiring, Methewkasly Pratama; Verina, Wiwi
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.v16i2.288

Abstract

This study aims to apply data mining techniques using multiple linear regression to predict inventory at PT. Sumber Jaya Motor's website. In today's digital era, companies face challenges in managing inventory, which can impact operational efficiency and customer satisfaction. Therefore, accurate inventory prediction is essential to improve inventory management efficiency. The multiple linear regression method was chosen because of its ability to link multiple independent variables with the dependent variable, thus providing more accurate predictions regarding required inventory. The data used in this study includes information related to sales, suppliers, and demand obtained from PT. Sumber Jaya Motor. The results of the multiple linear regression application indicate that the developed model can provide inventory predictions with a high degree of accuracy. This system is implemented on a website to facilitate real-time data-driven monitoring and decision-making. With the implementation of this method, it is hoped that PT. Sumber Jaya Motor can manage inventory more efficiently, reduce inventory costs, and improve customer service.
Designing a Website-Based Study Program Management Application and Digital Attendance for Informatics Study Programs Dwi Tri Putra, Bainul; Labiba Sarwoko, Nisrina; Gian Febriantama, Rizal; Bintang Maharani Sigalingging, Miranda; Naufal Arits Fikri, Muhammad; Ahmad Firdaus, Eryan
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.v16i2.233

Abstract

Conventional attendance and conventional management of academic activities often cause problems, such as recording errors, delays, and lack of access to organized information. In addition, academic communication still relies on external platforms, such as Telegram, which does not support the needs of lecture activity management. This research aims to design a web-based application called ClassTIcs, which provides digital attendance features, lecture schedule management, lecture material upload, and textbased discussion forums. This application involves three main actors: staff as super admin, lecturers, and students. The research uses the Rapid Application Development (RAD) method, which includes the stages of requirements planning, system design, prototype development, and testing. With ClassTIcs, staff can manage attendance data and schedules, lecturers can conduct digital attendance and upload lecture materials, and students can monitor lecture schedules and discuss through text forums. The results show that this application can help overcome the obstacles that often arise in the attendance process and management of study program activities while providing convenience for all actors involved. Further development is recommended to add other features to support the various needs of study programs in the future.
K-Nearest Neighbor (K-NN) Method for Disturbance Classification of Customer Wifi Networks at PT. Global Karya Wanda Cahyani, Muhairoh Indah; Ummi, Khairul
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.v16i2.256

Abstract

This study aims to develop and implement a Wi-Fi network disturbance classification system using the K-Nearest Neighbor (K-NN) algorithm at PT. Global Karya Wanda. The purpose of this research is to identify and classify Wi-Fi network conditions based on standard categories such as interference, troubleshooting, disconnection, and signal loss, thereby improving the efficiency and accuracy of network monitoring. The system was designed and developed using PHP and MySQL, with datasets obtained from PT. Global Karya Wanda’s operational network records. The classification process employed the K-NN algorithm to distinguish between Disturbance and Not Disturbance network states. The experimental results demonstrate that the K-NN method provides fast, automatic, and accurate classification performance, supporting the company in optimizing its troubleshooting workflow and enhancing customer service reliability. From a practical standpoint, the model enables more systematic network performance monitoring and proactive disturbance management. Scientifically, this research contributes to the application of machine learning algorithms in network performance analysis and telecommunications service optimization. Future studies are recommended to integrate hybrid approaches such as KNN–SVM or machine learning API integration to improve classification accuracy, scalability, and real-time responsiveness in larger and more dynamic network environments.
Application for Searching for Livestock Distributor Locations at the North Sumatra Plantation and Livestock Service Using the Euclidean Distance Method Aulia, Prayogi; Syahrin, Elvin
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.v16i2.257

Abstract

The Plantation and Livestock Agency of North Sumatra Province plays an important role in supporting the availability and distribution of livestock for farmers and livestock business actors. However, information about the locations of livestock distributors, which are scattered across various regions, is often difficult to access quickly and accurately. This situation makes the process of finding distributor locations inefficient and time-consuming. Therefore, a system is needed to help users find the nearest livestock distributor locations automatically and accurately. This study aims to design and develop an Android-based application that provides information on livestock distributor locations in North Sumatra Province and determines the nearest distance between the user and the distributors using the Euclidean Distance method. This method is used to calculate the distance between two points based on their geographic coordinates (latitude and longitude), allowing users to get recommendations for the nearest distributor from their current position. The result of this study is an Android application that can display a list of livestock distributors, show their locations on a map using GPS services, and provide real-time information about the closest distance. This application is expected to make the process of finding livestock distributor locations more effective and efficient, as well as help the Plantation and Livestock Agency of North Sumatra Province provide digital information services to the public.
Augmented Reality in Learning the Introduction to the Toba-Simalungun Batak Language Using the Marker-Based Tracking Method Simbolon, Andreas; Nasution, M Irfan Aldy
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.v16i2.258

Abstract

Regional languages ​​are invaluable cultural treasures and are inseparable from socio-cultural values ​​that reflect the principles of regional behavior and identity. In the areas where tribes are spread in North Sumatra Province, there are several languages ​​such as Batak Toba and Simalungun which are used in several areas in North Sumatra. Global developments have made the use of Batak Toba and Simalungun languages ​​​​decrease and the intention to learn from children to adults is decreasing and the lack of learning facilities to understand Batak Toba and Simalungun languages. This study will aim to combine Batak Toba and Simalungun languages ​​​​with augmented reality (AR) technology in order to obtain more efficient, interesting and interactive learning facilities for children to adults in learning Batak Toba and Simalungun languages. The application development method uses Marker Based Tracking as an augmented reality (AR) reference in the development of the Android-based Batak Toba-Simalungun Language Introduction Learning Application Marker Based Tracking Method. The Marker-Based Tracking method for the Toba-Simalungun Batak language, produced in this application, is a 3-dimensional (3D) image of the language. When the user points the camera at an object or 3D image, the application displays a 3-dimensional (3D) image of the Toba-Simalungun Batak language pattern captured by the user's camera.
System Application Design Inventory Management in Sales Using Genetic Algorithms Siahaan, Reinhard Parningotan; Triandi, Budi
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.v16i2.259

Abstract

PT. Kao Indonesia, a company engaged in the production and distribution of consumer goods, requires an efficient inventory management system to ensure a smooth and responsive sales process. One of the main challenges faced is the discrepancy between stock levels and market demand, which often leads to overstocking or stockouts and ultimately financial losses. This study aims to design and develop an inventory management system application that optimizes stock levels using a Genetic Algorithm (GA). The GA method is employed to determine the optimal inventory quantity by analyzing historical sales data and evaluating various stock-level scenarios to find the most efficient solution. The application was developed using the PHP programming language and a MySQL database. A case study at PT. Kao Indonesia involving sales and product inventory data over a specific period demonstrates that the system effectively enhances stock management efficiency, minimizes inventory discrepancies, and supports more accurate and data-driven decision-making in the company’s inventory management process.
Application of AHP and Profile Matching Methods in Teacher Performance Assessment at XYZ School Hidayat, Wahyu; 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.v16i2.260

Abstract

Teacher performance evaluation plays a critical role in improving educational quality; however, manual assessment systems often lead to subjectivity, inefficiency, and a lack of data-driven decision-making. This study aims to develop and implement a decision support system for teacher performance evaluation using the Analytical Hierarchy Process (AHP) and Profile Matching (PM) methods to produce more objective, transparent, and systematic assessments. The research was conducted at XYZ School with a sample of 20 teachers, evaluating five key performance aspects: learning planning, learning implementation, assessment and evaluation, classroom management, and communication with students. The AHP method was applied to determine the weight of each criterion through pairwise comparisons, while the Profile Matching method was used to align individual teacher competencies with ideal performance profiles. The system generated a ranking of teachers, identifying Drs. Hendarto Wijaya, Masriyanti, S.Pd, and Nurlia Syafina, S.Pd as the top three performers. The results indicate that combining AHP and PM effectively reduces subjectivity, enhances assessment accuracy, and accelerates the evaluation process. Furthermore, the web-based implementation allows automated reporting and easier data access, improving efficiency in teacher development planning. The implications of this study highlight that integrating multi-criteria decision-making models in educational management can strengthen evidence-based performance evaluation practices. Future studies should expand this framework across multiple institutions and incorporate advanced analytical methods to enhance system adaptability and scalability.
Comparison of K-Means Clustering and Fuzzy Tsukamoto Algorithms for Grouping Student Data of NU Medan Middle School Based on Academic Achievement Pahlevi, Abdul Rasyid; Amrullah, Amrullah
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.v16i2.262

Abstract

The increasing need for data-driven decision-making in education has encouraged the use of intelligent algorithms to evaluate and classify student academic performance more effectively. However, differences in algorithmic approaches often lead to variations in interpretation and categorization outcomes. This study aims to compare the performance of the K-Means and Fuzzy Tsukamoto algorithms in clustering student achievement data at SMP NU Medan to determine which method provides a more accurate and interpretable classification model. The research employs quantitative analysis using students’ semester grades processed through Python and Microsoft Excel, where K-Means utilizes centroid-based clustering (75, 85, and 95) and Fuzzy Tsukamoto applies fuzzy logic with weighted membership values (0, 5, and 10). The results reveal that K-Means produces a more proportional and stable clustering structure, effectively differentiating student achievement levels within the same population, while Fuzzy Tsukamoto offers a simpler, rule-based classification system aligned with fixed academic standards. The findings indicate that K-Means is more suitable for analyzing relative performance variations, whereas Fuzzy Tsukamoto is better suited for absolute classification and administrative evaluation. Both methods are easily implemented and can be integrated into educational management systems to enhance instructional decision-making. The study implies that a hybrid combination of these two algorithms may provide a more comprehensive analytical framework for evaluating student performance.
Data Mining for Drug Inventory Using Web-Based FP-Growth Method Puspita, Della; Fahrozi, Wirhan
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.v16i2.263

Abstract

Pharmacy inventory management plays a critical role in ensuring product availability and preventing financial losses due to overstock or stock shortages. However, many pharmacies, including Global Medcare Pharmacy in Medan, still experience challenges in maintaining optimal inventory levels due to the absence of data-driven management systems. This study aims to develop and implement a website-based data mining application using the FP-Growth algorithm to identify frequent itemsets and uncover association patterns within pharmaceutical sales transactions. The FP-Growth algorithm was applied to 300 transaction records to generate frequent item combinations with a minimum support threshold of 3%. The results reveal strong associations among specific drugs, such as Amlodipine 10mg, Azithromycin 500mg, and Cetirizine 10mg, with confidence levels reaching up to 100%. These findings demonstrate that FP-Growth effectively identifies purchasing patterns that can guide pharmacies in forecasting demand, managing stock levels, and designing promotional bundles. The practical implication of this research is that integrating FP-Growth into pharmacy information systems can enhance decision-making accuracy, improve service quality, and increase operational efficiency. Nevertheless, the study is limited to a single-site dataset and static analysis; future research should employ larger datasets and hybrid predictive approaches for real-time implementation across multiple pharmacy networks.
Implementation of the Apriori Method in Consumer Purchasing Patterns at PT. Ouzen Anugerah Indonesia Yustio, Muhammad Alif; 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.v16i2.264

Abstract

The rapid growth of the cosmetics industry has created intense competition among retail companies, including PT. Ouzen Anugerah Indonesia, which faces challenges in understanding consumer purchasing behavior due to the lack of systematic data analysis. This study aims to analyze consumer purchasing patterns and identify frequently purchased product combinations using the Apriori algorithm as a data mining technique. The research employed a descriptive quantitative approach based on 5,000 sales transactions recorded between January and December 2024, consisting of transaction IDs, dates, and purchased product lists. Through the Apriori algorithm, the study generated association rules that reveal relationships among products, represented by support and confidence values. The results show that several cosmetic items exhibit strong associative relationships, indicating consistent consumer purchasing tendencies that can be utilized for cross-selling strategies, promotional bundling, and inventory optimization. These findings highlight the effectiveness of data mining in transforming raw transaction data into actionable insights that support data-driven business decision-making. The study contributes theoretically by reinforcing the application of association rule learning in medium-scale retail contexts and practically by providing a framework for developing marketing and operational strategies based on data analysis. The research also suggests that future studies integrate broader datasets and additional variables, such as consumer demographics and temporal factors, to enhance analytical depth and model generalization.

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