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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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Decision Support System in Marketing Strategy Using Data Mining Techniques Harahap, Leliana; Purba, Sartika Dewi; Panggabean, Jonas Franky R; Sirait, Kamson
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.v16i2.299

Abstract

The increasing complexity of market competition and the rapid growth of enterprise data have made traditional marketing decision-making approaches inadequate in addressing information asymmetry and dynamic market changes. Conventional decision support systems (DSS) are often limited to data-level reporting and lack advanced analytical capabilities to uncover hidden patterns and strategic insights. This study aims to design and evaluate an intelligent marketing Decision Support System by integrating data warehousing, Online Analytical Processing (OLAP), and data mining techniques to enhance the quality and effectiveness of marketing decisions. The proposed method adopts a data-driven DSS architecture that performs extract–transform–load (ETL) processes to build a unified subject-oriented data warehouse, followed by multidimensional analysis and knowledge discovery using decision tree classification and neural network models. Experimental validation was conducted using FoodMart 2000 sales data to assess the predictive performance and decision support capability of the system. The results demonstrate that the three-layer BP neural network model achieved a mean absolute percentage error of 15.13% in sales prediction, indicating satisfactory forecasting accuracy, while simulation and sensitivity analyses reveal a positive correlation between promotional investment and corporate profit growth. These findings confirm that the proposed marketing DSS can effectively reduce information asymmetry, improve forecasting reliability, and support strategic marketing decisions related to pricing, promotion, and market expansion. The study implies that integrating data mining with DSS provides a robust analytical foundation for data-driven marketing management and sustainable enterprise competitiveness in complex market environments.
Evaluation of the usability of the faculty performance assessment questionnaire using the system usability scale (SUS) method Situmorang, Sutrisno; Manullang, Jontinus; Lubis, Harmoko; Manik, Aditiarno
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.v16i2.308

Abstract

The digital transformation of lecturer performance evaluation systems in higher education has improved efficiency in data collection and reporting; however, system effectiveness depends significantly on its usability. A system that is difficult to use may reduce student participation and compromise the quality of evaluation data. This study aims to evaluate the usability level of an online lecturer performance assessment questionnaire system using the System Usability Scale (SUS). The research employed a descriptive quantitative approach involving 30 active students as respondents who had completed the online questionnaire. Data were collected using a 10-item SUS instrument with a five-point Likert scale and analyzed according to standard SUS scoring procedures, followed by descriptive interpretation and validity testing using Pearson correlation. The results showed that the average SUS score was 49.75, which falls into the “Poor” category, indicating that the system’s usability level has not yet reached an acceptable standard. Although the system is generally accessible and relatively easy to learn, several aspects—particularly navigation clarity, interface consistency, and user feedback mechanisms—require improvement. The validity test confirmed that all questionnaire items were statistically valid. These findings imply that systematic redesign and iterative usability evaluation are necessary to enhance user experience, increase student participation, and strengthen the effectiveness of lecturer performance evaluation as part of sustainable academic quality assurance in higher education institutions.
Product Demand Prediction in E-Commerce Systems Using the Monte Carlo Method Fahrizal, Muhammad Arif; Lumbansiantar, Irwan; Herlianto, Rebi; Panjaitan, Krisfan Suganda
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.v16i2.309

Abstract

The rapid growth of e-commerce has intensified demand uncertainty, creating significant challenges in inventory management due to the risks of overstock and stockout conditions. Fluctuating consumer behavior and dynamic digital market trends require forecasting approaches capable of modeling probabilistic variability rather than relying solely on deterministic estimates. This study aims to analyze and implement the Monte Carlo simulation method to forecast product demand in an e-commerce system and to evaluate its effectiveness in supporting optimal inventory decision-making. The research adopts a quantitative approach using historical monthly sales data of laptop products collected over a ten-month period. The Monte Carlo method was applied by constructing probability distributions, calculating cumulative probabilities, defining random number intervals, and performing repeated simulations to generate demand predictions. The simulation results produced an average predicted demand of 139 units, closely aligned with the historical average of 137 units, with a Mean Absolute Deviation (MAD) of 2 units, indicating a low prediction error. These findings demonstrate that the Monte Carlo approach effectively captures demand variability and provides accurate probabilistic estimates. The study implies that integrating Monte Carlo simulation into e-commerce inventory planning can enhance risk-based decision-making, improve stock control accuracy, and reduce potential financial losses associated with demand uncertainty.

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