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ITEJ (Information Technology Engineering Journals)
Published by Universitas Islam Negeri Siber Syekh Nurjati Cirebon
ISSN : 25482130     EISSN : 25482157     DOI : https://doi.org/10.24235/itej.v7i1
Core Subject :
ITEj (Information Technology Engineering Journals) is a peer-reviewed journal that focuses on the Development of information systems, electronic-based learning, and the application of algorithms and methods in informatics engineering and software engineering. Besides that, the focus is also on developing intelligent systems and artificial intelligence. ITEj (Information Technology Engineering Journals) is published by UIN Siber Syekh Nurjati Cirebon collaborates with Asosiasi Prakarsa Indonesia Cerdas(APIC). Publishing two times a year, ie Issue 1 and Issue 2 in June and December. The journal publishes original research articles and case studies focused on e-learning and information technology. All papers are peer-reviewed by reviewers. The scope of the system discussed is attached but not limited; Systems and software engineering Artificial Intelligence Technology Internet of Thing and Big Data Smart Education systems and components Informatics Management Information Technology etc
Arjuna Subject : -
Articles 137 Documents
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Integration of Multi-Item EOQ and EPQ to Minimize Total Inventory Cost of Outsole Raw Materials Yahya Kusuma; Iriani Iriani
ITEJ (Information Technology Engineering Journals) Vol. 10 No. 2 (2025): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i2.284

Abstract

This study analyzes the integration of the multi-item Economic Order Quantity (EOQ) and Economic Production Quantity (EPQ) methods to minimize the inventory costs of outsole raw materials at UD. Santoso Mojokerto. The main problem lies in the high storage costs caused by the mismatch between purchasing quantities and the production needs of women’s flat sandals in sizes 37–39. Using demand, production, and inventory cost data for the period of August 2024–July 2025, EOQ is applied to determine the optimal order quantity, while EPQ is used to establish the optimal production quantity. The results show that integrating EOQ and EPQ can reduce total inventory costs compared to the company’s current method, while also maintaining raw material availability and ensuring smooth production processes.
Tsukamoto Fuzzy Logic Method for Determining Mental Health Levels of Final-Year University Students Nabila Rizky Sarip; Sriani Sriani
ITEJ (Information Technology Engineering Journals) Vol. 10 No. 2 (2025): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i2.291

Abstract

Mental health is an important aspect that affects the ability of final-semester students to complete their studies and face academic and non-academic pressures. The problem that arises is the difficulty in assessing mental health conditions because it is subjective and complex. The Tsukamoto fuzzy method is used because it is able to handle data uncertainty and provides results in the form of measurable crisp values. This study aims to apply the Tsukamoto fuzzy logic method in determining the level of mental health of final-semester students in a more objective and measurable manner. This system model uses four input variables, namely stress level, sleep quality, emotional exhaustion, and duration of gadget use, with 81 rules (rule base) that form relationships between variables. The inference process is carried out through the stages of fuzzification, rule inference, and defuzzification with increasing and decreasing linear triangular membership functions. Testing was carried out using MATLAB by comparing the prediction results to actual data to calculate the model accuracy level using the Mean Absolute Percentage Error (MAPE). The results showed that the total MAPE value was 19.34%, which is in the range of 10%–20% so it is included in the good accuracy category. This demonstrates that the Tsukamoto fuzzy method can provide fairly accurate predictions of the mental health of final-semester students. Therefore, this system can be used as a tool for evaluating and early detection of student mental health in higher education settings.
Waste Analysis in the Aluminum Extrusion Process utilizing Lean Six Sigma and Failure Mode and Effect Analysis (FMEA) Gracia Wiranatalie Damanik; Enny Aryanny
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.293

Abstract

This study aims to identify the dominant types of waste, investigate the underlying causes of quality failures, and formulate improvement recommendations to enhance the efficiency of the production process. The research employs an integrated approach combining Lean Six Sigma and Failure Mode and Effect Analysis (FMEA) to systematically identify inefficiencies and prioritize corrective actions. In contrast to previous studies that primarily focus on a single Critical to Quality (CTQ) characteristic within aluminum extrusion processes, this study examines four dominant CTQs, thereby providing a more comprehensive framework for quality improvement. The findings indicate that defects constitute the most critical form of waste, with the highest weight value of 0.1206, followed by waiting (0.0905) and transportation (0.0854). Further analysis using fishbone diagrams and FMEA reveals that denting defects represent the most significant failure mode, yielding the highest Risk Priority Number (RPN) of 336. Based on the proposed improvement initiatives, the company has the potential to eliminate all non-value-added (NVA) activities and substantially reduce necessary non-value-added (NNVA) activities. The implementation of these recommendations decreases the total production lead time from 2,435.59 minutes to 1,870.41 minutes, while increasing the proportion of value-added (VA) activities from 40.86% to 46.34%. These results demonstrate that the integration of Lean Six Sigma and FMEA provides an effective and systematic approach to improving process efficiency and product quality in aluminum extrusion manufacturing.  
SCM For Monitoring Stock And Demand Online Shop Products Nadrah Umi Kalsum; Riki Andri Yusda; Sumantri
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.297

Abstract

The rapid growth of e-commerce has increased the complexity of managing product inventory and responding to dynamic customer demand. Ineffective coordination between stock availability and demand forecasting can lead to stock shortages, overstock conditions, delayed order fulfillment, and reduced customer satisfaction. This study aims to develop and implement a Supply Chain Management (SCM) system for monitoring stock levels and demand for online shop products in real time. The proposed system integrates inventory management, demand tracking, and reporting functions to provide accurate and timely information that supports operational decision-making. The system records product inflows and outflows, monitors current stock conditions, analyzes demand trends based on transaction data, and generates informative reports to assist managers in planning procurement and replenishment activities. The implementation results demonstrate that the SCM-based monitoring system improves inventory visibility, enhances responsiveness to fluctuations in customer demand, and supports more effective stock control. Consequently, the proposed approach contributes to improving supply chain performance, minimizing inventory-related risks, and increasing the efficiency and competitiveness of online retail businesses.
Application of Simple Additive Weighting (SAW) for Best Decor at Mainaka Decoration Anggi Melisa Nasution; William Ramdhan; Zulkarnain Sirait
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.298

Abstract

The rapid development of the event decoration service industry demands companies to be able to provide optimal services and meet client needs quickly and objectively. Mainaka Decoration as a company engaged in the field of event decoration services faces problems in the process of selecting the best decoration concept which is still done subjectively based on the designer's experience and intuition. This condition causes the decision-making process to be less effective, not standardized, and has the potential to cause a mismatch between client expectations and the decoration results provided. Therefore, a system is needed that is able to assist management in determining the best decoration concept based on measurable criteria. This study aims to design and build a Decision Support System (DSS) in selecting the best decoration concept by applying the Simple Additive Weighting (SAW) method. The SAW method is used to rank several alternative decoration concepts based on predetermined criteria, namely budget, aesthetics, work time, theme suitability, and durability of decoration materials. This study uses a quantitative method with data collection techniques through observation, interviews, and literature studies. The system is designed using Unified Modeling Language (UML) and implemented web-based using PHP programming language with MySQL database. The results of this study indicate that the application of the SAW method in the Decision Support System is able to provide recommendations for the best decoration concepts objectively and systematically according to client needs. The system built can help increase effectiveness and efficiency in the decision-making process, minimize subjectivity in assessing decoration alternatives, and improve service quality and customer satisfaction at Mainaka Decoration
Stunting Risk Cluster Analysis In Petatal Plantation Village Using K-Means Clustering Approach Dewi Andini Putri; Dewi Maharani; Ahmad Muhazir
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.299

Abstract

Stunting remains a critical public health issue in rural communities, particularly in plantation-based villages where socioeconomic conditions, nutrition access, sanitation, maternal knowledge, and health service utilization may vary across households. This study aims to analyze stunting risk clusters in Petatal Plantation Village using the K-Means Clustering approach. The research applies a quantitative data mining method by grouping household or child-level data based on several risk indicators, including child age, nutritional status, birth weight, exclusive breastfeeding history, maternal education, household income, access to clean water, sanitation conditions, immunization status, and frequency of visits to health service facilities. The K-Means algorithm was used to classify the data into several clusters representing different levels of stunting risk. The clustering process involved data preprocessing, normalization, determination of the optimal number of clusters, model implementation, and interpretation of cluster characteristics. The results of the study are expected to identify distinct risk groups, such as low-risk, moderate-risk, and high-risk clusters. Households in the high-risk cluster are generally characterized by limited economic capacity, poor sanitation, low maternal nutrition awareness, inadequate dietary diversity, and irregular access to health services. Meanwhile, the moderate-risk cluster may show partial vulnerability, while the low-risk cluster reflects better nutritional and environmental conditions. This clustering analysis provides a data-driven basis for village authorities, health workers, and local stakeholders to design more targeted stunting prevention programs. Instead of applying a uniform intervention, the proposed approach supports priority-based decision-making according to the specific characteristics of each risk cluster. Therefore, K-Means Clustering can be considered an effective analytical tool for mapping stunting vulnerability and strengthening evidence-based public health intervention strategies in Petatal Plantation Village.
K-Means Clustering as a Method for Identifying Consumer Behavior Patterns In Taqimart Urba Yesha Hasibuan; Arridha Zikra Syah; Sudarmin
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.300

Abstract

Taqimart, as a grocery store developing amid competition from modern retail, still faces challenges in analyzing consumer data, where the transaction data generated has not been optimally utilized to understand consumer shopping behavior patterns. This study aims to identify and classify the shopping behavior patterns of Taqimart consumers by applying the K-Means Clustering method. The data used consist of consumer data and transaction data that reflect shopping behavior characteristics, such as purchase frequency and total spending. The K-Means Clustering method is used to group consumers into several clusters based on the similarity of their shopping behavior. The results of this study can provide more structured consumer segmentation information, helping Taqimart develop more targeted marketing strategies, increase the effectiveness of promotions, and support data-driven business decision-making to enhance business competitiveness
E-CRM System Development Strategy As An Effort To Improve Computer Services And Sales Azmi Ainul Rifky; Herman Saputra; Elly Rahayu
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.301

Abstract

The rapid development of digital technology has significantly influenced business strategies and customer behavior, particularly in the context of service and sales management. However, many businesses still face challenges in managing customer data that is not integrated, which affects service quality and marketing effectiveness. This study aims to design and implement an Electronic Customer Relationship Management (E-CRM) system to improve customer service and sales performance. The research method used is descriptive analysis with data collection conducted through observation and interviews to identify system requirements and existing problems. The system was developed using a web-based approach to integrate customer data, transaction history, and communication processes. The results show that the implemented E-CRM system is able to improve customer data management, facilitate customer behavior analysis, enhance service responsiveness, and support more targeted marketing strategies. In addition, the system contributes to increasing customer satisfaction and loyalty through better interaction and personalized services. In conclusion, the development of a web-based E-CRM system provides an effective solution for optimizing customer relationship management and improving business competitiveness in the digital era.
Building a Product Category System for a Shopping App with Inheritance in Java Saluky Saluky; Revi Injani; Citta Amelia
ITEJ (Information Technology Engineering Journals) Vol. 9 No. 2 (2024): December
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v9i2.155

Abstract

This article discusses the development of a product category system for an online shopping application using the concept of inheritance in Java programming. In a shopping application, efficient product category management is essential to make it easier for users to search for items according to their type and preferences. This system is designed using an inheritance structure to define various product categories hierarchically. The parent category will provide basic attributes and methods, while the child category will inherit and develop these functionalities according to the specific needs of the product. The use of inheritance allows for more modular and manageable coding, and increases the ability to expand the application in the future. With this system, the shopping application not only simplifies the product search process but also allows for more structured and flexible product grouping. The implementation in Java is done using base classes and child classes, as well as a polymorphism mechanism to optimize interactions between product category objects. The results of this study indicate that an inheritance-based approach in Java can improve the efficiency and readability of code in developing shopping applications with dynamic and easily developed product categories.
Clustering Analysis of Cocoa-Producing Areas Using the Gaussian Mixture Model Algorithm (A Case Study in Southeast Aceh Regency) Pathia Pathia; Taufiq Taufiq; Sujacka Retno
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/93m0r329

Abstract

This study aims to identify and cluster agricultural areas in Southeast Aceh Regency using the Gaussian Mixture Model (GMM) algorithm. The dataset consists of village-level agricultural data, including land area, production volume, productivity, and the number of farmers. To ensure comparability across variables, Z-Score normalization was applied. The optimal number of clusters was determined using the Bayesian Information Criterion (BIC), resulting in three distinct groups: high, medium, and low production areas. Clustering performance was evaluated using the Silhouette Score (0.3893) and the Davies-Bouldin Index (0.8548), indicating moderate clustering quality with reasonable separation between clusters. To improve accessibility and practical use, a web-based information system was developed to visualize agricultural data, clustering outcomes, and evaluation metrics interactively. These findings highlight the value of GMM-based machine learning in supporting data-driven decision-making and prioritizing agricultural development efforts by local governments.

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