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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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Sentiment Analysis of Instagram Users Toward the Animated Film Merah Putih: One For All Using the Naïve Bayes Method Nabila Wafa; Yoannes Romando Sipayung
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.v11i1.295

Abstract

Instagram is a widely used social media platform where users express opinions on various forms of entertainment, including animated films. The animated film Merah Putih One For All, as one of Indonesia’s local animation works, has received diverse responses from Instagram users that reflect positive, neutral, and negative sentiments. This study aims to analyze and classify the sentiment of Instagram user comments related to the film using the Naïve Bayes algorithm. This research utilized 200 Instagram comments, which were categorized into three sentiment classes. Text preprocessing was applied prior to classification. The dataset was evaluated using the Split Validation method with a 60:40 ratio, where 60% of the data were used for training and 40% for testing. Model performance was assessed using a confusion matrix along with accuracy, precision, and recall metrics. The experimental results show that the Naïve Bayes algorithm achieved an accuracy of 76,67%. The positive sentiment class obtained the highest recall value of 100%, followed by the neutral class with 83,33%, while the negative sentiment class recorded the lowest recall at 46,67%. These findings indicate that the model performs better in identifying positive and neutral sentiments than negative sentiment. Overall, the results demonstrate that the Naïve Bayes algorithm is sufficiently effective for sentiment analysis of Instagram comments, although further improvements are required to enhance the classification of negative sentiment.  
Analysis of Waste in Finished Feed Product Warehouse Using Lean Warehouse Approach Pratiwi Candra Kurniawati Yolanda; Dira Ernawati
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.v11i1.296

Abstract

Inefficient warehouse operations often generate various forms of waste, including excessive waiting times, unnecessary transportation, and redundant activities, which ultimately increase lead times and reduce operational performance. Despite the critical role of finished feed product warehouses in maintaining supply chain responsiveness, studies addressing waste reduction in this context remain limited. This study aims to identify non-value-added activities in the finished feed product warehouse process and propose improvement strategies to enhance warehouse efficiency using a Lean Warehouse approach. The study integrates Value Stream Mapping (VSM), Value Stream Analysis Tools (VALSAT), fishbone diagram analysis, and the 5W+1H method to systematically identify, analyze, and eliminate waste within warehouse operations. The findings reveal that the current warehouse process has a total lead time of 447 minutes. Following the implementation of the proposed improvements, the future-state lead time is reduced to 269 minutes, representing a 39.8% reduction. In addition, Process Cycle Efficiency (PCE) improves from 20.81% to 34.57%, indicating a 13.76 percentage-point increase in operational efficiency. The identified improvements primarily address delays, excessive movement, and procedural redundancies in the warehouse flow. These results demonstrate that the Lean Warehouse approach provides an effective framework for minimizing waste and improving the performance of finished feed product warehouses. The study contributes practical insights for warehouse managers seeking to enhance operational efficiency and supports the broader application of lean principles in warehousing environments.
Customer Relationship Management (CRM) Strategies For Enhancing Promotional Effectiveness: A Case Study Of Zakiyah Shop Era Fazira; William Ramdhan; Akmal
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.v11i1.302

Abstract

The rapid development of information technology has encouraged businesses to adapt to improve the effectiveness of marketing and customer service. Toko Zakiyah Serba Ada, a retail business specializing in fashion and daily necessities, still faces various challenges, such as manual promotional strategies, limited access to product information, and the lack of an integrated system for managing customer data and promotional activities. These conditions result in low promotional effectiveness and suboptimal customer relationships. This study aims to analyze the implementation of a web-based Customer Relationship Management (CRM) system to support more effective promotional strategies and increase customer loyalty. The research method used is a qualitative method with data collection techniques through interviews, observations, and literature studies. The data used comes from customer transaction history for the period November 2024 to October 2025. The results show that the implementation of a web-based CRM system is able to integrate customer data, transactions, and promotions into one structured system. This system provides various features such as live chat, messages, shopping carts, promotions, and reviews that support two-way interactions between the store and customers. In addition, the CRM system also enables customer behavior analysis so that promotional strategies can be implemented in a more targeted and data-driven manner. The conclusion of this study is that implementing web-based CRM positively impacts the effectiveness of promotional strategies and improves the quality of store-customer relationships. Therefore, a CRM system can be an appropriate solution for facing increasingly competitive business environments in the digital age.
Application of The SCM Concept in Maintaining Smoothness of Cabinet Production Chalista Alfina; Maulana Dwi Sena; 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.v11i1.303

Abstract

This study examines the application of Supply Chain Management (SCM) concepts in maintaining the smoothness and efficiency of cabinet production processes. In highly competitive manufacturing environments, production continuity is crucial to meeting customer demand and ensuring operational effectiveness. This research adopts a qualitative descriptive approach, collecting data through observations, interviews, and documentation within a cabinet manufacturing company. The findings reveal that the implementation of SCM practices such as supplier integration, inventory control, demand forecasting, and coordinated production planning significantly enhances material availability, reduces production delays, and minimizes operational disruptions. Furthermore, effective collaboration among supply chain actors improves communication flow and responsiveness to market changes. The study concludes that adopting SCM concepts not only ensures smoother production processes but also contributes to cost efficiency and improved customer satisfaction. These results highlight the strategic importance of SCM in strengthening manufacturing performance and sustaining business competitiveness in dynamic industrial environments..
Supply Chain Risk Mitigation Analysis Using the House of Risk (HOR) and Interpretive Structural Modeling (ISM) Methods Approach Najwa Ratna Dewanty; Dira Ernawati
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.v11i1.304

Abstract

Supply chain disruptions have become increasingly complex due to uncertainty in global markets, operational inefficiencies, and external environmental factors. Effective risk mitigation strategies are therefore essential to ensure supply chain resilience and sustainability. This study aims to analyze and prioritize supply chain risk mitigation strategies using an integrated approach of House of Risk (HOR) and Interpretive Structural Modeling (ISM). The HOR method was employed to identify risk events and risk agents, evaluate their aggregate risk potential, and determine priority mitigation actions based on their effectiveness-to-difficulty ratio. Subsequently, ISM was applied to analyze the interrelationships among selected mitigation strategies and to establish a hierarchical structural model for implementation. The results indicate that several dominant risk agents significantly influence supply chain performance, including supplier delivery delays, inaccurate demand forecasting, and inadequate information sharing. The HOR analysis identified the most critical preventive actions, while the ISM results revealed the driving and dependent factors among mitigation strategies, enabling a systematic implementation sequence. The integration of HOR and ISM provides a comprehensive framework for both quantitative risk prioritization and structural strategic planning. This study contributes to supply chain risk management literature by offering a practical decision-support model for organizations seeking to enhance supply chain resilience through structured and prioritized mitigation planning.
Analysis of Waste in Plastic Packaging Products Warehousing Activities Using a Lean Warehousing Approach Clairine Aurellia Sanjaya; Dira Ernawati
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.v11i1.305

Abstract

The manufacturing industry is required to meet customer demands quickly and precisely in the midst of fierce competition, so efficient operations are needed. PT. XYZ as a plastic packaging manufacturer is facing inventory buildup due to the uncertainty of delivery schedules. This condition leads to various wastes, including excess capacity, product damage, and unnecessary movements, which ultimately increase lead time and reduce warehouse efficiency. This research intends to pinpoint inefficiencies in warehouse operations by employing a lean warehousing strategy with the WAM and VALSAT techniques, while suggesting enhancements through a 5W+1H analysis. The findings indicate that the dominant wastes are inventory (20,21%), overproduction (19,60%), and defects (17,24%). The improvement proposal is focused on improving inventory management through the implementation of a real-time-based barcode/RFID system, which is supported by standardization of work procedures, routine evaluation, layout optimization and determination of storage time limits. The proposed improvements were able to increase efficiency by reducing the processing time by 827 minutes, from 1.881 minutes to 1.054 minutes. The number of activities was also reduced from 43 to 35 activities. In addition, process efficiency has increased, as shown by an increase in Process Cycle Efficiency (PCE) by 1,59%, from 2,02% to 3,61% in warehouse operations
Application of MobileNetV3 and U-Net Architectures in Deep Learning for Brain Tumor Detection Hisanah Nakhwah Aulia Faruly; Lindawati; Sarjana Sarjana
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.v11i1.307

Abstract

Brain tumors are neurological conditions that affect essential bodily functioning, necessitating a prompt and precise diagnosis. Brain tumors are frequently detected with magnetic resonance imaging (MRI), but manual interpretation is laborious and heavily reliant on radiologists' skill. Deep learning has become a popular method for improving medical image analysis. This paper suggests a hybrid deep learning architecture for MRI image-based brain tumor identification that combines MobileNetV3 and U-Net. U-Net was utilized for tumor region segmentation, and MobileNetV3 was utilized for tumor categorization. A publicly available Kaggle Brain Tumor MRI dataset and the BraTS 2021 Dataset were used in the study. The accuracy, precision, recall, F1-score, confusion matrix, AUC-ROC, dice coefficient, and intersection over union (IoU) metrics were used to assess the model's performance. The MobileNetV3 model exhibited good precision, recall, and F1-score values along with 95% classification accuracy. Furthermore, outstanding classification performance was demonstrated by the AUC-ROC values, which reached 0.99 and 1.00. The U-Net model received a Dice Coefficient of 0.9404, an IoU score of 0.8940, and a validation accuracy of 0.9936 for segmentation. For precise brain tumor detection and visualization on MRI images, the suggested hybrid architecture successfully integrates classification and segmentation tasks.

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