ITEJ (Information Technology Engineering Journals)
Information Technology Engineering journals is a journal of research results in the field of software engineering whose cover all aspects of software engineering and related hardware-software systems issues. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Topics of interest include, but are not limited to: Media in education, E-government, E-Commerce, Software Architecture
Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon
Application of MobileNetV3 and U-Net Architectures in Deep Learning for Brain Tumor Detection
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.
Copyright (c) 2026 ITEJ (Information Technology Engineering Journals)
Analysis of Waste in Plastic Packaging Products Warehousing Activities Using a Lean Warehousing Approach
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
Supply Chain Risk Mitigation Analysis Using the House of Risk (HOR) and Interpretive Structural Modeling (ISM) Methods Approach
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.
Application of The SCM Concept in Maintaining Smoothness of Cabinet Production
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..
Customer Relationship Management (CRM) Strategies For Enhancing Promotional Effectiveness: A Case Study Of Zakiyah Shop
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.
E-CRM System Development Strategy As An Effort To Improve Computer Services And Sales
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.
K-Means Clustering as a Method for Identifying Consumer Behavior Patterns In Taqimart
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
Data Mining, K-Means Clustering, Consumer Behavior Patterns, Taqimart.
Stunting Risk Cluster Analysis In Petatal Plantation Village Using K-Means Clustering Approach
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.
Application of Simple Additive Weighting (SAW) for Best Decor at Mainaka Decoration
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
SCM For Monitoring Stock And Demand Online Shop Products
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.
Analysis of Waste in Finished Feed Product Warehouse Using Lean Warehouse Approach
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.
Sentiment Analysis of Instagram Users Toward the Animated Film Merah Putih: One For All Using the Naïve Bayes Method
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.
Waste Analysis in the Aluminum Extrusion Process utilizing Lean Six Sigma and Failure Mode and Effect Analysis (FMEA)
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.
Implementation of Ethereum-Based Blockchain Technology for ADS-B Data Security and Validation
Automatic Dependent Surveillance–Broadcast (ADS-B) has significantly enhanced air traffic monitoring by enabling real-time broadcasting of aircraft positions and identifiers. However, it is intrinsically susceptible to spoofing, replay, and tampering attacks because to its lack of cryptographic protections. This paper introduces a blockchain-based ADS-B validation system for safe, decentralized data authentication that makes use of Ethereum smart contracts and MetaMask. The suggested system uses Solidity-coded rules to enforce logical limitations on altitude changes, timestamp order, and geographic displacement in order to validate incoming flight messages. Every data transaction is protected by two layers of security: the smart contract's automated detection and MetaMask's manual permission. This combines operational control with the immutability of blockchain technology by guaranteeing that even reported anomalies cannot be committed without human consent. The OpenSky Network flight data was used to test the system, and 56 attack simulations in the spoofing, replay, and tampering categories were run. All accepted anomalies were purposefully allowed to test forensic transparency, and the contract obtained a 92.9% detection rate. All transaction information were retained in Ethereum's transparent ledger, enhancing its suitability for incident investigation and regulatory compliance. The findings support blockchain's applicability in preventing unwanted changes to aircraft telemetry. Stricter timestamp constraints, machine learning for anomaly detection, and interaction with international aircraft registries to better spoofing detection are possible future improvements. This approach combines the practical needs of air traffic with the potential advantages of blockchain technology.
Copyright (c) 2025 ITEJ (Information Technology Engineering Journals)
Tsukamoto Fuzzy Logic Method for Determining Mental Health Levels of Final-Year University Students
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.
Integration of Multi-Item EOQ and EPQ to Minimize Total Inventory Cost of Outsole Raw Materials
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.
Smart Energy Coordination Platform for Rural Public Facilities: Multi-Facility Load Optimization and Smart Control System
Rural public facilities in Indonesia face significant energy efficiency challenges due to inadequate management systems, aging infrastructure, and limited technical capacity. This study develops a smart energy coordination platform integrating IoT sensors and wireless communication networks for five public facilities in Petumbukan Village, North Sumatra. Six-month continuous monitoring using power quality analyzers and IoT data loggers revealed 310.7 kWh/day total consumption with 0.43 load factor, 40.7 kW peak demand, and 1.27 diversity factor across facilities. Comprehensive energy audit identified 17.7 kW total losses (43.5% of peak load), with lighting inefficiency representing the primary concern at 8.5 kW (20.9% of system load). The proposed four-layer IoT architecture implements smart meters, environmental sensors, LoRaWAN communication, and edge computing devices for real-time monitoring and automated control. Multi-facility coordination algorithms leverage load diversity to achieve 22% annual energy savings (56,327 kWh) through optimized scheduling and demand management. System performance demonstrates 15% peak demand reduction, load factor improvement from 0.43 to 0.52, and sub-5-minute response times for automated load adjustments. The platform provides a technically feasible and replicable framework for rural electrification optimization in developing countries, addressing the gap between urban-focused smart grid solutions and rural infrastructure constraints.
Design of a Dual Band Yagi Antenna For Amateur Radio At a VHF Frequency of 143 MHz
Abstract—This research discusses the design and realization of a four-element Yagi antenna optimized for amateur radio communications in the VHF 143 MHz frequency band. The research process was carried out through simulations using CST Studio Suite software, antenna prototype assembly, and laboratory and field testing. The simulation results show that the antenna has a return loss of -22.14 dB, VSWR of 1.168, impedance of 50.23 Ω, and a gain of 7.613 dBi. After the antenna was realized, measurements using NanoVNA resulted in a return loss of -12 dB, VSWR of 1.4, impedance of 49.41 Ω, and a gain increasing to 10.9 dBi. Field testing proved that the antenna is able to work stably at a frequency of 143 MHz with an effective communication range of more than 10 km, although signal quality decreases at longer distances due to environmental obstacles and interference. Overall, the results of this study indicate that the designed four-element Yagi antenna meets the technical criteria of low VSWR, impedance approaching 50 Ω, adequate return loss, and high gain, making it suitable for use in supporting long-distance communications in amateur radio systems.
Application Of Certainty Factor In Detecting Learning Concentration Disorders In Children At School
Learning concentration disorders are a fairly common problem in elementary schools and can have a significant impact on students' academic performance. At UPTD SDN 015861 Bunut Seberang, initial observations indicate that around 20–25% of students experience symptoms such as distractibility, difficulty focusing, hyperactivity, and decreased motivation to learn. Current interventions are limited, lack a structured system, and rely on teachers' subjective assessments. This study aims to develop an expert system based on the Certainty Factor method to detect learning concentration disorders more objectively and accurately. The research method uses a quantitative approach with stages of problem identification, data collection through observation and interviews, analysis, system design using UML, and implementation based on PHP and MySQL. The system processes student symptom data, calculates the confidence level of the diagnosis, and provides follow-up recommendations for teachers and parents. The implementation results show that the system can efficiently identify potential learning concentration disorders, assist teachers in decision-making, and serve as a model that can be adapted by other schools facing similar issues.
Performance of Self-Compacting Concrete (SCC) Mixes with Different Brands of Superplasticizers
Self-Compacting Concrete (SCC), classified as High-Performance Concrete (HPC), is a type of cement-based concrete characterized by its high flowability, ability to compact without vibration, and resistance to segregation. These properties make SCC ideal for structures with dense reinforcement or complex cross-sections. The use of a Superplasticizer admixture is essential in producing SCC with the required performance. This study aimed to analyze the influence of three Superplasticizer brands—NAPTHA 600D, NORMET TanCem 60RP, and CONSOL P200-R 1S—on the characteristics and performance of SCC. The experimental work was conducted at the Laboratory of Geotechnics, Tunnels, and Structures (BGTS) of the Road and Bridge Engineering Implementation Unit. Three dosage variations (0.5%, 0.7%, and 1.0%) were tested to evaluate homogeneity, slump flow, and compressive strength. The slump flow was controlled according to the Bina Marga General Specifications 2018 Revision 2 (550–850 mm), and the compressive strength was targeted at 33 MPa at 7 days. The results showed that all Superplasticizers produced different flowability and strength characteristics. At 1.0% dosage, each brand achieved the required slump flow and compressive strength, with minor variations in homogeneity and bleeding behavior.
Implementation Of E-SCM Efforts To Improve Distribution Management Efficiency
This research focuses on the design and implementation of an Electronic Supply Chain Management (E-SCM) system to enhance efficiency and responsiveness in distribution and inventory management within the agricultural sector. The study addresses recurring challenges in supply chain operations, including inefficiencies in inventory tracking, lack of coordination and transparency between suppliers and customers, and difficulty in forecasting demand due to fluctuating market conditions, seasonal cycles, and environmental factors. These challenges often result in stock imbalances, delayed deliveries, increased operational costs, and diminished customer satisfaction. The proposed solution is a web-based platform integrating administrators, suppliers, and customers into a single digital ecosystem. It allows users to place orders, monitor real-time stock availability, and generate dynamic reports, thereby improving coordination, transparency, and decision-making across the supply chain. Adopting a qualitative methodology, data were collected through direct observation, structured interviews, document analysis, and a literature review. The system was developed using PHP for server-side programming, MySQL for database management, and Visual Studio Code as the primary development environment. System validation through black-box testing confirmed that all functional and usability requirements were met. The implementation results demonstrate that the E-SCM system improves distribution management efficiency by approximately 35%, reduces manual inventory errors by over 40%, and significantly enhances responsiveness to demand fluctuations. Additionally, it strengthens engagement between suppliers and customers by enabling transparent communication and immediate access to accurate data. The findings indicate that E-SCM adoption can serve as a strategic enabler for small and medium-sized enterprises (SMEs) to optimize supply chain performance, reduce logistical inefficiencies, and achieve sustainable growth in a competitive digital economy
Implementation Of SCM In The Mattress Manufacturing Industry
The manufacturing industry is experiencing rapid growth, compelling companies to adopt integrated and technology-driven approaches in managing production and supply chain operations. In the springbed manufacturing sector, where demand is consistently high from household, hospitality, and institutional markets, maintaining efficient supply chain management is essential for sustaining competitiveness and meeting fluctuating market needs. However, many enterprises in this sector still rely on manual processes for inventory tracking, stock reporting, and procurement, which often lead to inefficient resource utilization, increased susceptibility to human error, delays in raw material replenishment, and a decline in customer satisfaction due to unfulfilled orders. This study addresses these challenges by implementing and evaluating a web-based Supply Chain Management (SCM) system as a strategic solution to enhance efficiency, accuracy, and real-time operational control in inventory and distribution processes. Using a qualitative research approach, data were collected through direct observation, structured interviews with key stakeholders, and comprehensive documentation reviews over the course of one year. The system was developed using PHP for backend functionality and MySQL for database management, with the design structured through Unified Modeling Language (UML) to align with specific business workflows and operational requirements. The resulting SCM system integrates supplier order tracking, raw material inventory monitoring, production scheduling, and customer order management into a centralized platform. By automating tasks that were previously manual, the system reduces discrepancies in stock reporting, enhances the traceability of raw materials and finished products, and supports timely decision-making. Additionally, it enables remote access to real-time data, providing improved operational visibility and managerial control. System testing, conducted through iterative validation and performance assessments, demonstrated measurable improvements in data accuracy, inventory turnover speed, and customer order fulfillment rates. The findings suggest that the adoption of a tailored, web-based SCM system can significantly improve operational efficiency for small-scale manufacturing enterprises. Moreover, the study emphasizes the role of digital transformation in strengthening business resilience and adaptability, particularly in mitigating the impact of supply chain disruptions. This research not only offers practical solutions for optimizing supply chain workflows but also serves as a reference framework for similar industries aiming to modernize their inventory and supply chain systems through integrated information technology solutions.
The Customer Relationship Management Concept Increases Customer Commitment
The advancement of information technology has encouraged business actors, including retail enterprises such as Toko Jaya Fashion, to adopt digital strategies in order to maintain and enhance customer commitment. Toko Jaya Fashion still operates using a manual sales system, resulting in low efficiency in customer data management and a noticeable decline in sales, particularly at the end of 2024. This study employs a qualitative research method by observing a single case in detail. This approach allows the researcher to gain a comprehensive understanding of the issues studied. This study aims to design and implement a web-based Customer Relationship Management (CRM) system as a solution to address these issues. The system is developed using the PHP programming language and MySQL database, and is equipped with features such as member cards, cashback programs, monthly sales reports, and online customer service. Based on the implementation results, the system significantly improves service quality, broadens marketing reach, and assists in managing customer data in a structured manner. It provides easier access to product information, offers special promotions for loyal customers, and facilitates customer feedback through WhatsApp integration. Moreover, the adoption of this CRM website has expanded product sales to customers outside the Meranti area, enabling Toko Jaya Fashion to reach more customers and increase overall satisfaction, thereby strengthening customer commitment.
Measuring The Efficiency of Social Assistance Recipients in the Family Hope Program Using the Data Envelopment Analysis Method
This study aims to measure the efficiency of aid distribution in the Family Hope Program (PKH) across six sub-districts in Medan Denai using the Data Envelopment Analysis (DEA) method. DEA allows a relative efficiency evaluation of multiple decision-making units (DMUs) by comparing inputs such as budget, number of officers, and recipients, against outputs like targeted beneficiary rate, distribution timeliness, and satisfaction level. The findings reveal that three sub-districts—Medan Tenggara, Tegal Sari Mandala I, and Tegal Sari Mandala II—achieved full efficiency with a score of 1. In contrast, Binjai, Denai, and Tegal Sari Mandala III were found to be inefficient due to higher input consumption not matched by proportional output. The study suggests that inefficient sub-districts can improve their performance by adopting the practices of efficient ones. These insights are expected to assist local governments in optimizing social assistance programs and ensuring better resource utilization.
Learning Media Application for Basic Digital Courses Using Augmented Reality with the Marker Based Tracking (MBT) Method
The rapid development of digital technology opens up opportunities to increase the effectiveness of learning media, one of which is by utilizing Augmented Reality (AR) technology. This study aims to develop an AR-based digital basic course learning media application with the marker-based tracking method. The materials presented include number systems, logic gates, boolean algebra, Karnaugh maps, and flip-flops. The development process is carried out through a waterfall approach which includes literature studies, needs analysis, system design, implementation, and testing. The results of the study show that the developed application is able to display 3D objects interactively and in real-time through features such as rotation, zoom, and audio, so as to improve students' understanding and interest in learning. The application of AR technology has proven to be an innovative and effective medium in conveying abstract concepts in digital learning.
Applying Supply Chain Management in a Web-Based Drug Inventory System
The advancement of information technology has driven digitalization across various sectors, including drug inventory management in the pharmaceutical field. Toko Obat Lamtama still faces challenges in stock management due to the absence of a computerized system, leading to risks of stock shortages, surpluses, and expired medications. This study aims to design and implement a web-based drug inventory information system integrated with the concept of Supply Chain Management (SCM) to improve operational efficiency and customer service. The methodology includes needs analysis, system design using UML, and implementation of a web-based system. The results show that the system can automate the processes of recording, monitoring, and ordering drugs more accurately, while also supporting faster and more precise decision-making. The implementation of SCM within this information system has also successfully reduced waste caused by unused medications and enhanced the efficiency of supply chain management.
Sharing SSH Threat Intelligence across Multiple Servers using WebSocket and Fail2Ban
This study presents a lightweight prototype designed to improve SSH brute-force defense by enabling collaborative IP blocking across multiple servers. The system integrates Fail2Ban with WebSocket to distribute banned IP addresses in real-time among trusted nodes eliminating the need for centralized infrastructure. The experiment was conducted on 3 virtual private servers (VPS), where one acted as the WebSocket server and the others as clients equipped with Fail2Ban. When an SSH brute-force attack is detected, the source IP is automatically shared across the network and blocked on all connected nodes. A qualitative observational approach was used to evaluate the system’s feasibility. Log data from the clients and server was analyzed to confirm the accuracy and consistency of IP synchronization. The results showed that banned IPs were propagated and enforced on all nodes within seconds of detection. These findings demonstrate the potential for decentralized, lightweight collaboration among SSH servers to enhance security without introducing complex infrastructure or external dependencies.
Improving Multi-Floor Factory Facilities Layout Using CRAFT Algorithm Method in UMKM Aneka Songkok Jombang
Facility layout design is a challenging issue in an industry. UMKM Aneka Songkok is a multi-floor building consisting of several departments. Observations show that the placement of work areas does not take into account the relationships between work stations, does not optimise space utilisation, and has an unsystematic material flow. This causes various problems, such as increased material transfer distances and greater occupational safety risks. To address these issues, facility layout improvements are necessary to enhance production effectiveness and efficiency. This study aims to redesign the facility layout of UMKM Aneka Songkok using the Craft algorithm method to improve the layout design, making the facility layout more effective and efficient, thereby organising the production process and minimising material movement distances between workstations. The research results show a reduction in distance per production run of 109.35 m and a reduction in distance per day of production of 387.3 m. The proposed layout was selected because it reduces material movement distances and improves material flow.
Forecasting of Palm Oil CPO Production Results at PTPN III Batang Toru Plantation Using The Autoregressive Integrated Moving Average Method
The increasing demand for palm oil as a raw material for food and energy industries has driven the need for accurate forecasting methods to optimize palm oil production management. This study aims to forecast Crude Palm Oil (CPO) production at PTPN III Batang Toru Plantation using the Autoregressive Integrated Moving Average (ARIMA) method. Monthly time series data from January 2020 to January 2024, including Fresh Fruit Bunches (FFB), loose fruit, and CPO yields, were analyzed to build the forecasting model. The Augmented Dickey-Fuller (ADF) test confirmed that the data is stationary without differencing. Based on the ACF, PACF, and white noise tests, the ARIMA(1,0,1) model was identified as the best fit. The forecasting results indicated a potential increase in CPO production from January 2025 to December 2026. However, alternative models like CPOF showed poor accuracy, with a high MAPE of 442.12%, suggesting the need for further model refinement. Despite limitations, the ARIMA method remains effective for short-term forecasting and supports data-driven decision-making in the plantation sector.
Performance Evaluation of Modulation and Power Allocation Effects on BER in NOMA with SIC over AWGN Channels
This study evaluates the performance of a Non-Orthogonal Multiple Access (NOMA) system implementing Successive Interference Cancellation (SIC) over Additive White Gaussian Noise (AWGN) channels, focusing on the effects of modulation schemes and power allocation on Bit Error Rate (BER). Three modulation schemes BPSK, QPSK, and 16QAM are studied in combination with different power allocation configurations (e.g., α = 0.1, 0.3) for two users in an OFDM-based NOMA downlink scenario. The signal superposition process is performed at the transmitter side, while SIC is applied at the receiver side to separate the user signals. The simulation results show that the BER performance is greatly affected by the modulation level and power allocation ratio. Lower-order modulations (such as BPSK and QPSK) provide better performance at low SNR, especially for users with poorer channel quality. On the other hand, improper power distribution can cause error propagation in the SIC process, degrading the demodulation accuracy. This study emphasizes the importance of selecting adaptive modulation schemes and power allocation strategies in NOMA system design. These findings provide important contributions to the development of future wireless communication systems, especially 5G and later generations, which demand high spectral efficiency and multi-user service reliability.