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
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.
Copyright (c) 2025 ITEJ (Information Technology Engineering Journals)
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.
Developing Pedagogically Aligned AR Media for Teaching Polyhedra in Junior Secondary Education
This study presents the design and evaluation of marker-based augmented reality instructional media using Unity and Vuforia to support polyhedra learning in junior high school geometry. The development followed the 4D model and was grounded in a five-phase learning trajectory consisting of exploration, manipulation, analysis, application, and reflection. The AR media features interactive 3D polyhedron models, net unfolding animation, and formula overlays, each activated through printed markers. The design was informed by students’ learning needs, particularly difficulties in visualizing shapes, distinguishing surface area from volume, and applying geometric formulas correctly. Expert validation using Aiken’s V confirmed strong instructional alignment. Student perception data, classroom observations, and teacher interviews indicated that the media enhanced spatial reasoning, improved engagement, and encouraged verbal mathematical discourse. The findings support the integration of AR into structured pedagogical sequences and demonstrate its potential to improve geometry instruction in diverse classroom settings.
augmented reality, geometry learning, Unity, Vuforia, spatial reasoning
Classification of Family Hope Program Assistance Recipients Using the C4.5 Algorithm with Z-Score Normalization (Case Study in Atu Lintang District)
One of the challenges in distributing social assistance is determining recipients who are truly eligible objectively and efficiently. This study develops a classification system for Family Hope Program (PKH) recipients by utilizing the C4.5 algorithm combined with Z-Score normalization to group citizen data into Eligible or Ineligible categories. The data used came from 551 residents of Atu Lintang District and included attributes such as house status, wall type, toilet facilities, occupation, and income. The research stages started from data preprocessing, attribute normalization, training the model, to evaluating its performance through metric such as accuracy, precision, recall, and F1-score. The evaluation results showed that the model achieved an accuracy of 94%, precision 0.96, recall 0.90, and F1-score 0.93 for the Eligible category. Based on the confusion matrix, the model was able to correctly classify 47 Eligible residents and 57 Ineligible residents. Analysis of the attributes showed that occupation was the most influential feature in the classification process. These results prove that the application of the C4.5 algorithm can be applied effectively to build a decision support system in the distribution of social assistance, and provide accurate and easy-to-understand results. This study also opens up opportunities for improving model performance by adding more data and testing with alternative algorithms going forward.
Real-Time Object Detection for Smart City Surveillance and Traffic Management Systems
The rapid growth of urban populations necessitates the development of intelligent systems to manage city infrastructure effectively. This study presents a real-time object detection framework designed to enhance surveillance and traffic management in smart city environments. Leveraging deep learning-based models, specifically optimized versions of YOLO (You Only Look Once), the system detects and classifies vehicles, pedestrians, and other urban entities from live video streams. The proposed method integrates edge computing for low-latency inference, enabling timely decision-making in scenarios such as traffic flow optimization, pedestrian safety, and anomaly detection. Experiments were conducted using publicly available urban datasets and real-time feeds from city surveillance cameras. The results demonstrate high detection accuracy (mAP > 85%) with inference speeds exceeding 30 FPS on edge devices, proving its suitability for deployment in resource-constrained environments. This work contributes to the ongoing advancement of intelligent urban infrastructure by providing a scalable and efficient solution for real-time object perception in smart cities.
Analysis Of Defect Waste Reduction In Metal Forming Process Using Lean Six Sigma
This study investigates the reduction of defect waste in the metal forming process at PT XYZ, with a specific focus on the production of the reff D nose component. Recognizing the critical importance of quality in the aerospace industry, where even minimal defects can compromise safety and operational integrity, this research applies Lean Six Sigma methodology to address the observed defect rate, which averaged 4.6% over the period 2018–2023—significantly exceeding the company’s target of 1%. A comprehensive DMAIC (Define, Measure, Analyze, Improve, Control) framework was employed to systematically identify, quantify, and analyze the sources of defects. Data were collected from both primary sources, including direct observations and stakeholder interviews, and secondary sources such as production records and defect reports. Advanced tools such as SIPOC diagrams, Pareto analysis, Interpretive Structural Modeling (ISM), and the 5 Whys method for Root Cause Analysis (RCA) were utilized to determine the critical factors affecting process performance. The analysis revealed that issues related to raw material quality, suboptimal process parameter settings, machine conditions, operator competency, and production environment stability are the primary contributors to the elevated defect rates. Based on these findings, targeted improvement strategies were proposed to optimize process efficiency and enhance product quality.
Risk Analysis in the Palm Oil Harvesting Process Using the Mix Method (Case Study at PT XYZ South Kalimantan)
The harvesting process in oil palm plantations is one of the critical stages that greatly affects the productivity and quality of production results. The risks that arise in this process, both from operational risks, environmental risks, and human resource risks can have a significant impact on operational efficiency. This study aims to determine the level of risk in the harvesting process of oil palm products at PT XYZ South Kalimantan and to find out the proposed improvements that are in accordance with the problems faced using the mixed method, which combines qualitative and quantitative techniques. The results of the study showed that there were three levels of risk with the highest risk values. Namely the risk of work accidents with a risk value of 20 and a high risk level, the risk of damage to harvesting equipment with a risk value of 10 and a moderate risk level, and the risk of replacing harvesting equipment with a risk value of 10 and a moderate risk level. These three risks are risks with the highest priority level of handling. For this reason, it is recommended to focus on human control and SOPs, control of tools and procedures, and change management. With proper risk management, the harvesting process can run more efficiently, safely, and sustainably.
A Application of Six Sigma and FMEA Methods for Defect Reduction in Woven Bag Production
This study aims to reduce the number of defects in the production of woven bags, which experiences product defects ranging from 4.1% to 5.4%. The method used is Six Sigma with the DMAIC (Define, Measure, Analyze, Improve, Control) approach to improve product quality. Root cause analysis was carried out using fishbone diagrams and Failure Mode and Effect Analysis (FMEA) which identified four main factors causing defects, namely humans, machines, materials, and methods. The results showed that the defects that had the most effect on the production process were perforated weaving, stretched weaving, and miss print. From the data analyzed for the period January to December 2024, a Defects Per Million Opportunities (DPMO) value of 6,746,655 and a sigma level of 3.97 were obtained, which shows that even though the production process is good enough, there is still room for improvement. Improvement recommendations include increased quality supervision and regular calibration of machines to achieve better quality targets. With the application of the Six Sigma method, it is hoped that the company can achieve a significant reduction in the defect rate and improve overall production performance.
IoT-Based Cooking Oil Quality Monitoring System Using Thresholding Method on Android Application
Repeated use of cooking oil can reduce its quality and have a negative impact on health, but these changes are difficult to recognize with the naked eye. This research aims to develop an Internet of Things (IoT)-based cooking oil quality monitoring system with a thresholding method connected to an Android application. The system is equipped with a TCS3200 sensor to detect color, an LDR sensor to measure clarity, and a pH sensor to monitor oil acidity. The readings from the three sensors are used to classify the oil quality into three categories : good, medium, and unfit. The final classification is determined using unified decision logic based on the majority values from the sensors. Tests were conducted on six oil samples with a reading frequency of 60 times per sample. Data was sent in real-time to firebase and displayed through an android app. In addition, the system sent automatic notifications via telegram for remote monitoring. The results show that one-time use oil is classified as good, 2 to 4 times use is moderate, and 5 times use is categorized as unfit for consumption. The system offers a practical and efficient solution for digital and real-time monitoring of oil quality.
A Optimizing Helmet Production Quality Using The Six Sigma DMAIC Approach and FMEA
This study was conducted to analyze the main causes of defects in the Bogo helmet production process at PT Sidoarjo Helmet. The research method used is Six Sigma with DMAIC (Define, Measure, Analyze, Improve, Control) approach combined with FMEA (Failure Mode and Effect Analysis) analysis. This research was conducted during the period January-December 2024 with a total inspection of 44,249 units and a total defect of 4,670 units. The purpose of this research is to identify the types of defects that occur most frequently, calculate DPMO values and sigma levels to determine the capability of the production process, and provide suggestions for improvement based on the priority of failures found. Based on the calculation results, the DPMO value is 433,860 which shows that the level of defects is still relatively high, with a sigma level of only 1.67. The results of the FMEA analysis show that bubbly paint has the highest RPN (Risk Priority Number) value, so it is the main focus of improvement efforts. Suggested corrective actions include improving operator training, strengthening work procedures, and controlling the quality of raw materials and production processes. At the control stage, the company is advised to implement regular quality control using control maps and internal audit systems. This research is expected not only to reduce the product defect rate, but also to improve the process efficiency and competitiveness of the company in the national helmet market.
A Analysis of Preventive Maintenance Schedule of Xym1900 Vertical Roller Mill Crusher Machine using MTBF (Mean Time Between Failures) Method Based on RCA (Root Cause Analysis)
The reliability and performance of heavy machinery are critical to maintaining consistent production in the cement industry. This study analyzes the preventive maintenance schedule of the XYM1900 Vertical Roller Mill crusher machine at PT XYZ, where frequent breakdowns have contributed to production inefficiencies and increased operational costs. The research employs the Mean Time Between Failures (MTBF) method to evaluate the reliability of key machine components and identify optimal maintenance intervals. Additionally, Root Cause Analysis (RCA) is used to trace the underlying causes of recurring failures. Maintenance and failure data were collected over a one-year period to calculate MTBF values for critical components, including the grinding roller, hydraulic system, and gearbox. RCA findings indicate that improper lubrication, delayed part replacements, and environmental factors such as dust contamination are the primary contributors to equipment failure. Based on the analysis, revised maintenance schedules were proposed to reduce unplanned downtime and extend component life. The results demonstrate that implementing a data-driven preventive maintenance plan can significantly enhance equipment reliability and support operational efficiency.
Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method
Learning styles play an important role in determining the most effective teaching strategies by aligning instructional methods with students’ individual preferences in receiving, processing, and understanding information. However, classroom teaching is often applied uniformly, disregarding the differences in learning styles among students. This can hinder the effectiveness of the learning process. This research aims to develop a web-based expert system using the Euclidean Probability method to identify the dominant learning styles of students at SMK Negeri 3 Lhokseumawe. The system processes input data representing student characteristics and calculates the proximity to each learning style category using the Euclidean distance formula. A total of 110 student data entries were analyzed, revealing that 32 students (29.09%) had a Visual learning style, 26 students (23.64%) were Auditory, 16 students (14.55%) were Read/Write, and 36 students (32.73%) were Kinesthetic learners. The results showed that the Kinesthetic learning style was the most dominant among students. Therefore, this expert system can efficiently assist in determining students' learning styles, allowing for quick and accurate identification of their learning preferences. This supports the development of more personalized and adaptive learning strategies, which are expected to enhance student engagement and learning outcomes.
A Analysis of Machine Maintenance System Using Preventive Maintenance Method with Always Better Control (ABC) Classification and Modular Design
Companies must pay special attention to the machines used to make goods because if the machine is damaged, the resulting product will be damaged or the product will take a long time in the production process. By performing regular machine maintenance, companies can maintain and extend the service life of the machine. PT XYZ is a company in Indonesia that has an installed production capacity of 29 million tons of cement per year. The company uses a continuous production system, namely ensuring that all machines are in good condition so that the production process does not experience delays or losses. The results of observations show that the packer operation is the production process with the longest waiting time, with the Roto packer 638PM1 being the machine with the longest waiting time. Corrective and preventive maintenance are two types of maintenance currently used, and the current preventive maintenance strategy is currently suboptimal. This study aims to develop an efficient preventive maintenance system by providing preventive maintenance recommendations using the design modularity method and the Always Better Control (ABC) classification. By combining the classification of machines between critical levels and the utility value of each machine component, operational efficiency will increase, the risk of production disruptions caused by critical component shortages and unnecessary storage costs will be reduced. By applying this method, the total maintenance cost incurred is Rp. 771,782,456, this result has a difference of Rp. 406,113,204 smaller than the total maintenance cost currently used by the company, which is Rp. 1,177,895,660. The results demonstrate that the proposed maintenance method is effective and feasible, achieving a 34.47% cost efficiency improvement over the company’s current maintenance system.
Warehousing Process Flow Analysis Using Lean Warehousing Approach For Waste Minimization At PT XYZ
This research aims to identify and minimize waste in the warehousing process at PT XYZ using the lean warehousing approach. The problems faced are non-value added activities such as waiting time, repetitive movements, and inappropriate material placement. The methods used include observation, interviews, data collection, and analysis using Value Stream Mapping (VSM) and Process Activity Mapping (PAM). The results showed five main wastes, namely waiting time, defects, unnecessary motion, and excess inventory with the highest weights of 3.75 to 3.25. Implementation of improvements reduced six NVA activities, decreased the number of activities from 31 to 25, and saved activity time from 338 minutes to 243 minutes. In addition, Process Cycle Efficiency (PCE) increased by 25.36% from 46.74% to 72.1%. This research is expected to improve warehouse operational efficiency and become a reference for the application of lean warehousing in the electricity sector.
Analysis of Finished Product Warehouse Activity Flow Using Lean Warehouse Method
This company is a light steel processing manufacturing company that specializes in the production of hollow structural sections. A warehouse is the main component that enables a company's operational functions. The role of the warehouse is not only focused on storage and distribution but also on operational efficiency which can have an impact on business competitiveness. Warehousing activities at this company ain’t been optimal due to waste. Waste that occurs such as searching for empty areas for storage, waiting time for the next process, repeated product inspections, has an impact on the flow of warehousing activities. This study seeks to assess the amount of waste and provide recommendations to minimize waste within the finished product warehousing operations at this company. This study uses the Lean Warehousing method consisting of Value Stream Mapping, Process Activity Mapping, and 5 Whys Analysis. The results of this study identify 4 wasteful activities with the highest time such as waiting for the customer’s cargo truck to pick up products (I10), moving products to the truck loading bed (C5), moving products from the production floor to the storage area (S5), waiting for the next process in order to move products (P2). The proposed improvements can reduce 8 non-value-added activities and trim activity time by 1015 minutes. The suggested improvements also increase Process Cycle Efficiency by 15.32% from 11.17% to 26.49%. This proves that the implementation of lean warehouse can improve efficiency and service quality as a whole.
Hospital Recommendation and Mapping System Using Analytical Hierarchy Process Method and Dijkstra Algorithm Based on Website
This study aims to develop a web-based hospital recommendation and mapping system utilizing Dijkstra's algorithm and the Analytical Hierarchy Process (AHP) approach. The AHP method is used to determine the best hospital based on various criteria, such as the number of inpatient rooms, number of doctors, security, hospital class type, and available facilities. Meanwhile, the shortest path to the destination is determined using Dijkstra's method to the recommended hospital. 12 hospitals served as the research subjects for a case study carried out in Lhokseumawe City. The results show that Cut Meutia Lhokseumawe Hospital is the best recommendation with the highest ranking value of 0.21. Furthermore, Dijkstra's algorithm for finding the shortest route showed that the distance from the starting point in Gampong Batuphat Timur to the recommended hospital is 17,375 meters. This developed system can assist the public in selecting the best hospital while also finding the fastest route to reach it.
Decision Support System for Determining the Eligibility of Economically Disadvantaged Students for Assistance Using the K-Means and MOORA Methods
The determination of recipients for student financial aid often faces challenges related to subjectivity in the selection process, necessitating a system capable of conducting objective analysis. This study develops a Decision Support System using the K-Means method to cluster students based on similar socioeconomic characteristics and the MOORA method to rank aid recipients more accurately. The K-Means method is applied to classify students into three clusters based on parental income, number of dependents, and academic performance. The clustering results indicate that students in Cluster 1 belong to the lowest economic group, making them the top priority in the selection process. Subsequently, the MOORA method is used to rank students within Cluster 1 based on an optimal value calculated from the weighted benefit and cost criteria. This calculation produces a priority ranking that is more transparent and objective compared to conventional selection systems. The findings show that the combination of K-Means and MOORA methods enhances accuracy in selecting aid recipients while reducing subjectivity in the selection process. With this system, schools or relevant institutions can expedite decision-making and ensure that aid is distributed to the students most in need. This study is expected to serve as a solution for educational institutions in improving the effectiveness and efficiency of student welfare programs.
Eligibility for Aid for Economically Disadvantaged Students