ITEJ (Information Technology Engineering Journals)

Hospital Recommendation and Mapping System Using Analytical Hierarchy Process Method and Dijkstra Algorithm Based on Website

2025-04-19T17:30:46SE Asia Standard Time

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

2025-04-19T17:22:41SE Asia Standard Time

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.

Analysis of Finished Product Warehouse Activity Flow Using Lean Warehouse Method

2025-04-20T15:00:14SE Asia Standard Time

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.

Warehousing Process Flow Analysis Using Lean Warehousing Approach For Waste Minimization At PT XYZ

2025-06-12T09:54:11SE Asia Standard Time

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.

A Analysis of Machine Maintenance System Using Preventive Maintenance Method with Always Better Control (ABC) Classification and Modular Design

2025-06-12T10:22:07SE Asia Standard Time

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.

Development of an Expert System for Identifying Students' Learning Styles Using the Euclidean Probability Method

2025-06-12T10:33:22SE Asia Standard Time

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 Preventive Maintenance Schedule of Xym1900 Vertical Roller Mill Crusher Machine using MTBF (Mean Time Between Failures) Method Based on RCA (Root Cause Analysis)

2025-06-12T10:52:38SE Asia Standard Time

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.

A Optimizing Helmet Production Quality Using The Six Sigma DMAIC Approach and FMEA

2025-06-12T11:10:45SE Asia Standard Time

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.

IoT-Based Cooking Oil Quality Monitoring System Using Thresholding Method on Android Application

2025-06-12T11:35:03SE Asia Standard Time

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 Application of Six Sigma and FMEA Methods for Defect Reduction in Woven Bag Production

2025-06-12T12:11:18SE Asia Standard Time

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.

Risk Analysis in the Palm Oil Harvesting Process Using the Mix Method (Case Study at PT XYZ South Kalimantan)

2025-06-12T12:23:36SE Asia Standard Time

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.

Analysis Of Defect Waste Reduction In Metal Forming Process Using Lean Six Sigma

2025-06-12T12:39:18SE Asia Standard Time

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.

Real-Time Object Detection for Smart City Surveillance and Traffic Management Systems

2025-06-12T15:57:27SE Asia Standard Time

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.

Classification of Family Hope Program Assistance Recipients Using the C4.5 Algorithm with Z-Score Normalization (Case Study in Atu Lintang District)

2025-06-16T10:51:59SE Asia Standard Time

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.