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PT. X is a textile company that produces greige fabric. One of the crucial stages in the greige fabric production process is inspection. Currently, the facility layout of the inspection area at PT X is still suboptimal. This results in longer fabric movement distances and increased time, leading to delivery delays and higher material handling costs. This study aims to design an optimal facility layout in the inspection area using the Activity Relationship Chart (ARC) method and to determine the distances, times, and costs for the proposed layout. In the initial production layout, the fabric movement distance reached 26,130 meters in one working day, and the fabric movement time was 936 minutes. Furthermore, the initial production layout generated a relatively high OMH of Rp 13,742,980 per month. After designing the facility layout using the Activity Relationship Chart (ARC) and Blockplan methods, the proposed layout was arranged according to the production process sequence, resulting in a smoother material flow. The proposed layout yields 16,830 meters of fabric movement per workday, 660 minutes of required time, and a monthly OMH of Rp 7,945,622, with an efficiency of 39.32%.

Heart disease is the leading cause of death in Indonesia and globally, necessitating an early screening system that is both accurate and clinically trustworthy. Although XGBoost demonstrates high predictive performance, its black-box nature undermines clinical trust, while low recall risks missed diagnosis an unacceptable consequence in population screening, especially in middle-income countries with limited healthcare resources. This study aims to develop a sensitive, transparent, and implementation-ready heart disease screening framework through the integration of SHAP-based Explainable AI. The CDC's Indicators of Heart Disease dataset (319,795 samples) was processed according to WHO/CDC standards, followed by class imbalance handling, hyperparameter optimization using RandomizedSearchCV, evaluation based on metrics sensitive to minority classes (AUC, recall, F1-score, AUC-PR), and threshold tuning to maximize recall. The baseline model showed a very low recall of 12.18%. After optimization and threshold tuning at 0.10, the model achieved recall >96% (96.79%) with a G-mean of 0.7477, supported by SHAP interpretation stability and the ability to capture non-linear interactions between advanced age (AgeCategory_WHO) and poor general health (GenHealth). SHAP analysis confirmed the alignment of dominant features with medical evidence, and its visualizations provide transparent explanations for healthcare professionals indicating its potential implementation as an interpretable clinical decision support system.

This research develops a machine learning model to classify customer loyalty using the Random Forest algorithm. Customer churn is a critical issue that reduces revenue and increases acquisition costs. A dataset of 50,000 customers from global e-commerce and subscription platforms was processed through data cleaning, imputation, outlier handling, and class balancing with SMOTE. The Random Forest model was built as a baseline and optimized with hyperparameter tuning. Evaluation using accuracy, precision, recall, and F1-score shows that the optimized model achieved 90.81% accuracy and 83.87% F1-score, outperforming previous Naïve Bayes approaches. Feature importance analysis highlights customer service interactions, lifetime value, and demographic factors as key predictors of churn. These findings demonstrate Random Forest’s effectiveness in churn prediction and provide practical insights for customer retention strategies

The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

This study focuses on the analysis of traffic density in Jember City, particularly at the Wirolegi Intersection, which is known to have a high density level. This condition often triggers congestion that hinders public mobility, so that appropriate and data-based handling efforts are needed. The purpose of this study is to identify and analyze the level of density at critical congestion points through a statistical approach using the One Way ANOVA method. The research method used is quantitative descriptive with a descriptive observational approach. Primary data was collected directly through a field survey in 2025 at the Wirolegi Intersection as one of 3 intersections in Jember City. The data obtained were then processed using normality tests, homogeneity tests, and One Way ANOVA with the help of SPSS software. The results of the analysis show that the traffic flow density on the three routes studied, namely Jalan Gunung Haryono, Jalan Brigjen Katamso, Jalan Yos Sudarso, does not show a significant difference. The significance value of the ANOVA test is greater than 0.05 which indicates the similarity of density levels between routes. Further testing (post hoc testing) also strengthens this finding. The conclusion of this study shows that handling congestion at the Wirolegi Intersection needs to be done comprehensively through traffic control and evaluation of the transportation system to improve smoothness and mobility in Jember City.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

This study aims to analyze the effectiveness of technical personnel management in handling main engine failures during extreme weather conditions at sea. The main focus of this study is to assess the extent to which technical competence, communication, coordination, and preparedness of technical personnel contribute to the effectiveness of damage management. The method used is a descriptive quantitative approach with data collection through a closed-ended questionnaire based on a Likert scale. A total of 100 respondents who are ship engineering officers currently studying at a maritime campus were sampled. The results of the analysis show that the four independent variables (technical competence, communication, coordination, and preparedness) simultaneously have a significant effect on the effectiveness of handling main engine failures. From the results of the multiple linear regression test, the coefficient of determination (R²) value of 0.897 indicates that 89.7% of the variation in damage management effectiveness can be explained by these four variables. This finding indicates that good technical personnel management plays a significant role in reducing the risk of engine system failure during extreme weather.

Coal quality must be controlled from the pit area to the ROM stockpile to ensure compliance with market specifications. However, hauling and stockpiling processes often lead to changes in coal characteristics. This study aims to analyze variations in proximate parameters between coal from Pit B1 and ROM Stockpile Km4 at PT Trisensa Mineral Utama and to identify factors contributing to these changes. The methodology includes field sampling at both locations, sample preparation based on ASTM standards, and laboratory testing of inherent moisture, residual moisture, ash content, volatile matter, and fixed carbon. The results indicate that coal undergoes quality changes after being stored in the stockpile, marked by a decrease in inherent moisture of 2.54% (from 17.64% to 15.10%), a decrease in residual moisture of 1.42% (from 17.17% to 15.75%), a slight reduction in ash content of 0.16%, a decline in volatile matter of 0.28%, and a reduction in fixed carbon of 0.18%. These changes are influenced by field conditions, material contamination during mining, rainfall, coal porosity, and handling activities at the stockpile. The findings highlight the need for improved sampling management, better surface water control, and stricter material handling procedures to minimize coal quality degradation.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

In the automotive manufacturing industry, efficiency in quality control is a crucial factor to ensure consistent product quality. Conventional Quality Assurance (QA) processes using manual record-keeping often face challenges such as delayed reporting, human errors, and difficulty in tracking historical data. This study aims to design and implement a QA performance dashboard based on digital forms at PT Dharma Polimetal, Tbk, to enhance efficiency in production quality control. The research methodology includes direct field observation, collection of production and QA data, mapping of QA process flows, interactive dashboard interface design, and system trial implementation. The designed dashboard focuses on four main aspects: QA Incoming, QC Line, QC Gate, and Customer Handling, each containing measurable performance indicators and quality parameters. Initial implementation results indicate significant improvements in QA process monitoring, faster reporting of inspection results, and easier real-time data access for both production teams and management. The system enables early detection of potential quality issues, supports rapid decision-making, and facilitates internal and external audits. Moreover, the use of digital forms within the dashboard enhances data accuracy, minimizes human error, and creates structured historical records for long-term analysis. This study provides a tangible contribution to the digitalization of QA systems, strengthening sustainable quality control practices in the automotive industry, thereby ensuring consistent productivity and product quality.

In manufacturing industries, machining processes play a critical role in ensuring product quality, precision, and production efficiency. However, in the production of swing arm parts, the machining process has been identified as a bottleneck due to its non-optimal cycle time. One of the main issues contributing to this inefficiency is the disorganized handling of circlip inner parts. These components are often scattered without a designated placement system, which creates significant difficulties for operators when retrieving and installing circlips onto the swing arm. Such abnormalities disrupt workflow continuity, extend production time, and reduce overall productivity. To address this challenge, a circlip feeder machine was designed as a supporting device to assist operators and streamline the machining process. The design emphasizes efficiency, integration, and systematic operation by utilizing readily available workshop materials. The developed feeder machine is equipped with a robust frame construction and has a storage dimension capable of accommodating up to 200 circlips. In addition, mechanical analysis demonstrates that the feeder structure can withstand a maximum applied force of 31,475 N, ensuring durability and reliability during operation. The introduction of this circlip feeder machine directly impacts the production process by reducing operator workload, minimizing delays caused by disorganized parts, and ensuring faster and more accurate installation of circlips. Consequently, the overall machining cycle time is shortened, thereby improving production flow and enhancing the efficiency of swing arm part manufacturing. Beyond immediate time savings, the use of the feeder machine contributes to better resource utilization, reduced ergonomic strain on operators, and improved consistency in product quality. This study highlights the significance of simple yet effective mechanical innovations in overcoming production bottlenecks and optimizing manufacturing processes in automotive component industries.

This study aims to improve ground handling personnel to support operational safety in the airside area of Yogyakarta International Airport. The main focus of this study is the level of compliance with the technical provisions stipulated in SKEP 140/VI/1999. The approach used is descriptive qualitative, with data collected through direct field observation, interviews with competent parties, and review of related documents. The results of the study indicate that there are still many violations of standard operating procedures (SOPs), especially related to the use and placement of Ground Support Equipment (GSE) that does not comply with standards and the operation of vehicles that do not reach properly. Some of the main causes identified are weak supervision, low work discipline, limited understanding of safety procedures, and a lack of routine training for officers. This has the potential to increase safety risks and disrupt smooth operations at the airport. As a solution, this study recommends corrective measures that include strengthening field supervision, ongoing training to improve understanding of safety procedures, implementing strict sanctions for violators, and reorganizing equipment storage and use areas. In addition, it is important to develop a stronger safety culture among officers and utilize technology to improve operational oversight. These measures are expected to create a safer and more efficient environment in the airside area of Yogyakarta International Airport. This study also identified the need for regular evaluation and updating of existing procedures to align with technological developments and international standards. By paying attention to safety aspects, operational efficiency can be maintained and the risk of accidents or incidents can be minimized. By strengthening safety management and work discipline, better performance levels and improved service quality in the aviation sector can be achieved.

This study aims to analyze the role of the Terminal Inspection Service (TIS) Unit in supervising landside facilities at Minangkabau International Airport. Landside facilities include passenger terminals, drop-off areas, parking lots, and other public spaces that serve as key interaction points between airport services and users. Effective supervision of these areas is essential to ensure safety, comfort, and order within the airport environment. A qualitative descriptive approach was employed in this research, with data collected through field observations, in-depth interviews with TIS personnel and related stakeholders, as well as documentation analysis of existing regulations and supervision procedures.The findings reveal that the TIS Unit plays a strategic role in maintaining service quality and the security of landside facilities. This role is carried out through regular monitoring activities, early detection of potential disruptions, and the handling of various violations in public areas. However, the implementation of TIS duties still faces several challenges. These include limited personnel, which restricts comprehensive supervision of all areas; slow response from relevant units when operational issues are reported; and low discipline and compliance among service users, which further complicates enforcement efforts.To enhance the effectiveness of supervision, this study proposes several recommendations. These include strengthening inter-unit coordination within the airport, providing ongoing training for TIS personnel to improve competency and responsiveness, and implementing an integrated digital reporting system to streamline reporting and follow-up processes. Such measures are expected to support more optimal and sustainable supervision of landside facilities at the airport.

This study aims to analyze the implementation of compensation for flight delays at Sultan Aji Muhammad Sulaiman Sepinggan International Airport, Balikpapan. This study uses a qualitative descriptive approach, with data collection techniques through direct observation, in-depth interviews with airline staff, and documentation during On the Job Training (OJT) activities. The main focus of the study is to evaluate the extent to which passenger rights have been fulfilled by airlines in accordance with applicable regulations, specifically the Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 89 of 2015 concerning Handling Flight Delays. The results of the study indicate that the implementation of compensation by several airlines is still not optimal. There are discrepancies between regulatory provisions and implementation in the field, such as late delivery of information, disproportionate compensation, and uneven service among airlines. Passengers often do not receive clarity regarding their rights, and complaint mechanisms are still limited. Factors causing delays include technical problems, bad weather, inefficient operational management, and limited number and capacity of human resources on duty. This study emphasizes the need to improve the internal and external communication systems of airlines so that information related to delays and compensation can be conveyed transparently and in a timely manner. In addition, airlines need to conduct regular evaluations of standard operating procedures (SOPs), strengthen customer service training for staff, and foster synergy between work units to improve responsiveness to unforeseen situations. Regulators and airport authorities need stricter oversight and the implementation of strict administrative sanctions for violations of passenger rights. These findings provide important contributions to the formulation of public policy in the air transportation sector and provide input for improving services that are more humane, accountable, and oriented toward customer satisfaction.