Publication Search

Background: The increasing complexity of industrial production systems requires machine condition monitoring solutions that are capable of operating in real time with high accuracy and responsiveness to support predictive maintenance strategies. Conventional cloud based monitoring systems often experience limitations such as high latency and dependence on stable network connectivity, which can delay decision making processes in critical industrial operations. Objective: This study aims to design and evaluate an Industrial Internet of Things (IIoT) architecture based on edge computing to improve the efficiency of industrial sensor data processing and accelerate anomaly detection in industrial machines. Method: The research adopts an experimental approach by designing a system architecture consisting of a sensor layer, edge computing layer, and cloud layer. Industrial sensors, including vibration, temperature, and current sensors, continuously collect machine operational data, which are then processed locally at the edge node using a machine learning based anomaly detection algorithm. System testing is conducted in a simulated manufacturing environment to evaluate performance based on latency, reliability, and detection accuracy. Results: The results indicate that edge based data processing significantly reduces latency compared with cloud-based processing and enables faster responses to machine condition changes. Additionally, the implemented anomaly detection algorithm achieves high accuracy in identifying abnormal sensor data patterns.

Background: The development of industrial technology in the Industry 4.0 era has encouraged the implementation of intelligent monitoring systems to improve machine reliability and operational efficiency. However, machine fault diagnosis systems based on artificial intelligence often face limitations in terms of interpretability because the models used are complex and difficult to explain. Objective: This study aims to develop a deep learning-based industrial machine fault diagnosis system integrated with an Explainable Artificial Intelligence (XAI) approach to improve diagnostic accuracy while providing interpretable insights for users. Method: The research method involves collecting data from industrial machine sensors consisting of vibration signals, temperature measurements, and acoustic signals, followed by data preprocessing and feature extraction processes. The processed data are then used to train a deep learning-based diagnostic model, after which explainability methods such as SHAP or LIME are applied to analyze the contribution of each feature to the model’s prediction results. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results: The results indicate that the proposed deep learning model achieves better performance compared to conventional machine learning methods such as Support Vector Machine and Random Forest. Furthermore, the explainability analysis reveals that vibration amplitude, increases in machine component temperature, and anomalies in acoustic signals are the main factors influencing machine fault detection. Therefore, the proposed system not only improves the accuracy of machine fault diagnosis but also provides transparency in the decision-making process, thereby supporting the implementation of predictive maintenance in smart manufacturing environments.

The decline in the performance of the die casting machine in 1998 after a long period of producing copper terminals showed dimensional defects and instability in product quality, especially in nozzle clogging, reduced copper flow, crust buildup on the gooseneck, plunger movement obstruction, and hydraulic pressure leaks. The purpose of planning the replacement and repair of die-casting machine components is to obtain replacement and repair costs, replacement and repair schedules for the period 2026, and the ratio of maintenance costs to profits. The replacement and repair planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, predicting component service life, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting the time to replace spare parts or reinstall repaired components, estimating replacement and repair costs for the period 2026, and calculating the ratio of replacement and repair costs to profits. The planning results obtained replacement and repair costs for the 2026 period are 75.770.000,- IDR with an estimated die casting machine rental rate of  1,500,000 IDR/hour which has the potential to be rented for 1,200 hours/year, and the ratio of maintenance costs to profits is 10,02 % which implies that the die casting machine with a capacity of 40 units/hour is still suitable for use and has the prospect of generating profits for the next few years.

The problem with a 4-stroke gasoline engine-driven electric generator is a decrease in tool performance due to wear on important components for the stator, cooling fan, air filter, oil filter, and gasket. The purpose of component replacement planning is to obtain replacement costs, maintenance schedules in 2027, and the ratio of maintenance costs to profits. The component replacement planning method includes collecting maintenance data from previous years, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component lifespan, predicting labor costs, predicting supporting equipment to be used in maintenance, predicting spare part replacement times, predicting maintenance costs in 2027, and calculating the ratio of maintenance costs to profits. The results of the replacement planning obtained maintenance costs in 2027 amounting to IDR 570,007,- with an estimated electric generator rental rate of IDR 30,000,-/hour which has the potential to be rented for 128 hours/year, a profit of IDR 3,840,000,- was obtained, and the ratio of maintenance costs to profits was 14.84% which implies that a 2.5 kW electric generator that uses gasoline-pertalite fuel of around 1.5 liters/hour at maximum power is still suitable for use in the next few years and has the potential to generate profits.

The problem with a passenger car with a capacity of 15 people lies in its unscheduled maintenance and having broken down on the road. The purpose of component replacement planning is to obtain component replacement costs, maintenance and repair schedules for the 2026 period, and the maintenance cost-to-profit ratio. The planning method includes collecting previous maintenance data, applying the inspection-replace-repair-overhaul (IRRO) method, evaluating component conditions, estimating component lifespan, estimating labor costs, estimating supporting equipment to be used in maintenance, estimating the time to replace spare parts or reinstall repaired components, estimating maintenance and repair costs for the 2026 period, and calculating the maintenance cost-to-profit ratio. The results of component replacement planning obtained costs for the 2026 period are IDR 11,780,000 with an estimated passenger car rental rate of IDR 800,000/24 ​​hours (day) which has the potential to be rented for 4,320 hours/year, and the ratio of maintenance costs to profits is 10.33% which implies that passenger cars with a capacity of 15 people are still prospective to generate profits and are suitable for use for the next few years.  

This study aims to design and develop an electrical installation panel by applying engineering safety principles in the water spray booth of a vehicle body painting system. Field observations indicate that electrical panels in painting rooms often do not meet safety standards, which can lead to short circuits and potential fire hazards. The research employed a Research and Development (R&D) method using a simplified Borg and Gall model consisting of nine stages, starting from problem identification to effectiveness testing. Expert validation results obtained a score of 87.5% (highly valid), practicality testing yielded 90% (very practical), and effectiveness tests showed an average current of 4.1 A, with both the MCB and emergency stop functioning optimally. The developed panel product is declared feasible to be used as a practical learning media for automotive electrical systems. Based on the test results, the panel product was declared suitable for use and can be used as a learning medium in automotive electrical practice, helping to increase understanding of the application of safety in electrical installations in the automotive industry.  

Unexpected downtime of a 2 kg/hour coffee grinder is crucial in cafe operations, thus less guaranteeing the availability of the grinder. The purpose of component replacement and repair planning is to obtain a prediction of the maintenance and repair schedule and costs in the 2026 period. The component replacement planning method includes collecting previous maintenance and repair data, applying the inspection-replace-repair-overhaul (IRRO) method, assessing component conditions, predicting component life, predicting technician costs, predicting supporting work equipment and supporting materials to be used in maintenance, predicting the time to replace spare parts or reinstall components after repair, estimating maintenance and repair costs for the 2026 period, and calculating the ratio of maintenance costs to profits. The results of component replacement and repair planning obtained maintenance costs for the 2026 period are IDR 2,350,000, - with an estimated coffee grinder rental rate of IDR 25,000/hour which has the potential to be rented for 1440 hours/year, and the ratio of maintenance costs to profits is 6.5% which implies that the coffee grinder with a capacity of 2 kg / hour is still suitable for use for the next few years and still has the opportunity to make a profit.

The use of protective equipment in the form of helmets is an important aspect of ensuring motorcycle rider safety. However, violations of helmet usage still frequently occur and are difficult to monitor continuously. This study proposes a real-time helmet detection system using the YOLOv8 object detection method. The YOLOv8n model was trained using a helmet and no-helmet image dataset that underwent data augmentation to improve the model’s robustness against variations in environmental conditions. The system was implemented using the Python programming language with the support of the Ultralytics and OpenCV libraries. The system input was obtained from a webcam with a resolution of 640×640 pixels, where each video frame was processed in real time to detect the Helmet and No Helmet classes. The system displays bounding boxes and class labels in real time and is equipped with a violation duration calculation mechanism. When a no-helmet condition is detected continuously, the system generates pop-up alerts and automatic notifications via the Telegram application. The experimental results show that the system is capable of detecting helmet usage and no-helmet violations in real time with stable performance. The integration of violation duration calculation helps reduce momentary detection errors and improves the reliability of identifying valid violations

This study develops an interpretable machine learning model to predict the settlement status of Hajj fees in Jambi Province, Indonesia. Utilizing the XGBoost algorithm on a dataset of 4,332 prospective pilgrims from 2025, the research addresses the critical challenge of class imbalance where only 28.5% of samples are labeled "Unsettled". The baseline XGBoost model achieved a ROC-AUC of 0.7778, with a recall of 0.3482 for the minority class. SHAP (SHapley Additive exPlanations) analysis was employed to interpret model predictions, revealing that financial features specifically NILAI_VA (Virtual Account Value), JML_SETORAN (Deposit Amount), and JML_PELUNASAN (Settlement Amount) are the most significant factors influencing repayment risk, with negative SHAP values indicating increased default probability. The findings demonstrate that an interpretable XGBoost framework can provide both predictive accuracy and actionable insights for policymakers, enabling targeted interventions such as flexible payment schemes and enhanced financial monitoring for high-risk pilgrims..

The development of information technology has driven digital transformation in various sectors, including the food and beverage (F&B) industry. However, many small to medium-scale F&B businesses still rely on manual ordering systems, resulting in long queues, order recording errors, limited menu information, and suboptimal user experience. This study aims to design the user interface (UI) and user experience (UX) of a web-based Smart Ordering System that provides convenience, efficiency, and comfort in the food ordering process. The research method used is the Design Thinking approach, which includes empathize, define, ideate, prototype, and testing stages. The design process involves user needs analysis, user flow development, wireframe creation, and high-fidelity prototype development using Figma. Usability testing is conducted using the Single Ease Question (SEQ) method to evaluate ease of use and user satisfaction. The results indicate that the proposed UI/UX design provides a clear ordering flow, intuitive interface, and easy-to-understand user experience. Based on the SEQ results, most users experienced no difficulty in using the system, indicating that the design meets usability criteria with a very good category and is suitable for implementation in the F&B industry.

Managing Regional Public Service Agency (Badan Layanan Umum Daerah/BLUD) hospitals requires planning and budgeting processes that are accountable, measurable, and aligned with service performance. In practice, BLUD planning is still constrained by fragmented applications (hospital information system/SIMRS, finance, human resources, e-office, and procurement), duplicate data entry, approval delays, and limited monitoring of process compliance. This study aims to analyze requirements and design a web-based BLUD planning information system using an Enterprise Application Integration (EAI) approach through middleware to improve cross-system interoperability, data consistency, and the timeliness of executive reporting. The study adopts the Design Science Research (DSR) framework, comprising problem identification, definition of solution objectives, artifact design and development, demonstration, evaluation, and communication/report writing. The proposed system includes a unit-based budget proposal module and item management, a role-based approval workflow (RBAC) with SLA tracking, a budget ceiling (pagu) master to benchmark proposals, audit trails and report exports, and an executive dashboard integrating budget perspectives, service indicators (e.g., bed occupancy rate/BOR and patient visits), and process compliance. It also provides an integration design via middleware (ESB/message broker) supported by a canonical data model (CDM) and traceable logging (trace_id/correlation_id). Evaluation using black-box testing and API contract testing indicates that the main planning workflow operates as intended and the integration interfaces are consistently defined, providing a foundation for staged implementation and further performance evaluation.

Conventional management of sports services often leads to inefficiency and limited public access to experts and facilities. Reliance on manual systems poses a high risk of scheduling conflicts or human error. This study aims to develop the User Interface (UI) and User Experience (UX) design for the Movement and Athletic Talent Hub (MATCH) application as an integrative digital solution. The approach employed is the Design Thinking method, encompassing the stages of empathize, define, ideate, prototype, and testing. The design process resulted in an interactive prototype featuring key functions such as facility booking, trainer search, and a digital payment system. Evaluation was conducted using the System Usability Scale (SUS) method involving target users. The test results yielded an average score of 79.5, categorizing the MATCH application within the Good rating and Acceptable status. These findings indicate that the design is effective in meeting user needs and is viable for further development as a digital sports ecosystem.

SMP Negeri 5 Merlung is a public junior high school in Merlung Subdistrict that has utilized the DAPODIK system for online data processing management, enabling efficient sending and receiving of information to the government. This research analyzes IT governance on the DAPODIK system using the COBIT 5 framework, specifically the MEA01 domain (Monitor, Evaluate and Assess Performance and Conformance), which focuses on monitoring and evaluating performance and conformance. The research background is based on the need to maximize the utilization of the system at the school level. The main objectives are to determine the current and expected capability levels, as well as to provide improvement recommendations to achieve higher process maturity. The research method applies Assessment Process Activities, covering observation, interviews, identification of findings, gap analysis, and recommendations. The results show that the current capability level is at level 3 (established process), while the expected capability level is directed toward level 4 (predictable process). The implications of these findings provide practical recommendations such as routine monitoring enhancements, staff training, and integration of automation tools to bridge the capability gap, thereby improving the effectiveness of IT governance at SMP Negeri 5 Merlung sustainably.

The fitness industry has shown rapid growth as public awareness of the importance of healthy lifestyles increases in today's world. Gyms no longer function solely as a place to exercise, but also offer value and a sustainable experience for their members. The ability to manage a gym and retain members in this increasingly competitive era encourages gym owners to be more sensitive to factors that could potentially affect member satisfaction and loyalty. To improve the quality of the gym, it is necessary to evaluate the price and quality offered in the form of well-maintained and complete facilities. The suitability of the price to the benefits felt by members will make them more loyal to training at the gym. Likewise, comfortable facilities can increase member loyalty, which in turn drives member satisfaction, especially at Velcro Gym.

Magnetic resonance imaging (MRI) provides rich anatomical contrast for brain tumor assessment, yet routine interpretation remains time-intensive and demands high precision. This work develops a pipeline for four-class brain MRI image classification (glioma, meningioma, pituitary tumor, and no tumor) by combining automated brain-region cropping, data augmentation, and transfer learning with EfficientNetB1. Experimental results demonstrate exceptional performance, achieving an overall accuracy of 0.99 (99%) on the test set. Specifically, the model reached an F1-score of 1.00 for the no tumor class, 0.99 for pituitary, and 0.98 for both glioma and meningioma classes. Beyond reporting numerical performance, the study utilizes Grad-CAM heatmaps to verify that predictions rely on clinically plausible regions rather than spurious background cues. These results indicate that an efficiency-oriented backbone, paired with systematic preprocessing, can achieve reliable and interpretable performance for brain tumor classification tasks.

The implementation of the Electronic-Based Government System (SPBE) is essential for achieving efficient, transparent, and accountable village governance. Sido Rukun Village in Merangin Regency, Jambi Province, has begun using several government applications but lacks a structured enterprise architecture aligned with the national SPBE framework. This study aims to develop an enterprise architecture for SPBE in the business process domain at Sido Rukun Village. The research employs the TOGAF ADM (The Open Group Architecture Framework – Architecture Development Method) approach, involving stages such as identifying current business processes, designing a target architecture, and conducting a gap analysis between the as-is and to-be states. The findings include a business process architecture blueprint compliant with Presidential Regulation No. 95 of 2018 and Presidential Regulation No. 132 of 2022 on the National SPBE Architecture. This blueprint encompasses BPMN-based business process models and supporting artifacts that serve as a foundation for integrated information systems at the village level. The study’s implications are significant: it provides Sido Rukun Village with a practical and standardized technical blueprint for implementing a sustainable electronic-based government system, thereby supporting its transformation toward a Smart Village capable of adapting to evolving information and communication technology trends.

This study aims to design the User Interface (UI) and User Experience (UX) on the VINIX Showcase Website as a personal branding platform and digital Skill Passport for participants of the VINIX Seven Aurum Program using the Design Thinking method. The background of this research is the absence of an integrated digital platform that can systematically and easily document and display participants' skills, projects, certificates, and professional identity. The design process is carried out through five stages of Design Thinking, namely Empathize, Define, Ideate, Prototype, and Test, starting with exploring user needs, formulating problems, developing solution ideas, creating Prototypes, and Usability Testing. The results of the study consist of the UI/UX design of the VINIX Showcase Website, which includes registration and Login features, user Dashboard, Skill Passport, project upload, public Showcase, and automatic CV generation feature. Testing using the Usability Testing method showed that the resulting design has a good level of ease of use and comfort and is acceptable to users. This research is expected to be an effective digital solution in supporting personal branding, skills documentation, and improving the professionalism of VINIX Seven Aurum Program participants.

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.

Chronic Kidney Disease (CKD) is a heterogeneous disorder that gradually affects the structure and function of the kidneys, is difficult to recover, and causes the body to be unable to maintain metabolism and fail to maintain fluid and electrolyte balance, leading to increased urea levels. Chronic kidney disease data was obtained from Kaggle, in this study a comparison was made between two classification algorithms, namely Naïve Bayes Classifier (NBC) and Random Forest because it is not yet known what algorithm is best in classifying chronic kidney disease (CKD). Both algorithms are evaluated based on performance metrics such as accuracy, precision, recall, and confusion matrix. The results of the evaluation showed that in a dataset of 400 samples, the performance  of the Naïve Bayes Classifier (NBC) algorithm obtained an accuracy of 94%, while Random Forest had an accuracy of 93%. Then in the small dataset (158 data), Random Forest got a better accuracy score with 87% compared to the Naïve Bayes Classifier (NBC) of 78%. Based on the results of the evaluation, Random Forest has a more stable performance on small datasets, while Naïve Bayes Classifier (NBC) provides higher performance on larger datasets in the context of chronic kidney disease classification.

Sudden Infant Death Syndrome (SIDS) is a sudden and unexpected death in infants that is often associated with the prone sleeping position. This study aims to develop an automated monitoring system capable of detecting SIDS risk factors using the YOLOv8 algorithm and to analyze the effect of data augmentation on model performance. The dataset consists of two classes, baby-lying-on-back (supine) and baby-lying-on-stomach (prone), which were processed through model training and evaluation using precision, recall, F1-score, and mAP metrics. The model was trained under two scenarios, without data augmentation and with data augmentation. The results show that the model without augmentation achieved a precision of 90%, recall of 85%, F1-score of 86%, and mAP50 of 93.7%. After applying augmentation, performance improved to a precision of 90%, recall of 87%, F1-score of 88%, and mAP50 of 95.1%. These findings indicate that augmentation increases detection accuracy and enhances model generalization, including robustness against variations in lighting and camera angles. Furthermore, testing with image and video inputs revealed that the non-augmented model exhibited a tendency toward overfitting, particularly in favor of the baby-lying-on-stomach, whereas the augmented model successfully classified both classes accurately. The developed system is also equipped with an alarm feature and early-warning notifications via Telegram to smartphone when a prone position is detected for a certain duration. Overall, the results demonstrate that YOLOv8 with data augmentation is effective for an automated, non-invasive monitoring system for infants, making it suitable for detecting and preventing potential SIDS risk factors.