Journal La Multiapp

Efficiency and Performance Analysis of the Design and Construction of a 30 Kg/Hour Coffee Grinding Machine

Thu, 16 Apr 2026 00:00:00 +0700

This study aims to evaluate the performance of a disk mill coffee grinding machine with a design capacity of 30 kg per hour for small and medium scale coffee processing. The machine was developed as a practical gasoline powered grinder to support coffee processing activities, particularly in areas where access to electricity may be limited. A performance test was conducted using roasted coffee beans with three input loads, namely 500 g, 750 g, and 1000 g. The observed parameters included grinding time, grinding capacity, product yield, residual material, material loss, material conversion efficiency, energy efficiency, and grinding quality based on particle size distribution. The results show that the machine achieved an average grinding capacity of 29.68 kg per hour, which is close to the intended capacity of 30 kg per hour. The average product yield and material conversion efficiency reached 94.55 percent, while the average material loss was 1.11 percent. However, residual material remained at 4.00 percent, indicating that the grinding chamber and discharge system still require improvement. The energy efficiency was reported at 73.8 percent, although the engine power specification needs further clarification. In terms of grinding quality, 70 percent of the particles were within the desired range of 500 to 700 micrometers. These findings indicate that the machine is technically feasible for small scale coffee production, but further refinement is needed to improve material discharge, reduce residual particles, and strengthen particle size consistency.

Thermal Effectiveness Analysis of Lube Oil Cooler Fan with Capacity of 40.332 Kg/S with Pressure of 5 Bar

Wed, 29 Apr 2026 13:55:09 +0700

The Lube Oil Cooler Fan is an essential component in the lubrication system of a gas turbine because it maintains lubricating oil temperature within a safe operating range. This study aims to analyze the thermal performance and effectiveness of the Lube Oil Cooler Fan on Gas Turbine GT 1.1 at PT XYZ. The study employed a descriptive quantitative approach using field observation data and heat transfer calculations. The analysis was conducted through the Log Mean Temperature Difference method and heat exchanger effectiveness approach by considering fluid temperature changes, mass flow rates, thermophysical properties, flow characteristics, convective heat transfer coefficients, overall heat transfer coefficient, heat transfer rate, and thermal effectiveness. The results show that the lubricating oil temperature decreased from 61°C to 49°C, while the cooling air temperature increased from 32°C to 53.5°C. The tube side heat transfer coefficient was 40.71 W/m²°C, the shell side heat transfer coefficient was 308 W/m²°C, and the overall heat transfer coefficient was 30.61 W/m²°C. The calculated heat transfer rate was 992.57 W or approximately 0.993 kW. The lubricating oil was identified as the minimum heat capacity fluid, with a heat capacity rate of 33.13 kW/°C. The thermal effectiveness of the Lube Oil Cooler Fan was 41.4%, indicating that the cooler was able to perform its cooling function, although its performance remained moderate. Routine monitoring, stable airflow control, and periodic cleaning are recommended to improve thermal performance.

Strategic Adaptive Federated Learning Framework for Privacy-Preserving Image Data Mining in Highly Heterogeneous Medical Environments

Mayyadah Jabbar Gailan — Mon, 04 May 2026 10:43:16 +0700

The data mining of medical imaging data is being increasingly restricted by stringent data privacy regulations like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Even though FL offers a decentralized framework for model training, it suffers from significant performance degradation in heterogeneous settings characterized by non-IID data. In this work, a novel framework, namely Adaptive Privacy-Preserving Federated Learning, is proposed. This framework combines an adaptive weighting scheme with Differential Privacy to address the issue of divergence caused by statistical heterogeneity. As per the experimental evaluation of the MedMNIST dataset, a classification accuracy of 94.2% is achieved with a privacy budget of ε = 1.0.

Development of an Automated Infrared Therapy System Based on MLX90614 Sensor for Muscle Pain Treatment

Fri, 08 May 2026 15:44:58 +0700

This study aims to design and evaluate an automatic infrared therapy system capable of maintaining temperature stability within a safe and controlled range. The system utilizes a non-contact infrared temperature sensor MLX90614, an ultrasonic distance sensor HC-SR04, an Arduino Uno microcontroller, and an infrared lamp regulated by a pulse width modulation (PWM) signal. Temperature control is implemented using a proportional control (P-Control) method, while sensor data is processed using a moving average filter to reduce measurement noise. Experimental testing was conducted by comparing system responses without control and with P-Control at three distance variations, namely 15 cm, 30 cm, and 45 cm. The results show that the uncontrolled system produced a temperature deviation of ±1.8°C, while the implementation of P-Control reduced the deviation to ±0.6°C at a setpoint of 45°C. However, the achieved average temperature varied depending on distance, with values ranging from 35.27°C to 42.61°C across all test conditions. These results indicate that the application of P-Control combined with a moving average filter is effective in improving temperature stability, although the heating performance is influenced by the distance between the sensor and the object. This system demonstrates potential for safer and more controlled infrared therapy applications.

Comparative Analysis of CNN Architectures for Clean and Non-Clean Outfit Classification in Fashion Images

Noviana Wahyu Basuki, Imam Yuadi — Sat, 30 May 2026 19:17:47 +0700

The purpose of this study is to create and contrast image classifiers which are able to identify clean clothing from dirty using machine learning and deep learning techniques. Our utilized dataset contains 200 images of an outfit which are obtained from different official fashion brand sites and well-known e-commerce platforms in Indonesia. The information is used from secondary data which are digital images of the two style categories. Image preprocessing (resizes, normalizations and data augmentations), feature extraction from VGG-16, VGG-19 and inception v3 is done. The extracted features are then fed into the classifiers namely Logistic Regression, Neural Network and Support Vector Machine (SVM). The evaluation of the model is performed by different metrics (e.g., AUC, accuracy, F1-score, precision, recall and MCC) and visual examination using MDS plot and Silhouette Plot. The results demonstrate that the integrated model involving VGG-16 and Logistic Regression performs best obtaining highest AUC when compared with other model combinations. The MDS and Silhouette Plot visualizations also supported that VGG-16 has the most superior feature separation between clean outfits and non-clean outfits. In a word, our study unveils that fashion style recognition accuracy can be improved significantly through CNN-based feature extraction and traditional classification model. We hope that our work will encourage the comparison of CNN feature extraction and classification algorithms, and also can lay the foundation for further research in image-based outfit guidance systems serving a range of fashion industry and service sectors where professional appearance is a criterion.

The Effect of Cooling Water Inlet Emperature (CWIT) on Efficiency USC 1050 MW Steam Power Plant

Hiro Nayaparana, Purwanto Purwanto, Endang Kusdiyantini — Tue, 30 Jun 2026 10:23:48 +0700

This research explores the impact of the Cooling Water Inlet Temperature (CWIT) on the efficiency and operational costs of the Ultra Supercritical (USC) 1050 MW Coal-Fired Power Plant (PLTU) in Indonesia. With Indonesia's growing dependence on PLTU as a primary source of electricity, maintaining operational efficiency is critical. The study was conducted at the PLTU Jawa 7 change to 1050 USC type power plan using quantitative descriptive-analytical methods, and data was collected from both the Distributed Control System (DCS) and BMKG. The analysis focuses on how varying CWIT affects condenser pressure, thermal efficiency, Net Plant Heat Rate (NPHR), and fuel consumption. The findings indicate that an increase in CWIT results in higher condenser pressure, reduced vacuum quality, and lower thermal efficiency, leading to an increase in NPHR and higher fuel costs. Furthermore, the research assesses the economic impact, highlighting daily fuel cost penalties and opportunity losses due to reduced electricity generation. The study also provides strategies for mitigating the negative effects of CWIT, including enhanced cooling water flow, condenser cleaning, and the addition of cooling towers. In conclusion, the research emphasizes the importance of controlling CWIT to optimize plant performance and reduce operational costs.

Analysis of Hard Box Product Failures Using Failure Mode and Effects Analysis (FMEA) and DMAIC Methods at PT XYZ

Mellyani Nurmaya Fauzizah, Hendi Herlambang, Setiawan `Setiawan — Mon, 06 Jul 2026 14:04:26 +0700

PT XYZ is a manufacturing company in the packaging industry that produces hard box products with high precision requirements. Based on production data from January to December 2025, the defect rate reached an average of 3.39% with a maximum value of 6.80%, exceeding the company standard of 3%, indicating that the production process is not yet fully under control and requires systematic quality improvement. This study aims to identify dominant defect types, analyze the root causes of failures, and determine effective improvement priorities. The method used is the DMAIC (Define, Measure, Analyze, Improve, Control) approach integrated with Failure Mode and Effect Analysis (FMEA) to assess failure risks based on the Risk Priority Number (RPN). The results show that the dominant defects are dimensional inaccuracy (22.91%), glue contamination (20.76%), and dented/damaged products (18.82%), contributing 62.49% of the total defects. Based on FMEA analysis, glue contamination has the highest RPN value of 384, making it the top priority for improvement. The main causes of defects are related to human factors, machine conditions, and unstandardized work methods. The proposed improvements include process standardization, development of standard operating procedures (SOP), use of supporting tools, enhancement of operator competence, and regular machine maintenance and calibration. The integration of FMEA within the DMAIC framework provides a more systematic and structured analytical approach and is projected to reduce the defect rate to meet or approach the company’s quality standard.

Implementation of Job Safety Analysis and HIRADC as Occupational Risk Control Strategies in the Preventive Maintenance Process at PT XYZ

Annisa Safitri Mawaddah, Setiawan Setiawan, Cipto Purwanto — Mon, 06 Jul 2026 14:39:09 +0700

This research focuses on potential hazards and the risk of accidents that may occur during preventive maintenance, which requires awell planned approach. The objective of this study is to identify hazards, analyze the extent of the risks, and evaluate methods that canbe used to control those risks. Data collection was conducte dusing the JSA (Job Safety Analysis) method to identify hazards at each stage of the work and the HIRADC (Hazard Identification, Risk Assessment, and Determining Control) to assess risk levels and select appropriate control measures. The findings of this study revealthe presence of various potential hazards, such as mechanical, electrical, and human factors, with varying threat levels ranging from low to high Risk management includes administrative controls, the use of personal protective equipment, and inspections of the tools used. It is hoped that the application of the JSA and HIRADC methods can effectively assist in the identification and control of risks, although their implementation still requires improvement particularly regarding monitoring of worker compliance. It is recommended to enhance training on workplace safety and conduct periodic evaluations of risk control measures.

Analysis of Improving the Efficiency and Effectiveness of Company Resource Utilization Using CPM and PERT Approaches: A Case Study

Wagimin Wagimin, Anwar Fattah — Wed, 08 Jul 2026 10:49:18 +0700

Effective project management is essential for technology service companies to improve resource efficiency and minimize project delays. PT XYZ has experienced an increase in project demand, but several projects have not been completed according to the planned schedule, resulting in financial losses and potential client dissatisfaction. This study aims to analyze the scheduling of the Hospital Informatics Project at PT XYZ using the Critical Path Method (CPM) and Program Evaluation and Review Technique (PERT). A descriptive quantitative case study approach was applied by identifying project activities, determining activity dependencies, allocating human resources, developing a project network, and calculating project duration and completion probability. The CPM analysis shows that the critical path of the project is C–F–H–J, with an optimal completion time of 45 months. The PERT analysis indicates that the probability of completing the project within the target duration is 24.51%. The findings also show that project delays may be influenced by work methods, environmental conditions, material availability, human resources, and extraordinary circumstances. Therefore, the integration of CPM and PERT can help PT XYZ improve project scheduling, monitor critical activities, reduce delay risks, and enhance the efficiency and effectiveness of company resources.

Developing an Accountable Budget Baseline for Infrastructure Projects: A Consolidated Cost and Schedule Estimation Framework and Maturity Assessment

Haryo Utomo Wisnu Nugroho, Silvianita Silvianita — Fri, 17 Jul 2026 14:18:42 +0700

Organizations frequently rely on multiple international references for cost estimating and schedule development, yet these references use different terminology, levels of detail, and emphases, which can leave budgeting stage estimation fragmented and difficult to audit. This paper develops a consolidated, stepwise framework by synthesizing prescriptive practices from PMI, AACE, NASA, and GAO, and applies the framework as an auditable process maturity assessment instrument. A benchmarking synthesis approach was used to screen candidate references for capital project relevance and explicit guidance for cost estimating and schedule development. Process steps were extracted, harmonized across sources, and consolidated into two aligned end to end process frameworks for cost estimating and schedule estimating. The consolidated steps were converted into assessment items, rated on a five level maturity scale, and summarized using achieved versus maximum scoring. Application to PT Pertamina Patra Niaga (PPN)’s budgeting stage practices indicates that the current cost estimating process reaches 56/70 (80%) maturity, while the current schedule estimating process reaches 46/90 (51%). The previous cost estimating process scores 43/70 (61%). The most prominent gaps relate to the formal documentation of ground rules and assumptions, typically captured in a Basis of Estimate, and to schedule model discipline, particularly coding, logic structure, and systematic schedule quality checks. Overall, the findings show that the framework is practical to implement and supports evidence based prioritization of process improvements toward more transparent, accountable, and reliable budgeting baselines for capital and infrastructure related investments.

Evaluation of Time Performance in Construction Project Implementation: A Case Study of Building X and Building Y

Dian Nazwa Qurratul A'ini, Fathur Rohman Robandi — Fri, 17 Jul 2026 15:03:45 +0700

This study aims to evaluate the implementation of construction project time management in the Building Development Project at RSUD X. The research focuses on analyzing the conformity between planned schedules and actual project execution, identifying time deviations, determining critical path activities, and examining factors causing delays during project implementation. The study employed a descriptive quantitative approach with a case study method. Data were collected through observation, documentation, interviews, project schedules, S-curve data, and weekly progress reports. The analysis was conducted using S-Curve comparison and Critical Path techniques to evaluate project performance in terms of time control. The findings indicate that several project activities experienced delays caused by technical, managerial, and external factors, including material delivery constraints, coordination issues, and field conditions. Critical path analysis identified several key activities that significantly influenced the overall project duration. The study concludes that effective time management, continuous monitoring, and proper scheduling control are essential to minimize delays and improve construction project performance.

Analysis of Factors Causing Material Waste Using the Pareto Method and a Fishbone Diagram in Building Construction Projects

Amelia Ayuni Agustina, Aryati Indah K — Fri, 17 Jul 2026 15:50:57 +0700

Material waste is one of the major problems in construction projects that can lead to increased costs, project delays, and reduced project efficiency. This study aims to analyze the factors causing material waste in building construction projects using the Pareto and Fishbone Diagram methods, with a case study of Hotel X Construction Project in West Java. The research employed a quantitative method with a descriptive-analytical approach through field observations, project documentation, interviews, and questionnaires distributed to respondents directly involved in project material management. Pareto analysis was used to identify materials contributing the greatest losses, while the Fishbone Diagram was applied to analyze the root causes of material waste based on factors of man, method, material, machine, and management. The results showed that the total loss caused by material waste reached IDR 32,109,075.00, with D22 reinforcing steel contributing 36,64% and concrete contributing 28,76%, resulting in a cumulative contribution of 65,40%. The main factors causing waste included improper material cutting, weak field supervision, inefficient work methods, material damage during delivery, and suboptimal material management. Based on the findings, waste control recommendations include the implementation of cutting plans, improvement of work supervision, optimization of material storage systems, and enhancement of coordination among project divisions. This research is expected to serve as a reference for improving material use efficiency and minimizing losses in building construction projects.