Jurnal Komputer Indonesia (Ju-Komi)

DEVELOPMENT OF ECO-FRIENDLY LUBRICATING GREASE FROM PALM KERNEL OIL WITH POLYPROPYLENE ADDITIVE: A SUSTAINABLE APPROACH

2025-10-11T17:05:53+00:00

This study explores the development of eco-friendly lubricating grease from palm kernel oil with polypropylene additive, adopting a sustainable approach. The research focuses on formulating high-performance greases suitable for industrial and automotive applications. Through experimental synthesis and testing, including worked penetration, dropping point, and water washout resistance, the study evaluates the grease's properties. Results show that the formulated grease with polymer additive exhibits improved thermal stability (dropping point of 187°C) and suitable consistency (worked penetration of 250), meeting NLGI Grade 2 and 3 standards (Table 4.3). The grease also demonstrates excellent water resistance and anti-wear characteristics. This research contributes to sustainable lubrication science, offering a viable alternative to conventional greases and supporting environmentally friendly practices in various industries..

SUSTAINABLE EXTRACTION AND COMPARATIVE ANALYSIS OF OIL FROM MORINGA AND SOYBEAN SEEDS USING PETROLEUM ETHER: AN ECONOMIC COST ANALYSIS

2025-10-11T16:56:44+00:00

The increase demand and application for oils have engendered more searches for vegetable and seed oil that are of high quality. In this work, extraction and phytochemical analysis and physiochemical characterization of moringa seed and soya bean seed oil was carried out. The seed oil of the plants were extracted using solvent (petroleum ether), standard method was adopted to extract the oil. The parameters of both were determined by physiochemical analysis and calculation. 382g of grounded moringa seed and soya bean seed powder were weighed, and mixed with 1000ml of the petroleum ether in a round bottom flask of soxhlet extraction unit. The extraction process was carried out for three hours (180mins) for the seed powders respectively. The pH of moringa seed oil and soya bean seed oil were recorded as 5.8pH and 5.9pH respectively. The moringa seed yielded 185ml oil which represent 48.4% yield while soya bean seed yielded 61ml of oil which represent 16% yield. The density of both oils in the study research; 0.8363g/ml for moringa seed oil and 0.904g/ml, represent low and medium density food grade oil respectively. Density of oil > 0.92g/ml are regards as high density oil (Abbas A, et al, 2020).The phytochemical analysis showed that both seed oils are healthy plants based oils for human, domestic and industrial application.

REVOLUTIONIZING SMALL-SCALE LNG BUSINESS: OPTIMAL STRATEGIES FOR AN ADAPTIVE AND SUSTAINABLE SUPPLY CHAIN

2025-10-10T16:47:03+00:00

This groundbreaking research tackles the intricate challenges facing the small-scale LNG market, including logistical complexities, high operational costs, limited infrastructure, fluctuating demand, and environmental concerns. By harnessing the power of machine learning techniques, such as reinforcement learning, recurrent neural networks, online learning, and graph theory, we develop a revolutionary intelligent system for optimizing LNG pickup and delivery routes. Our innovative approach transforms the selection and planning process, yielding unprecedented efficiency gains, cost reductions, and faster delivery times. Our linear regression model reveals a significant relationship between LNG supply chain cost and independent variables, with a coefficient of determination (R-squared) of 0.85. The time series analysis shows a trend coefficient of 0.05, indicating a steady increase in LNG supply chain performance metrics. The ARIMA model demonstrates a strong autoregressive component, with a coefficient of 0.80. Our multiple linear regression model shows that transportation cost, storage cost, demand, and supply are significant predictors of LNG supply chain cost, with an R-squared of 0.90. The stochastic frontier analysis estimates an efficiency score of 0.85, indicating room for improvement in the LNG supply chain.

The vector autoregression model reveals significant relationships between LNG supply chain performance metrics, with an AIC of 120.56. The generalized autoregressive conditional heteroskedasticity model estimates a significant ARCH coefficient of 0.20 and GARCH coefficient of 0.70, indicating volatility clustering in LNG supply chain performance metrics. The panel data model shows that transportation cost and storage cost are significant predictors of LNG supply chain cost, with an R-squared of 0.88. Our machine learning model achieves an R-squared of 0.92, outperforming traditional statistical models. By implementing optimization strategies, we achieve a 15% reduction in transportation costs, a 20% reduction in transportation times, a 12% increase in tank utilization, an 8% reduction in transportation costs through using larger vessels, a 6% reduction in transportation costs through optimizing routes, and a 4% reduction in overall supply chain costs through improving demand forecasting and supply chain planning.

DEVELOPMENT OF A FACE RECOGNITION AND GEOFENCING BASED ATTENDANCE INFORMATION SYSTEM USING THE PROTOTYPING METHOD

2025-10-18T17:34:14+00:00

An attendance information system is a crucial component in managing attendance in educational institutions and organizations. This research aims to develop an attendance system that integrates face recognition and geofencing technology to improve the accuracy and efficiency of the attendance recording process. Face recognition technology recognizes users' faces in real-time, while geofencing ensures users are within a designated area when taking attendance. The system development method used is prototyping, allowing the design process to be carried out iteratively by involving direct feedback from users. The results of this research are a mobile and web-based attendance information system that can automatically detect faces and locations, and store attendance data securely and structured. The developed system is expected to be an innovative solution in realizing a more modern, accurate, and reliable attendance process.

Literature Review on the Development and Applications of Data Science in Various Fields

2025-10-23T11:52:33+00:00

This study is a literature review aimed at describing the development and application of Data Science across various sectors of life. The method used involves a review of scientific literature from multiple academic sources published between 2018. The findings indicate that Data Science has evolved from classical statistical approaches to artificial intelligence–based systems that support decision-making in the health, finance, education, agriculture, industry, and government sectors. This review also highlights the integration of Big Data, Machine Learning, and Artificial Intelligence technologies as the main drivers of global digital transformation.