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INDONESIA
Jurnal Komputer Indonesia (JU-KOMI)
Published by SEAN INSTITUTE
ISSN : -     EISSN : 29630460     DOI : https://doi.org/10.54209
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering
Jurnal Komputer Indonesia (JU-KOMI) is a scientific journal in the field of Computers which includes: Information System Analysis & Design, Artificial Intelligence, Data Mining, Cryptography & Steganography, Decision Support System, Software Engineering, Computer Network and Architecture, Fuzzy Logic, Information Security, Content-Based Multimedia Retrievals, Data analysis, Fuzzy Logic, Genetic Algorithm, Image Processing, Computer Network, Embedded System, Virtual/Augmented Reality, Computer Security, Neural networks, e-Healthcare, e-Learning, e-Manufacturing, e-Commerce, Media, Game and Mobile Technologies
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 39 Documents
 1   2   3   4 
SUSTAINABLE EXTRACTION AND COMPARATIVE ANALYSIS OF OIL FROM MORINGA AND SOYBEAN SEEDS USING PETROLEUM ETHER: AN ECONOMIC COST ANALYSIS Theodore U. Nwaneri; Nnadikwe Johnson
Jurnal Komputer Indonesia (Ju-Komi) Vol. 4 No. 01 (2025): Jurnal Komputer Indonesia (JU-KOMI), October 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v4i01.747

Abstract

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.
DEVELOPMENT OF ECO-FRIENDLY LUBRICATING GREASE FROM PALM KERNEL OIL WITH POLYPROPYLENE ADDITIVE: A SUSTAINABLE APPROACH Ugochukwu Chukwuemerie Wisdom; Ibe Raymond Obinna; Nnadikwe Johnson; Iheme Chigozie
Jurnal Komputer Indonesia (Ju-Komi) Vol. 4 No. 01 (2025): Jurnal Komputer Indonesia (JU-KOMI), October 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v4i01.748

Abstract

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..
Implementation of C4.5 Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.751

Abstract

Diarrheal disease remains one of the major health problems among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, mothers’ hand hygiene, and immunization status play an important role in influencing the occurrence of diarrhea. This study aims to analyze the application of the C4.5 algorithm in developing a predictive model for diarrhea among toddlers using secondary data from a Public Health Center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The analysis process was carried out through entropy calculation, information gain assessment, and decision tree construction to obtain classification patterns. The results showed that the C4.5 model achieved an accuracy of 92%, precision of 87.5%, recall of 87.5%, F1-score of 87.5%, and specificity of 94.12%. These values indicate that the C4.5 algorithm is capable of making predictions with a good level of accuracy and balance in detecting both positive and negative cases. This study contributes to the utilization of data mining, particularly the C4.5 algorithm, as a decision-support tool in the health sector for the prevention of diarrheal disease among toddlers.
Comparison and Evaluation of Euclidean and Canberra Distances in the Adaptive K-Means Algorithm for Classifying the Food Security Status of Indonesian Provinces Cinthya Agatha Sinaga; Paska Marto Hasugian
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.752

Abstract

Food security issues in Indonesia are a major concern because they affect the sustainability of people's livelihoods and regional disparities. This study was conducted to classify food security conditions between provinces based on two main indicators, namely the Food Security Index and the Percentage of Adequate Food Consumption. The method used is the K-Means Adaptive algorithm with a comparison of two types of distance measurements, namely Euclidean and Canberra. The selection of centroids is done gradually using a probabilistic approach to improve the stability of the clustering results. Before conducting a comprehensive test, the method is first tested using sample data to see the characteristics of each distance function. Subsequently, all data were analyzed using Python programming, and the results were evaluated using the Silhouette Score metric. The analysis results showed that the Canberra distance function provided better clustering quality than the Euclidean function with a value of 0.415. This approach is expected to serve as a reference for more accurate and informative regional-based food security analysis.
Implementation of Random Forest Algorithm for Diarrhea Prediction Sipra Barutu; Siska Simamora
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.753

Abstract

Diarrhea is one of the leading causes of morbidity among toddlers in Indonesia. Environmental factors such as drinking water quality, sanitation, maternal hand hygiene, and immunization status contribute significantly to the incidence of diarrhea. This study aims to analyze the application of the Random Forest algorithm in developing a predictive model for diarrhea in toddlers using secondary data from a community health center (Puskesmas), consisting of 200 records divided into 150 training data and 50 testing data. The model was constructed by generating multiple decision trees and combining them using a majority voting technique. The results show that the Random Forest algorithm achieved an accuracy of 88%, precision of 77.78%, recall of 87.5%, F1-score of 82.35%, and specificity of 88.24%. These values indicate that Random Forest is quite reliable in detecting positive diarrhea cases, although some limitations remain in reducing misclassification of negative data. This study contributes to the utilization of machine learning algorithms, particularly Random Forest, as a decision-support tool in the health sector for diarrhea prevention among toddlers.
DEVELOPMENT OF A FACE RECOGNITION AND GEOFENCING BASED ATTENDANCE INFORMATION SYSTEM USING THE PROTOTYPING METHOD Situmorang, Caesar Juanda Theodorus; Hasugian, Paska Marto
Jurnal Komputer Indonesia (Ju-Komi) Vol. 4 No. 01 (2025): Jurnal Komputer Indonesia (JU-KOMI), October 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v4i01.755

Abstract

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.
Optimizing Big Data Analytics in the Era of Digital Transformation Sipra Barutu
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.757

Abstract

Digital transformation requires organizations to manage large, diverse, and high-velocity data to support effective decision-making. Big Data Analytics (BDA) plays a strategic role in this process through its ability to transform raw data into valuable insights for organizations. This study aims to analyze strategies for optimizing BDA in the digital transformation era by emphasizing the integration of technology, information systems, and organizational capabilities. The research uses a qualitative descriptive approach through a literature review of journals, industry reports, and scientific publications from 2018 to 2025. The findings indicate that the success of BDA optimization is determined by three key synergies: (1) robust and scalable technology, (2) integrated and secure information systems, and (3) adaptive and innovative organizational capabilities. The integration of these three aspects enables organizations to enhance operational effectiveness, strengthen competitiveness, and accelerate the success of digital transformation. Therefore, optimizing BDA is not merely a matter of technological implementation but a comprehensive transformation of how organizations think, make decisions, and create value in the digital era.
Patient Hypertension Modeling Using Decision Tree: Analysis of Age, Symptoms, Fatty Food Intake, Salt Intake, Medication Count, and Blood Pressure Using RapidMiner Manahan Tua Tinambunan; Sipra Barutu
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 02 (2025): Jurnal Komputer Indonesia (JU-KOMI), April 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i02.758

Abstract

Hypertension is a chronic disease characterized by persistently elevated blood pressure and remains a major global health problem. Various interacting factors, including age, salt and fatty food intake, medication use, and blood pressure, influence the risk and symptoms of hypertension. This study aims to identify patterns and characteristics of hypertension patients and determine the most influential factors using data mining techniques. A quantitative approach with the Decision Tree algorithm was applied using RapidMiner Studio. The analysis involved data preprocessing, model training and validation, and identification of influential variables. The Decision Tree analysis revealed that medication use is the main determinant of symptom patterns in hypertension. In patients not taking medication, symptoms were mainly influenced by salt intake and blood pressure, where low salt intake was associated with nausea and moderate salt intake with varied symptoms, especially headaches. In patients taking medication, symptom patterns were affected by the combination of salt and fatty food intake. High salt and fat consumption were associated with dizziness, while moderate intake was related to fatigue. Hypertension symptoms are determined not only by blood pressure but also by lifestyle factors and medication use. The Decision Tree model effectively identifies hierarchical relationships among these factors, providing valuable insights for healthcare professionals to design more targeted hypertension management and prevention strategies.
Literature Review on the Development and Applications of Data Science in Various Fields Margaret, Margaret
Jurnal Komputer Indonesia (Ju-Komi) Vol. 4 No. 01 (2025): Jurnal Komputer Indonesia (JU-KOMI), October 2025
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v4i01.759

Abstract

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.

Page 4 of 4 | Total Record : 39

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