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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
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Implementation of C4.5 Algorithm for Disease Classification: Literature Review Sari Siregar
Jurnal Komputer Indonesia (Ju-Komi) Vol. 2 No. 02 (2024): Jurnal Komputer Indonesia (JU-KOMI), April 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v2i02.585

Abstract

Several studies have shown that the C4.5 algorithm is capable of achieving a high level of accuracy in diagnosing various diseases. For example, a study by Konstas et al. (2010) showed that the C4.5 algorithm was capable of achieving an accuracy level of 89% in diagnosing coronary heart disease. This study aims to discuss the application of the C4.5 Algorithm in disease classification as has been done by previous studies. This study is a literature review, which includes references related to the cases or problems identified. The author uses data from a literature study, a method used to collect data or sources related to the topic discussed in a study. The results of this Algorithm literature review can help doctors diagnose diseases more quickly, accurately, and efficiently. However, it is important to remember that the C4.5 algorithm is only a tool, and cannot replace the diagnosis of a professional doctor.
Application of the TOPSIS Method (Technique for Order Preference by Similarity to Ideal Solution) in Product Selection Rosa, Rosa; Sari Siregar
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.586

Abstract

In an era of increasingly fierce business competition, the right product selection is one of the company's successes. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method offers a systematic approach to evaluate various product alternatives based on predetermined criteria. This research aims to apply the TOPSIS method in product selection, focusing on factors such as price, quality, and features. The results of the analysis show that this method improves the efficiency and objectivity of decision-making, allowing companies to identify the best products that are close to the ideal solution. In addition, the use of TOPSIS also increases the transparency of the evaluation process, supports adaptation to market changes, and assists companies in achieving competitive advantage. These findings confirm the importance of the TOPSIS method as a tool for intelligent, data-driven decision-making in a dynamic business context.  
Development of Decision Making Information System for Student Executive Board Election of Faculty of Computer Science, Santo Thomas Catholic University Using Topsis Method Marizda Hizkia Angelique; Dita Oktavia Manurung; Cherina Ateta Br Ginting; Marselina br sitepu
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.587

Abstract

This study aims to solve the problems in the recruitment of prospective BEM members at the Catholic University of Santo Thomas Medan. To select competent and fair members of the Student Executive Board (BEM) of the Catholic University of Santo Thomas Medan, several stages of selection are carried out. However, decision making in recruitment is prone to collusion and nepotism at the student level on campus. To overcome this, a decision support system (DSS) for the recruitment of BEM members was designed using the TOPSIS method. DSS was designed using the following criteria: minimum GPA of 3.00, experienced in student organizations, and to become the Chair of BEM must be a 7th semester student. The result of this study is a decision support system using the TOPSIS method. The result of the TOPSIS method calculation process is in the form of information on the selection of the Student Executive Board (BEM) at the Catholic University of Santo Thomas Medan and getting the results of the selection in the system.
The Influence of Information Technology on Community Culture Tandak Berutu
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.588

Abstract

Information technology has developed rapidly and brought major changes in various aspects of people's lives, including culture. This study aims to analyze the influence of information technology on traditional culture. This study uses a qualitative method with a case study approach. Data were collected through observation, interviews, and documentation. The results of the study show that information technology is used to disseminate information and knowledge about traditional culture through various platforms and media, such as social media, websites, mobile applications, and online mass media. The use of information technology has benefits, such as increasing access to information, expanding reach, increasing awareness, and promoting cultural preservation. However, there are also challenges that need to be overcome, such as the digital divide, misinformation, and cultural commodification. This study shows that information technology has great potential to help preserve traditional culture. Efforts from various parties are needed to ensure that information technology is used optimally to disseminate accurate and useful information about traditional culture.
Decision Making Technique Project “Scholarship Acceptance Using Simple Additive Weighting Method” Saveria Silvi Yanti Zebua; Dian Oktavian Simanjuntak; Rusdita Purnama Sari br. Girsang; Christian Purba; Simson Manatar Siahaan
Jurnal Komputer Indonesia (Ju-Komi) Vol. 2 No. 02 (2024): Jurnal Komputer Indonesia (JU-KOMI), April 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v2i02.591

Abstract

Scholarships are important financial aid programs for students from various sources, such as governments and universities, to support their education. Santo Thomas Catholic University offers various types of scholarships, but the selection process is often not on target and faces problems such as document falsification. To improve fairness and efficiency in the selection process, this study proposes the use of the Simple Additive Weighting (SAW) method, which can provide objective assessments based on the weight of the established criteria. The SAW method is known to be effective in managing data and producing transparent decisions. The results of the study show that the application of this method is able to produce a ranking of prospective scholarship recipients that can be used as a reference by the Scholarship Selection Team, thereby increasing the validity of the overall selection process.
The Effect of Height and Weight on Toddler Nutritional Status Using Logistic Regression Adinda Breskyn Tha Sembiring; Harta Monika Putri Sinaga; Risma Sitanggang; Putrienna M Nababan; Nelly P Hutasoit
Jurnal Komputer Indonesia (Ju-Komi) Vol. 2 No. 02 (2024): Jurnal Komputer Indonesia (JU-KOMI), April 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v2i02.592

Abstract

The issue of nutritional status of toddlers in Indonesia is a serious concern, considering that this age group is vulnerable to malnutrition, which can impact their health and quality of life in the future. This study used logistic regression to analyze the effect of height and weight on the nutritional status of toddlers in Karang Songo Village, Bantul. The results of the analysis showed that the weight variable had a significant effect on nutritional status, while height did not have a significant impact. These findings emphasize the importance of special attention to weight in efforts to improve the health and well-being of toddlers. This study provides valuable insights that can be used to design more effective interventions and policies in addressing nutritional problems in Indonesia, to ensure that future generations have optimal health.
K-Means Clustering of Student Mid-Term and Final Exam Data Nella Ane Br Sitepu; Agnesia Rointan Sijabat; Cindy Rounali Limbong; Lenny Evalina Pasaribu; Einson O.B Nainggolan; Michael Manulang
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.593

Abstract

This study examines the use of the k-means clustering method in grouping students based on UAS and UTS scores to identify patterns of academic achievement. Clustering is an effective data mining technique for grouping data based on similar characteristics. By applying the k-means algorithm, this study aims to make it easier for lecturers to identify student abilities, so that they can provide appropriate support to those who need help. Data were taken from UTS and UAS scores of students at a university in Indonesia, and the results of the analysis showed that k-means clustering can group students according to their level of achievement. These findings are expected to help in the development of more effective teaching strategies and interventions, improving the quality of education and overall academic performance of students.
Application of Simple Additive Weighting Method in Student Decision Making in Choosing Study Programs Ricko Gurning; Riska Ananda; Febri Sinabutar; Desi Damanik; Renny Karo Karo
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.594

Abstract

This study discusses the application of the Simple Additive Weighting (SAW) method in helping students of Santo Thomas Catholic University in choosing the right study program. Choosing the right study program is very important, because it can affect students' careers and future lives. Through the SAW method, this study identifies important criteria such as career prospects, personal interests, and academic achievements, each of which is weighted according to its level of importance. Each study program is evaluated and scored based on these criteria, and the results are summed up to obtain a final score. This study found that SAW is effective in providing objective and structured study program recommendations, but determining the right weighting is very important for the accuracy of the results. In addition, evaluating student satisfaction with the chosen study program also contributes to increasing the relevance of the recommendations given. Thus, the SAW method can be a useful tool to improve student satisfaction and learning outcomes in higher education.
Analysis of Malaria Disease Classification Based on Age in the Work Area of Idanogawo Health Center, Idanogawo District, Nias Regency Using the Decision Tree Algorithm Sipra Barutu
Jurnal Komputer Indonesia (Ju-Komi) Vol. 3 No. 01 (2024): Jurnal Komputer Indonesia (JU-KOMI), Oktober 2024
Publisher : SEAN Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/ju-komi.v3i01.609

Abstract

Malaria is an infectious disease caused by the Plasmodium parasite, which is transmitted through the bite of the Anopheles mosquito. This disease can affect individuals of all age groups, but its prevalence varies between age groups. This study aims to analyze the relationship between age and malaria incidence in the working area of ​​the Idanogawo Health Center, Nias Regency, using the Decision Tree algorithm. Secondary data collected from medical records at the Idanogawo Health Center were processed through preprocessing to obtain age and malaria incidence attributes. The results of the analysis showed that the age group <30 years had the highest percentage of malaria incidence (80%), followed by the age groups 30-39 years and 40-49 years, each of which recorded an incidence rate of around 73%. The age group >=50 years also showed a high incidence of malaria (100%), although the sample size was small. These findings indicate that malaria attacks younger people more, but older age groups are also significantly affected.
REVOLUTIONIZING SMALL-SCALE LNG BUSINESS: OPTIMAL STRATEGIES FOR AN ADAPTIVE AND SUSTAINABLE SUPPLY CHAIN 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.v4i01.745

Abstract

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.

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