Jurnal Komputer Indonesia (Ju-Komi)

Application of the TOPSIS Method (Technique for Order Preference by Similarity to Ideal Solution) in Product Selection

2024-10-24T15:56:00+00:00

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

2024-10-30T16:54:58+00:00

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

2024-10-30T17:32:17+00:00

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.

K-Means Clustering of Student Mid-Term and Final Exam Data

2024-11-04T08:10:47+00:00

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

2024-11-04T10:08:48+00:00

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

2024-11-26T16:37:33+00:00

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.