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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) KLIK: Kajian Ilmiah Informatika dan Komputer
Jhiro Faran
Universitas Nasional, Jakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

 1 
Sistem Pendukung Keputusan untuk Penentuan Jurusan dengan Metode Simple Additive Weighting (SAW) dan Pembobotan ROC Jhiro Faran; Rima Tamara Aldisa
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 3 (2023): Desember 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i3.1541

Abstract

When someone chooses a major, they will focus on a particular area of study throughout their learning period. Usually, each department has a curriculum that has been specially prepared to provide in-depth understanding, specific skills and knowledge in that discipline. Decisions in choosing a major are often based on personal interests, talents, environmental influences, including parental views and personal career goals. Some courses may have specific entry requirements or may involve developing specific skills over the course of your studies. Apart from that, choosing a major can also influence the direction and career you will follow after graduating. Therefore, it is very important to make careful decisions and obtain the views of educational advisors or career counselors according to personal educational and career goals. This research discusses studies at a high school, where there are only two major choices, namely science and social studies. In the process of selecting majors, schools use special selection methods other than the general test route. The problem that arises is the need for flexibility and accuracy in decision making. To overcome this problem, researchers used a decision support system method by combining the ROC and SAW methods. ROC is used to calculate criteria weights, while SAW is used to assess preferences. The results of applying these two methods show that the initial major choices made by students are often different from the majors determined using these two methods. A total of five students were assigned to major in science, including alternatives A2, A3, A7, A8 and A10, while five other students were assigned to major in social sciences, namely alternatives A1, A4, A5, A6 and A9.
Penerapan Algoritma K-Means Data Mining untuk Clustering Kinerja Karyawan Koperasi Jhiro Faran; Agung Triayudi
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1728

Abstract

Employees are people who are the main element in every organization/company. An employee is someone who can carry out work and provide the results of their work to the employer or agency where the employee works, where the results of their work are in accordance with the profession or work based on their expertise. The role of employees in cooperatives is the same as the role of employees in general in every other organization/company. Giving rewards to employees is a form of company appreciation for its employees. Reward or recognition is a form of gratitude from the company for the dedication and performance of employees, namely those who have good quality work and have met the criteria for employees with good performance. The problem faced is that currently there is no process that has been carried out to group employee performance. Grouping employee performance is a fairly important problem and must be resolved immediately by the company. The solution to this problem can be solved by paying attention to patterns based on processes or data that occurred in the past. Data mining is the right way to solve this problem. Data mining is a process of processing data and extracting data to get information back from a collection of data. Clustering is a process of grouping data contained in a dataset. Grouping data in a dataset using clustering is done based on the similarity values or characteristics of each data. The K-Means algorithm is part of clustering data mining, where the K-Means algorithm can be used to form new groups of data. The results obtained from the research are that the formation of new groups/clusters is based on a total of 15 data, so there are 2 (two) clusters where in cluster 1 there is 7 data and cluster 2 there is 8 data
Co-Authors Agung Triayudi Rima Tamara Aldisa