Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Building of Informatics, Technology and Science

Application of Support Vector Machine (SVM) Algorithm in Classification of Low-Cape Communities in Lampung Timur Aldino, Ahmad Ari; Saputra, Alvin; Nurkholis, Andi; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.539 KB) | DOI: 10.47065/bits.v3i3.1041

Abstract

Classification is a technique for grouping and categorizing specific standards as material for compiling information, making conclusions, or making decisions. This paper discusses data classification for underprivileged communities in Tanjung Inten, Purbolinggo, East Lampung using the Support Vector Machine (SVM) algorithm, then grouped into two label classes, namely the less fortunate and capable label classes. From the data that has been collected, 1154 data. The data goes through processing, scoring, labeling, and testing, producing two classes of results, namely less fortunate and capable. From the test data using the Support Vector Machine (SVM) method, the accuracy score is 97%, the precision score is 97%, the Recall score is 100%, and the F1-Score is 98%. This test resulted in a proportion of classification with the capable label is 87% and less fortunate label is 13%
Decision Support System for Determining the Best Internship Students Using the Combined Compromise Solution Method Pasaribu, A. Ferico Octaviansyah; Aldino, Ahmad Ari; Surahman, Ade; Setiawansyah, Setiawansyah
Building of Informatics, Technology and Science (BITS) Vol 5 No 3 (2023): December 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i3.4231

Abstract

Interns are individuals who are undergoing a period of practical learning in an organization or company as part of their educational curriculum. During the internship, students have the opportunity to apply the knowledge they learn in class to real-world situations, as well as gain valuable work experience. The selection of the best intern can involve several problems or challenges. One of them is the difficulty in evaluating students' practical skills based solely on their academic performance. The Decision Support System (DSS) to determine the best internship students using the Combined Compromise Solution Method provides a holistic approach in the selection process. This method combines elements of the Compromise Solution Method that consider compromise solutions between alternatives. With this comprehensive approach, DSS can assist institutions or companies in selecting internship students that best suit their needs and expectations, as well as ensure the success of internships that are beneficial to both parties. The results of the ranking of the best internship student alternatives showed that rank 1st with a value of 5.7847 was obtained by Jonathan, rank 2nd with a value of 5.2625 was obtained by Handoko R, and rank 3rd with a value of 4.6117 was obtained by M. Ali Fikri. The results of this ranking help companies determine the best internship students by applying the combine compromise solution method