Article Per Year (5 Year)
p-Index From 2021 - 2026
2.085
P-Index
This Author published in this journals
All Journal JTIK (Jurnal Teknik Informatika Kaputama) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Indonesian Journal of Science, Technology, and Humanities Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Saturnus: Jurnal Teknologi dan Sistem Informasi
I Gusti Prahmana
Unknown Affiliation
Religion Automotive Engineering Computer Science & IT Control & Systems Engineering Mathematics Social Sciences

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Korelasi Antara Karakteristik TKI dengan Jenis Pekerjaan Menggunakan Metode Apriori Elfira Iriani; I Gusti Prahmana; Yani Maulita
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): November : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.218

Abstract

This study addresses the issue of Indonesian migrant workers (TKI) whose characteristics do not match the jobs assigned abroad, often leading to complaints from agencies and companies. This mismatch is caused by incorrect job placements and insufficient training, which prompts TKI to leave their assigned jobs. The research aims to better understand the characteristics of TKI that influence successful job placement. The **apriori** method was used to identify patterns and relationships between TKI characteristics, destination countries, and suitable job types. Based on a 30% minimum support, 3 and 4 itemset combinations were produced, showing correlations between TKI characteristics and job positions. Using lowerboundminsupport 0.001 and minmetric 0.1, this study generated 6 itemsets from 13 data points, providing significant correlations between TKI characteristics and more accurate job placements.
Penggunaan Metode Rough Set pada Tingkat Kecemasan (Anxietas) Mahasiswa dalam Menyusun Tugas Akhir Nadilla Ayudia Pasa; Yani Maulita; I Gusti Prahmana
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): November : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.243

Abstract

This study investigated college students' anxiety levels in completing their final projects, which is an important requirement for graduation. Anxiety is a common problem faced by students, which is often caused by the long and complicated process of preparing a final project. Using the Zung Self-Assessment Anxiety Scale (SAS/SRAS), this study aims to measure the level of anxiety and identify the main factors that contribute to it. The Rough Set Method, an efficient technique for analyzing uncertainty, was applied to identify patterns and relationships between factors influencing college students' anxiety. Data was collected through questionnaires from students who are currently completing their final projects. By applying the Rough Set Method, this research succeeded in identifying significant factors that influence anxiety levels, such as psychological, physical and positive responses. These findings provide valuable insight for educators and counselors to better understand and address college students' anxiety during the final years.
Prediksi Pengaruh Kegiatan MBKM terhadap Mahasiswa menggunakan Metode K-Nearest Neighbor Farida Hanum; Yani Maulita; I Gusti Prahmana
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 4 (2025): November : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.249

Abstract

The Merdeka Belajar Kampus Merdeka (MBKM) program provides students the opportunity to study for one semester outside of their major, aiming to develop the soft and hard skills required in the workforce. One key component of this program is internships or practical work, which gives students hands-on experience in the professional world and the chance to build professional networks. This research uses the K-Nearest Neighbor (K-NN) method to predict the impact of MBKM activities on undergraduate students at STMIK Kaputama. Using the RapidMiner application, student data was tested to obtain the accuracy of predicting students' engagement in the MBKM program in the future. The test results show that the K-NN model has an accuracy of 75.34%, indicating that the model is fairly good at predicting the impact of the MBKM program on students.
Grouping Number of Library Members For Determining the Location of Socialization Using Clustering Method Sella Dwi Pratiwi; Achmad Fauzi; I Gusti Prahmana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.270

Abstract

The high use of smartphones at this time led to a decline in public interest in reading books in the library directly. Especially students and students. This is certainly a problem for the Langkat Regency Archives and Libraries Office. Socialization is needed to increase efforts to read interest in the community. The right socialization location must have several criteria so that the socialization carried out is right on target. The existence of a database for each member of the library will facilitate the location selection process. Data mining techniques can classify the number of library members based on the results of large data analysis into information in the form of patterns. The clustering method is a method in data mining that can analyze data with the aim of grouping data based on the same characteristics. The K-Means algorithm is a simple algorithm for classifying a large number of objects with certain attributes into clusters which are usually used in data mining.
Co-Authors Abdi Prayogi ACHMAD FAUZI Artika Dini Anggriani Br Bangun, Tiara Buaton, Relita Desva Karliana br Sembiring Dicha Mutia Dhani Dinda Firdawati Simamora Elfira Iriani Elsa Risqi Amalia Farida Hanum Fauzi, Achmad Magdalena Simanjuntak muhammad arief Nadilla Ayudia Pasa Novriyenny Novriyenny Pardede, Akim Manaor Hara Rusmin Saragih, Rusmin Salsabilla, Nur Sella Dwi Pratiwi Simanjuntak, Magdalena Yani Maulita Zulkifli Zulkifli