Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal ULINA: Jurnal Pengabdian kepada Masyarakat Scripta Technica: Journal of Engineering and Applied Technology
Dede Prabowo Wiguna
Unknown Affiliation
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi Program Kampus Merdeka Dalam Kegiatan MSIB7 Studi Independen Pada Yayasan Decoding Indonesia Sembiring, Lismardiana; Duma Lasmaria Siagian; Monang Tarigan; Dede Prabowo Wiguna; Jenheri Rejeki Tarigan
ULINA: Jurnal Pengabdian kepada Masyarakat Vol 3 No 1 (2025): Januari
Publisher : Universitas Mandiri Bina Prestasi (UMBP)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58918/ulina.v3i1.282

Abstract

MBKM (Merdeka Belajar – Kampus Merdeka) is a program provided by the Ministry of Education, Research, and Technology (Kemenristek) to improve the quality of education in Indonesia. One of the most popular MBKM programs is the MSIB Program (Certified Independent Study and Internship Program). Internship based on MBKM (Merdeka Belajar Kampus Merdeka) is an internship/work practice program that can be taken by students who have completed at least semester 5 of their studies. This program is a form of university collaboration with industry to provide independent learning. The purpose of MBKM is to hone hard skills and soft skills to suit the needs of the times and be ready to welcome the world of work. MSIB (Certified Independent Study and Internship), this program allows students to do internships at companies or take learning to increase their knowledge in the world of work. Independent Study Program is a non-degree learning program organized by organizations or industries that provide knowledge and skills with a high level of relevance in the world of work and business in the form of short courses, bootcamps, massive open online courses (MOOCs), and others, which are continued with collaborative activities with fellow participants or partner organization personnel in a project or case study. The following are the characteristics of Independent Study certified by Kampus Merdeka. For MSIB7 as a partner of MBKM Yayasan Decoding Indonesia, the MBKM team is entrusted as a means of certified learning assisted by DPP (Program Supervisor) whose participants are all lecturers throughout Indonesia who have been declared to have passed as DPP by Kampus Merdeka.
Aplikasi Google Colab Berbasis Python dalam Menerapkan Teori Pohon dengan Algoritma Random Forest Classifier Dede Prabowo Wiguna; Lisda Juliana pangaribuan; Sakaria Efrata Ginting
Journal of Engineering and Applied Technology Vol 1 No 2 (2025): December: Scripta Technica: Journal of Engineering and Applied Technology
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65310/ybbpgv52

Abstract

This study examines the application of Python-based Google Colab in implementing tree theory through the Random Forest Classifier algorithm for income classification in data science, artificial intelligence, and machine learning professions. The research adopts an experimental quantitative approach using secondary data sourced from a global employment dataset. The methodological process includes data preprocessing, feature selection, class balancing, model training, and performance evaluation within the Google Colab environment. The results demonstrate that Random Forest effectively represents tree theory through ensemble decision structures capable of handling complex and heterogeneous data. Model evaluation indicates a satisfactory level of accuracy, confirming the classifier’s ability to generalize patterns across different income categories. Feature importance analysis reveals that job title, experience level, and company location play a significant role in determining income classification. These findings highlight the relevance of Random Forest as both a predictive and interpretative model, while emphasizing Google Colab’s effectiveness as a computational platform for machine learning experimentation. Overall, the study contributes to the practical understanding of tree-based algorithms and their application in analyzing labor market dynamics within the digital economy.    
Co-Authors Duma Lasmaria Siagian Ginting, Sakaria Efrata Jenheri Rejeki tarigan Lisda Juliana Pangaribuan Monang Tarigan Sembiring, Lismardiana