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All Journal Jurnal Teknologi Informasi dan Komunikasi KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Teknologi Komputer dan Informasi JGK (Jurnal Guru Kita) Jurnal Pengabdian Masyarakat (Jubdimas) Scripta Technica: Journal of Engineering and Applied Technology
Lisda Juliana Pangaribuan
Universitas Mandiri Bina Prestasi
Arts 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 Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

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Journal : Scripta Technica: Journal of Engineering and Applied Technology

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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 Berlin Barus Bertha N Siahaan Catra Indra Cahyadi Cherlina Helena Purnamasari Panjaitan Dede Prabowo Wiguna Fauzi Haris Simbolon Ginting, Sakaria Efrata Jaidup Banjarnahor Melvin Bismark Hamonangan Sitorus - Panjaitan, Cherlina Helena P Salman, Aulia Siagian , Dumai Lasmaria Sinaga, Sarman Surbakti, Benar Susi Diriyanti N Sylvia, Tiara