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All Journal Aptisi Transactions on Management ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) IAIC Transactions on Sustainable Digital Innovation (ITSDI) ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) International Transactions on Education Technology (ITEE) Blockchain Frontier Technology (BFRONT) Saturnus: Jurnal Teknologi dan Sistem Informasi Journal Collabits
Saputro, Janu Ilham
Universitas Raharja
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

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Journal : journal collabits

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Comparison of Random Forest and Naive Bayes Algorithms in Classification of Song Popularity on the Spotify Platform Saputro, Janu Ilham; Fantomi, Rian; Simbolon, Saoloan; Arizka, Puput Nur; Ramadani, Berlina
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37578

Abstract

The purpose of this study is to use machine learning to rank Spotify songs based on how popular they are. Because there is so much music data out there, musicians and artists need to know if a song will be popular or not. The dataset has 8,778 songs, each with different features like how popular the artist is, how many followers they have, and other song details. This research evaluates the efficacy of two classification algorithms: Random Forest and Naive Bayes. Artist popularity, artist followers, explicit album total tracks, and track number are the main things that are used to make models. The results of the experiment show that the Random Forest algorithm works better than the Naive Bayes algorithm. The Random Forest algorithm was right 76.54% of the time, but the Naive Bayes algorithm was only right 72.21% of the time. The f1-score for both popularity classes is also better for Random Forest. This finding shows that ensemble-based models, like Random Forest, work better with the features of music popularity data than basic probabilistic models do.
Comparison of Random Forest and Naive Bayes Algorithms in Classification of Song Popularity on the Spotify Platform Janu Ilham Saputro; Rian Fantomi; Saoloan Simbolon; Puput Nur Arizka; Berlina Ramadani
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37578

Abstract

The purpose of this study is to use machine learning to rank Spotify songs based on how popular they are. Because there is so much music data out there, musicians and artists need to know if a song will be popular or not. The dataset has 8,778 songs, each with different features like how popular the artist is, how many followers they have, and other song details. This research evaluates the efficacy of two classification algorithms: Random Forest and Naive Bayes. Artist popularity, artist followers, explicit album total tracks, and track number are the main things that are used to make models. The results of the experiment show that the Random Forest algorithm works better than the Naive Bayes algorithm. The Random Forest algorithm was right 76.54% of the time, but the Naive Bayes algorithm was only right 72.21% of the time. The f1-score for both popularity classes is also better for Random Forest. This finding shows that ensemble-based models, like Random Forest, work better with the features of music popularity data than basic probabilistic models do.
Web-Based Multi-Cafe Information System in the West Jakarta Area Luthfia Fauzia Dewi Ariyanti; Janu Ilham Saputro; Eduard Hotman Purba; Muhammad Nur Habibie
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.39597

Abstract

Evolving urban lifestyles have driven increased public interest—particularly in West Jakarta—in visiting cafes for relaxation, work, and socializing. However, information regarding cafe locations, menus, prices, facilities, and operating hours remains fragmented and difficult to access centrally, making it challenging for potential customers to make informed choices. This research aims to design and develop a web-based information system that integrates data from various cafes in West Jakarta into a single, unified platform. The system development follows the Waterfall model, encompassing requirements analysis, system design, implementation, testing, and maintenance. The system was built using web-based programming languages and a relational database to store cafe information, incorporating features such as search functionality, location- and category-based filtering, and user reviews. System testing utilized the Black Box Testing method to ensure all functions operated according to requirements, alongside usability testing to assess the system's ease of use for end-users. The results indicate that this multi-cafe information system enables users to quickly and accurately search for and compare cafe information while simultaneously assisting cafe owners in digitally promoting their businesses. This system is expected to serve as a beneficial, unified information solution for both the public and cafe business owners in West Jakarta.
Co-Authors Agus Sofyan Ahmad Syaefud Daeroby Arizka, Puput Nur Berlina Ramadani Cici Alviona Roza Eduard Hotman Purba Fadhillah, Muhammad Daffa Fantomi, Rian Fauzi, Rizky Amien Gojali, Muhamad Fikri Harfizar, Harfizar Hasibuan, Irfan Yuanda Haydar Muhammad Nadhif Luthfia Fauzia Dewi Ariyanti Mahesa, Dimas Malik Al Makkah Marini Ekawati Martono, Dedi Muhamad Alfarel Julianto Muhamad Miqbad Attamami Muhammad Faisal Muhammad Nur Habibie Mulki syahrian Nissa, Nadia Khaerun Nisya Istiqomah Arifin Nugroho, Indrawan Octavia, Dwiana Octipany, Rulita Panji Adam Rustambi Praditya Aliftiar Prastyo, Doni Puput Nur Arizka Putri Ika Wati Rahmadani, Ade Agung Ramadani, Berlina Ramadhanti, Kinanti Dara Rian Fantomi Rulita Octipany Sa'adah, Lailatus Ferahma Saoloan Simbolon Silaban, Nia Adelina Simbolon, Saoloan Siti Sunariah Sriyono, Diaz Mauludin Syifa, Yasyfiyani Utami, Wiranti Sri Wati, Putri Ika Wibisono, Hurdjanto Yulandha, Nadia Yuliyanto, Hizkia