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Mutaqin Akbar
Universitas Mercu Buana Yogyakarta
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Journal : bulletin of network engineer and informatics bufnets

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SONG POPULARITY PREDICTION ON THE SPOTIFY PLATFORM USING MULTIPLE LINEAR REGRESSION WITH MIN-MAX NORMALIZATION Rismaria Sipayung; Mutaqin Akbar
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738832

Abstract

This study critically investigates whether three commonly used audio-level features of a song, duration, tempo, and energy, are by themselves, statistically capable of predicting song popularity on the Spotify platform. Using a multiple linear regression model estimated via Ordinary Least Squares (OLS) on 849 songs retrieved from Kaggle "Spotify Tracks Dataset", a publicly available collection spanning multiple genres and release periods, filtered to exclude duplicate records and songs with a popularity score of zero, we show that this is not the case. Prior to modeling, Min-Max normalization was applied to standardize variable scales. The model yields the equation Y = 42.4073 + (-5.2462)*X1 + (1.0306)*X2 + (-4.1247)*X3, but the overall F-test is not significant (F = 1.1789 < F-table = 2.6154, p = 0.3167), and none of the three predictors reach individual significance (all |t| < 1.53, p > 0.12). Pairwise correlations between each predictor and popularity are negligible (r = -0.037 for duration, r = -0.006 for tempo, r = -0.053 for energy), confirming that the linear structure captures essentially no signal in this dataset. We conclude that duration, tempo, and energy have effectively zero standalone predictive capability for Spotify popularity, and that normalization, while statistically inert with respect to R² and RMSE, remains necessary for producing comparable, interpretable coefficients. As a secondary, practical contribution, the analysis pipeline was deployed as an interactive web application (SpotiPredict, built with Python Flask) that exposes the full normalization and regression computation transparently to the user.
LINEAR INTERPOLATION AS A BASELINE TOOL FOR ONLINE RIDE-HAILING FARE ESTIMATION DURING PEAK HOURS Prima Yalesta Sarumaha; Mutaqin Akbar
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/738830

Abstract

The dynamic pricing system on Grab causes frequent fare fluctuations, complicating travel budget planning. This study analyzes GrabBike Reguler fare patterns from Campus 3 of Universitas Mercu Buana Yogyakarta (UMBY) to Mirota Kampus Babarsari. It implements linear interpolation as a lightweight numerical baseline for rough estimation, rather than a robust predictive model. Primary data were collected through direct observation over seven days at 15-minute intervals during morning (07:00–09:00 WIB), noon (12:00–13:00 WIB), and evening (16:00–19:00 WIB) peak periods, noting rainy and non-rainy conditions. This method was deployed via a Python-Flask web application. Results indicate the morning fare stabilizes at IDR 12,000 after a peak of IDR 14,500, while the noon fare remains constant at IDR 12,000. The evening period shows the highest volatility, peaking at IDR 14,342 (17:30 WIB, non-rainy) and IDR 21,250 (18:45 WIB, rainy). Accuracy evaluation across 21 paired data points from morning and evening sessions yielded MAE = IDR 1,047.62, MSE = 4,117,767.24, and RMSE = IDR 2,029.23, representing an 8.7% average error relative to the IDR 12,000 base fare. This margin confirms linear interpolation serves only as a rough baseline reference tool, not a reliable predictive model.
Co-Authors Adella Maharani, Putri Adrian, Reka Adwin Nurhasananda Agung Firmansyah Agus Salim Ahsan, Moh An-Naufal Nuha, Alfian Anisyah Jatu Siti Nurjanah Aprisia Bahagia, Grace Arifadillah, Elang Arita Witanti Ascha, Nugrah Pratama Astri Wulandari Audita Nuvriasari Auditya, Yonathan Bagus Dwi Kurniawan, Bagus Dwi Bambang Agus Setyawan Budi Sulistiyo Jati Budi Sulistiyo Jati Budianto, Alexius Endy Dangin, Dangin Dian Kartika Sari, Dian Kartika Diski Ijtima Putri Dwiyati Pujimulyani Elsa Anggraini Maili Fajriansyah, Dede Febri Rahmadsyah Fiki Ertandi Firdaus Alfajar Sudarsih Hardiyanto, Andri Hendri Tri Cahya Leksana Hilda Sukma Pertiwi Ichlasia Ainul Fitri Ikram, Rauf Al Indah Susilawati Intan Aulia Irfan Nur Fahrudin Irfan Pratama Jeremias Quintino Tilman Junianto Bagas Prasetyo Kafilahudin, Fahrul Advis Kartadinata, Arifqi Khuzaini*, Nanang Kuswandaru, Kuswandaru Marfianto, Jodhy Dwi Muhammad Abdul Gofur Muhammad Ali Ma'mun Muhammad Pratiwo Muhammad Syadham, Syahrun Muhammad Syaifudin Musa, Rahmat Nafida Hetty Marhaeni Nanang Khuzaini, Nanang Nanik Triatmi Nugroho, Yassir Ahmad Nur Alamsyah Nurdiarti, Rosalia Prismarini Nusantara, Bondan Surya Prasetyaningrum, Putri Taqwa Prima Yalesta Sarumaha Primananda, Muhammad Izra Priscilia Amanda Leza Priyanto Purnama, Arif Pria Putu Sangyoga, Titus Bintang Pekiek Rahmat Musa Ramos, Sarah Vega Refky Satria Bima Reny Yuniasanti Rio Setya Pambudi Rismanto, Septa Rismaria Sipayung Rivansyah Subagyo, Ibnu Rivka Novi Cahyati Riyanto, Agung Rizky, Muhammad Rafi Fajar Rofiqi, Lutfi Rohmad, Arinadi Nur Rosalia Prismarini Nurdiarti Saputra, Aldi Dwi Saputra, Andika Dwi Sari, Prima Wulan Sedyarsa, Hanif Fauzan Septa Rismanto Setyaningsih, Putry Wahyu Sidiq Purnomo, Agus Sri Muhammad Kusumantomo Subhan Bole Boly Suharjo, Imam Supatman Supatman Thimoty Pascal Munthe Umul Aiman Wakidi Wakidi Wibowo, Sigit Heri Wisnu Adi Yulianto Wulandari, Astri Yusanto, Yoga Zada Maulana