Komparasi linear regression, random forest regression, dan multilayer perceptron regression untuk prediksi tren musik TikTok
DOI:
https://doi.org/10.24246/aiti.v20i2.191-205Keywords:
Popular Music, Comparison, Prediction, Machine Learning, TikTokAbstract
Predicting how audio features correlate with popular songs on TikTok is essential in the music industry. Armed with data that has several audio features, a study was conducted using the Linear Regression, Random Forest Regression (RFR), and Multilayer Perceptron Regression (MLP Regression) methods to compare models that can effectively predict popularity and features that influence song popularity on TikTok, then Exploratory Data Analysis (EDA) was also carried out to gain insight into the data. The results of the EDA process are that the most popular of songs is in the range of 40-80, the duration of songs is between 2-3 minutes, feature loudness is positively correlated with energy, and so is between artist_pop and track_pop. The set feature importance in the LR and RFR models for the feature target track_pop is artist_pop, loudness, and duration_ms. The LR method has the most effective results between RFR and MLP Regression for the dataset used, with MSE of 0.0313, RMSE of 0.177, and MAE of 0.118.
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R. Rachmadayanti, F. Andini, V. V. Susilo, and W. S. Syahroni, “Dampak Algoritma Tiktok Terhadap Konsumsi Musik,” Semin. Nas. Ke-III Univ. Tarumanagara Tahun 2021, pp. 2069–2076, 2021.
R. Ferira, “Analisis Aplikasi TikTok sebagai Platform Membangun Jaringan Bisnis Generasi Millenial,” Munazzama J. Islam. Manag. Pilgr., no. June, pp. 1–15, 2022.
D. Martín-Gutiérrez, G. Peñaloza, A. Belmonte-Hernández, and F. García, “A Multimodal End-to-End Deep Learning Architecture for Music Popularity Prediction,” Inst. Electr. Electron. Eng., vol. 8, pp. 39361–39374, 2020.
Y. Essa, A. Usman, T. Garg, and M. K. Singh, “Predicting the Song Popularity Using Machine Learning Algorithm,” Int. J. Sci. Res. Eng. Trends, vol. 8, no. 2, pp. 1054–1062, 2022.
E. Kyauk, E. Park, and J. Pham, “Predicting Song Popularity,” Dept. Com- put. Sci., Stanford Univ., Stanford, CA, USA, Tech. Rep., 2016, vol. 26, p. 2012, 2012.
X. Liu, “Music Trend Prediction Based on Improved LSTM and Random Forest Algorithm,” J. Sensors, vol. 2022, 2022, doi: 10.1155/2022/6450469.
M. Reiman and P. Örnell, “Predicting Hit Songs with Machine Learning,” http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1214146&dswid=-7013, 2018, [Online]. Available: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229705.
C. V. S. Araujo, M. A. P. Cristo, and R. Giusti, “A model for predicting music popularity on streaming platforms,” Rev. Inform. Teor. e Apl., vol. 27, no. 4, pp. 108–117, 2020, doi: 10.22456/2175-2745.107021.
P. Pareek, P. Shankar, P. Pathak, and N. Sakariya, “Predicting Music Popularity Using Machine Learning Algorithm and Music Metrics Available in Spotify,” J. Dev. Econ. Manag. Res. Stud., vol. 9, no. 11, pp. 10–19, 2022.
D. Sartika and I. Saluza, “Penerapan Metode Principal Component Analysis (PCA) Pada Klasifikasi Status Kredit Nasabah Bank Sumsel Babel Cabang KM 12 Palembang Menggunakan Metode Decision Tree,” Generic, vol. 14, no. 2, pp. 45–49, 2022.
G. A. Sandag, “Prediksi Rating Aplikasi App Store Menggunakan Algoritma Random Forest,” CogITo Smart J., vol. 6, no. 2, pp. 167–178, 2020, doi: 10.31154/cogito.v6i2.270.167-178.
K. Sahoo, A. K. Samal, J. Pramanik, and S. K. Pani, “Exploratory data analysis using python,” Int. J. Innov. Technol. Explor. Eng., vol. 8, no. 12, pp. 4727–4735, 2019, doi: 10.35940/ijitee.L3591.1081219.
M. H. Meinanda, M. Annisa, N. Muhandri, and dan K. Suryadi, “Prediksi masa studi sarjana dengan artificial neural network,” Internetworking Indones. J., vol. 1, no. 2, pp. 31–35, 2009.
I. Muslim and K. Karo, “Implementasi Metode XGBoost dan Feature Importance untuk Klasifikasi pada Kebakaran Hutan dan Lahan,” J. Softw. Eng. Inf. Commun. Technol., vol. 1, no. 1, pp. 10–16, 2020.
S. Jain, P. Chhabra, and S. Johari, “Predict-the-Hit: Prediction of Hit Songs based on Multimodal Data,” Int. J. Sci. Res. Publ., vol. 12, no. 9, pp. 302–309, 2022, doi: 10.29322/ijsrp.12.09.2022.p12940.
K. N. Abd Halim*, A. S. Mohd Jaya, and A. F. A. Fadzil, “Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 3, pp. 272–277, 2020, doi: 10.35940/ijitee.c8472.019320.
P. Misra and A. S. Yadav, “Impact of Preprocessing Methods on Healthcare Predictions,” SSRN Electron. J., no. Ml, 2019, doi: 10.2139/ssrn.3349586.
S. Kappal, “Data Normalization using Median & Median Absolute Deviation (MMAD) based Z-Score for Robust Predictions vs. Min-Max Normalization,” London J. Res. Sci. Nat. Form. Vol., vol. 19, no. 4, pp. 39–44, 2019.
K. Simarmata and K. Hartomo, “Analisa Rekomendasi Fitur Persetujuan Pinjaman Perusahaan Financial Technology Menggunakan Metode Random Forest,” J. Tek. Inform. dan Sist. Inf., vol. 9, no. 3, 2022, [Online]. Available: https://doi.org/10.35957/jatisi.v9i3.2258.
R. Indrakumari, T. Poongodi, and S. R. Jena, “Heart Disease Prediction using Exploratory Data Analysis,” Procedia Comput. Sci., vol. 173, no. 2019, pp. 130–139, 2020, doi: 10.1016/j.procs.2020.06.017.
M. Sholeh, S. Suraya, and D. Andayati, “Machine Linear untuk Analisis Regresi Linier Biaya Asuransi Kesehatan dengan Menggunakan Python Jupyter Notebook,” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 8, no. 1, pp. 20–27, 2022, [Online]. Available: https://jurnal.untan.ac.id/index.php/jepin/article/view/48822.
G. Kou, P. Yang, Y. Peng, F. Xiao, Y. Chen, and F. E. Alsaadi, “Evaluation of feature selection methods for text classification with small datasets using multiple criteria decision-making methods,” Appl. Soft Comput. J., vol. 86, p. 105836, 2020, doi: 10.1016/j.asoc.2019.105836.
C. Haryanto, N. Rahaningsih, and F. M. Basysyar, “KOMPARASI ALGORITMA MACHINE LEARNING DALAM MEMPREDIKSI HARGA RUMAH,” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 533–539, 2023.
B. P. Zen, D. Wicaksana, and H. Alfidzar, “Analisis Sentimen Tweet Vaksin Covid 19 Sinovac Menggunakan Metode Support Vecor Machine,” J. Data Min. dan Sist. Inf., vol. 3, no. 2, pp. 21–27, 2022, doi: 10.33365/jdmsi.v3i2.1926.
V. Sari, F. Firdausi, and Y. Azhar, “Perbandingan Prediksi Kualitas Kopi Arabika dengan Menggunakan Algoritma SGD, Random Forest dan Naive Bayes,” Edumatic J. Pendidik. Inform., vol. 4, no. 2, pp. 1–9, 2020, doi: 10.29408/edumatic.v4i2.2202.
G. A. Sandag, J. Leopold, and V. F. Ong, “Klasifikasi Malicious Websites Menggunakan Algoritma K-NN Berdasarkan Application Layers dan Network Characteristics,” CogITo Smart J., vol. 4, no. 1, pp. 37–45, 2018, doi: 10.31154/cogito.v4i1.100.37-45.
H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 364, 2019, doi: 10.22146/jnteti.v8i4.537.
W. Apriliah, I. Kurniawan, M. Baydhowi, and T. Haryati, “Prediksi Kemungkinan Diabetes pada Tahap Awal Menggunakan Algoritma Klasifikasi Random Forest,” Sistemasi, vol. 10, no. 1, p. 163, 2021, doi: 10.32520/stmsi.v10i1.1129.
S. Saadah and H. Salsabila, “Prediksi Harga Bitcoin Menggunakan Metode Random Forest,” J. Komput. Terap., vol. 7, no. 1, pp. 24–32, 2021.
F. Y. Pamuji and V. P. Ramadhan, “Komparasi Algoritma Random Forest dan Decision Tree untuk Memprediksi Keberhasilan Immunotheraphy,” J. Teknol. dan Manaj. Inform., vol. 7, no. 1, pp. 46–50, 2021, doi: 10.26905/jtmi.v7i1.5982.
Ardianto, A. Raharjo, and D. Purwitasari, “Random Forest Regression Untuk Prediksi Produksi Daya Pembangkit Listrik Tenaga Surya,” vol. 7, no. 4, pp. 1058–1075, 2022.
A. H. Raza and K. Nanath, “Predicting a Hit Song with Machine Learning: Is there an apriori secret formula?,” 2020 Int. Conf. Data Sci. Artif. Intell. Bus. Anal. DATABIA 2020 - Proc., no. July, pp. 111–116, 2020, doi: 10.1109/DATABIA50434.2020.9190613.
I. Oktavianti, E. Ermatita, and D. P. Rini, “Analisis Pola Prediksi Data Time Series menggunakan Support Vector Regression, Multilayer Perceptron, dan Regresi Linear Sederhana,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 282–287, 2019, doi: 10.29207/resti.v3i2.1013.
Z. A. Khan, T. Hussain, I. U. Haq, F. U. M. Ullah, and S. W. Baik, “Towards efficient and effective renewable energy prediction via deep learning,” Energy Reports, vol. 8, pp. 10230–10243, 2022, doi: 10.1016/j.egyr.2022.08.009.
A. H. Afolayan, B. A. Ojokoh, and A. O. Adetunmbi, “Performance analysis of fuzzy analytic hierarchy process multi-criteria decision support models for contractor selection,” Sci. African, vol. 9, p. e00471, 2020, doi: 10.1016/j.sciaf.2020.e00471.
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