Analisis Sentimen Komentar pada Lagu “Takut” Idgitaf Menggunakan SVM Kernel Linear dan Polynomial

Desi Daomara Sitanggang, Wahyu S. J. Saputra, Muhammad Nasrudin

Abstract


The development of social media, particularly YouTube, has become a platform for people to express their opinions about musical works. The song “Takut” by Idgitaf is one of the pieces that has attracted much attention from listeners because it tells about fear and anxiety in entering adulthood. The great enthusiasm of listeners has generated thousands of diverse comments on the music video. Sentiment analysis of the comments uses a Natural Language Processing (NLP) approach to detect emotional patterns based on two labels, namely positive and negative. This study aims to conduct sentiment analysis on comments about the song “Takut” using the Support Vector Machine (SVM) algorithm by comparing the linear and polynomial kernels. Before the analysis, the data go through preprocessing, labeling, and feature extraction stages. In data labeling, a lexicon-based method is used and then manually updated, where words such as takut, nangis, and sedih are categorized as positive sentiments. The study also applies random undersampling to address data imbalance. Based on testing of 13,037 comments, SVM with a linear kernel showed superior performance with an accuracy of 91%, compared to the polynomial kernel which achieved 87%. This indicates that the linear kernel is more effective in classifying sentiments in this type of text data.

References


Abel Laia, Nico, and Simon Prananta Barus. 2025. “Analisis Sentimen YouTube: ‘Di Balik Ambisi Jokowi Dalam IKN.’” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) 5(1): 07–12. doi:10.55382/jurnalpustakaai.v5i1.891.

Alam, S., & Sulistyo, M. I. 2023. “Analisis Sentimen Berdasarkan Ulasan Pengguna Aplikasi Mypertamina Pada Google Playstore Menggunakan Metode Naïve Bayes 1,2,3).” 2(3): 100–108.

Alviyanti, Risma, Nur Hidayat, Muh Said, Universitas Islam, and Negeri Alauddin. 2025. “Respon Publik Terhadap Strategi Dakwah Melalui Stand Up Comedy Pada Platform YouTube.” 4: 21–31.

Ananda, Fadhilah Dwi, and Yoga Pristyanto. 2021. “Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine Sentiment Analysis of Twitter Users on Internet Service Providers Using Support Vector Machine Algorithm.” 20(2): 407–16. doi:10.30812/matrik.v20i2.1130.

Kurniawan, Y. W., & Rizki, R. C. 2023. “REPRESENTASI QUARTER LIFE CRISIS MELALUI ANALISIS WACANA PADA LIRIK LAGU ‘ TAKUT ’ KARYA IDGITAF TERHADAP REALITA MENTAL HEALTH.” 5(2): 486–510.

Natasuwarna, Amar P, Jurusan Sistem Informasi, Analisis Sentimen, and Pembelajaran Daring. 2020. “Seleksi Fitur Support Vector Machine Pada Analisis Sentimen Keberlanjutan Pembelajaran Daring.” 19(4): 437–48.

Permata Aulia, Thalita Meisya, Nur Arifin, and Rini Mayasari. 2021. “Perbandingan Kernel Support Vector Machine (Svm) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19.” SINTECH (Science and Information Technology) Journal 4(2): 139–45. doi:10.31598/sintechjournal.v4i2.762.

Pratama, Muhamad Rizky, Yudhi Raymond Ramadhan, and Mutiara Andayani Komara. 2023. “Analisis Sentimen BRImo Dan BCA Mobile Menggunakan Support Vector Machine Dan Lexicon Based.”

Purnomo, Bambang Setio, Putri Taqwa Prasetyaningrum, Universitas Mercu, Buana Yogyakarta, Data Mining, and Algoritma K-means. 2021. “PENERAPAN DATA MINING DALAM MENGELOMPOKKAN KUNJUNGAN WISATAWAN DI KOTA YOGYAKARTA MENGGUNAKAN METODE K-MEANS.” 1(1): 27–32.

Rabbani, Salsabila, Dea Safitri, and Nadila Rahmadhani. 2023. “Comparative Evaluation of SVM Kernels for Sentiment Classification in Fuel Price Increase Analysis Perbandingan Evaluasi Kernel SVM Untuk Klasifikasi Sentimen Dalam Analisis Kenaikan Harga BBM.” 3(October): 153–60.

Rahman, O. H., Abdillah, G., & Komarudin, A. 2021. “Klasifikasi Ujaran Kebencian Pada Media Sosial Twitter Menggunakan.” 1(10): 17–23.

Rofiqi, Lutfi, and Mutaqin Akbar. 2024. “Analisis Sentimen Terkait RUU Perampasan Aset Dengan Support Vector Machine.” 4(3).

Shevira, Sheila, I Made Agus, Dwi Suarjaya, and Putu Wira. 2022. “Pengaruh Kombinasi Dan Urutan Pre-Processing Pada Tweets Bahasa Indonesia.” 3(2).

Suryadi, Sudi. 2019. “Implementasi Normalisasi Dalam Perancangan Database Relational.” 3(2): 1–5.

Wahyuni, N. A., Ayu, D. P., & Irsyad, H. 2024. “Analisis Sentimen Di Youtube Terhadap Kenaikan UKT Menggunakan Metode Support Vector Machine Salah Satu Teknik Atau Metode Yang Paling Efektif Untuk Analisis Sentimen Ini Adalah Support Vector Machine ( SVM ). SVM Merupakan Teknik Machine Learning Yang Ba.” 4(1): 57–71.


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