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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IKRA-ITH Informatika : Jurnal Komputer dan Informatika IJID (International Journal on Informatics for Development) Jurnal Ilmiah Sinus JPM: JURNAL PENGABDIAN MASYARAKAT Jurnal Responsif : Riset Sains dan Informatika Decode: Jurnal Pendidikan Teknologi Informasi Journal Computer Science and Informatic Systems : J-Cosys Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Technologica SENTRI: Jurnal Riset Ilmiah Jurnal Keilmuan dan Keislaman INCOME: Indonesian Journal of Community Service and Engagement Journal of Artificial Intelligence and Digital Business Jurnal Saintifik Journal of International Multidisciplinary Research Journal of Data Analytics, Information, and Computer Science (JDAICS) Applied Information Technology and Computer Science (AICOMS) Cahaya Pengabdian
Nova Tri Romadloni
Universitas Muhammadiyah Karanganyar
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Uncovering Insights in Spotify User Reviews with Optimized Support Vector Machine (SVM) Tri Romadloni, Nova; Kurniawan, Wakhid
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.4903

Abstract

The rapid growth of user-generated reviews on platforms like Spotify necessitates efficient analytical techniques to extract valuable insights.  This study employs a Support Vector Machine algorithm, optimized using Forward Selection, Backwards Elimination, Optimized Selection, Bagging, and AdaBoost, to effectively classify user reviews. A dataset of approximately 10,000 Spotify reviews was compiled from diverse online sources, ensuring a representative sample. The analysis reveals sentiment patterns across positive, negative, and neutral categories, with positive reviews dominates the landscape. These patterns help highlight Spotify’s strengths while identifying areas for improvement. However, the SVM algorithm faces challenges in classifying minority classes, particularly negative sentiments, due to class imbalance. To address this, advanced optimization techniques are utilized to enhance classification precision and recall. Preprocessing steps, including data cleansing, tokenization, stemming, and stopword removal, refine the dataset, while TF-IDF converts text into numerical features for effective feature selection. The results show that the Optimized Selection method achieves the highest accuracy of 84.5%, outperforming other approaches. This research contributes significantly to developing balanced sentiment analysis models. Future studies may explore deep learning techniques to further improve classification accuracy and mitigate current limitations in data representation.
A Hybrid Approach of Pearson Correlation and PCA in Feature Selection for Opinion Mining Tri Romadloni, Nova; Kurniawan, Wakhid; Ariyadi, Muhammad Yusuf; Efendi, Burhan
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5195

Abstract

This study proposes a hybrid feature selection approach that combines Pearson Correlation and Principal Component Analysis (PCA) to improve classification performance in opinion mining tasks. The rapid growth of e-commerce on social media platforms, such as TikTok, has generated a significant volume of user-generated reviews, which are valuable sources of consumer sentiment. However, the high dimensionality of textual data poses challenges in achieving accurate sentiment classification. To address this issue, the proposed method first applies Pearson Correlation to remove irrelevant features with weak correlation to sentiment labels, followed by PCA to reduce dimensionality. The dataset consists of user reviews from the TikTok Seller platform. Experiments using SVM, Naive Bayes, and Random Forest show that the hybrid approach achieves the highest accuracy of 86.2% (SVM and RF), improving over PCA-only by +0.9% and recovering 13.8% accuracy loss for Naive Bayes (from 72.0% to 83.1%). The results demonstrate that integrating correlation- and projection-based methods yields a more compact and effective feature set. This approach is especially suited for opinion mining in noisy, high-dimensional e-commerce data.
Co-Authors Avianti, Risqi Nur Budilaksono, Sularso Cucut Hariz Pratomo Cucut Hariz Pratomo Efendi, Burhan Erwinda Sam Anafih Fanani, Galih Pramuja Inngam Hana Mulia, Pamela Handoko, Frendy Prasetyo Hani Setiani Hasna Karima, Fatimah Hendri Setiawan Hilman F Pardede IMAM SANTOSO Kurniaji, Ganno Tri Buana Kurniawan, Wakhid Lenny Puspita Dewi Mariyanto Mariyanto Mariyanto Miswanto Muhammad Ardiansyah Muhammad Irfan Luthfi Muhammad Qolbi Shobri Muhammad Syahriandi Adhantoro Muhammad Yusuf Ariyadi Mukhlis Mukhlis Mulia, Pamela Hana Mulyawati, Theana Lativa Nashiruddin, Abid Nawwar, Nabil Abiyu Nisa Dwi Septiyanti Nova Ellyanza, Marcellina Nugraheni, Anidhya Siwi Nurul Iksani, Salma Pratomo, Cucut Hariz Primananda, Masdicka Maulana Rahmawati, Aderima Rauhulloh Ayatulloh Khomeini Noor Bintang Resi Intan Penatari Rianti , Ria Rika Putri Utami Saefudin Samsi Samsi Sari, Desi Lajar Setyawati, Yayuk Sholefah, Wahyu Putri Sya’bani, Nur Hayati Tristanti, Novi Wakhid Kurniawan Wakhid Kurniawan Wiwit Supriyanti YAN ADITYA PRADANA Yayu Sri Rahayu Yitno Puguh Martomo