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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Capture : Jurnal Seni Media Rekam ANALISIS - SCIENTIFIC JOURNAL OF ECONOMICS, MANAGEMENT, BUSINESS, AND ACCOUNTING Indonesian Journal of Data and Science JURNAL ILMIAH GLOBAL EDUCATION JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Multidisiplin Sahombu Journal of Economics and Business (JECOMBI)
Willdahlia, Ayu Gede
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Music-Structure Segmentation in Balinese Gamelan (Tabuh Lelambatan) with SSM, Checkerboard Novelty, and HMM Pertiwi, Ni Nyoman Sucianta; Ariana, Anak Agung Gde Bagus; Meinarni, Ni Putu Suci; Willdahlia, Ayu Gede; Ariantini, Made Suci
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15494

Abstract

This study aims to automatically segment the musical structure of Balinese gamelan by combining the Self-Similarity Matrix (SSM) method, the Checkerboard Novelty kernel, and Hidden Markov Models (HMM). Balinese gamelan has a complex musical structure that is cyclical and based on a colotomik system, requiring an adaptive analytical approach to repetitive patterns and transitions between musical sections. The research data consists of 30 Tabuh Lelambatan gamelan audio recordings obtained from public digital sources and validated through expert annotation to produce ground truth. The segmentation process was carried out through feature extraction using Constant-Q Transform (CQT), SSM formation to detect acoustic similarity patterns, application of the checkerboard kernel to mark transitions between segments, and temporal sequence modeling using HMM to refine boundary detection. System performance evaluation was carried out by comparing the segmentation results with ground truth using precision, recall, and F1-score metrics. The test results showed an average macro precision value of 0.998, a recall of 0.705, and an F1-score of 0.818, indicating that this method is capable of detecting the main boundaries of musical structures with high accuracy and consistent stability. However, the model still tends to miss gradual micro transitions. This research contributes to the field of Music Information Retrieval (MIR) and supports efforts to preserve traditional Balinese music through data-based analysis and the development of music computing technology.
Public Response on X to the Revocation of Indonesia’s 3-Kg LPG Retail Ban: A Support Vector Machine Study Wahyuni, Ni Nyoman Asti Sri; Sudipa, I Gede Iwan; Sastaparamitha, Ni Nyoman Ayu J.; Willdahlia, Ayu Gede; Aristamy, I Gusti Ayu Agung Mas
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.349

Abstract

This study examines public responses on X to the 3-Kg LPG retail ban implemented on February 1, 2025, and revoked on February 4, 2025, which caused widespread shortages, long queues, and limited access, particularly for citizens living far from official distribution points. A total of 2,524 Indonesian-language tweets were collected via crawling and systematically processed through text cleaning, tokenization, normalization, stopwords removal, and stemming, followed by automatic labeling using the Indonesian Sentiment (InSet) Lexicon. After removing 229 neutral tweets, 1,405 tweets (61.2%) were classified as negative and 890 tweets (38.8%) as positive, with the study focusing on these two sentiment classes. Text features were extracted using TF-IDF, and classification was conducted using a linear-kernel Support Vector Machine (C = 0.1) with an 80:20 train-test split. The model achieved an overall accuracy of 84%, with precision, recall, and F1-score of 0.82, 0.94, and 0.88 for the negative class, and 0.87, 0.68, and 0.76 for the positive class. Results indicate that negative sentiment was dominated by criticism related to LPG shortages and insufficient policy communication, while positive sentiment reflected user relief over restored supply and hopes for fairer distribution in the future. These findings suggest that revoking the ban did not fully restore public perception, highlighting the necessity for more effective policy dissemination and stricter monitoring of 3-Kg LPG distribution. The study also emphasizes the importance of leveraging social media, particularly X, as a real-time source for monitoring public opinion and evaluating the effectiveness of energy distribution policies in Indonesia.
Co-Authors Adiftia, Mohammad Ryan Aniek Suryanti Kusuma, Aniek Suryanti Ariana, Anak Agung Gde Bagus Aristamy, I Gusti Ayu Agung Mas Atmaja, Ketut Jaya Budayasa, I Putu Gede Budiarthana, I Wayan Desak Made Dwi Utami Putra I Gede Iwan Sudipa I Gusti Agung Indrawan I Made Marthana Yusa I Nyoman Agus Suarya Putra Libraeni, Luh Gede Bevi Made Suci Ariantini Maisusanto, Eko Meinarni, Ni Putu Suci Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Pande, Ni Kadek Nita Noviani Pertiwi, Ni Nyoman Sucianta Putra, I Putu Satria Udyana Qinantha, I Kadek Mahesa Chandra Rahayu, Ni Made Astini Sandika, I Kadek Budi Sastaparamitha, Ni Nyoman Ayu J. Sudianti , Ni Made Sudianti, Ni Made Wahyuni, Ni Nyoman Asti Sri Wiguna, I Made Surya Winatha, Komang Redy