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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Ekuitas: Jurnal Pendidikan Ekonomi JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING WIDYABHAKTI Jurnal Ilmiah Populer IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Sains dan Teknologi Ekuitas Jurnal Pnedidikan Ekonomi KOMET
I Gusti Agung Indrawan
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Video Company Profile Sebagai Media Promosi Pratama Widya Pasraman Rare Semesta Anggara, I Gede Adi Sudi; Setiawan, Antonio Henry; Indrawan, I Gusti Agung; Setiawan, I Ketut; Fittryani, Yuri Prima
Jurnal KOMET Vol 1 No 1 (2024): Jurnal Komet: Kolaborasi Masyarakat Berbasis Teknologi : Volume 1 Nomor 1, Juni 2
Publisher : Yayasan Sinergi Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/komet.v1i1.10

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pengetahuan dan kesadaran masyarakat tentang Pratama Widya Pasraman Rare Semesta dan pentingnya pendidikan usia dini di Desa Besakih. Berdasarkan analisis situasi, ditemukan bahwa banyak orang tua tidak mengetahui keberadaan sekolah ini dan menganggap pendidikan usia dini tidak penting, yang berdampak pada rendahnya jumlah siswa. Solusi yang diambil adalah merancang dan memproduksi video company profile sebagai media promosi. Proses produksi meliputi pengumpulan data melalui wawancara, observasi, dan kuesioner, serta pembuatan video dalam tiga tahap: praproduksi, produksi, dan pascaproduksi. Hasil pengujian video menunjukkan penilaian sangat baik dari orang tua (98,2%), ahli konten (100%), dan ahli media (93,7%). Video ini efektif dalam menyampaikan informasi dan meningkatkan kesadaran orang tua tentang pentingnya pendidikan usia dini, serta berhasil memperkenalkan Pratama Widya Pasraman Rare Semesta kepada masyarakat.
Public Sentiment Analysis of the Free Nutritious Meals Program (MBG) on Social Media X Using the Naive Bayes Method Aprianti, Ni Nyoman; Desmayani, Ni Made Mila Rosa; Libraeni, Luh Gede Bevi; Indrawan, I Gusti Agung; Radhitya, Made Leo
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11420

Abstract

This study aims to analyze public sentiment towards the Free Nutritious Meals Program (MBG) launched by the government, utilizing data from the X (Twitter) platform using the Naïve Bayes method. The background of this study is based on the high level of public attention towards the MBG program, which targets school children, toddlers, pregnant women, and nursing mothers, as well as the prevalence of diverse opinions on social media. Data was collected through a crawling process during the period of April 28 to May 28, 2025, using keywords related to MBG, resulting in 12,310 tweets. The data processing stages included text preprocessing (cleansing, case folding, tokenizing, filtering, stemming), word weighting with TF-IDF, training and test data division, and testing using a confusion matrix. The results show that the Naïve Bayes method is capable of classifying sentiment into three categories: positive, negative, and neutral, with optimal performance on an 80:20 data split, resulting in an accuracy of 86.78%, precision of 86.86%, recall of 86.78%, and an F1-score of 86.58%. The majority of public sentiment towards the MBG program was positive, reflecting support for the program's benefits in improving the nutrition of school children and alleviating the economic burden on families. This study is expected to serve as a reference for the government in evaluating public policy and communication strategies, as well as contributing academically to the development of text mining and sentiment analysis studies on social media.
Classification of Gamelan Selonding Music Using Convolutional Neural Network Ni Putu Diah Pradnya Savitri; Anak Agung Gde Bagus Ariana; Ni Kadek Nita Noviani Pande; I Made Dwi Putra Asana; I Gusti Agung Indrawan
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.v6i3.358

Abstract

Introduction: Balinese Selonding gamelan is an endangered sacred repertoire, and automatic recognition of its musical pieces can support documentation and preservation. Method: This study investigates the automatic classification of Selonding gamelan music using a Convolutional Neural Network (CNN). The dataset consists of 10 traditional Selonding compositions. Recordings were segmented into fixed 15-second excerpts, converted to WAV, normalized, and transformed into time–frequency features using two approaches: Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform (CQT). A CNN-based classifier was trained and evaluated using 5-fold cross-validation for each feature representation. Results: The MFCC-based model achieved stable high performance, with mean accuracy of 94.67% (±2.11%), mean precision of 94.97% (±1.90%), mean recall of 94.67% (±2.11%), and mean F1-score of 94.63% (±2.12%) across folds. In contrast, the CQT-based model performed notably worse, reaching only 58.04% mean accuracy and 53.28% mean F1-score, with large variance across folds. These results indicate that MFCC features capture the discriminative timbral characteristics of Selonding more effectively than CQT under the current experimental setting. Conclusion: Overall, the findings show that a CNN trained on MFCC features can reliably distinguish Selonding compositions using only short (15-second) audio segments, despite limited data. This suggests that deep learning is a feasible strategy for indexing, retrieval, and long-term preservation of Balinese gamelan repertoires.
Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube Ni Wayan Indah Juliandewi; Aniek Suryanti Kusuma; Kompiang Martina Dinata Putri; I Gusti Agung Indrawan; I Gusti Ayu Agung Mas Aristamy
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.v6i3.366

Abstract

The advancement of digital technology has increased public engagement in expressing opinions and responding to issues on social media platforms such as X and YouTube. A prominent topic of recent public debate concerns Danantara's management of state-owned banks. This study analyzes public sentiment regarding this issue by comparing the performance of the Naïve Bayes and Random Forest classification methods. A dataset comprising 25,565 entries was collected from both platforms between January 2025 and May 2025. The data underwent text pre-processing, labeling with the InSet Lexicon, and feature weighting using term frequency-inverse document frequency (TF-IDF). The dataset was split at 80:20, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) prior to classification. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that Random Forest performed stably, achieving 84% accuracy both before and after sampling. In contrast, Naïve Bayes achieved 74% accuracy before sampling, which increased to 79% after sampling. These findings suggest that Random Forest is more robust to data imbalance than Naïve Bayes, which is more susceptible to bias toward the majority class.
Co-Authors Anak Agung Gde Bagus Ariana Aniek Suryanti Kusuma Aprianti, Ni Nyoman Aranta, Arik Aristamy, I Gusti Ayu Agung Mas Asana, I Made Dwi Putra Atmaja, Ketut Jaya Ayu Gede Wildahlia Ayu Manik Dirgayusari Christina Purnama Yanti I Gede Adi Sudi Anggara I Gede Agung Chandra Wijaya I Gusti Ayu Anom I Ketut Setiawan I Komang Arya Ganda Wiguna I Made Marthana Yusa I Nyoman Agus Suarya Putra I Nyoman Anom Fajaraditya I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Dwi Guna Ambara Putra I Putu Eka Giri Gunawan I Wayan Agus Surya Darma Ida Bagus Ary Indra Iswara Kompiang Martina Dinata Putri Libraeni, Luh Gede Bevi Lie Jasa Made Sudarma Murpratiwi, Santi Ika Ni Kadek Ariasih Ni Kadek Ariasih, Ni Kadek Ni Kadek Nita Noviani Pande NI LUH PUTU MERY MARLINDA Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Diah Pradnya Savitri Ni Putu Sutramiani Ni Wayan Indah Juliandewi Pramitha, Gede Dana Prawira, I Made Karang Satria Putra, I Putu Dwi Guna Ambara Putra, I Putu Satria Udyana Putu Andy Novit Pranartha Putu Surya Wedra Lesmana Putu Wirayudi Aditama Qinantha, I Kadek Mahesa Chandra Radhitya, Made Leo Sandika, I Kadek Budi Santi Ika Murpratiwi Setiawan, Antonio Henry Sutarwiyasa, I Ketut Tjokorda Istri Agung Pandu Yuni Maharani Welda Widiartha, Komang Kurniawan Willdahlia, Ayu Gede Yuri Prima Fittryani, Yuri Prima