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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Infotech Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI International Journal of New Media Technology Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal ULTIMA Computing Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Tekno Kompak Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Tekinkom (Teknik Informasi dan Komputer) G-Tech : Jurnal Teknologi Terapan BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Komunikasi Journal of Applied Data Sciences DEDIKASI PKM Prosiding Konferensi Nasional PKM-CSR Indonesian Community Journal Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Scientech Research and Development Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Journal of Artificial Intelligence and Digital Business
Ni Made Satvika Iswari
Universitas Primakara
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Peningkatan Keterampilan Digital Siswa SMKN 1 Sukawati melalui Pelatihan Pengembangan Aplikasi Web Terintegrasi Artificial Intelligence Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya
I-Com: Indonesian Community Journal Vol 6 No 3 (2026): I-Com: Indonesian Community Journal (September 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i3.10611

Abstract

Kemajuan teknologi web dan Artificial Intelligence (AI) menuntut siswa untuk memiliki keterampilan digital yang semakin baik, terutama pada jenjang Sekolah Menengah Kejuruan (SMK). Akan tetapi, kesempatan siswa untuk memperoleh pengalaman praktik menggunakan teknologi terbaru masih relatif terbatas. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan keterampilan digital siswa SMKN 1 Sukawati melalui pelatihan pembuatan aplikasi web yang dikombinasikan dengan teknologi AI. Pelaksanaan kegiatan dilakukan melalui pelatihan praktik langsung dengan pendekatan pembelajaran berbasis proyek, yang mencakup materi HTML, CSS, JavaScript, serta penerapan API AI pada aplikasi sederhana. Evaluasi kegiatan dilakukan melalui pre-test, post-test, penilaian proyek, dan observasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan praktis siswa dalam mengembangkan aplikasi web berbasis AI, meskipun masih ditemukan kendala dalam proses adaptasi terhadap materi baru. Kegiatan ini menunjukkan bahwa pelatihan yang berorientasi pada praktik mampu mendukung peningkatan kompetensi digital siswa dan relevan diterapkan dalam pengembangan pembelajaran berbasis teknologi di SMK.
Analyzing Student Sentiments and Insights on Generative AI for Independent Learning in Universities Ni Made Satvika Iswari; I Nyoman Yudi Anggara Wijaya; Ni Putu Widya Yuniari
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1083

Abstract

Transformations in higher education brought about by Generative AI have significantly changed how university students’ access, comprehend, and develop learning materials. This study explores Indonesian university students’ perceptions and experiences regarding the use of Generative AI for independent learning, employing qualitative surveys together with sentiment analysis powered by machine learning. Data were collected from open-ended questionnaires and analyzed using four key algorithms, such as Naive Bayes, Logistic Regression, Random Forest, and Linear SVM, to classify student sentiments towards generative AI technologies. These four classical machine learning models were employed as baseline algorithms commonly used in sentiment analysis to benchmark performance on small, imbalanced educational datasets before applying more complex transformer-based methods. In addition to quantitative analysis, this study also implements thematic analysis of open-ended responses to identify prominent issues, challenges, and student recommendations concerning the use of generative AI in learning. Evaluation results identified Linear SVM as the most consistent model, with the highest weighted F1-score (0.63), although all models showed limitations in detecting negative sentiment due to class imbalance (only three negative samples out of forty responses), which affected model generalization. Key findings indicate that students perceive Generative AI as a supportive tool that accelerates understanding, creativity, and reference searching; however, they remain wary of risks related to dependency, reduced originality, and academic integrity dilemmas. This article recommends the implementation of ethical policy, AI digital literacy training, and enhancement of campus infrastructure to ensure that AI technologies enrich the learning process without compromising student independence and integrity.
Enhancing Aspect-Based Sentiment Analysis in Tourism Reviews Through Hybrid Data Augmentation Ni Made Satvika Iswari; Nunik Afriliana
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.842

Abstract

The increasing reliance on online reviews in tourism has made User-Generated Content (UGC) an invaluable resource for understanding visitor perceptions. However, extracting meaningful insights from these reviews remains challenging due to their unstructured nature, aspect imbalance, and the prevalence of code-mixing between languages such as Indonesian and English—particularly in multicultural destinations like Bali. Aspect-Based Sentiment Analysis (ABSA) offers a promising solution by associating sentiment polarity with specific aspects of tourist experiences. Yet, its performance is often constrained by limited and imbalanced datasets, especially for underrepresented aspects such as sanitation and amenities. This study proposes a hybrid data augmentation framework that integrates three complementary strategies: generative augmentation using ChatGPT, semantic filtering via Sentence-BERT (SBERT), and domain refinement through Masked Language Modeling (MLM). The framework is designed to improve ABSA performance on multilingual tourism reviews by generating synthetic aspect-relevant data while preserving semantic integrity and contextual nuance. Using 398 reviews of Kuta Beach in Bali, we evaluate the effectiveness of the proposed approach across five tourism aspects: scenery, dusk, surf, amenities, and sanitation. Results show that the hybrid strategy reduces hallucination rates from 12% (using ChatGPT alone) to 3.8%, increases F1-scores for underrepresented aspects by up to 5.1%, and improves cross-lingual alignment (Cohen’s κ = 0.78). These improvements demonstrate the synergy between generative and semantic augmentation in addressing real-world ABSA challenges. The proposed method not only advances the state of multilingual ABSA but also offers practical implications for tourism analytics, allowing destination managers to better understand and respond to aspect-specific visitor feedback. The framework is extensible to other low-resource domains, were linguistic diversity and data scarcity present similar limitations.
Co-Authors A. A. Istri Ita Paramitha Adhi Kusnadi Adnyani, Dewa Ayu Anggi Afriliana, Nunik Agustyan Hidayat Andre Rusli Andre Utomo Martliong Anugrah Cahya Dewi, Putri Bagus Nugraha Sindhu, I Gd Putu Bimo Satriantoro David Domarco Dennis Gunawan, Dennis Dewi, Putri Anugrah Cahya Dharma, Eddy Muntina Dwi Eva Pramesti, Ni Made Dwiyanti Gunaswari, Made Dyah Ayu Anggreini Tuasikal Eddy Muntina Dharma Eko K. Budiardjo Fredlina , Ketut Queena I Gede Juliana Eka Putra I Gede Juliana Eka Putra I Gede Prana Yoga I Gede Putu Krisna Juliharta I Komang Adi Galang Permana Galang I Made Dwi Arya Wiguna I Made Surya Kumara I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Nyoman Yudi Anggara Wijaya I Putu Abdiparta I Putu Eka Indrayana Juliana Eka Putra, I Gede Kadek Intan Janeta Pratiwi Ketut Queena Fredlina Ketut Tri Budi Artani Lin, Lin Andriani Luthfialmas Fakhrizki Malik Abdul Ghani Marlinda Vasty Overbeek MB Nugraha Moeljono Widjaja Mulyosaputro, Matthew Alden Muntina Dharma, Eddy Nengah Widya Utami Ni Kadek Juliani Ni Putu Gunaprya Dharmapatni Ni Putu Noviyanti Kusuma Ni Putu Widya Yuniari Nyoman Purnama, Nyoman PERMANA, LEON FARREL AGUNG Putri, Ni Luh Putu Adela Sartian Putu Krisna Juliharta, I Gede Ranny Ranny Reza Setiabudi Risa Rahayu Setiawati, Ni Wayan Robi Rianto Ryan Adiputra Samuel Christoper Santo Syakh Alam, Helmy Wella Wella Wella Wella Widya Utami, Nengah Winarno * **, Winarno * Yaman Khaeruzzaman Zainal Arifin Hasibuan