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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) JE (Journal of Empowerment) Journal of Engineering and Scientific Research (JESR) Indonesian Journal of Data and Science Jurnal Scientia Journal of Computer Networks, Architecture and High Performance Computing Jurnal Krisnadana Jurnal Krisnadana Journal of Social Work and Empowerment International Journal of Artificial Intelligence in Medical Issues TECHNOVATE KOMET Jurnal Nasional Teknologi Informasi dan Aplikasinya
Radhitya, Made Leo
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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PKM Media Digital Interaktif Lontar Prasi Arjuna Wiwaha Berbasis AR untuk Meningkatkan Minat dan Pemahaman Budaya Lokal Putu Wirayudi Aditama; Ida Bagus Gede Sarasvananda; Rizkita Ayu Mutiarani; Made Leo Radhitya; Ida Ayu Gede Dwi Jentari
Journal of Social Work and Empowerment Vol 4 No 3 (2025): Vol 4 No 3 (2025): Journal of Social Work and Empowerment - (Mei- Juli 2025)
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/jswe.v4i3.843

Abstract

Tujuan dari kegiatan ini adalah mengembangkan media edukasi interaktif berbasis Augmented Reality (AR) untuk mengenalkan dan melestarikan budaya Lontar Prasi Bali, khususnya cerita Arjuna Wiwaha, kepada generasi muda. Program ini ditujukan untuk meningkatkan pemahaman, minat, dan keterlibatan peserta, terutama siswa SMA/SMK, dalam proses pembelajaran budaya lokal melalui pendekatan teknologi digital yang inovatif dan menarik. Metode pengabdian meliputi: analisis kebutuhan melalui survei dan wawancara; pengembangan aplikasi AR berbasis Unity dan ARCore; digitalisasi konten Lontar Prasi dengan ilustrasi, narasi audio, dan animasi; penyusunan modul digital dan cetak; pelatihan penggunaan media bagi peserta; serta evaluasi dan perbaikan berdasarkan umpan balik pengguna. Prototipe diuji secara langsung pada peserta mitra. tim pelaksana merancang dan mengembangkan media edukasi berbasis Augmented Reality (AR) yang menampilkan visualisasi cerita Arjuna Wiwaha dalam bentuk gambar 3D, teks, suara, dan animasi interaktif. Teknologi ini memberikan pengalaman belajar yang lebih menarik dan memudahkan peserta dalam memahami isi Lontar Prasi. Selain itu, digitalisasi konten dan penyusunan modul pembelajaran baik dalam bentuk digital maupun cetak telah memperkaya sumber belajar yang dapat diakses secara mandiri. Melalui pelatihan, sosialisasi, dan evaluasi menyeluruh, hasil kegiatan ini menunjukkan adanya peningkatan pemahaman peserta terhadap isi Lontar Prasi, serta tumbuhnya minat untuk melestarikan budaya lokal. Penggunaan teknologi dalam pelestarian budaya terbukti mampu menjembatani generasi muda dengan warisan budaya Bali secara efektif dan relevan. Dengan demikian, pengembangan media edukasi berbasis AR ini diharapkan dapat menjadi model pembelajaran inovatif yang berkelanjutan dalam mendukung pelestarian seni dan budaya Bali, khususnya Lontar Prasi.
The Performance of Support Vector Machine in Classifying Public Sentiment toward Student Suicide Cases I Gusi Gede Bagus Ngurah Sarjana; Made Leo Radhitya; Ni Wayan Suardiati Putri
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.431

Abstract

Introduction: The rapid growth of social media has generated large volumes of user-generated content that can be analyzed to understand public responses to sensitive social issues. This study evaluates the performance of Support Vector Machine (SVM) in classifying public sentiment toward a widely discussed student suicide case based on YouTube comments. Method: A total of 5,000 comments were collected from a video on the Denny Sumargo YouTube channel using the YouTube Data API and categorized into positive and negative sentiments. Text preprocessing included cleaning, normalization, tokenization, stop-word removal, and stemming. Term Frequency-Inverse Document Frequency (TF-IDF) was used for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) addressed class imbalance. The dataset was divided into 80% training and 20% testing data, and SVM was applied for binary sentiment classification. Results and Discussion: The SVM model achieved 99.96% training accuracy and 89.25% test accuracy, with precision, recall, and F1-score consistently around 89%. These results indicate that the TF-IDF, SMOTE, and SVM pipeline effectively classified Indonesian social media comments despite the linguistic complexity of discussions surrounding sensitive issues. Conclusion: SVM demonstrates effective and robust performance for classifying public sentiment in Indonesian YouTube comments and provides a useful approach for analyzing public responses to sensitive social phenomena.
Face Images Classification using VGG-CNN Astawa, I Nyoman Gede Arya; Radhitya, Made Leo; Ardana, I Wayan Raka; Dwiyanto, Felix Andika
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).
Co-Authors Adityo Permana Wibowo Admojo, Fadhila Tangguh Agus Harjoko Aprianti, Ni Nyoman Asana, I Made Dwi Putra Asana, Made Dwi Putra Atmaja, I Made Ari Dwi Suta Atmaja, Ketut Jaya Bagus Tresna Bramstya Dwiyanto, Felix Andika Edi Faizal Felix Andika Dwiyanto Gede Wirya Wardhana Giri, Gst. Ayu Vida Mastrika Gst. Ayu Vida Mastrika Giri Gunawan, I Komang Agus Bryan Hamada Zein I Dewa Gede Agung Pandawana I Gede Iwan Sudipa I Gusi Gede Bagus Ngurah Sarjana I Gusti Agung Indrawan I Made Subrata Sandhiyasa I Made Yoga Apriliana I Nyoman Alit Arsana I Nyoman Gede Arya Astawa I Nyoman Tri Anindia Putra I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Eka Andika Cakra I Wayan Raka Ardana I Wayan Supriana Ida Ayu Gede Dwi Jentari Ida Bagus Gede Sarasvananda Ika Arfiani Ketut Laksmi Maswari Libraeni, Luh Gede Bevi Made Wahyu Adhiputra Naswin, Ahmad Ni Made Diah Nandita Pangestu Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Widantari Suandana Ni Wayan Jeri Kusuma Ni Wayan Suardiati Putri Olivia Sutriani Jelni Putra, Putu Satria Udyana Putu Wirayudi Aditama Raharja, Made Agung Rizkita Ayu Mutiarani Saputra, I Wayan Kelvin Widana Selivan, Diana Semadi, Ketut Ngurah Siti Marlina, Siti Subawa, I Gede Arika Triadi, Wilfridus Bambang Waas, Devi Valentino Wahyudi, I Putu Alfin Teguh Widiantari, Ni Komang Mira Wiguna, I Komang Arya Ganda Wiguna, I Wayan Darma Wijaya, Bagus Kusuma