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All Journal International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JPTK: Jurnal Pendidikan Teknologi dan Kejuruan Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Pendidikan Teknologi dan Kejuruan KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Sains dan Teknologi Jurnal Simetris Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnal Sistem dan Informatika KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Multimedia JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Informatika dan Komputer Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of System and Computer Engineering INSERT: Information System and Emerging Technology Journal KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Sains dan Komputer International Journal of Management Science and Information Technology (IJMSIT) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Locus Penelitian dan Pengabdian Paradigma Digital Transformation Technology (Digitech) MASALIQ: Jurnal Pendidikan dan Sains Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Artificial Intelligence and Digital Business Bulletin of Network Engineer and Informatics (BUFNETS) INOVTEK Polbeng - Seri Informatika
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Pengembangan Video Animasi 3 Dimensi Sejarah Terbentuknya Kampung Loloan Jembrana Didik Nurrahman; I Nengah Eka Mertayasa; I Made Gede Sunarya
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 15 No. 2 (2026): [ONGOING] Karmapati Vol 15 No 2 Tahun 2026
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v15i2.116818

Abstract

Video Animasi 3 Dimensi Sejarah Terbentuknya Kampung Loloan Jembrana menceritakan awal mula kampung Loloan berdiri serta bagaimana nilai-nilai sejarah yang tekandung didalamnya seperti nilai gotong royong, persatuan dan toleransi. Penelitian ini bertujuan untuk (1) Mengembangkan video nimasi 3 dimensi Sejarah Kampung Loloan Jembrana (2) Mengetahui respon pengguna terdahap video nimasi 3 dimensiSejarah Kampung Loloan Jembrana. Pengembangan video nimasi 3 dimensi Sejarah Kampung Loloan Jembrana menggunakan Metode Multimedia Development Life Cycle (MDLC) dengan enam tahap, yaitu concept, design, material collecting, assembly, testing, dan distribution. Hasil penelitian berdasarkan uji ahli isi dan uji ahli media memperoleh validitas 1,00 dengan tingkat validitas “Sangat Tinggi”, dan , uji respon pengguna memperoleh nilai rata-rata 34,17 serta sebanyak 92,5% responden memberikan respon dengan kualifikasi “Sangat Positif, dan 7,5% responden memberikan kualifikasi “Positif”. sehingga video animasi 3 dimensi Sejarah Kampung Loloan Jembrana dapat diterima dengan baik oleh masyarakat khususnya masyarakat Kelurahan Loloan Barat dan Kelurahan Loloan Timur. Dengan dibuatnya video animasi 3 dimensi Sejarah Kampung Loloan Jembrana diharapkan masyarakat khususnya generasi muda mengetahui dan ikut melestarikan sejarah Kampung Loloan beserta nilai-nilai yang terkandung didalamnya.
Perbandingan Algoritma Naive Bayes Berbasis Feature Selection Gain Ratio dengan Naive Bayes Kovensional dalam Prediksi Komplikasi Hipertensi I Made Arya Adinata Dwija Putra; I Made Gede Sunarya; I Gede Aris Gunadi
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2024): May
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i1.488

Abstract

High blood pressure is a significant public health problem with a high prevalence in the Indo-nesian population. 2018 Riset Kesehatan Dasar (Riskesdas) data shows a prevalence of hyperten-sion of 34.1% in individuals aged 18 years and over, with the highest figure in South Kalimantan and the lowest in Papua. Complications arising from hypertension can have serious impacts on organs such as the brain, eyes, heart and kidneys. The Naive Bayes algorithm is generally used in disease prediction, but the Naive Bayes algorithm has problems when selecting attributes, because Naive Bayes itself is a statistical classification method that is only based on Bayes' Theorem so it can only be used with the aim of predicting the probability of membership in a group or class. So attribute weighting is needed to increase accuracy more effectively. This research introduces Gain Ratio as an attribute weighting method to increase the accuracy of Naive Bayes. The aim of this study was to compare conventional Naive Bayes with Naive Bayes Gain Ratio in predicting complications of high blood pressure. The research results show that feature selection with gain ratio weighting can increase the accuracy of naive Bayes classification, with an average increase in accuracy of 20% compared to naive Bayes without feature selection. The precision value increased by 21% in the naive Bayes gain ratio algorithm for the kidney failure class, an increase of 3% in the heart class, and an increase of 31% in the stroke class, for the recall value the naive Bayes gain ratio increased by 35% in the heart class while in the kidney failure and stroke classes did not increase the recall value.
Recognition of Balinese Traditional Ornament Carving Images with Convolutional Neural Network and Discrete Wavelet Transform Ni Luh Putu Kurniawati; Made Windu Antara Kesiman; I Made Gede Sunarya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 4 (2022): Desember
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24360

Abstract

Balinese carvings are less known to the public due to the lack of information about Balinese carvings. Minimum information about Balinese carvings can be overcome by utilizing advances in information technology in the field of image processing, namely the introduction of Balinese carving patterns. In the pattern recognition model of an image, there are several things that can be analyzed, such as the recognition method used, feature extraction, including the model in preprocessing to reduce noise in a Balinese carving image. In this study, the Convolutional Neural Network (CNN) was used to classify Balinese carving images combined with Discrete Wavelet Transform (DWT) in extracting image features. The introduction was made to 25 categories of Balinese carving ornaments. Tests are generated based on the level of accuracy generated in the testing process. Analysis of the results was carried out on the resulting model, namely the analysis of the combination of CNN with DWT and without DWT. Testing the data set with 212 training data and 129 testing data using all DWT channels. Based on the results of the tests that have been carried out, it is found that using the DWT extraction feature produces a higher testing accuracy value, namely 35.66% for 25 classes and 74, 42% for 3 carving classes. Meanwhile, without using DWT, it produces an accuracy value of 32.56% for 25 classes and 66.67% for 3 carving classes. In future research, it is hoped that there will be an improvement in the data set and good shooting with a balanced and adequate number for the 25 carving classes that have been obtained.
Detecting the Same Pattern in Choreography Balinese Dance Using Convolutional Neural Network and Analysis Suffix Tree I Komang Hendra Trinium Jaya; Made Windu Antara Kesiman; I Made Gede Sunarya
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.24461

Abstract

The Balinese dances that are popular today were created by maestros who have existed since time immemorial. To develop the dances made by the existing maestro, one must know the characteristics of each dance based on the motion used. The help of digital image processing and string algorithm analysis methods will help to determine the characteristics of a dance. The algorithm used for dance analysis is the Suffix Tree, where the suffix tree is one of the algorithms that can be used to find patterns from input strings. The string to be analyzed is a series of codes performed by the classifier. The classifier used is Convolutional Neural Network. This method uses an image as its input, which will later perform convolution operations and perform a full-connected layer. The results were obtained using the Convolutional Neural Network method with Alexnet architecture as the classification and confusion matrix to calculate the level of accuracy of the test set, the best accuracy for the head is by using parameter learning rate 0.001, epoch 150, and RGB color space obtained 95% accuracy, 88% precision, 78% recall, and 82% f1-score. For the full body, using a learning rate of 0.01, epoch 150, and RGB color space, the accuracy is 85%, precision is 79%, recall is 64%, and f1-score is 69%. For the legs, using a learning rate of 0.001, epoch 150, and RGB color space, the accuracy is 92%, precision is 84%, recall is 59%, and f1-score is 65%. The results of the suffix tree analysis between codes that use ground truth and classification results have similar values, although the results of the movement patterns obtained by the suffix tree algorithm have not varied, which is dominated by class A because class A is the dominant class in each dance.
Comparison of Linear and Ridge Regression for Estimating Indonesia’s IHSG, 2010–2024 Indah Saraswati, I Dewa Ayu; Yunita Dewi, Kadek; Rehatta, Jullio; Sunarya, I Made Gede; Oka Gunawan, I Made Agus
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 3 (2026): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.117861

Abstract

This study aims to estimate the movement of the Indonesia Composite Stock Price Index (IHSG) using linear regression and Ridge Regression based on monthly data from 2010 to 2024, where IHSG serves as a key indicator of Indonesia’s capital market and requires a simple yet reliable estimation model to support economic and investment decisions. The methodology applies linear regression as a baseline model and Ridge Regression to address potential multicollinearity among independent variables, with model performance evaluated using 5-fold cross-validation and metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that linear regression achieves MAE = 0.068829, MSE = 0.007987, and RMSE = 0.087823, while Ridge Regression performs slightly better with MAE = 0.068547, MSE = 0.007970, and RMSE = 0.087732. Although the differences are relatively small, Ridge Regression consistently produces lower and more stable error values, indicating that it is a more robust alternative for IHSG estimation, particularly for medium- to long-term analysis.
Benchmarking CNN and YOLO Models for Automated Classification of Fish Freshness I Gede Andika Diana Putra; I Gede Aris Gunadi; I Made Gede Sunarya
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 2 (2026): Edumatic: Jurnal Pendidikan Informatika (IN PRESS)
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i2.35019

Abstract

Fish freshness assessment is essential for ensuring food quality and consumer safety; however, conventional visual inspection remains subjective and inconsistent. Although deep learning has shown promising performance in image classification, standardized benchmarking of Convolutional Neural Networks (CNN) and YOLOv8 Classification under identical experimental settings for fine-grained fish freshness classification remains limited. This study compares both models using the same dataset, preprocessing pipeline, augmentation strategy, and training configuration to evaluate predictive performance and computational efficiency. The dataset comprised digital images of tongkol and slungsung fish categorized into four classes: Fresh Tongkol, Rotten Tongkol, Fresh Slungsung, and Rotten Slungsung. Model performance was evaluated using accuracy, precision, recall, F1-score, training time, and inference speed. CNN achieved superior predictive performance with 99.25% accuracy, 99.13% precision, 99.13% recall, and 99.13% F1-score, whereas YOLOv8 Classification achieved 89.88% accuracy, 89.96% precision, 89.88% recall, and 89.89% F1-score. Conversely, YOLOv8 required only 15 minutes for training and 9 ms per image for inference, compared with 23 minutes 20 seconds and 18 ms for CNN. These findings establish a robust benchmark for selecting deep learning architectures by balancing predictive accuracy and computational efficiency in automated fish freshness inspection systems.
Comparison of the Performance of Vector Space Model and Latent Semantic Indexing Algorithms in Book Search Information Retrieval Ida Bagus Satriya Satriya Wibawa; I Gede Aris Gunadi; I Made Gede Sunarya
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6503

Abstract

Selecting an appropriate method for an information retrieval system is a critical factor in achieving accurate and efficient search performance. This study aims to compare the performance of the Vector Space Model (VSM) and Latent Semantic Indexing (LSI) in the context of book information retrieval, with particular emphasis on semantic capability and computational efficiency. The dataset was constructed by merging two book datasets obtained from the Kaggle platform, which were originally sourced from Amazon's book catalog. After data normalization and duplicate removal, the final dataset consisted of 133,491 book records. The analysis focused on two primary attributes: book titles and book descriptions. The evaluation was conducted through two experimental scenarios: polysemy and synonymity testing, assessed using the Mean Absolute Percentage Error (MAPE) across 20 documents and five search queries, and retrieval speed testing, measured by response time on Google Colab using 20 dataset size variations. The experimental results indicate that LSI outperformed VSM in three of the five search queries, achieving the best MAPE score of 33.20%, whereas VSM recorded its lowest MAPE of 36.35% but deteriorated to 72.01% for queries with high semantic ambiguity. In contrast, VSM demonstrated superior computational efficiency in the retrieval speed evaluation, with response times ranging from 0.4583 ms to 7.2597 ms, substantially faster than LSI, which required between 2.5290 ms and 27.6777 ms. Both algorithms exhibited a linear increase in response time as the dataset size increased, with coefficients of determination of R² = 0.999 for VSM and R² = 0.997 for LSI. The findings reveal a significant trade-off between semantic accuracy and computational efficiency: LSI provides superior semantic understanding for information retrieval, whereas VSM offers substantially faster retrieval performance.
Comparison of SMOTE, Class Weighting, and Classical Machine Learning Models on the ID-SMSA Indonesian Stock Market Dataset I Komang Adyanata; I Gede Aris Gunadi; I Made Gede Sunarya
Journal of System and Computer Engineering Vol 7 No 3 (2026): JSCE: July 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i3.2688

Abstract

Sentiment classification of social-media text related to the Indonesian stock market is a growing research area. The ID-SMSA dataset is the publicly available labelled corpus for this domain, yet class-imbalance handling strategies on this dataset have not been systematically compared across multiple classifiers. This paper evaluates Multinomial Naive Bayes, linear Support Vector Machine (SVM), and Random Forest under three imbalance-handling conditions: no handling, class weighting, and SMOTE. All experiments use the full 3,287-tweet dataset with an 80:20 stratified split and report macro F1 as the primary metric. SMOTE consistently improves macro F1 across all classifiers. The largest gain is on Naive Bayes (+0.137, from 0.589 to 0.726). The best configuration is SVM with SMOTE, achieving macro F1 of 0.752 and accuracy of 0.784. Class weighting benefits Random Forest (+0.011) but slightly reduces SVM, confirming that linear SVM on TF-IDF is robust to moderate imbalance at IR = 2.41. Per-issuer evaluation reveals macro F1 variation from 0.647 on TPIA to 0.881 on BBNI, shaped by vocabulary consistency, class dominance, and domain specificity. These results provide a transparent and reproducible classical baseline that situates transformer-based and deep-learning approaches on ID-SMSA within a well-defined reference frame.
Internet Network Analysis with Hierarchy Token Bucket Method at Dhyana Pura University Trywanto Rina; Kadek Yota Ernanda Aryanto; I Made Gede Sunarya
Paradigma - Jurnal Komputer dan Informatika Vol. 25 No. 2 (2023): September 2023 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v25i2.2354

Abstract

Bandwidth management is indispensable in computer networks. Not only to manage the needs of each individual, but also to keep the data traffic running smoothly. Dhyana Pura University is a private university that utilizes information technology in achieving optimal performance. Observation results with throughput, delay, packet loss and jitter parameters show that bandwidth management has not been done properly. Implementation of bandwidth management is done on Mikrotik Cloud Core Router and PC Router based on Ubuntu server version 16.04. One way to reduce performance degradation is to manage bandwidth. Good bandwidth management is expected to provide the right Quality of Service (QoS) for each internet service. The Hierarchy Token Bucket (HTB) method as a queuing method that regulates bandwidth usage to be given to each internet user shows more optimal results and is easier to use according to the desired needs. This is because the bandwidth is divided evenly and prevents one user from spending excessive bandwidth, so that it can increase employee satisfaction in using internet services. The results of the analysis of measuring the level of employee satisfaction with the Customer Satisfaction Index (CSI) method show that the HTB method has a total satisfaction index of 66.154% in the very satisfied category, while for troughput variables of 65.32%, delay of 67.14%, packet loss of 66.50% and jitter of 65.40%. Thus the implementation on the internet network at Dhyana Pura University using the Hierarchy Token Bucket (HTB) method is feasible to implement with a satisfied predicate.
Analysis of the Success of the PLN Mobile Application Using the DeLone and McLean Ni Putu Viky Aryani; ⁠I Made Candiasa; I Made Gede Sunarya
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2yzf2m17

Abstract

The advancement of digital technology has driven a transformation in public services, including the electricity sector. PT PLN (Persero) responded to this change by launching the PLN Mobile application; however, a gap between user expectations and actual experiences remains evident. This study aims to evaluate the success of the PLN Mobile application using the DeLone and McLean (D&M) model, focusing on five dimensions, system quality, information quality, user interest, user satisfaction, and net benefits. A quantitative method was employed through Structural Equation Modeling (SEM) with AMOS based on data from 150 active respondents. The goodness-of-fit results confirmed that the model was acceptable (RMSEA = 0.039; CFI = 0.977; TLI = 0.975). Path analysis revealed that system quality (β = 0.240; p = 0.006) and information quality (β = 0.381; p < 0.001) positively influence user interest. User interest enhances satisfaction (β = 0.501; p < 0.001), while satisfaction significantly affects net benefits (β = 0.420; p < 0.001). In contrast, the direct effects of system and information quality on satisfaction and the direct effect of user interest on net benefits were not significant. The novelty of this study lies in validating the D&M model within the context of a public utility application in Indonesia, emphasizing the mediating role of user satisfaction. These findings highlight that improving system and information quality not only stimulates user interest but also ensures satisfaction as the key mediator driving the realization of net benefits from the PLN Mobile application.
Co-Authors ., Dewa Ayu Kade Diah Arindia Putri ., Gede Agus Udayana ., I Putu Eka Dharma Cahyadi ., Km Pita Setiarini ., Ni Kd Putri Ariani ., Ni Made Erna Maygayanti ., Novitasari Putri ., Putu Sanistya h Aan Yudianto Acep Taufik Hidayat Ade Widiyantara, I Putu Adi Arta Wibawa, I Gede Made Adi Saputra Yasa, I Gede Agoes Gelgel Aryawan, I Komang Agung Ayu Hanna Cahyani Agung Istri Ariningrat, I Gusti Agung Purnama Putra, I Gede Agung Wahyu Prayoga, I Gusti Agus Permadi, I Nyoman Agus Sutrisna, I Kadek Agus Tria Pradnyana Udayana Agus Tria Pradnyana Udayana, Agus Tria Pradnyana Ali Djamhuri Anak Agung Gde Wahyu Sukma Erlangga Anak Agung Sri Farida Sari Dewi Andika, I Gede Antara, I Gede Wija Aprilia Monica Sari Ardipa, Gede Sukra Ari Kamelia Dewi, Ni Made Arief Hadi Prasetyo Arief Hadi Prasetyo, Arief Hadi Arisandi, Ni Made Desi Artika Winati Mapet, Made Ayu Elviani, Ni Komang Ayu Nirma Lestari, Gusti Bagus Maha Putra, I Gusti Budiana, I Wayan Budiastawa, I Dewa Gede Bunga Anindya, Made Cahyani, Agung Ayu Hanna Cahyo, Kukuh Adhicahyo Candiasa , ⁠I Made Darma Putra, I Kadek Agus Dessy Seri Wahyuni Dewa Ayu Kade Diah Arindia Putri . Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewa Ngakan Putu Eka Juniarta Dewa Nyoman Adi Sista Dewa Nyoman Adi Sista, Dewa Nyoman Adi Dewa Putu Doniawan Dewi , Luh Joni Erawati Dewi, Made Sulatri Dewi, Ni Putu Dita Ariani Sukma Dharma Laksana, I Kadek Diani, Ni Komang Didik Nurrahman Dika Prasetya, I Putu Dinata, I Made Anom Mahartha Doniawan, Dewa Putu Drs. I Wayan Darsana,M.Ed . Dwi Suparyanta, Kadek DWI SURYANTO Dwipayoga, Dewa Made Wisma Eka Aditya Saputra Eka Putra Widiantara Eka Swastika, I Putu Eko Mulyanto Yuniarno Endrawati, Ni Komang Ayu Erlangga, Anak Agung Gde Wahyu Sukma Farida Sari Dewi, Anak Agung Sri Firda Riani Gede Agus Putra Yasa Gede Agus Udayana . Gede Arna Jude Saskara Gede Doni Agustina Gede Doni Agustina Gede Doni Agustina, Gede Doni Gede Nova Kertiana Putra Gede Noverdi Indrawirawan Gede Saindra Santyadiputra Gede Saindra Santyadiputra Gede Saindra Santyadiputra, Gede Saindra Gede Sukra Ardipa Gede Surya Mahendra Giri, I Gusti Putu Yada Gusti Ayu Nirma Lestari Gusti Ngurah Wira Satryawan Hanna Cahyani, Agung Ayu Hartariani, Luh Lina Hartawan, I Kadek Priyogi Giri Hermawan, Norma I G. Uttaram I Gede Adi Saputra Yasa I Gede Agung Purnama Putra I Gede Agus Pebriana I Gede Andika Diana Putra I Gede Aris Gunadi I Gede Bendesa Subawa I Gede Bintang Arya Budaya I Gede Bintang Arya Budaya I Gede Eka Artha Putra I Gede Eka Udiyana I Gede Eka Udiyana, I Gede Eka I Gede Kesumayudha Widiana I Gede Made Adi Arta Wibawa I Gede Mahendra Darmawiguna I Gede Merta I Gede Nyoman Agung Jayarana I Gede Sudirtha I Gede Wija Antara I Gusti Agung Istri Ariningrat I Gusti Agung Mia Pradita I Gusti Agung Wahyu Prayoga I Gusti Ayu Agung Diatri Indradewi I Gusti Bagus Maha Putra I Gusti Gede Raka Wiradarma I Gusti Made Wahyu Krisna Widiantara I Gusti Nyoman Tri Jayendra I Gusti Putu Yada Giri I Kadek Agus Darma Putra I Kadek Agus Sutrisna I Kadek Dharma Laksana I Kadek Dwi Gitayana Putra I Ketut Dedi Kusuma Rena I Ketut Eddy Purnama I Ketut Resika Arthana I Ketut Semara Yasa I Ketut Semara Yasa, I Ketut Semara I Komang Adyanata I Komang Agoes Gelgel Aryawan I Komang Hendra Trinium Jaya I Komang Sureadiputra Diwangkara . I Komang Sureadiputra Diwangkara ., I Komang Sureadiputra Diwangkara I Komang Susena I Made Agus Oka Gunawan I Made Agus Wirawan I Made Ardwi Pradnyana I Made Arya Adinata Dwija Putra I Made Candiasa I Made Kresna Dana I Made Putrama I Made Tirta Murdika I Made Widnyana, I Made I Made Yoga Antara I Made Yudiantara I Made Yudiantara I Md. Dendi Maysanjaya I Nengah Eka Mertayasa I Nengah Eka Mertayasa I Nyoman Agus Permadi I Nyoman Indhi Wiradika I Nyoman Narmada I Nyoman Narmada, I Nyoman I Nyoman Sudiartayasa Adiputra I Putu Ade Widiyantara I Putu Dika Prasetya I Putu Eka Dharma Cahyadi . I Putu Eka Swastika I Putu Gd Sukenada Andisana I Putu Hendra Tresnadana Sueca I Putu Hery Antara I Putu Hery Antara, I Putu Hery I Putu Nata Susila I Putu Surya Dharma Putra I Putu Surya Pratama Wardhana I Putu Wijaya Merta I Wayan Ady Juliantara I Wayan Arya Gina Widyatmaja I Wayan Eka Purnama Putra . I Wayan Eka Purnama Putra ., I Wayan Eka Purnama Putra I Wayan Indra Diatmika I Wayan Indra Diatmika, I Wayan Indra I Wayan Nuarsa I Wayan Sudarsana I Wayan Treman I Wayan Wahyu Nuarsa I Wayan Wijaya Kusuma Ida Ayu Putu Purnami Ida Bagus Jyotisananda Ida Bagus Mahendra Ida Bagus Satriya Satriya Wibawa Ida Bagus Yudha Surya Pradipta Ida Bagus Yudha Surya Pradipta, Ida Bagus Yudha Surya Ida Purnamasari, Putu Ika Hendriana, Komang Inayaturrahman . Inayaturrahman ., Inayaturrahman Indah Saraswati, I Dewa Ayu Indradewi, Gusti Ayu Agung Diatri Indrawirawan, Gede Noverdi Ismoyo Sunu Joko Priambodo Juliantara, I Wayan Ady Kadek Artawan Kadek Artawan, Kadek Kadek Dedi Krisma Prayudi Kadek Dodi Permana Kadek Dodi Permana Kadek Dodi Permana, Kadek Dodi Kadek Dwi Suparyanta Kadek Dwi Yoga Adi Palguna . Kadek Dwi Yoga Adi Palguna ., Kadek Dwi Yoga Adi Palguna Kadek Rido Setiawan Kadek Rido Setiawan, Kadek Rido Kadek Suwis Satria Atmaja Kadek Yota Ernanda Aryanto Kadek Yota Ernanda Aryanto Kertiana Putra, Gede Nova Ketut Agustini Ketut Intan Kusuma Wardani Ketut Sukreni Ketut Sukreni, Ketut Ketut Widiantara Km Pita Setiarini . Komang Devi Kristianti Komang Ika Hendriana Komang Trya Chandra Resmawan . Kristianti, Komang Devi Kumara, I Ketut Bagus Surya Kusuma Wardani, Ketut Intan Lalu Rendy Syahrial Lanang Nugraha, Made Lika Hanifah Luh Asri Ramayanthi Luh Asri Ramayanthi, Luh Asri Luh Joni Erawati Dewi Luh Lina Hartariani Luh Putu Eka Damayanthi Luh Putu Eka Damayanthi Luh Putu Eka Damayanthi, Luh Putu Eka Luh Putu Risma Noviana Risma M.Cs S.Kom I Made Agus Wirawan . Made Artika Winati Mapet Made Bunga Anindya Made Lanang Nugraha Made Sulatri Dewi Made Suyasa Dwi Putra Made Widnyana Made Windu Antara Kesiman Made Windu Antara Kesiman Mahendra, Komang Maryati, Ni Made Rai Mauridhi Hery Purnomo Merta, I Gede Mita Puspita dewi Mita Puspita Dewi, Ni Putu Nata Susila, I Putu Natih, I Dewa Gede Agung Wibhisana Negara, I Made Wahyu Guna Neno, Joseph Extrada Ngakan Putu Eka Juniarta, Dewa Ni Desak Made Sri Adnyawati Ni Kadek Dina Agustina Ni Kadek Dina Agustina, Ni Kadek Dina Ni Kadek Dwi Trisna Rahayu Ni Kd Putri Ariani . Ni Ketut Ayu Purnama Sari . Ni Ketut Ayu Purnama Sari ., Ni Ketut Ayu Purnama Sari Ni Ketut Catur Wahyu Puspitawati Ni Komang Arista Tri Wahyuni Ni Komang Ayu Elviani Ni Komang Ayu Endrawati Ni Komang Oktari Permata Sari Ni Luh Putu Kurniawati Ni Made Ari Kamelia Dewi Ni Made Desi Arisandi Ni Made Erna Maygayanti . Ni Made Nafta Sukendry Ni Made Pradnya Paramita Kusumawati Kusumawati Ni Made Sudiartini Ni Nyoman Emang Smrti Ni Putu Anik Mentayani Ni Putu Ayu Wijayanti Ni Putu Eka Apriyanthi Ni Putu Mita Puspita Dewi Ni Putu Ratna Puspitarini Ni Putu Ratna Wiryani Ni Putu Ratna Wiryani, Ni Putu Ratna Ni Putu Viky Aryani Ni Wayan Martiningsih Novitasari Putri Novitasari Putri . Novitasari Putri, Novitasari Nugraha, Putu Zasya Eka Satya Nyoman Sugihartini P. WAYAN ARTA SUYASA Padama Nyoman Crisnapati Padma Nyoman Crisnapati Padma Nyoman Crisnapati Padma Nyoman Crisnapati Pathni, Ida Ayu Wisma Anggaritha Pebriana, I Gede Agus Permana, Made Ody Gita Pinem, Deby Natalia Br Pradiktha, Wisnu Dwijaya Pradita, I Gusti Agung Mia pramana, i gede pramana ade saputra Prasetia, I Putu Widia Prawira, Putu Yoka Angga Priambodo, Joko Prianka Vedanty, Putu Puspitarini, Ni Putu Ratna Putra Yasa, Gede Agus Putra, I Gede Eka Artha Putra, I Kadek Nurcahyo Putu Alan Arismandika Putu Angga Septiana Putra . Putu Angga Septiana Putra ., Putu Angga Septiana Putra Putu Ary Darma Yasa Putu Ary Darma Yasa, Putu Ary Putu Deri Ariyasa Dana Putu Hendra Suputra Putu Ida Purnamasari Putu Kartika Widya Swari Putu Kartika Widya Swari, Putu Kartika Putu Maha Putra Putu Merta Putu Sanistya h . Putu Sava Adikara Budi Putu Soni Ermawati Putu Suarningsih Putu Wendy Ariyani Putu Yoka Angga Prawira Putu Yudia Pratiwi Putu Zasya Eka Satya Nugraha Rahayu, Ni Kadek Dwi Trisna Rehatta, Jullio Rena, I Ketut Dedi Kusuma Rendy Syahrial, Lalu Riani, Firda Rika Rokhana Rika Rokhana Rizki Anom Raharjo Rokhana, Rika Rudy Satya Wira Dharma, Kadek Santra, Wayan Saputri, Ni Kadek Tesya Ari Sarasmayana, Ketut Yoga Sariyasa . Shodiq Damanhuri Sidik, Purnama Sindu, I Gede Partha Soni Ermawati, Putu Suarningsih, Putu Sudiartayasa Adiputra, I Nyoman Sudiartini, Ni Made Sudiasta Putri, Nyoman Dinda Indira SUGIYANTI, NI PUTU HAPPY VALENTINA Sukendry, Ni Made Nafta Sulatri Dewi, Made Sumantara, I Gusti Lanang Trisna Suputra, I Putu Arsana Surya Diputra, I Gusti Nyoman Anton Surya Pratama Wardhana, I Putu Susena, I Komang Suyasa Dwi Putra, Made Swastika, I Putu Eka Taufik Ismail Taufik Ismail Tirta Murdika, I Made Tita Karlita Tita Karlita Tita Karlita Tresnadana Sueca, I Putu Hendra Tri Arief Sardjono Trywanto Rina Trywanto Rina Uttaram, I G. Uttaram, I G. Viky Aryani, Ni Putu Wahyu Eka Putra, I Gusti Agung Wardana, I Komang Tri Edi Wardhana, I Putu Surya Pratama Wayan Andre Pratama Wayan Andre Pratama Wayan Santra Widiantara, Eka Putra Widiantara, I Gusti Made Wahyu Krisna Widiantara, Ketut Widiantara, Ketut Wija Antara, I Gede Wijaya Kusuma, I Wayan Wijaya Merta, I Putu Wijaya, Ni Made Pradnyaswari Wijayanti, Ni Putu Ayu Wilhelmus Sabatani Jangku Wiradarma, I Gusti Gede Raka Wisnu Dwijaya Pradiktha Yoga Antara, I Made Yoka Angga Prawira, Putu Yudiantara, I Made Yundari, Yundari Yunita Dewi, Kadek ⁠I Made Candiasa