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All Journal International Journal of Advances in Applied Sciences IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Pendidikan Fisika Undiksha Jurnal Pendidikan Teknologi dan Kejuruan Prosiding Seminar Nasional MIPA Wahana Matematika dan Sains Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika Mimbar Pendidikan: Jurnal Indonesia untuk Kajian Pendidikan KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer SINTECH (Science and Information Technology) Journal Jurnal Teknik Informatika UNIKA Santo Thomas Jurnal Serambi Engineering Jurnal RESISTOR (Rekayasa Sistem Komputer) International Journal of Community Service Learning Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika Jurnal Ilmiah Sinus EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Jurnal Mantik Jurnal Teknologi Informasi dan Terapan (J-TIT) JURNAL ILMU KOMPUTER INDONESIA Jurnal Teknologi Informasi dan Multimedia Bali Medika Jurnal Indonesian Journal of Electrical Engineering and Computer Science INFOKUM Didaktik : Jurnal Ilmiah PGSD STKIP Subang Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Journal of System and Computer Engineering J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Locus Penelitian dan Pengabdian Malcom: Indonesian Journal of Machine Learning and Computer Science Bulletin of Network Engineer and Informatics (BUFNETS) INOVTEK Polbeng - Seri Informatika
I Gede Aris Gunadi
Universitas Pendidikan Ganesha
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Enhancement performance of the Naïve Bayes method using AdaBoost for classification of diabetes mellitus dataset type II I Gusti Agung Putu Mahendra; I Made Agus Wirawan; I Gede Aris Gunadi
International Journal of Advances in Applied Sciences Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i3.pp737-746

Abstract

In using technology, especially in health sciences, machine learning modeling can make it easier to predict disease treatment. Naïve Bayes optimization with AdaBoost is needed because even though Naïve Bayes has the advantage of minimal parameters, its accuracy is susceptible to too many features. AdaBoost is used to overcome sensitivity to an excessive number of features and optimize its ability to handle complex datasets. This research aims to analyze the classification results of the Naïve Bayes method with the help of the AdaBoost method. This data comes from Community Health Centers I, II, and III Mengwi District, Bali Province patient medical records. The classification process uses the Naïve Bayes method and Naïve Bayes with AdaBoost, which is then evaluated using a confusion matrix. Two scenarios were used in testing: Naïve Bayes and AdaBoost-based Naïve Bayes. The algorithm is implemented on the dataset and tested directly using cross-validation. The evaluation results show that the Naïve Bayes method experienced an increase in accuracy of 5.92% at 5-fold and 5.93% at 10-fold on a dataset with 890 data. The addition of the AdaBoost method to diabetes classification has been proven to improve the accuracy performance of the Naïve Bayes method.
Evaluasi Sistem Informasi Rapor SP Menggunakan Metode Pieces dan Metode Topsis (Studi Kasus SMKN 1 Kuta Selatan) Ni Kadek Erna Supriathi; I Gede Aris Gunadi; Sariyasa Sariyasa
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 5 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i5.5614

Abstract

Penilaian hasil belajar dalam pendidikan bertujuan untuk menyeimbangkan antara peningkatan capaian hasil belajar siswa dan proses perkembangan pembelajaran, dengan memanfaatkan perkembangan ilmu pengetahuan dan teknologi yang semakin maju guna meningkatkan kualitas pembelajaran. Rapor Satuan Pendidikan (Rapor SP), sebagai sistem informasi yang digunakan pada tingkat sekolah dasar, menengah pertama, menengah atas, kejuruan, dan pendidikan khusus, belum dimanfaatkan secara optimal. Sebagian guru masih memerlukan pendampingan dalam penggunaan sistem tersebut, dan pada periode tertentu seperti akhir semester, aplikasi sering mengalami kendala, seperti akses yang lambat serta proses login yang kurang efisien. Penelitian ini bertujuan untuk melakukan evaluasi secara komprehensif terhadap permasalahan implementasi Aplikasi E-Rapor SP di SMKN 1 Kuta Selatan dengan menggunakan metode PIECES untuk mengidentifikasi kelemahan sistem, serta menentukan dan memprioritaskan rekomendasi solusi perbaikan yang paling efektif dan efisien menggunakan metode TOPSIS. Teknik pengumpulan data meliputi kuesioner dengan skala Likert, wawancara, dan diskusi yang dianalisis menggunakan metode PIECES dan metode TOPSIS. Hasil penelitian menunjukkan bahwa prioritas utama rekomendasi perbaikan ditetapkan pada tiga solusi, yaitu penerapan sistem pencadangan data (backup) mendekati waktu nyata (near real-time) (A2), penguatan sosialisasi pengisian data email untuk proses reset password (A13), serta penerapan validasi format input secara otomatis (A10). Hasil analisis juga menunjukkan bahwa perbaikan pada infrastruktur sistem (stabilitas, keamanan, dan kualitas data) merupakan prasyarat yang lebih mendesak dan efektif (prioritas A2) dibandingkan dengan solusi berupa bimbingan teknis atau sosialisasi semata.
Perencanaan Strategis Sistem Informasi dan Teknologi Informasi di SMA N 1 Singaraja Berbasis Ward and Peppard dan COBIT 2019 Lika Hanifah; I Gede Aris Gunadi; I Made Gede Sunarya
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 5 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i5.5844

Abstract

Perkembangan Sistem Informasi dan Teknologi Informasi (SI/TI) di sektor pendidikan menuntut institusi sekolah memiliki perencanaan strategis yang terarah agar mampu mendukung efektivitas proses bisnis dan peningkatan kualitas layanan. Penelitian ini bertujuan menyusun perencanaan strategis SI/TI pada SMA Negeri 1 Singaraja menggunakan metode Ward and Peppard serta mengevaluasi tata kelola SI/TI berdasarkan kerangka COBIT 2019. Metode penelitian yang digunakan adalah deskriptif kualitatif dengan pendekatan studi kasus melalui observasi, wawancara, dokumentasi, dan kuesioner kepada pihak manajemen sekolah serta pengguna SI/TI. Analisis dilakukan menggunakan SWOT, PEST, Value Chain, Porter’s Five Forces, Critical Success Factors, dan McFarlan’s Strategic Grid, sedangkan evaluasi tata kelola menggunakan domain APO02, APO03, APO06, dan APO07. Hasil penelitian menunjukkan bahwa SI/TI telah mendukung aktivitas akademik dan administrasi, tetapi masih memerlukan peningkatan pada integrasi sistem, pengelolaan sumber daya, kompetensi SDM, keamanan data, serta dokumentasi kebijakan tata kelola. Pemetaan McFarlan’s Strategic Grid menghasilkan portofolio aplikasi pada kuadran strategic, high potential, key operational, dan support. Evaluasi COBIT 2019 menunjukkan capability level secara umum berada pada level 2, sehingga diperlukan standardisasi dan pengukuran proses yang lebih baik. Penelitian ini menghasilkan rekomendasi strategi SI bisnis, strategi TI, strategi manajemen SI/TI, portofolio aplikasi, roadmap implementasi, serta peningkatan tata kelola agar pengelolaan SI/TI sekolah lebih efektif, terarah, dan selaras dengan kebutuhan organisasi.
PERFORMANCE ANALYSIS OF NAÏVE BAYES CLASSIFIERS BASED ON THE INFORMATION GAIN-BASED FEATURE SELECTION WITH MULTICOLLINEARITY ANALYSIS Luh Putu Risma Noviana Risma; I Gede Aris Gunadi; I Made Gede Sunarya
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/732114

Abstract

This study aims to analyze the performance of the Naïve Bayes Classifier algorithm by comparing several feature selection methods, namely Information Gain-based feature selection, Multicollinearity-based feature selection, and a combination of Information Gain and Multicollinearity. The dataset used in this study consists of 337 toddler stunting cases obtained from Kintamani I and VI Public Health Centers. The experiment was conducted using four testing scenarios: (1) Naïve Bayes Classifier without feature selection, (2) Naïve Bayes Classifier with Information Gain feature selection, (3) Naïve Bayes Classifier with Multicollinearity feature selection, and (4) Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity feature selection. All experiments used a data split of 70% training data and 30% testing data, while model performance was evaluated using a confusion matrix. In the Information Gain feature selection stage, several features achieved the highest gain values, namely BPJS with a gain value of 1.0, immunization with a gain value of 1.0, age with a gain value of 0.842, maternal pregnancy history with a gain value of 0.791, and smoking habits with a gain value of 0.756. These features were retained in the final combined model because they contributed the most to the stunting classification process. In addition to improving predictive performance, the combination of Information Gain and Multicollinearity was also able to reduce feature redundancy, resulting in a more stable classification model. The results showed that the accuracy of the Naïve Bayes Classifier without feature selection was 90.10%, the Naïve Bayes Classifier with Information Gain feature selection achieved 95.05%, the Naïve Bayes Classifier with Multicollinearity feature selection achieved 93.07%, and the Naïve Bayes Classifier with a combination of Information Gain and Multicollinearity achieved the highest accuracy of 96.04%. These findings indicate that the combination of Information Gain and Multicollinearity produced the best performance among all tested methods. In addition, a coefficient of determination (R Square) test was conducted using SPSS, resulting in a value of 0.577, indicating that 57.7% of stunting classification was influenced by independent variables such as age, BPJS, immunization, smoking habits, and maternal pregnancy history, while the remaining 42.3% was influenced by other factors outside the scope of this study. The results also indicate that the Naïve Bayes algorithm combined with Information Gain feature selection and multicollinearity testing can be used as a stable and effective approach for early stunting classification to support decision-making in public health services.
An Integrated Smart Village Information System for Digital Public Services and BUMDes Marketplace in Temesi Village Made Junindra Maha Arta Sang; I Gede Aris Gunadi; Gede Rasben Dantes
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/6kh1s185

Abstract

The digital transformation of village governance requires information systems that not only provide administrative services but also support transparency, community participation, and local economic activities. Previous village information systems mostly focused on basic administration or information publication, while integrated public services, community reporting, budget transparency, and village-owned enterprise marketplace features remain limited. This study aims to develop an integrated web-based Village Information System based on the Smart Village concept in Temesi Village, Gianyar Regency. The system was developed using the software development life cycle with the waterfall model, including requirements analysis, system design, implementation, testing, deployment, and maintenance. Data were collected through interviews, focus group discussions, and observations involving village officials and community representatives. The system was implemented using Laravel, MySQL, and a model-view-controller architecture. The developed system provides population data management, administrative service requests, service status tracking, village information, community complaints, budget transparency, village maps, galleries, and a village-owned enterprise marketplace. Functional testing showed that the main features worked according to the expected scenarios. Usability testing involving 50 respondents, consisting of 6 information technology experts, 4 village officials, and 40 village residents, obtained an average System Usability Scale score of 86.15, categorized as Excellent. These results indicate that the system is feasible, usable, and supports Smart Village implementation in Temesi Village.
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.
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.
Improving Butterfly Fish Image Classification Accuracy using HSV Feature Extraction and SMOTE-Based Data Balancing I Putu Arya Putra; I Made Agus Wirawan; I Gede Aris Gunadi
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/vw52nn48

Abstract

Class imbalance in image data can reduce the accuracy of classification models, especially when the minority class data is much smaller than the majority class. This research focuses on enhancing the classification accuracy of butterflyfish images through the application of the Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, combined with the K-Nearest Neighbor (KNN) algorithm utilizing HSV-based feature extraction. The datasets were collected in two conditions, namely conditioned (controlled background and lighting) and unconditioned (varied background and natural lighting). The research stages include preprocessing, HSV feature extraction, data balancing with SMOTE, and classification using KNN with various k values (3, 5, 7, 9) and cross-validation (k-fold 5 and 10). The experimental results show that SMOTE consistently improves accuracy on both types of datasets, with the best performance at k = 3 and k-fold = 10, namely 85.32% (conditioned) and 87.59% (unconditioned). This improvement occurs because a more balanced data distribution allows the model to optimally recognize features between classes. This study proves that the integration of SMOTE and KNN is effective in overcoming class imbalance in image classification, with potential applications in the fields of digital image technology, ecosystem management, and species identification.  
Co-Authors ., Ketut Suma ., Putu Sonia Virgawati Pratiwi Adi Sista, Dewa Nyoman Agus Ariwanta, I Putu Yesha Agus Gunawan Agus Harjoko Agus Harjoko Agus Harjoko Ahmad Asroni Ahmad Asroni, Ahmad Anandita, Ida Bagus Gede Andiny T T Arditaloka, I Wayan Angga Ariasa, Komang Artama, Made Bella Eka Wahyuningtias Cipta, I Putu Agus Eka Yatna Cokorda Oka Birawidya David Juli Ariyadi Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi Oktofa Rachmawati Dharmana, I Wayan Dinata, I Made Anom Mahartha Erlangga, Anak Agung Gde Wahyu Sukma Fauzi, Muhammad Rizki Galih Cahyaningsih, Agung Ukki Gede Indrawan Gede Rasben Dantes Hajrin, M. Heryanto, I Wayan Agus I Gede Andika Diana Putra I Gusti Agung Putu Mahendra I Ketut Agus Indra Diatmika I Ketut Paramarta I Komang Adyanata I Made Arya Adinata Dwija Putra I Made Candiasa I Made Gede Sunarya I Made Gede Sunarya I Made Pradipta I Nyoman Sukajaya I Nyoman Wahyu Semeru Putra I Putu Agus Eka Yatna Cipta I Putu Aris Sanjaya I Putu Aris Sanjaya, I Putu Aris I Putu Arya Putra I Putu Dody Suarnatha I Putu Putra Damana I Wayan Agus Heryanto I Wayan Gede Suweca Antara I Wayan Pio Pratama I Wayan Rosiana I Wayan Sadia I Wayan Santyasa I Wayan Sukra Ida Ayu Mirah Cahya Dewi Ida Bagus Satriya Satriya Wibawa Jana Satvika, Gd. Aditya Kadek Kusuma Wardana Kadek Yota Ernanda Aryanto Ketut Suma Ketut Suma . Ketut Suma . Komang Ariasa Komang Setemen Lika Hanifah Luh Joni Erawati Dewi Luh Putu Budi Yasmini Luh Putu Budi Yasmini Luh Putu Risma Noviana Risma Luh Rumni Oktaria M. Hajrin M.Cs S.Kom I Made Agus Wirawan . Made Artama Made Junindra Maha Arta Sang Made Wahyu Aditya Arta Made Windu Antara Kesiman Made Windu Segara Matius Ivan Bimasena Mimin Yeli Sholekah Moh. Heri Setiawan MS Prof. Dr. Ketut Suma . N Dinda Maharani Ni Kadek Erna Supriathi Ni ketut Lisa Maheni Ni Komang Rai Mirayanti NI LUH PUTU MANIK WIDIYANTI Ni Made Dwi Andayani Ni Made Yeni Dwi Rahayu Ni Putu Eka Apriyanthi Nugraha, I Gede Pradipta Adi Nugraha, I Gusti Agung Satria Oktaria, Luh Rumni Pathni, Ida Ayu Wisma Anggaritha pramana, i gede pramana ade saputra Prof. Dr. Ketut Suma, MS . Putra, I Kadek Nurcahyo Putra, I Nyoman Wahyu Semeru Putra, I Putu Arya Putu Eka Parianthana Putu Sonia Virgawati Pratiwi . Putu Wendy Ariyani Rai Sujanem Risha, Nurfa Sandhiyasa, I Made Subrata Saputri, Ni Kadek Tesya Ari Sariyasa Sariyasa Sariyasa Sariyasa Sawitri D U Segara, Made Windu Sidik, Purnama Sisilia Fhelly Djun Sonia Dewi Parna.T Sri Hartati Suputra, I Putu Arsana Suryawan, I Made Yuda Sutarno, Erwan Sutarno, Erwan Suweca Antara, I Wayan Gede T, Andiny T U, Sawitri D Wardana, I Komang Tri Edi Wayan Eka Ariawan Yogi Duwi Antara