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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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Perbandingan Kinerja Algoritma Naive Bayes dan K-Nearest Neighbor dalam Menganalisis Sentimen Pengguna Game Free Fire Sudiasta Putri, Nyoman Dinda Indira; Maysanjaya, I Made Dendi; Sunarya, I Made Gede
Jurnal Pseudocode Vol 12 No 2 (2025): Volume 12 Nomor 2 September 2025
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.12.2.53-59

Abstract

Free Fire is one of the most popular online games in Indonesia, yet it continues to receive a wide range of user reviews regarding gameplay experiences. These reviews reflect diverse user perceptions, including both praise and criticism, making sentiment analysis essential to understanding user satisfaction. This study aims to classify user sentiments toward Free Fire using a combined dataset collected from the Google Play Store and App Store, and to compare the performance of two text classification algorithms: Naive Bayes and K-Nearest Neighbor (KNN). The data were collected using web scraping techniques and manually labeled by expert validators. Text preprocessing involved cleansing, tokenizing, stopword removal, and stemming, followed by term weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method. The experimental results show that the Naive Bayes algorithm achieved the highest accuracy of 72.78%, while the KNN algorithm recorded a maximum accuracy of 45.91%. Based on these findings, Naive Bayes is proven to be more effective in classifying user sentiments related to Free Fire. The results of this study are expected to provide constructive insights for developers to improve the quality and user experience of the game.
SISTEM PENDUKUNG KEPUTUSAN BERBASIS WEB DALAM PENENTUAN SISWA BERPRESTASI DI SMK NEGERI BALI MANDARA MENGGUNAKAN METODE MARCOS Rudy Satya Wira Dharma, Kadek; Sunarya, I Made Gede; Mahendra, Gede Surya
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.9001

Abstract

Pemilihan siswa berprestasi di SMK Negeri Bali Mandara saat ini masih dilakukan secara manual menggunakan spreadsheet, sehingga rentan terhadap kesalahan manusia dan penilaian subjektif. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) berbasis web yang objektif dan transparan menggunakan metode Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Pengembangan sistem mengadopsi model Agile Development untuk menjamin fleksibilitas terhadap kebutuhan sekolah. Penelitian melibatkan 44 siswa kelas XI TKJ sebagai alternatif yang dievaluasi berdasarkan lima kriteria penilaian: nilai rapor, nilai IPC, prestasi, absensi, dan ekstrakurikuler. Hasil pengujian fungsional melalui Black Box menunjukkan sistem berjalan 100% sesuai spesifikasi. Evaluasi kepuasan pengguna menggunakan System Usability Scale (SUS) menghasilkan skor 77,25, yang termasuk dalam kategori "Good" dan tingkat penerimaan "Acceptable". Implementasi metode MARCOS terbukti memberikan hasil pemeringkatan yang stabil melalui penentuan hubungan antara alternatif dengan solusi ideal dan anti-ideal. Sistem ini memberikan solusi praktis bagi pihak manajemen sekolah dalam menentukan siswa berprestasi secara akurat, transparan, dan akuntabel.
Tinjauan Sistematis Literatur Tentang Sistem Deteksi Penyakit Berbasis Deep Learning Hartawan, I Kadek Priyogi Giri; Sunarya, I Made Gede
Digital Transformation Technology Vol. 6 No. 1 (2026): Periode Maret 2026
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v6i1.7503

Abstract

Perkembangan teknologi deep learning telah memberikan kontribusi yang signifikan terhadap pengembangan sistem deteksi penyakit pada berbagai domain, seperti pertanian, medis, dan peternakan. Berbagai penelitian telah memanfaatkan metode deep learning untuk meningkatkan akurasi dan efisiensi deteksi penyakit berbasis citra, namun keberagaman pendekatan, dataset, dan metrik evaluasi yang digunakan menunjukkan perlunya kajian yang lebih komprehensif. Penelitian ini bertujuan untuk melakukan Systematic Literature Review (SLR) guna mengidentifikasi metode deep learning yang dominan digunakan, tren performa model, serta tantangan dan peluang penelitian dalam bidang deteksi penyakit. Metode SLR dilakukan dengan mengikuti tahapan perencanaan, pelaksanaan, dan pelaporan, dengan menganalisis 21 artikel ilmiah yang dipublikasikan pada rentang tahun 2018–2025. Artikel dikumpulkan melalui beberapa basis data, antara lain Publish or Perish, Google Scholar, serta Garuda Kemdikbud. Hasil kajian menunjukkan bahwa Convolutional Neural Network (CNN) dan pendekatan transfer learning menggunakan model pralatih seperti ResNet dan MobileNetV2 merupakan metode yang paling banyak digunakan dan mampu menghasilkan performa yang tinggi. Selain itu, metode YOLO menunjukkan keunggulan dalam tugas deteksi objek secara real-time, khususnya pada domain pertanian. Secara umum, penelitian pada domain pertanian menghasilkan tingkat akurasi di atas 90%, sedangkan pada domain medis berkisar antara 85% hingga 97%. Meskipun demikian, tantangan seperti keterbatasan dataset, ketidakseimbangan kelas, dan kebutuhan komputasi yang tinggi masih menjadi permasalahan utama. Oleh karena itu, penelitian ini menyimpulkan bahwa deep learning merupakan pendekatan yang andal untuk deteksi penyakit serta memiliki peluang pengembangan lebih lanjut melalui penerapan model ringan, peningkatan kualitas dataset, dan explainable AI.
Perangkat Cerdas Berbasis U-Net untuk Memantau Kerusakan Daun Pakcoy Akibat Serangan Hama Diani, Ni Komang; Kesiman, Made Windu Antara; Sunarya, I Made Gede; Nugraha, Putu Zasya Eka Satya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6957

Abstract

Implementasi Pertanian modern di Smart Green Garden Universitas Pendidikan Ganesha saat ini menghadapi tantangan yang signifikan dalam efisiensi mendeteksi serangan hama pada tanaman Pakcoy (Brassica rapa L.). metode pemantauan secara manual terbukti tidak efektif dan memakan waktu yang cukup lama serta mengalami kesulitan dalam mengenali kerusakan daun pakcoy dalam skala besar. Penelitian ini berfokus pada pengembangan perangkat cerdas yang menggabungkan arsitektur deep learning U-Net untuk segmentasi semantik dengan perangkat Internet of Things (IoT). Sistem ini menggunakan kamera CCTV yang dimodifikasi dengan Stepper motor Nema 23 dan Arduino Nano untuk melakukan pemindaian area tanam secara otomatis dalam radius 360 derajat secara otomatis. Dataset penelitian berjumlah 405 citra hasil augmentasi berkualitas tinggi dengan resolusi 512x512 piksel untuk memperkuat generalisasi model. Model U-Net dilatih menggunakan strategi 5-fold cross validation. Hasil evaluasi menunjukkan bahwa Model U-Net pada (Fold-2) menghasilkan performa paling unggul dengan tingkat Akurasi Global 96,8% dan Mean Intersection over Union (mIoU) sebesar 0,683. Sistem ini mampu mendeteksi serta memvisualisasikan area kerusakan daun, seperti lubang dan bercak, secara real-time melalui koneksi Real-Time Streaming Protocol (RTSP) pada dashboard website, hal ini menjadi alternatif pemantauan yang lebih presisi dibandingkan metode deteksi objek konvensional untuk deteksi dini serangan hama. Sehingga sistem cerdas ini diharapkan mampu membantu petani untuk efisiensi waktu pemantauan.
Comparison of Multinomial, Bernoulli, and Gaussian Naïve Bayes for Complaint Classification in Pro Denpasar Application Ida Bagus Mahendra; I Made Gede Sunarya; I Made Agus Wirawan
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24828

Abstract

Pelayanan Rakyat Online Denpasaror PRO Denpasar is a Denpasar City Government application intended as a public service mall to support Denpasar to become a smart city. This application was built since 2014 and is actively used in channeling public complaints so optimization is continuously needed to increase the efficiency of application use. Application optimization is carried out by developing decision support tools to determine complaint categories that are still done manually. The application of the properartificial intelligence method can be used as a solution in classifying complaint categories to become a decision support tool for operators. This study compares three classification methods including multinomial naïve bayes, bernoulli naïve bayes and gaussian naïve bayes by applying TF-IDF feature extraction to determine the best complaint category classification method. Based on eight comparison scenario results by applying a comparison of 25%, 50%, 75% and 100% of complaint descriptions with 5-fold cross validation and 10-fold cross validation, it was found that the multinomial naïve Bayes method provided the best result in seven combined comparisons involving the test parameters accuracy, precision, recall, f1-score and processing time.
Pemanfaatan Google ML Kit untuk Face Recognition sebagai Mekanisme Monitoring CBT Edu Mobile I Kadek Dwi Gitayana Putra; I Made Gede Sunarya; I Ketut Resika Arthana
MASALIQ Vol 6 No 1 (2026): JANUARI
Publisher : Lembaga Yasin AlSys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/masaliq.v6i1.8349

Abstract

Mobile-based academic evaluation systems, such as Computer-Based Tests (CBT), are vulnerable to cheating, including impersonation (proxy test-taking) and illegal collaboration, thereby necessitating more reliable monitoring mechanisms to safeguard exam validity. This study aimed to develop and implement a Face Recognition feature utilizing Google ML Kit as a monitoring system in the CBT Edu application and to examine user responses to its implementation. The research employed a research and development (R&D) method using the ADDIE model (Analyze, Design, Development, Implementation, Evaluation), with exam participants and prospective users as the subjects and Google ML Kit as the research object. Data were collected through questionnaires used to assess system functionality and user experience. The results show that the Face Recognition-based exam monitoring system was successfully designed and implemented in the CBT Edu application. Functional testing using white-box and black-box methods validated all features, while device compatibility and bandwidth consumption testing demonstrated stable performance across various devices and network conditions. User responses measured using the User Experience Questionnaire (UEQ) fell into the “very good” category, with the six dimensions of Attractiveness (5.93), Perspicuity, Efficiency, Dependability, Stimulation, and Novelty (5.72) achieving average scores above 5.7. These findings indicate that the integration of Face Recognition in the CBT Edu application is effective in enhancing exam validity and provides a practical solution to minimize cheating in mobile-based academic evaluation systems, while simultaneously delivering a positive user experience.
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.
Continuous Regression Models for Mapping the Smartphone Addiction Spectrum Using Random Forest Regressor Eka Aditya Saputra; I Made Gede Sunarya; Putu Hendra Suputra
International Journal of Management Science and Information Technology Vol. 6 No. 1 (2026): January - June 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i1.7098

Abstract

This study proposes a predictive modeling approach to measure the level of smartphone addiction in adolescents by transforming a conventional binary classification model into a continuous regression model. The use of categorical labels often fails to capture the complex spectrum of addictive behaviors, so this study implemented the Random Forest Regressor algorithm to predict addiction scores on a scale of 1.0 to 10.0. The experimental results show that the regression model is able to provide high prediction accuracy, as evidenced by the coefficient of determination obtained R^2 of 0.8607 and a Mean Absolute Error (MAE) of 0.2854. These findings confirm that the regression approach offers better data resolution in mapping the degree of digital dependency than classification methods. In practice, this model produces a continuous score that provides a dynamic tool for mental health professionals. This approach allows for objective monitoring of patient’s behavioral progress during recovery. Furthermore, this model can facilitate multilevel psychological interventions and tailored care, from early prevention to therapy for high-risk addicts.
User Experience Evaluation of the Internal Quality Assurance Information System (E-AMI) using the User Experience Questionnaire Plus (UEQ+) Ni Putu Anik Mentayani; I Made Candiasa; I Made Gede Sunarya
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6483

Abstract

This study evaluates the user experience (UX) of the E-AMI system at Primakara University, an academic quality assurance system managed by the Quality Assurance Institution (LPM). A sequential explanatory mixed-method design was employed, combining quantitative measurement using the User Experience Questionnaire Plus (UEQ+) across 15 scales with qualitative data collected through observations and semi-structured interviews. A total of 41 respondents participated, consisting of administrators, auditors, and auditees actively involved in the Internal Quality Audit (AMI) process. UEQ+ scores range from -3 (very negative) to +3 (very positive), with scores above 1.0 generally classified as good. The quantitative results yielded an average KPI score of 2.10, indicating good user experience quality, with the highest scores recorded in usefulness (M = 2.51), efficiency (M = 2.26), and perspicuity (M = 2.38). These findings were corroborated by qualitative data confirming the system's effectiveness in supporting core AMI processes. However, both quantitative and qualitative findings converged on several persistent UX weaknesses: Importance-Performance Analysis (IPA) identified visual aesthetics (M = 1.41), novelty (M = 1.90), attractiveness (M = 1.91), stimulation (M = 1.95), and value (M = 1.98) as priority improvement dimensions, consistent with qualitative findings of unintuitive navigation, insufficient system feedback, interface inconsistencies, and incomplete feature support. It is concluded that while the E-AMI system performs well in functional dimensions, further development in visual, experiential, and feature dimensions is required to achieve optimal UX quality, with future improvements recommended to adopt a user-centered design approach.
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