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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 Website Network Automation untuk Manajemen Bandwidth Mikrotik dengan Metode Hierarchical Token Bucket (HTB) di SMP Negeri 8 Singaraja Dwipayoga, Dewa Made Wisma; Saskara, Gede Arna Jude; Sunarya, I Made Gede
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 14 No. 2 (2025)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v14i2.94491

Abstract

Penelitian ini bertujuan untuk mengimplementasikan website network automation manajemen bandwidth mikrotik menggunakan metode HTB (Hierarchical Token Bucket) di SMPN 8 Singaraja, serta mengevaluasi efisiensi waktu pengelolaan jaringan dibandingkan dengan metode manual. Pengembangan sistem dilakukan menggunakan metode waterfall, yang mencakup tahapan requirement analysis, system design, implementation, system testing, dan maintenance. Pengujian sistem mencakup efisiensi waktu konfigurasi, white box testing dengan metode basis path testing, black box testing, serta UAT (User Acceptance Testing). Pada pengujian konfigurasi tiga MikroTik secara bersamaan ,hasil penelitian menunjukkan bahwa konfigurasi manual menggunakan Winbox membutuhkan waktu 2341.20 detik, sedangkan dengan website network automation hanya membutuhkan waktu 38.08 detik. Hasil ini membuktikan bahwa website network automation yang dikembangkan mampu meningkatkan efisiensi waktu sampai 98%, menyederhanakan proses konfigurasi, dan memungkinkan konfigurasi banyak perangkat dalam satu kali proses. Hasil black box testing menunjukkan bahwa seluruh fitur berjalan sesuai harapan, sedangkan white box testing terhadap 12 fungsi menghasilkan 49 jalur independen, dengan CC (Cyclomatic Complexity) yang konsisten, menandakan efisiensi struktur kode sistem. Pada pengujian UAT, yang dilakukan dengan dua responden, yaitu pengelola jaringan di SMPN 8 Singaraja, tingkat penerimaan sistem mencapai 91%, yang termasuk dalam kategori Sangat Setuju (SS). Kesimpulan dari penelitian ini menunjukkan bahwa implementasi website network automation terbukti meningkatkan efisiensi waktu konfigurasi bandwidth mikrotik, dengan langkah yang lebih sederhana dan lebih cepat dibandingkan metode manual. Untuk penelitian selanjutnya, disarankan untuk mengeksplorasi teknik konfigurasi berbasis API sebagai alternatif SSH, guna meningkatkan integrasi, keamanan, dan efisiensi komunikasi dengan perangkat.
Optimalisasi Penggunaan 2 ISP dengan Metode PCC serta Failover di SMP Negeri 4 Singaraja Neno, Joseph Extrada; Saskara, Gede Arna Jude; Sunarya, I Made Gede
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 14 No. 2 (2025)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v14i2.94941

Abstract

Ketersediaan jaringan internet yang stabil sangat penting di SMP Negeri 4 Singaraja, yang memiliki dua jalur ISP Telkom 100 Mbps. Namun, pemanfaatan kedua jalur belum optimal, dengan beban terpusat pada satu jalur. Penelitian ini bertujuan mengoptimalkan penggunaan kedua ISP melalui metode Per Connection Classifier (PCC) dan Failover. PCC mengalokasikan trafik berdasarkan karakteristik koneksi, sementara Failover memastikan ketersediaan jaringan dengan pengalihan ke jalur cadangan. Metodologi yang digunakan adalah PPDIOO. Hasil penelitian menunjukkan peningkatan signifikan dalam kualitas jaringan, dengan kecepatan akses yang lebih stabil. Implementasi PCC memisahkan akses konten (YouTube, TikTok, Instagram) ke ISP 2, sementara akses defaul (Dapodik, Eraport) ke ISP 1, dengan limitasi bandwidth untuk guru (download 5 Mbps, upload unlimited) dan siswa (download 3 Mbps, upload 2 Mbps). Pengujian menggunakan Winbox, CMD, dan QOS berdasarkan standar TIPHON menunjukkan throughput 2.547 Kbps, delay 2,51 ms, jitter 0,000146 ms, dan packet loss 1,21%, dengan kategori sangat baik. Implementasi PCC dan Failover berhasil mengoptimalkan penggunaan kedua jalur ISP.
Evaluasi Kualitas Jaringan Internet di SMP Negeri 3 Singaraja Menggunakan Metode Quality of Service (QoS) Kadek Dedi Krisma Prayudi; Saskara, Gede Arna Jude; Sunarya, I Made Gede
KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) Vol. 14 No. 2 (2025)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/karmapati.v14i2.96208

Abstract

Jaringan yang optimal harus mengutamakan mutu layanan yang diterima oleh pengguna. Dalam proses pembangunan sebuah jaringan, penting untuk mempertimbangkan Quality of Service (QoS). QoS adalah pendekatan yang digunakan untuk menilai seberapa baik mutu layanan jaringan yang ada. Berdasarkan standar TIPHON, parameter-parameter QoS meliputi throughput, jitter, delay, dan packet loss. Di SMP Negeri 3 Singaraja, hasil observasi dan kuesioner yang dilakukan terhadap guru, staf, dan siswa menunjukkan bahwa koneksi internet sering terputus dan aksesnya lambat. Mengingat permasalahan ini, perlu dilakukan evaluasi terhadap kualitas jaringan internet di SMP Negeri 3 Singaraja dengan menggunakan parameter QoS. Melalui penelitian yang menggunakan metode action research, ditemukan bahwa rata-rata throughput mencapai 1091 Kbps dengan indeks 3, yang termasuk dalam kategori baik. Rata-rata jitter tercatat sebesar 8,027 ms dengan indeks 3, juga tergolong baik. Sementara itu, nilai rata-rata delay adalah 8,027 ms dengan indeks 4, yang dikategorikan sangat baik. Selain itu, nilai rata-rata packet loss adalah 0,2% dengan indeks 3, yang berarti baik. Secara keseluruhan, nilai rata-rata QoS adalah 3,25, yang diklasifikasikan sebagai baik menurut standar TIPHON.
Comparison of Random Forest and Support Vector Machine Methods in Sentiment Analysis of Student Satisfaction Questionnaire Comments at ITB STIKOM Bali Sidik, Purnama; I Made Gede Sunarya; I Gede Aris Gunadi
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9617

Abstract

ITB STIKOM Bali is one of the higher education institutions in Bali that focuses on academic activities, particularly in the field of Information Technology. To maintain its educational quality, the Quality Assurance Department collaborates with the Center for Information and Communication (Puskom) to distribute a student satisfaction questionnaire at the end of each semester. In evaluating student satisfaction with campus facilities, the comment section is one of the key indicators, featuring the question: “Based on your experience, please describe which AAK services you found disappointing and in need of improvement.” This study compares the performance of the Random Forest and Support Vector Machine (SVM) methods in conducting sentiment analysis on historical student satisfaction comments. The research involved several stages, including literature review, data collection, preprocessing, transformation, data mining, evaluation, and visualization. The results demonstrate strong accuracy, precision, recall, and F1-scores for both methods using an 80:20 data split. Before applying the SMOTE technique, the best result was achieved by the Support Vector Machine method with a score of 0.90, while the Random Forest method yielded an accuracy of 0.81, precision of 0.85, recall of 0.81, and F1-score of 0.76. After applying SMOTE, both methods achieved an improved and equal score of 0.90. The study also produced an excellent classification result based on the ROC curve. It is expected that this research can serve as an additional reference for the assessment of student satisfaction at ITB STIKOM Bali at the end of each academic semester.
Security analysis of Indonesia e-commerce platform against the risk of phishing attacks Saskara, Gede Arna Jude; Permana, Made Ody Gita; Sunarya, I Made Gede
International Journal of Advances in Applied Sciences Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i2.pp533-541

Abstract

This research analyses the security of e-commerce platforms in Indonesia against the risk of phishing attacks using the social-engineer toolkit (SET) application. Of the 31 platforms tested, it was found that 22 platforms have a low-security level because they can be easily replicated to carry out phishing attacks. In contrast, 9 platforms showed a high level of security, as they implemented the step-wise authentication and embedded login methods, which proved effective in protecting the platform from phishing attacks. The effectiveness rate of the SET application in conducting tests was recorded at 70.9%; the percentage is included in the high category. This research also identified that most low-security platforms still use the single-page login method or a special URL for login, making them very vulnerable to phishing attacks. The action research method was used as the research framework, involving five stages: diagnosis, planning, action, evaluation, and learning. The results of this study provide important guidance for platform owners to improve security mechanisms, how to build a login page to avoid the risk of misuse by cybercrime actors to conduct phishing attacks, and for users as a reference to choose a more secure e-commerce platform.
Analisis Kualitas Sangraian Biji Kopi Berdasarkan Ekstraksi Fitur Bentuk Dan Glcm pramana, i gede pramana ade saputra; Gunadi, I Gede Aris; Sunarya, I Made Gede
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 1 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

This study aims to develop a digital image analysis system that can determine the quality and maturity level of roasted coffee beans, in terms of detecting roasted coffee beans that are suitable and unfit for consumption and sold as specialty coffee as contained in the standard coffee bean classification. provided by SNI No. 01-2907-1999. This study focuses on the physical changes of coffee beans after the roasting process, changes that affect in terms of quality, namely shape. Shape extraction uses Metric because the shape to be searched for is a circle shape, and texture extraction uses Gray Level Co-Occurrence Matric (GLCM). This research began by collecting data in the form of 2D digital images of roasted coffee beans. The system developed in this study consisted of two main stages, namely training and testing. The number of coffee bean image data used is 90 images. In analyzing the quality of coffee beans, the data used is an image of coffee beans which consists of two levels, that is good and bad beans. Classification using Naive Bayes algorithm. Based on the results of research on coffee bean quality analysis, the highest training accuracy was 88% and the highest test accuracy was 90%.
Analisis Kualitas Sangraian Biji Kopi Berdasarkan Ekstraksi Fitur Bentuk Dan Glcm pramana, i gede pramana ade saputra; Gunadi, I Gede Aris; Sunarya, I Made Gede
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 1 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

This study aims to develop a digital image analysis system that can determine the quality and maturity level of roasted coffee beans, in terms of detecting roasted coffee beans that are suitable and unfit for consumption and sold as specialty coffee as contained in the standard coffee bean classification. provided by SNI No. 01-2907-1999. This study focuses on the physical changes of coffee beans after the roasting process, changes that affect in terms of quality, namely shape. Shape extraction uses Metric because the shape to be searched for is a circle shape, and texture extraction uses Gray Level Co-Occurrence Matric (GLCM). This research began by collecting data in the form of 2D digital images of roasted coffee beans. The system developed in this study consisted of two main stages, namely training and testing. The number of coffee bean image data used is 90 images. In analyzing the quality of coffee beans, the data used is an image of coffee beans which consists of two levels, that is good and bad beans. Classification using Naive Bayes algorithm. Based on the results of research on coffee bean quality analysis, the highest training accuracy was 88% and the highest test accuracy was 90%.
Enhancing EEG-Based Stress Detection Using ICA, Relative Difference, and Convolutional Neural Networks Negara, I Made Wahyu Guna; Wirawan, I Made Agus; Sunarya, I Made Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14777

Abstract

: EEG-based stress detection is crucial for early mental health monitoring, but signal quality is often degraded by artifacts and baseline variability. This study proposes an optimized preprocessing method combining Independent Component Analysis (ICA) for artifact removal and Relative Difference for baseline reduction. Using the SAM-40 EEG dataset, features were extracted with Differential Entropy and structured into a 3D EEG cube to preserve spatial-frequency information. A Convolutional Neural Network (CNN) classified stress levels into low and high categories. The proposed approach achieved 94.44% accuracy, with 100% precision for the high stress class and 81.82% recall. These results highlight the effectiveness of combining ICA and baseline reduction to enhance deep learning-based EEG signal processing for stress detection.
Hyperparameter Optimization with MobileNet Architecture and VGG Architecture for Urban Traffic Density Classification Using Bali Camera Image Data Suputra, I Putu Arsana; I Gede Aris Gunadi; Sunarya, I Made Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14971

Abstract

Traffic congestion in urban areas is a critical issue, particularly in densely populated regions such as Bali. This study addresses the challenge by implementing a Convolutional Neural Network (CNN) method to classify traffic density levels based on images captured by road surveillance cameras. The primary focus of this research is hyperparameter optimization to enhance the model's performance in classifying traffic conditions. Various combinations of hyperparameters—such as the number of neurons in the dense layer, dropout rate, learning rate, batch size, and number of epochs—were tested on two popular CNN architectures: MobileNet and VGG16. MobileNet offers lightweight computing, while VGG16 provides strong feature extraction capabilities, albeit with higher computational resource demands. Quantitative results show that after hyperparameter tuning, the MobileNet architecture achieved an accuracy of 96.94% and an F1 score of 0.969, while the VGG16 architecture achieved an accuracy of 97.22% and an F1 score of 0.972 in traffic density classification. These findings confirm that hyperparameter optimization can significantly improve classification accuracy. The scientific contribution of this research lies in the structured approach to CNN hyperparameter optimization and the demonstration that this process directly impacts the enhancement of model performance in traffic image classification tasks. This study offers valuable insights for the development of intelligent traffic management systems, especially in urban areas with limited resources.
Optimalisasi Metode RBFNN Dengan Fuzzy C-Means Dalam Prediksi Import Barang Konsumsi Indonesia Budiastawa, I Dewa Gede; Sunarya, I Made Gede; Wirawan, I Made Agus
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8711

Abstract

Prediction or forecasting is an action that aims to find out future events based on indicators that influence an event. Consumer goods are products or goods purchased by people or households that are intended for direct consumption in the sense that they are not for further production purposes. Based on this, serious handling is needed to maintain the state of the Indonesian economy, especially in the industrial sector. Predicting the value of consumer goods imports is a step in finding out the value of consumer goods imports in the next period so that the government has a reference in determining policies. In this study, the prediction of the value of consumer goods imports was carried out based on factors that influence the value of consumer goods imports based on research in the field of economics. This study uses the Radial Basis Function Neural Network (RBFNN) method using a combination of clustering methods, namely Fuzzy C-Means Clustering to improve method performance. The RBFNN method is the best method used in predicting future data based on previous research and the FCM method is a clustering method that is able to overcome ambiguity in the prediction process. This study proves that the Fuzzy C-Means method is effective in optimizing the performance of the Radial Basis Function Neural Network method with a comparison of MAPE values in each combination, namely RBFNN - FCM 15.73%, RBFNN - K-Means 16.87% and RBFNN - Random centroid 17.70%. The learning rate parameter is directly proportional to the RBFNN - FCM model where the greater the learning rate, the better the model performance, indicating that the model does not need to do in-depth learning to recognize data patterns. In contrast to the fuzzification parameter which increases accuracy when the fuzzification value is lowered, indicating that the model does not require a very vague approach to recognize data patterns. The best architecture is 8 - 4 - 1 with a fuzzification parameter value of 1.5, a learning rate of 0.3 and a threshold error of 0.3 produced by a combination of RBFNN and FCM.
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 Gede Yudhi Paramartha 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 Ngurah Surya Ardika Dinataputra 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