p-Index From 2021 - 2026
15.106
P-Index
This Author published in this journals
All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi JUSIFO : Jurnal Sistem Informasi INFORMAL: Informatics Journal Proceeding of the Electrical Engineering Computer Science and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika International Journal of Artificial Intelligence Research JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jiko (Jurnal Informatika dan komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) International Journal of Natural Science and Engineering Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JE (Journal of Empowerment) Jurnal Mantik REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) INFOKUM Jurnal Sistem Komputer dan Informatika (JSON) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH JINAV: Journal of Information and Visualization International Journal of Engineering, Science and Information Technology Journal of Technology and Informatics (JoTI) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal Multidisiplin Madani (MUDIMA) East Asian Journal of Multidisciplinary Research (EAJMR) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Indonesia (JPMI) Digital Transformation Technology (Digitech) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Eduvest - Journal of Universal Studies Journal of Software Engineering and Information System (SEIS) Faedah: Jurnal Hasil Kegiatan Pengabdian Masyarakat Indonesia Bulletin of Informatics and Data Science Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) TECHNOVATE Galaksi KOMET BIOS: Jurnal Informatika dan Sains Jurnal Pengabdian Masyarakat Indonesia
Claim Missing Document
Check
Articles

ELECTRE-Based Decision Support Model for LPG Base Location Optimization Putri, Ida Ayu Putu Calista Kencana; Sudipa, I Gede Iwan; Ariana, Anak Agung Gede Bagus; Yanti, Christina Purnama; Ekayana, Anak Agung Gde
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15500

Abstract

The kerosene to LPG (Liquefied Petroleum Gas) 3 kg conversion program since 2007 has successfully improved household energy efficiency, but equitable access to bases in remote areas is still an obstacle. In Tabanan Regency, Bali, eight villages do not have access to 3 kg LPG bases, making it difficult for the community to obtain LPG at the Highest Retail Price (HET) and timely supply. This research develops a decision-making model using the ELECTRE method to recommend optimal base locations based on a case study of four villages: Pupuan Sawah, Dalang, Mundeh, and Belatungan. The model integrates 15 criteria including population density, infrastructure accessibility, existing base distance, and the presence of public facilities with a multi-stakeholder approach. The model is expected to be a tool for LPG agents and policy makers in determining the optimal base location and supporting equitable distribution of subsidized energy.
IndoBERT-Based Pediatric Disease Classification and Symptom-Based Traditional Medicine Recommendation from Lontar Usada Rare Winata, I Putu Erick Prawira; Sudipa, I Gede Iwan; Meinarni , Ni Putu Suci; Wulandari, Dewa Ayu Putri; Yanti, Christina Purnama
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15507

Abstract

This study aims to develop a Balinese traditional text-based pediatric disease classification model using a fine-tuned IndoBERT model on the Lontar Usada Rare dataset. The dataset used consists of 422 entries containing disease symptoms, disease types, medicinal ingredients, and treatment procedures obtained from transliteration of lontar manuscripts and interviews with traditional medicine experts. Pre-processing was done through case folding, cleansing, and normalization, followed by label encoding on 35 disease classes. The IndoBERT model was fine-tuned using the AdamW optimizer with a learning rate of 5e-5, batch size 8, and 15 epochs. Evaluation results showed the model was able to achieve 90.59% accuracy, 94.71% precision, 90.59% recall, and 90.99% F1-score, indicating excellent performance in understanding the linguistic context of traditional medical text. The developed recommendation system integrates model prediction with TF-IDF-based cosine similarity method to provide the most relevant treatment recommendations based on user symptom input. This research makes an important contribution to the digitization and preservation of Balinese traditional medical knowledge through the development of a structured and widely accessible digital knowledge base.
SVM-Based Pediatric Disease Classification Model from the Balinese Lontar Usada Rare Manuscript Bhawanaputra, I Gusti Made Ngurah Ari; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Aristamy, I Gusti Ayu Agung Mas; Pratistha, Indra
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15508

Abstract

Lontar Usada Rare is a traditional Balinese manuscript containing pediatric medical knowledge based on local wisdom, yet its narrative format limits accessibility and utilization in modern contexts, while its physical fragility threatens long-term preservation. This study aims to develop a pediatric disease classification model using a Support Vector Machine (SVM) combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting to support the digitalization of Balinese traditional medicine. A total of 422 data samples were collected through expert interviews and manuscript analysis, covering symptoms, disease types, herbal ingredients, and treatment procedures. The research stages included text preprocessing (cleansing, tokenizing, stopword removal, stemming), manual labeling into 35 disease classes, and model evaluation using five train–test split ratios (80:20 to 60:40) with variations of the complexity parameter C (0.5, 1, 10, 100, 1000). The best performance was achieved using C=10 with an 80:20 ratio, resulting in 87.06% accuracy, 91.55% precision, 87.06% recall, and an F1-score of 87.96%. Confusion matrix analysis showed strong classification performance for most classes, although minority classes with overlapping symptoms exhibited misclassification. Overall, the TF-IDF and linear SVM combination effectively classifies pediatric disease symptoms from Lontar Usada Rare and contributes to the preservation and digital transformation of Balinese traditional medical knowledge for potential modern healthcare applications.  
Fuzzy Time Series Chen Model for Dual-Commodity Agricultural Forecasting: Evidence from Indonesia’s Rice and Corn Production Wiguna, I Kadek Artha; Sudipa, I Gede Iwan; Meinarni, Ni Putu Suci; Atmaja, Ketut Jaya; Ekayana, Anak Agung Gede
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15584

Abstract

Indonesia's strategic food commodities, particularly rice and corn, exhibit strong seasonal fluctuations and irregular production shocks driven by climate anomalies and policy changes, generating nonlinear time-series patterns that conventional statistical models often fail to capture. This study evaluates the forecasting capability of the standard Chen Fuzzy Time Series (FTS) model for dual-commodity agricultural data under varying seasonal and anomaly conditions. Monthly production data from January 2021 to March 2025 from the Indonesian Central Bureau of Statistics (BPS) were processed through a complete FTS pipeline: universe-of-discourse construction, triangular membership function design, fuzzification, FLR and FLRG formation, and midpoint-based defuzzification. Forecast accuracy was assessed using MAE, MSE, RMSE, MAPE, and R², with residual distribution analysis, Shapiro-Wilk tests, and scatter plots conducted to validate model stability. The model achieved high precision with overall MAPE of 4.37% for rice and 8.12% for corn, both classified as Highly Accurate. Monthly accuracy revealed consistent stability during May-December, while transitional months (January-March) showed greater variability due to extreme anomalies such as the January 2024 production collapse. Residual analysis confirmed near-normal error distribution for rice (p = 0.062) and mild deviation for corn (p = 0.031), while scatter plots demonstrated strong linear relationships (Rice R² = 0.9876; Corn R² = 0.9654). The findings establish Chen's FTS as a transparent and operationally reliable baseline method for national food production forecasting, although its sensitivity to structural breaks highlights the need for future hybridization with climate and policy indicators.
A Comparative Study of MobileNetV2 and ResNet50 for Multi-Class AI- Generated and Real Image Classification Pramudita, I Gusti Ngurah Agus Ega Patria; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Iswara, Ida Bagus Ary Indra; Aristamy, I Gusti Ayu Agung Mas
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15682

Abstract

This study aims to classify AI-generated and real images using Convolutional Neural Network (CNN) architecture by comparing the performance of MobileNetV2 and ResNet50. Previous studies on AI-generated image detection have primarily focused on binary classification without explicitly analyzing object-level context in multi-class scenarios, leaving a gap in understanding model performance across diverse visual categories. The dataset consists of 23,941 images divided into two main classes of real and fake and five subclasses of human, animal, art, view, and vehicle. The training process employs data augmentation and a K-Fold Cross Validation strategy on the training and validation set to maintain balanced class proportions, while a separate unseen test set is used exclusively for final performance evaluation. Model evaluation is performed based on accuracy, precision, recall, and F1-score metrics on test data. The results showed that MobileNetV2 achieved the best accuracy of 89% at the 10th epoch, but experienced a decline in performance at the 30th and 50th epochs, indicating overfitting. In contrast, ResNet50 showed the most stable performance with the highest accuracy of 93% at the 30th epoch and consistently high precision, recall, and F1-score values. Thus, ResNet50 was found to be the most effective architecture for classification of AI-generated and real images on multi-class datasets, while MobileNetV2 remains relevant for implementation on devices with computational limitations.
Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province Gotama, I Gusti Agung Raditiya; Sudipa, I Gede Iwan; Brahma, Anak Agung Gede Raka Wahyu; Ariantini, Made Suci; Wulandari, Dewa Ayu Putri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15715

Abstract

Rice production has a seasonal pattern that depends on the planting cycle and environmental conditions, requiring forecasting methods that can accurately model temporal dynamics. This study aims to predict monthly rice production in Bali Province using the Stacked Gated Recurrent Unit (GRU) architecture. Monthly rice production data from 2018 to 2024 from the Central Statistics Agency (BPS) was used as the main source. The preprocessing stage includes data cleaning, Min-Max normalization, and feature engineering in the form of creating sin_month and cos_month features to capture seasonal patterns, as well as a 3-month rolling mean to extract short-term trends. The proposed stacked design with dual-layer GRU combined with seasonal features improves temporal pattern extraction compared to single-layer GRU baselines. The model was tested using three configurations, and Scheme 3 provided the best performance with an MAE value of 1610.21, an RMSE of 2055.90, and a MAPE of 14.29%, which is considered good accuracy. The model was able to follow seasonal production trends, including an increase at the beginning of the year and a decrease during the planting period. Long-term predictions for the next 12 months and quarterly forecasts per district/city also showed patterns consistent with historical data. The results of the study indicate that Stacked GRU is effective in forecasting seasonal rice production and can be used as a basis for decision support in food security planning in Bali.
A Structured Decision Intelligence Framework for Context-Aware Decision Making Sudipa, I Gede Iwan; Pandawana, I Dewa Gede Agung; Sandhiyasa, I Made Subrata
Jurnal Galaksi Vol. 2 No. 2 (2025): Galaksi - August 2025
Publisher : Yayasan Sraddha Panca Widya Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70103/galaksi.v2i2.96

Abstract

Decision Intelligence (DI) has emerged as an integrative paradigm that combines data, analytics, and artificial intelligence to enhance organizational decision-making. Despite this growing interest, many existing DI approaches place disproportionate emphasis on predictive intelligence while providing limited methodological guidance on how predictions are transformed into actionable and accountable decisions. Machine learning models are highly effective at forecasting and classification; however, they do not inherently incorporate organizational constraints, human preferences, or decision trade-offs. This study proposes a structured, end-to-end Decision Intelligence framework that explicitly integrates machine learning–based prediction with Decision Support System (DSS) modelling. The framework positions DSS as the core decision logic by employing the Analytic Hierarchy Process (AHP) to formalize contextual and human preferences and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to execute alternative ranking. Furthermore, contextual intelligence and outcome intelligence are embedded to ensure decision relevance, transparency, and continuous improvement. Using a Design Science Research approach, this study develops and demonstrates the proposed framework as a systematic solution for bridging the gap between predictive analytics and decision execution. The framework contributes to Decision Intelligence research by clarifying the role of DSS in AI-driven decision environments and by providing a replicable structure for integrating prediction, decision modelling, and outcome evaluation.
Model Peramalan Artificial Neural Network pada Peserta KB Aktif Jalur Pemerintahan menggunakan Artificial Neural Network Back-Propagation B. Herawan Hayadi; I Gede Iwan Sudipa; Agus Perdana Windarto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1273

Abstract

Pertumbuhan penduduk di Indonesia yang terus meningkat setiap tahunnya dan tidak disertai dengan ketersediaan lapangan pekerjaan yang mampu menampung seluruh angkatan kerja bisa menimbulkan pengangguran, kriminalitas, yang bersinggungan pula dengan rusaknya moralitas masyarakat. Oleh karena pemerintah memberikan serangkaian usaha untuk menekan laju pertumbuhan penduduk agar tidak terjadi ledakan penduduk yang lebih besar. Salah satu cara yang dilakukan adalah dengan menggalakkan program KB (Keluarga Berencana). Tujuan dari penelitian untuk membuat model prediksi dengan memanfaatkan Artificial Neural Network (ANN) pada peserta KB aktif jalur pemerintahan untuk melihat laju pertumbuhan penduduk kedepannya dalam rentang waktu tertentu guna mempermudah pemerintah dalam membuat rancangan perencanaan ke depannya. Back-propagation merupakan salah satu metode yang digunakan untuk melakukan peramalan yang merupakan bagian dari ANN. Hal ini perlu dilakukan mengingat jumlah kepadatan penduduk terus meningkat setiap tahunnya dan KB merupakan salah satu program pemerintah yang bertujuan mengendalikan laju kenaikan penduduk di Indonesia. Dataset yang digunakan yakni peserta KB aktif di Kota Pematangsiantar bulan agustus 2019 – januari 2020. Pengujian dilakuan dengan bantuan software matlab dengan menguji 5 model arsitektur (try error) yakni model 4-5-1; model 4-7-1; model 4-8-5-1; dan model 4-9-7-1. Hasil analisis diperoleh bahwa model arsitektur 4-8-5-1 merupakan yang terbaik dan dijadikan acuan untuk meramalkan peserta KB aktif pada jalur pemerintah dengan tingkat akurasi sebesar 71% (terbaik dari 4 model arsitektur lainnya). Model ANN tersebut dapat diimpementasikan untuk melakukan prediksi terhadap peserta KB aktif jalur pemerintahan sehingga pemerintah dapat melakukan rancangan untuk kedepannya.
Building the Virtual World: A Literature Review on the Integration of Metaverse and Blockchain Technology I Gede Iwan Sudipa; Putu Wirayudi Aditama; Christina Purnama Yanti
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 01 (2024): BIOS: Jurnal Informatika dan Sains, April 2024
Publisher : Sean Institute

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

Abstract

The present study explores the incorporation of blockchain technology within the metaverse, uncovering the potential for mutually beneficial outcomes that may revolutionise virtual environments, transaction security, and digital interaction. This study examines the recent advancements, obstacles, and uses of integrating blockchain and metaverse technologies through a literature review. It emphasises the critical role that AR/VR technology plays in the creation of immersive experiences. The findings indicate that the integration of blockchain technology and the metaverse presents a viable avenue towards enhanced virtual experiences that are interactive, decentralised, and secure. This has far-reaching implications across various sectors, including education, entertainment, culture, healthcare, and more. This study highlights the significance of additional progress and interdisciplinary cooperation in order to fully exploit the capabilities of this integrated digital ecosystem.
Artificial Intelligence-Assisted IoT Model for Water Level Monitoring and Prediction Systems: A Review and Analysis I Gede Iwan Sudipa; I Dewa Gede Agung Pandawana; I Made Subrata Sandhiyasa
BIOS: Jurnal Informatika dan Sains Vol. 2 No. 02 (2024): BIOS: Jurnal Informatika dan Sains, October 2024
Publisher : Sean Institute

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

Abstract

Flood disasters have become increasingly frequent and severe due to climate change and urban expansion. Traditional water level monitoring systems often lack real-time data processing and predictive capabilities. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) presents a promising solution for enhancing water level monitoring and flood prediction systems. This paper provides a comprehensive review and analysis of AI-assisted IoT models for water level monitoring and prediction. It examines system architectures, sensor networks, and the application of AI algorithms such as Fuzzy Logic and Long Short-Term Memory (LSTM) networks. The study highlights the benefits of combining real-time IoT data with AI-based predictive models to improve the accuracy and responsiveness of flood early warning systems. Challenges related to data quality, sensor network infrastructure, and model optimization are also discussed. This review aims to inform future research and development in intelligent disaster mitigation systems.
Co-Authors A.A. Tri Wulandari Mayun Achmad Selamat Fauzi Achmad Selamat Fauzi Aditama Kadek Aditama, Dewa Made Marta Agus Perdana Windarto Ahmad Jurnaidi Wahidin Alfiah, Agry Ali Zainal Abidin Alaydrus Ali, Raihan Anak Agung Gde Ekayana Andika, I Gede Anggaswara, Aditha Diva Ani Nida’ia Mustafida Aniek Suryanti Kusuma Apriana, Ketut Adhi Ariana, Anak Agung Gede Bagus Arianto Muditomo Aripurnama, I Kadek Ade Aristamy, I Gusti Ayu Agung Mas Aristana, Made Dona Wahyu Arslayandi, Fiqhan Artha, I Putu Mahesa Kama Aryati, Komang Sri Asana, I Made Dwi Putra Asana, Made Dwi Putra Atmaja, I Gede Bagastia Widi Atmaja, Ketut Jaya Ayu Gede Willdahlia B. Herawan Hayadi Bagus Kusuma Wijaya Bagus Kusuma Wijaya Bhawanaputra, I Gusti Made Ngurah Ari Brahma, Anak Agung Gede Raka Wahyu Cakranegara, Pandu Adi Cakranegara, Pandu Adi Cetiawan, Arie Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Darma Setiawan Putra Darmika, Kadek Jaya Deddy Kurniawan Desak Made Dwi Utami Putra Desak Putu Diah Kumala Dewi Devi Valentino Waas Devi Valentino Waas Devi Valentino Waas Devi Valentino Waas Dewa Ayu Novi Swijayanti Dewa Gede Agung Gana Kumara Dewantara, Rizki Dewi, Ni Kadek Feby Puspita Dewi, Ni Luh Putu Berliana Dirgayusari, Ayu Manik Dwi Putra Asana Dwijaputra, I Kadek Dwiki Setiawan Efendi Efendi Eka Ayu Purnama Lestari Ekayana, Anak Agung Gede Emy Yunita Rahma Pratiwi, Emy Yunita Rahma Erlina Putri, Ni Putu Mita Fajar Muharam Fajriana, Fajriana Gede Surya Mahendra Gotama, I Gusti Agung Raditiya Gunawan, I Komang Agus Bryan Gustiadi Gustiadi Gustiadi Gustiadi Hamid Wijaya Handika, I Putu Susila Hardiatama, Kadek Harjanti, Trinugi Wira Herry Rachmat Widjaja Hidayatulloh, Fachmi Hugo, Veronika Novia I Dewa Gede Agung Pandawana I Dewa Gede Agung Pandawana I Gede Sudiantara I Gede Totok Suryawan I Gede Totok Suryawan I Gusti Agus Adek Putra Ardiwinata I Gusti Ayu Agung Mas Aristamy I Gusti Made Ngurah Desnanjaya I Kadek Adiana Putra I Kadek Budi Sandika I Kadek Budi Sandika I Kadek Dwi Gandika Supartha I Kadek Hardiatama I Kadek Surya Arimbawa I Komang Arya Ganda Wiguna I Komang Arya Ganda Wiguna I Komang Arya Ganda Wiguna I Komang Arya Ganda Wiguna I Komang Arya Ganda Wiguna I Komang Wiratama I Komang Wiratama I Komang Wiratama I Made Angga Wijaya I Made Dwi Putra Asana I Made Dwi Putra Asana I Made Muryasa I Made Oka Widyantara I Made Subrata Sandhiyasa I Made Subrata Sandhiyasa I Nyoman Alit Arsana I Nyoman Jayanegara I Nyoman Tri Anindia Putra I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Candra Jumariana I Putu Eka Giri Gunawan I Putu H Permana I Putu Hendika Permana I Putu Hendika Permana I Putu Hery Setiawan I Putu Yudi Pratama I Wayan Dani Pranata I Wayan Dharma Suryawan I Wayan Sudiarsa I WAYAN SUDIARSA Ida Bagus Ary Indra Iswara Ida Bagus Gede Sarasvananda Ida Bagus Gede Sarasvananda Ida Bagus Nyoman Pascima Ika Arfiani Indra Pratistha Iwan Adicandra J.A, Qurrotul Ainia Jatila , I Gede Mahesa Putra Jaya, I Made Krisna Jayadi, I Dewa Gede Wahyu Jimmy H Moedjahedy Jullev Atmadji, Ery Setiyawan Kadek Ari Prayoga Putra Kencana Putri, Ida Ayu Putu Calista Ketut Jaya Atmaja Ketut Ngurah Semadi Ketut Sepdyana Kartini Khoirun Nisa Komang Kurniawan Widiartha Krinayanti, Ni Putu Krisna, I Gede Anugrah Adi Kusuma Dewi, Ni Wayan Jeri Kusuma, Kadek Ngurah Adi Laksono Trisnantoro Lalu Puji Indra Kharisma Legito . Libraeni, Luh Gede Bevi Made D W Aristana Made Dona Wahyu Aristana Made Dona Wahyu Arsitana Made Leo Radhitya Made Suci Ariantini Mahardika , Dewa Gede Candra Maharianingsih, Ni Made Maulidah, Salsa Bila Jihan Meinarni , Ni Putu Suci Meinarni, Ni Putu Suci Meliana, Putu Mita Melyawati, Ni Luh Putu Mesran, Mesran Moh. Erkamim Muammar Khaddafi Muh. Fahrurrozi Muhammad Habibi Muni, Gede Dharma Sahasra Murti , Ayu Jihwani Mustika Wati Alfia Ningtyas Ni Komang Dani Juniantari Ni Luh Ayu Prima Dania Ni Luh Putu Ayu Cintia Utami Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Chintya Sasri Ni Made Dwi Puspitawati Ni Made Maharianingsih Ni Made Sri Dadi Sukerthi Ni Nyoman Asti Sri Wahyuni Ni Nyoman Ayu J. Sastaparamitha Ni Putu Eka Kherismawati Ni Putu Eka Kherismawati Ni Putu Widantari Suandana Ni Putu Yuniawati Yunia Ni Wayan Eka Wijayanti Ni Wayan Suardiati Putri Nicodemus Mardanus Setiohardjo Nofirman, N Noris, I Kadek Novaria, Rachmawati Pande Putu Sukma Awantari Pande, Ni Kadek Nita Noviani Partama, I GD. Yudha Perani Rosyani Pramawati, I.D.A Tantri Pramita, Dewa Ayu Kadek Pramudita, I Gusti Ngurah Agus Ega Patria Pranata, I Wayan Dani Priadinata, I Putu Bramasta Puspitayani, Ida Ayu Dwi Putra, I Kadek Nurcahyo Putra, Putu Agus Febri Sedana Putra, Putu Satria Udyana Putri, Ida Ayu Putu Calista Kencana Putri, Rozza Maudina Ayuwan Putu Adreal Candranatha Putu Hendra Premana Putu Praba Santika Putu Praba Santika Putu Praba Santika PUTU SUGIARTAWAN Putu Sugiartawan Putu Sugiartawan Putu Wirayudi Aditama Rachmat Radhitya, I Made Leo Radhitya, Made Leo Revan Dwi Hanza Rezania Agramanisti Azdy, Rezania Agramanisti Rhaishudin Jafar Rumandan Riana, Roni Rikcy Sanusi Rini Komalasari risaldi, risaldi Riska Aryanti Rizkita Ayu Mutiarani Robbi Rahim Sandhiyasa, I Made Subrata Sandika, I Kadek Budi Sanusi, Rikcy Saputra, Gusti Bagus Arya Savitri, Ni Kadek Wiliya Sekarsari, Yulia Ayu Semadi, Ketut Ngurah Sepriano Sepriano Septiawan, I Kadek Jerry Sri Aryati, Komang Sudiani, Ni Made Sugiarta, Putu Agus Arya Sastra Sugihya Artha Dwipayani Sumiyatun Sunarya, I Wayan Susatyo Adhi Pramono, Susatyo Adhi Tika, I Gede Bagus Arya Merta Tukino, Tukino Tuti Marjan Fuadi Wahyudi, I Putu Alfin Teguh Wayan Gede Suka Parwita Welda Widiantara, I Komang Widiantari, Ni Komang Mira Widiari, Ni Putu Diva Septa Widiartha, Komang Kurniawan Widyatama, I Dewa Gede Surya Wiguna, I Kadek Artha Wiguna, I Komang Arya Ganda Wijaya, Bagus Kusuma Winata, I Putu Erick Prawira Winatha, Komang Redy Wulandari, Dewa Ayu Putri Yarimani Laia Yohana Jun Yuri Prima Fittryani, Yuri Prima