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All Journal Journal of Food and Pharmaceutical Science Jurnal Pendidikan Matematika Undiksha Scan : Jurnal Teknologi Informasi dan Komunikasi Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer Akta Kimia Indonesia WIDYABHAKTI Jurnal Ilmiah Populer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Networks, Architecture and High Performance Computing JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Tika JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Prioritas : Jurnal Pengabdian Kepada Masyarakat Dharma Jnana Jurnal Ilmiah Ilmu Terapan Universitas Jambi Jurnal Pengabdian Kepada Masyarakat Widya Mahadi Journal of Global Pharma Technology Jurnal Media Akademik (JMA) Prosiding SENAMA PGRI Jurnal Bisnis Inovatif dan Digital Router : Jurnal Teknik Informatika dan Terapan Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Jurnal Manajemen Bisnis Digital Terkini Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi KOMET Jurnal Manajemen Akuntansi dan Ilmu Ekonomi Router : Jurnal Teknik Informatika dan Terapan Jurnal Kendali Teknik dan Sains
I WAYAN SUDIARSA
STMIK STIKOM Indonesia
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Implementasi Pipeline ETL dan Pemodelan Prediktif ARIMA dalam Memetakan Pola Pembelian Konsumen pada Dataset Marketplace I Wayan Manik Mas Sri Dantya; I Wayan Sudiarsa; I Putu Kabinawa Raesa Putra; Brian Adi Sapurta; I Komang Hari Sastrawan
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.799

Abstract

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.
Data Pipeline Engineering untuk LSTM Forecasting Seismisitas Melalui Integrasi Proses ETL Katalog Gempa Indonesia Dewa Gde Agung Wisnu Anantha; I Wayan Sudiarsa; I Kadek Adi Erawan; I Ketut Okta Suastika; Gde Wardika Nugraha
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 1 (2026): Januari : Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i1.1426

Abstract

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.
Production of Solid Soap from Arabica Coffee Grounds (Coffea arabica L.) with Antibacterial Properties Sanjiwani, Ni Made Sukma; Ariani, Komang; Sunadi Putra, I Made Agus; Rahadi, I Wayan Surya; Mirah Mariati, Ni Putu Ayu; Sudiarsa, I Wayan; Udayani, Ni Nyoman Wahyu
Journal of Food and Pharmaceutical Sciences Vol 14, No 1 (2026): J.Food.Pharm.Sci
Publisher : Integrated Research and Testing Laboratory (LPPT) Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jfps.25035

Abstract

Coffee shops are now a favourite hangout for people of all ages, so many entrepreneurs are developing coffee shop businesses because of their high profit potential. Arabica coffee is the type of coffee most commonly used in coffee shops. The coffee-making process produces waste in the form of coffee grounds. Coffee grounds can be used as an ingredient in beauty products such as soap. Soap is the result of saponification, which is a reaction between a base or alkali and fatty acids, which acts as a skin cleanser. This study aims to examine the presence of secondary metabolite compounds in Arabica coffee grounds (Coffea arabica L.), test the physical quality of solid soap made from Arabica coffee grounds (C arabica L.), and assess the soap's ability to inhibit the growth of Staphylococcus aureus bacteria. Arabica coffee grounds were first analysed through qualitative phytochemical screening tests using test tubes and various reagents, then formulated into solid soap with different concentrations, namely 4%, 7%, and 9%. After that, physical properties testing and antibacterial activity evaluation against Staphylococcus aureus were carried out using the disc diffusion method. The research results data were presented using descriptive analysis. The tests revealed that Arabica coffee residues contain secondary metabolites in the form of alkaloids, flavonoids, tannins, saponins, and triterpenoids. Solid soap made from Arabica coffee grounds meets the physical quality test standards in accordance with SNI 3532:2021 and that solid soap made from Arabica coffee grounds with a percentage of 7% (formula 2) and 9% (formula 3) has the potential to inhibit the activity of Staphylococcus aureus bacteria with a moderate category. It can be concluded that Arabica coffee grounds contain secondary metabolites such as alkaloids, flavonoids, tannins, saponins, and triterpenoids. Solid soap with Arabica coffee grounds as an ingredient meets the physical quality requirements in accordance with SNI 3532:2021. Soap formulas with a coffee grounds concentration of 7% (F2) and 9% (F3) have the potential to inhibit the growth of Staphylococcus aureus with moderate efficacy.
Analisis Klasifikasi Pengaruh Kegagalan dan Keterbatasan Metode Pembayaran Digital terhadap Churn Pelanggan Menggunakan Decision Tree Dewa Ayu Putu Angelina Dewi; I Wayan Sudiarsa; Ni Made Dwi Junita Sariyani; Yuvensia Armelia Sumu; Gusti Ngurah Abhimanyu
Jurnal Bisnis Inovatif dan Digital Vol. 3 No. 1 (2026): Januari : Jurnal Bisnis Inovatif dan Digital
Publisher : Asosiasi Riset Ilmu Manajemen Kewirausahaan dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jubid.v3i1.1232

Abstract

The rapid development of digital technology has led to an increased adoption of digital payment methods in online transaction-based businesses. However, in practice, failures and limitations in the implementation of digital payment systems still occur, potentially disrupting transaction processes and reducing customer convenience. Payment related obstacles may result in transaction cancellations and increase the risk of customer churn. This study aims to analyze the impact of failures and limitations in digital payment methods on customer churn using a classification-based approach. The data used in this research are secondary e-commerce customer data obtained from the Kaggle platform, including transaction information, payment methods, customer behavior, and historical transaction records. The research methodology consists of data preprocessing, time-based feature engineering, and classification modeling using logistic regression, decision tree, and random forest algorithms. Model performance is evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the decision tree model demonstrates superior capability in identifying churn customers compared to the other models, although it does not always achieve the highest accuracy. In addition to digital payment methods, other factors such as purchase value, transaction frequency, purchase timing patterns, and product return rates also influence customer churn. The findings highlight the importance of optimizing digital payment systems as part of customer experience enhancement strategies and customer retention efforts in online transaction–based businesses.
Implementasi Algoritma Random Forest untuk Klasifikasi Rentang Harga Ponsel Berdasarkan Spesifikasi Teknis Yustinus Liguori; I Wayan Sudiarsa; I Made Jagat Dita; I Gusti Ngurah Galih Jimbar Baskara; Pande Wisnu Wijaya Putra
Router : Jurnal Teknik Informatika dan Terapan Vol. 4 No. 1 (2026): Maret : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i4.796

Abstract

The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.
Analisis Klasifikasi Keputusan Belanja Konsumen Pada Toko Online XX Menggunakan Algoritma Decision Tree Putri Maria Theresia Kehi; I Wayan Sudiarsa; Maria Oktaviani Suryati; Yosefina Dehadi; Maria Karlinda
Saturnus: Jurnal Teknologi dan Sistem Informasi Vol. 4 No. 1 (2026): Januari : Saturnus: Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v4i1.1436

Abstract

This study aims to analyze consumer purchasing behavior on e-commerce platforms using the Decision Tree algorithm as an easily interpretable classification method. The dataset used consists of 12,330 transaction records with 18 attributes representing visitor characteristics and user activities during interactions with the e-commerce platform. The research stages include data exploration to identify initial patterns, data preprocessing to handle missing values and class imbalance, splitting the data into training and testing sets, training the Decision Tree model, evaluating model performance, and visualizing the tree structure to analyze decision rules.The test results show that the Decision Tree model with a maximum depth of 3 achieves fairly good performance, with an average accuracy of 89.78%, precision of 69.82%, recall of 59.95%, and an F1-score of 64.51% for the buyer class. The visualization of the decision tree provides clear interpretation of the main attributes influencing purchasing decisions, thereby facilitating understanding for non-technical decision makers. Overall, this study demonstrates that the Decision Tree method is effective in modeling consumer purchasing behavior in e-commerce and can be utilized as a basis for data-driven business decision making, particularly in marketing strategies and improving sales conversion rates.
PREDIKSI PEMBATALAN PESANAN E-COMMERCE INDONESIA MENGGUNAKAN RANDOM FOREST DAN SMOTE Koten, Felixiana; Sudiarsa, I Wayan; Trisnawati, Ni Komang; Pera, Magdalena Matildis Palo; Ndinin, Maria Avilia
Jurnal Manajemen Akuntansi dan Ilmu Ekonomi Vol. 3 No. 1 (2026): April
Publisher : PT. ANAN PUBLISHER CENDEKIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70585/jumali.v3i1.192

Abstract

Tingginya angka pembatalan pesanan (order cancellation) menjadi tantangan serius bagi efisiensi operasional dan profitabilitas industri e-commerce di Indonesia. Penelitian ini bertujuan untuk mengidentifikasi variabel determinan yang memicu pembatalan serta membangun model prediktif berbasis machine learning. Metode yang digunakan adalah kuantitatif dengan pendekatan data mining menggunakan algoritma Random Forest. Sumber data berasal dari dataset sekunder Kaggle "Indonesia E-Commerce Sales and Shipping 2023-2025" yang mencakup 19.189 data transaksi. Tahapan penelitian meliputi pra-pemrosesan data, penggunaan teknik SMOTE (Synthetic Minority Over-sampling Technique) untuk menangani ketidakseimbangan kelas, pembangunan model, dan evaluasi menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa model Random Forest mampu memprediksi pembatalan dengan akurasi sangat tinggi sebesar 99,94%. Faktor utama yang paling berpengaruh terhadap keputusan pembatalan adalah metode pembayaran (khususnya Online Payment dan COD) serta nilai transaksi. Temuan ini memberikan implikasi manajerial bagi penyedia platform untuk memperketat verifikasi pembayaran dan meningkatkan transparansi biaya guna memitigasi risiko pembatalan di masa depan.  
ADAPTIVE MULTIRESOLUTION SEMIPARAMETRIC MODEL INTEGRATING TRUNCATED SPLINE AND WAVELET TO BALINESE VILLAGE CREDIT INSTITUTIONS (LPD) Mariati, Ni Putu Ayu Mirah; Sudiarsa, I Wayan; Kumalasari, Putu Diah
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 10 No. 2 (2026): Volume 10, Nomor 2, April 2026
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v10i2.53877

Abstract

In this research, an adaptive multiresolution semiparametric regression approach is proposed to examine the financial performance of Lembaga Perkreditan Desa (LPD) in Bali, Indonesia. In the research, the problem that arises from the inadequacy of traditional regression techniques in accounting for the nonlinear pattern in the data and the financial instability is overcome by introducing the truncated spline and wavelet components into the semiparametric regression analysis.This research utilizes a quantitative method based on secondary financial information collected for 50 LPDs between 2015 and 2024, providing around 500 observations. In order to obtain a more consistent dataset, the purposive sampling method will be used. Return on Assets (ROA) is chosen as the dependent variable, and explanatory variables include interest rates, the number of customers, capital adequacy ratio (CAR), total assets, and non performing loans (NPL). The model will be estimated using the penalized least squares with iterative backfitting estimation technique and will be assessed using the RMSE, MAE, and R² criteria based on Kfold cross validation. As it can be seen from the results, the hybrid model significantly improves the predictive power of traditional linear and spline methods, providing smaller error rates and better fit quality. Spline functions help to determine long term trends, whereas wavelets capture short term effects. This shows that multiresolution modeling increases predictability and interpretability. The model has practical utility for financial management and regulation through adaptive risk management and decision-making processes in microfinance firms.
Rancang Bangun Sistem Pendeteksi Kebocoran Gas Secara Portable pada Distributor Gas Rumah Tangga Zamzak , M.Arif; Sudiarsa, I Wayan; Putra , I Dewa Putu Gede Wiyata
Jurnal Kendali Teknik dan Sains Vol. 1 No. 3 (2023): Juli: Jurnal Kendali Teknik dan Sains
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jkts-widyakarya.v1i3.1058

Abstract

In this research we design and build a portable gas leak detection system for household gas distributors. The performance of the designed system is described and evaluated. The system aims to speed up the time in detecting gas leaks. System portability allows implementation and monitoring in the form of a website. System design and performance are assessed and proven effective in detecting gas leaks, providing a reliable and efficient solution for gas distributors. The method used is the conditional method where the gas leak detection system is made with Nodemcu esp8266, sensor mq6, buzzer, step-down lm2596, and led. The results obtained from testing the leak detection system explain the testing and evaluation of the gas cylinder inspection system. The system is designed to check 3kg gas cylinders, category level 1 with numbers 300-500, category level 2 with numbers 500-750, category level 3 with numbers 750-1000, and category level 4 with numbers higher than 1000. Detection of gas leaks in each valve takes about 13 seconds to detect leaking gas, send data to the website and provide output to the buzzer.
ANALISIS KESALAHAN SISWA DALAM MENYELESAIKAN SOAL CERITA MATERI TRIGONOMETRI DI SMK PGRI 1 DENPASAR TAHUN AJARAN 2024/2025 Ni Wayan Anggreni Prabawati Sutrisna; I Wayan Sudiarsa; I Made Surat
Prosiding SENAMA PGRI Vol. 4 (2026): Volume 4 Tahun 2026
Publisher : Program Studi Pendidikan Matematika Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/senama.v4.5142

Abstract

ABSTRAK Penelitian analisis kesalahan dalam menyelesaikan soal cerita materi trigonometri ini bertujuan untuk mengetahui jenis kesalahan yang dilakukan siswa serta penyebab terjadinya kesalahan tersebut. Jenis penelitian ini merupakan penelitian Kualitatif yang dilakukan di SMK PGRI 1 Denpasar dengan subjek penelitian yang terdiri dari 2 kelas yaitu kelas X Gabungan dan kelas X TKR. Pada penelitian ini, data didapatkan dari tes kemampuan siswa serta wawancara dimana, instrumen yang digunakan berupa tes uraian sebanyak 5 butir soal guna memeroleh data mengenai jenis kesalahan yang dilakukan dan dilanjutkan dengan wawancara guna mengetahui lebih dalam mengenai faktor penyebab dari kesalahan yang dilakukan. Hasil penelitian menunjukkan bahwa dari 5 soal yang diberikan, jenis-jenis kesalahan yang dilakukan oleh siswa kelas X SMK PGRI 1 Denpasar dalam menyelesaikan soal cerita materi trigonometri menurut kategori Kesalahan Newman, antara lain: Kesalahan membaca (Reading Error) dilakukan sebanyak 18 kali dengan persentase sebesar 5.17%, kesalahan memahami masalah (Comprehension Error) dilakukan sebanyak 88 kali dengan persentase 25.29%, kesalahan transformasi (Transformation Error) dilakukan sebanyak 85 kali dengan persentase 24.43%, kesalahan keterampilan proses (Process Skills Error) dilakukan sebanyak 44 kali dengan persentase 12.64%, dan kesalahan penulisan jawaban (Encoding Error) 113 kali dengan persentase 32.47%. Faktor-faktor penyebab terjadinya kesalahan yang dilakukan oleh siswa yaitu: 1) kesalahan membaca (Reading Error): subjek kurang teliti dalam membaca kalimat dalam soal, siswa salah membaca informasi berupa angka atau simbol pada soal sehingga keliru pada langkah penyelesaian selanjutnya, 2) kesalahan memahami masalah (Comprehension Error): subjek kurang teliti membaca dan mengartikan soal, subjek tidak memahami konsep pada soal yang diberikan, 3) kesalahan transformasi (Transformation Error): subjek tidak memahami konsep dasar yang berkaitan dengan konteks soal, 4) kesalahan keterampilan proses (Process Skills Error): subjek kehabisan waktu ketika menjawab soal, subjek tidak teliti melakukan proses perhitungan, subjek terburu-buru untuk menyelesaikan jawaban, 5) kesalahan penulisan jawaban (Encoding Error): subjek kehabisan waktu, subjek terburu-buru mengerjakan soal. Kata Kunci: Analisis Kesalahan, Soal Cerita, Trigonometri, Kategori Kesalahan Newman. ABSTRACT This study aims to identify the types of errors made by students and the causes of these errors in solving word problems related to trigonometry. This research is a qualitative study conducted at SMK PGRI 1 Denpasar, with research subjects consisting of two classes, namely class X Gabungan and class X TKR. In this study, data were obtained from student ability tests and interviews, using essay test instruments consisting of 5 questions to obtain data on the types of errors made, followed by interviews to explore the underlying factors of the errors committed. The results of the study showed that from the five questions given, the types of errors made by students of class X at SMK PGRI 1 Denpasar in solving trigonometry word problems according to the Newman Error Category, included: Reading Errors occurred 18 times (5.17%), Comprehension Errors occurred 88 times (25.29%), Transformation Errors occurred 85 times (24.43%), Process Skills Errors occurred 44 times (12.64%), and Encoding Errors occurred 113 times (32.47%). The factors causing these errors are as follows: 1) Reading Error: students were not careful in reading the sentences in the questions; they misread numerical information or symbols in the questions, leading to errors in the next steps. 2) Comprehension Error: students were not careful in reading and interpreting the questions; they did not understand the concepts presented in the problems. 3) Transformation Error: students did not understand the basic concepts related to the context of the problem. 4) Process Skills Error: students ran out of time when answering the questions, were not careful in performing calculations, or rushed to complete the answers. 5) Encoding Error: students ran out of time and hurried in answering the questions. Keywords: Error Analysis, Word Problems, Trigonometry. Newman’s Error Categories.
Co-Authors A. A. Gde Ekayana Agung Ari Chandra Wibawa Agung Narayana Adhi Putra Andika, I Gede Aniek Suryanti Kusuma Ariana, Anak Agung Gede Bagus Ariani, Komang Aslin Thanelab Nope Augreselia Novita Nuer Avento Maria Honestra Pratama Onggot Ayu Sri Dewi Brian Adi Sapurta Dewa Ayu Ika Pramitha Dewa Ayu Putu Angelina Dewi Dewa Ayu Sri Handani Dewa Gde Agung Wisnu Anantha Dewa Putu Yudhi Ardiana Dirgayusari, Ayu Manik Gandika Supartha, I Kadek Dwi Gde Wardika Nugraha Gede Agus Santiago Giri, Putu Agus Semara Putra Gusti Ngurah Abhimanyu I Dewa Made Krishna Muku I Dewa Putu Juwana I Gede Adnyana I Gede Andika I Gede Iwan Sudipa I Gusti Ayu Anom I Gusti Made Aditya Putra I Gusti Ngurah Agung Putra Wijaya I Gusti Ngurah Galih Jimbar Baskara I Gusti Ngurah Rangga Mahesa I Kadek Adi Erawan I Kadek Adi Gunawan I Kadek ShandyDwi Putra Andikha I Kadek Yukiarta Putra I Ketut Okta Suastika I Komang Dika Setiawan I Komang Hari Sastrawan I Komang Sukendra I Made Gde Bagus Baskara I Made Jagat Dita I Made Suarta I Made Suarta I Made Surat I Nyoman Agus Suarya Putra I Nyoman Buda Hartawan I P.Fajar Adi Pradipta I Putu Dicky Dharma Suryasa I Putu Diva Naratama I Putu Kabinawa Raesa Putra I Wayan Dharma Suryawan I Wayan Eka Saputra I Wayan Manik Mas Sri Dantya I Wayan Sumandya I Wayan Surya Rahadi Ida Ayu Agung Ekasriadi Ida Ayu Eka Sastradewi Indra Pratistha Jepri Martana, I Nyoman Kadek Agustine Yueyin Parisya Kadek Bagus Karunia Dwi Dharmayasa Kadek Suryati Koten, Felixiana Made Hanindia Prami Swari Made Hendra Wijaya Maharianingsih, Ni Made Maria Karlinda Maria Oktaviani Suryati Nadeerah Hani’ Fauziyyah Ndinin, Maria Avilia NI KADEK RINI PURWATI Ni Luh Putu Sandrya Dewi Ni Made Dwi Junita Sariyani Ni Made Lisma Martarini Ni Made Sukma Sanjiwani Ni Nyoman Padmawati Ni Nyoman Wahyu Udayani NI PUTU AYU MIRAH MARIATI Ni Putu Kania Mahadina Ni Putu Sri Indah Wulandari Ni Wayan Anggreni Prabawati Sutrisna Ni Wayan Sunita Pande Wisnu Wijaya Putra Pande, Ni Kadek Nita Noviani Pera, Magdalena Matildis Palo Pramana, I Made Wisnu Yoga Puguh Santoso Putra , I Dewa Putu Gede Wiyata Putra, I Made Agus Sunadi Putri Maria Theresia Kehi Putu Agus Aditya Putra Putu Diah Kumalasari Putu Paramita Rusaldi PUTU SUGIARTAWAN Rahadi, I Wayan Surya Sanjiwani, Ni Made Sukma Sartika Sartika Sastaparamitha, Ni Nyoman Ayu J. Satwika, I Kadek Susila Setya Cahyani, I Gusti Ayu Agung Dwita Socatama, I Putu Yoga Suradana, I Made Suyitno, Yoga Kristian Syamsiar, Syamsiar Tebai, Elisabeth Lydia Trisnawati, Ni Komang Wardani, Ni Wayan Willdahlia, Ayu Gede Wiyata Putra, I Dewa Putu Gede Yosefina Dehadi Yulianus Kevin Dharmawa Sagur Yustinus Liguori Yuvensia Armelia Sumu Zamzak , M.Arif