Article Per Year (5 Year)
p-Index From 2021 - 2026
6.837
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Reviu Akuntansi dan Keuangan Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmu Komputer Jurnal SPEKTRUM Majalah Ilmiah Teknologi Elektro Ecotrophic, Journal of Environmental Science TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JURNAL MANAJEMEN AGRIBISNIS PROSIDING CSGTEIS 2013 Jurnal Ilmiah Mahasiswa FEB TEMA (Jurnal Tera Ilmu Akuntansi) Infestasi Jurnal Akuntansi Aktual JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Journal of Computer Science and Informatics Engineering (J-Cosine) Logic : Jurnal Rancang Bangun dan Teknologi International Journal of Engineering and Emerging Technology Jurnal Manajemen Indonesian Journal of Electrical Engineering and Computer Science Reviu Akuntansi, Keuangan, dan Sistem Informasi Majalah Ilmiah Teknologi Elektro KREATIF: Jurnal Pengabdian Masyarakat Nusantara Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Made Sudarma
Udayana University
Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Social Sciences

Published : 211 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 211 Documents
Search

 7   8   9   10   11 
Ekstraksi Fitur Warna, Tekstur dan Bentuk untuk Clustered-Based Retrieval of Images (CLUE) I Gusti Rai Agung Sugiartha; Made Sudarma; I Made Oka Widyantara
Jurnal Teknologi Elektro Vol 16 No 1 (2017): (January - April) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (7138.005 KB)

Abstract

Picture (image) is a media that used for storing visual data, for example, two-dimensional images are often used to store an incident. Images on the internet media growth very rapidly. There are a lot of image, video, text or other content on the Internet. Image Index and image retrieval again become a topic of research in the last decade in which concentrated on how to get the meaning of an information contained in an image. Three methods outlined in the search for an image, the text-based image retrieval, content-based image retrieval and indexing images in the order of language. This study focuses on the preparation of the features of an image based on color and texture. Features colors using the average value of Hue image, texture features using Gray Level occurance Matrix (GLCM). Color, texture, and shape extraction technique resulted in eighteen (18) feature that can be used as features in the process of Clustering.DOI: 10.24843/MITE.1601.12
Implementasi Algoritma C5.0 pada Penilaian Kinerja Pegawai Negeri Sipil Putu Wirya Kastawan; Dewa Made Wiharta; Made Sudarma
Jurnal Teknologi Elektro Vol 17 No 3 (2018): (September - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (117.275 KB) | DOI: 10.24843/MITE.2018.v17i03.P11

Abstract

Employees become the spearhead of every Company, whether it is run the business on manufacture or service. The position and role of public government employees as an element of civil servant obligate them to provide fair public services. Refers to those facts, the performance of public government employees needs to be well managed. The algorithm C5.0 is one of the decision tree algorithm which can process the employees performance data become an input for decision-making. Based on evaluation result of 184 employees performance datas, there was a high accurracy data in level 96.08%. Due to that result, the algorithm system can be developed become e-performance system which can predict or giving an advice in order to decision-making processes whether for assigning promotion, ranking or giving performance allowances.
Sistem Monitoring Kehadiran Perkuliahan Menggunakan Face Detection Dengan Algoritma Viola Jones Zul Fachmi; Made Sudarma; Lie Jasa
Jurnal Teknologi Elektro Vol 18 No 1 (2019): (Januari - April) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1580.649 KB) | DOI: 10.24843/MITE.2019.v18i01.P18

Abstract

Presence of lectures is an important factors to take the final exam. It is necessary to have a presence system with computer vision technology that is capable to handling problems manually. Computer vision technology used is face detection and recognition in order to monitor attendance data system. The face detection process in this study uses the Viola-Jones algorithm, and this algorithm has four stages, namely Haar Like Feature, Integral Image, Adaboost learning and Cascade classifier. The results of this study Viola-Jones algorithm successfully applied to the face detection process and in the face recognition process using the KNN (K-Nearest Neighbor) method with an accuracy rate of 94.79%.
Clustering History Data Penjualan Menggunakan Algoritma K-Means Yogiswara Dharma Putra; Made Sudarma; Ida Bagus Alit Swamardika
Jurnal Teknologi Elektro Vol 20 No 2 (2021): (Juli-Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2021.v20i02.P03

Abstract

The company has a desire to develop an increase in its business so that it is not eroded by the very tight business competition. PT. Baliyoni Saguna is a company engaged in information technology and telecommunications which currently helps its customers to provide the best solutions according to customer needs. Product quality is a major factor in keeping customers alive and satisfied with the products provided by PT. Baliyoni Saguna. These products need to be reviewed in order to have a reference in creating the best products. Clustering is a method that can be used to see the level of sales that have been made based on the formed clusters. The K-Means algorithm is a method capable of processing sales history data owned by PT. Baliyoni Saguna in forming groups according to the item category of the item. The K-Means algorithm is able to provide convenience in processing large data so that it can be processed more quickly and efficiently. The results of the application of the K-Means algorithm formed 3 clusters representing the most desirable, least desirable, and least desirable categories. In the most desirable category there are 5 total items, 4 in the interested category there are 4 total items, and 14 items less desirable. These results are expected to help in creating quality goods so as to maintain product quality and customer satisfaction. Keywords – Clustering, K-Means Algorithm, PT. Baliyoni Saguna
Analisis Perilaku Mean Dataset Perubahan Garis Pantai pada Hasil Spasialtemporal Metode Empirical Orthogonal Function (EOF) Ida Ayu Putu Febri - Imawati; Made Sudarma; I Nyoman Satya Kumara
Jurnal Teknologi Elektro Vol 16 No 1 (2017): (January - April) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (720.112 KB)

Abstract

The purpose of this study is to apply EOF method on shoreline change resulting spatialtemporal analysis mode 1 and also to to prove the mean behavior on the spatial or temporal of its EOF outcomes. The data used was obtained from shoreline coordinates of shp file of the Bali island map and the result of breaking waves study, by using a one-dimensional modeling  yielded dataset or prediction shoreline changes during 91 months. Calculation EOF 1, the input matrix or dataset was initially not reduced by the mean but EOF 2 vice versa. Each matrix receive the same treatment were calculated covariance, eigen value, eigen vector and principal component. EOF calculations obtained the last five eigen value, the last five eigen vector, trace, principal component and variant data. Based on the results obtained were compared parameters of two matrices mentioned before. Spatially results both of  EOF 1 and EOF 2 shows the same eigen vector represented by the first mode of eigen vector. Similarly, the eigen value, trace and variance of data, produce the same information. Significant difference occurs in the principal component (temporal). EOF 1 shows that the value of the first month produces a positive value, second month until month 91th output are minus.  EOF 2 shows the value of the principal component the first month until the 37th month are in a positive position, then from month 38th to month 91th yielded negative results. Nevertheless EOF 1 and EOF 2 showed shoreline changes tend to be erosion.Tujuan dari penelitian ini adalah menerapkan metode EOF pada perubahan garis pantai sehingga menghasilkan analisis spasialtemporal mode ke-1 dan juga untuk membuktikan perilaku mean pada hasil spasial atau temporal hasil EOF tersebut. Data yang digunakan diperoleh dari koordinat garis pantai file shp dari peta pulau Bali dan hasil studi gelombang pecah, dengan menggunakan pemodelan satu dimensi menghasilkan dataset atau prediksi perubahan garis pantai selama 91 bulan. Perhitungan EOF 1, matriks input atau dataset awalnya tidak dikurangi dengan rata-rata tetapi EOF 2 sebaliknya. Setiap matriks menerima perlakuan yang sama dihitung covariance, eigen value, eigen vektor dan principal component. Dari perhitungan EOF diperoleh eigen value lima terakhir, eigen vcktor lima terakhir, trace, principal component dan variance data. Berdasarkan hasil yang diperoleh dibandingkan parameter dua matriks sebelumnya.Secara spasial hasil EOF 1 ataupun EOF 2 menunjukkan nilai eigen vector yang sama yang diwakili oleh eigen vector mode pertama. Demikian pula pada  eigen value, trace dan varian data,  EOF 1 dan EOF 2 menghasilkan informasi yang sama. Perbedaan yang siginifikan terjadi pada principal component (temporal). Dari EOF 1 didapatkan bahwa nilai temporal bulan ke-1 menghasilkan nilai positif, bulan ke-2 hingga bulan ke-91 output bernilai minus. Pada EOF 2 nilai principal component ke-1 hingga bulan ke-37 berada pada posisi positif, selanjutnya dari bulan ke-38 hingga bulan ke-91 menghasilkan nilai negatif.  Meskipun demikian  EOF 1 dan EOF 2 tetap menunjukkan garis pantai yang cenderung mengalami  erosi.DOI: 10.24843/MITE.1601.16 
Genetic K-Means Algorithms, ASSU Analisis Peningkatan Kompetensi Mahasiswa Menggunakan Model Pembelajaran ASSURE berbasis Project-Based Learning Asri Prameshwari; Rukmi Sari Hartati; Made Sudarma
Jurnal Teknologi Elektro Vol 17 No 3 (2018): (September - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (696.597 KB) | DOI: 10.24843/MITE.2018.v17i03.P16

Abstract

Sistem pembelajaran yang telah diterapkan dan dikembangkan bertujuan untuk meningkatkan, menguasai, memahami, dan menerapkan materi belajar untuk kemudian dijadikan suatu kompetensi dasar. Penelitian ini menganalisa hasil peningkatan kompetensi mahasiswa dalam mata kuliah teknologi informasi di STIKes Wira Medika Bali pada jenjang S1 Keperawatan dengan menggunakan sistem pembelajaran ASSURE berbasis Project-Based Learning. Metode yang digunakan dalam pengelompokkan hasil peningkatan tersebut menggunakan Genetic K-Means Algorithms, Metode ini dipilih karena mempunyai kinerja lebih optimal dari K-Means sederhana. Algoritma ini yang menggunakan natural selections untuk opitimalisasi menentukan initial seeds. Penentuan jumlah cluster yang digunakan dalam penelitian ini sebanyak tiga cluster dengan kategori tinggi, sedang dan rendah. Hasil dari penelitian ini untuk kategori sedang meningkat dengan range 3,92% dan 14%, untuk kategori rendah meningkat 31,37% dan 74%, untuk kategori tinggi menurun 35,29% dan 60%.
Desain Sistem Semantic Data Warehouse dengan Metode Ontology dan Rule Based untuk Mengolah Data Akademik Universitas XYZ di Bali Made Pradnyana Ambara; Made Sudarma; I Nyoman Satya Kumara
Jurnal Teknologi Elektro Vol 15 No 1 (2016): (January - June) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1815.215 KB) | DOI: 10.24843/MITE.2016.v15i01p02

Abstract

Data warehouse pada umumnya yang sering dikenal data warehouse tradisional mempunyai beberapa kelemahan yang mengakibatkan kualitas data yang dihasilkan tidak spesifik dan efektif. Sistem semantic data warehouse merupakan solusi untuk menangani permasalahan pada data warehouse tradisional dengan kelebihan antara lain: manajeman kualitas data yang spesifik dengan format data seragam untuk mendukung laporan OLAP yang baik, dan performance pencarian informasi yang lebih efektif dengan kata kunci bahasa alami. Pemodelan sistem semantic data warehouse menggunakan metode ontology menghasilkan model resource description framework schema (RDFS) logic yang akan ditransformasikan menjadi snowflake schema. Laporan akademik yang dibutuhkan dihasilkan melalui metode nine step Kimball dan pencarian semantic menggunakan metode rule based. Pengujian dilakukan menggunakan dua metode uji yaitu pengujian dengan black box testing dan angket kuesioner cheklist. Dari hasil penelitian ini dapat disimpulkan bahwa sistem semantic data warehouse dapat membantu proses pengolahan data akademik yang menghasilkan laporan yang berkualitas untuk mendukung proses pengambilan keputusan. DOI: 10.24843/MITE.1501.02
Penentuan Target Pajak Kendaraan Bermotor Di Provinsi Bali Menggunakan ARIMA Dan Algoritma Genetik I Gusti Ngurah Rai Dharma Widhura; Made Sudarma; Ruksi Sari Hartati
Jurnal Teknologi Elektro Vol 17 No 3 (2018): (September - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1363.134 KB) | DOI: 10.24843/MITE.2018.v17i03.P07

Abstract

Bali Regional Income Board is a regional organization tasked with determining the amount of local tax revenue target for the next fiscal year. Currently still done manually in accordance with existing upgrading trends from previous years. So it needs to be done in way that can be measured and accurate forecasting. In recent studies, it shows that forecasting by combining conventional and artificial intelligence (hybrid) methods results in better forecasting accuracy. By that reason, the writer tries to forecast the target of revenue from Motor Vehicle Tax (PKB and BBNKB), which contribute 70% to Bali Province income by combining ARIMA method and Genetic Algorithm. The data used consisted of five groups: yearly and new Vehicles that have linear data types, and as Reverse Names, Entrance Mutations and Output Mutations that have non-linear data types. Each data group consisted of PKB and BBNKB, where it’s monthly realization data from 2011 to 2016 used to be training data and realization data for 2017 as test data. The Combined forecasting mechanism is performed using ARIMA to forecast linear data and using Genetic Algorithms for non-linear data. As a benchmark for combined forecasting using ARIMA and Genetic Algorithms, forecasting using ARIMA and Genetic Algorithms independently is used for all data types (linear and nonlinear). Testing is done by comparing data of forecasting result with that 3 different methods for year 2017 with data realization year 2017. Then the error percentage is counted using MAPE. From the test results obtained for ARIMA MAPE value of 3.63, Genetic Algorithm 4.72 and combined ARIMA and Genetic Algorithm of 1.13. Thus, the result of forecasting with combination ARIMA and Genetic Algorithm have the best result and then used to forecasting target of PKB for 2018 and so on
Text Mining pada Sosial Media untuk Mendeteksi Emosi Pengguna Menggunakan Metode Support Vector Machine dan K-Nearest Neighbour I Made Dwi Ardiada; Made Sudarma; Dwi Giriantari
Jurnal Teknologi Elektro Vol 18 No 1 (2019): (Januari - April) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (210.139 KB) | DOI: 10.24843/MITE.2019.v18i01.P08

Abstract

Twitter social networking and microblog services that allow users to send and read text-based messages up to 140 characters, known as tweets. A text in a tweet does not only convey information from an information, but also contains information about human behavior including emotions. To detect the emotion of the text on Twitter social media services with unstructured data, it is necessary to do text analysis, one of them is by using Text Mining. Text mining tries to extract useful information from data sources through identification and exploration of an interesting pattern. Data sources are a collection of documents and interesting patterns that are not found in the form of record databases, but in unstructured text data. In this study proposes to do text mining research on Social Media to detect user emotions. Text-based emotional detection can be used in business, education, psychology, and any other field that is most important for understanding and interpreting emotions. From the tests carried out by the Support Vector Machine and K-Nearest Neighbor methods can produce an average value of precision of 0.45640904478933. Recall value is 0.50199332258158 and the accuracy value is 0.8140589569161 while from the K-Nearest Neighbor method the average value of precision is 0.34210487225193. Recall value is 0.45954538381009 and the accuracy value is 0.79705215419501. the results of testing with the SVM-KNN method showed that the suitability of emotional classification was better than the K-Nearest Neighbor method of the whole emotional categories.
Evaluasi Pengembangan Disaster Recovery Center untuk Data Center Universitas Udayana Kheri Arionadi Shobirin; Nyoman Putra Sastra; Made Sudarma
Jurnal Teknologi Elektro Vol 20 No 1 (2021): (Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2021.v20i01.P20

Abstract

Data Center has a vital and strategic role in supporting university operations. Based on Government Regulation No.17 of 2019 article 20 section 1: Every Data Center owner must have a Disaster Recovery Center. Evaluation of Disaster Recovery Center Development for Udayana University Data Center conducted by considering aspects of natural threats, human threats, environmental threats, existing Data Center specification, virtualization, and cloud technology used to maintain the availability of Data Center services for Udayana University with the most efficient development costs. Using cost comparation for DRC development and operation for 3 years, found that implementation cost of Cloud DRC 3 times higher compare to Conventional DRC. High cloud computing cost contribute 67% of Cloud DRC cost structure.
Co-Authors A. A. K. Oka Sudana A.A Ngurah Narendra A.A Raka Novi Aristi Adi Darmawan Ervanto Adinata Mas Pratama Aggry Saputra Aggry Saputra Agus Aan Jiwa Permana Agus Dharma Ahmad Catur Widyatmoko Ajeng Anandra Anak Agung Kompiang Oka Sudana Anak Agung Ngurah Prawira Yudha Andrew Sumichan Andrew Sumichan Ari Kamayanti Ariyady Kurniawan Muchsin Asri Prameshwari Casya Nova Nitali Ginting Charolina Devi Oktaviana Soleman Charolina Devi Oktaviana Soleman Dandy Pramana Hostiadi Darma Kotama, I Nyoman Darma Putra Dea Novim Kartikasari Dewa Ayu Putri Wulandari Dewa Made Wiharta Dima Nurfitri Apriani Dita Rizky Prahayuningtyas Duman Care Khrisne Erwin Saraswati Faraz Muhammad Aulia Fauziah, Farah Ferry Angga Irawan Gde Brahupadhya Subiksa Hanif Prio Ariantono Hardi yusa Hisyam Rahmawan Suharno Hisyam Rahmawan Suharno I Dewa Made Krisnayana I Dewa Nyoman Anom Manuaba I Dewa Nyoman Anom Manuaba I Gede Abi Yodita Utama I Gede Adnyana I Gede Harsemadi I Gede Herry Juniartha I Gede Sujana Eka Putra I Gede Totok Suryawan I Gede Wira Darma I Gst Agung Alit Wismaya I Gusti Agung Gede Mega Perbawa I Gusti Agung Indrawan I Gusti Agung Komang Diafari Djuni Hartawan I Gusti Kade Harta Kesuma Wijaya I Gusti Made Panji Indrawinatha I Gusti Ngurah Adhy Pradhana I Gusti Ngurah Agung Jaya Sasmita I Gusti Ngurah Agung Surya Mahendra I Gusti Ngurah Agung Surya Mahendra I Gusti Ngurah Gede Agung Suniantara I Gusti Ngurah Rai Dharma Widhura I Gusti Rai Agung Sugiartha I Kadek Arya Wiratama I Kadek Dwi Gandika Supartha I Kadek Sastrawan I Kadek Yuda Setiadi I ketut Gede Darma Putra I Komang Yogi Sutrisna I Made Adi Bhaskara I Made Arsa Suyadnya I Made Artawan I Made Budi Sentana I Made Dwi Ardiada I Made Dwi Jendra Sulastra I Made Gede Yudiana I Made Gede Yudiyana I Made Oka Widyantara I Made Sukarsa I Made Sukarsa I N Satya Kumara I Nyoman Adi Putra I Nyoman Gunantara I Nyoman Putu Suwindra I Putu Adi Pradnyana Wibawa I Putu Agung Bayupati I Putu Agus Eka Darma Udayana I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Agus Priska Suryana I Putu Alit Putra Yudha I Putu Arya Putrawan I Putu Astya Prayudha I Putu Gd Sukenada Andisana I Putu Oka Wisnawa I Putu Putra Diyastama I Putu Putrayana Wardana I Putu Sugi Almantara I Putu Warma Putra I Wayan Agus Surya Darma I Wayan Eka Krisna Putra I Wayan Suarna Ida Ayu Dwi Giriantari Ida Ayu Listia Dewi Ida Ayu Putu Febri Imawati Ida Bagus A. Swamardika Ida Bagus Agung Eka Mandala Putra Ida Bagus Dwijaya Kesuma Ida Bagus Gede Manuaba Ida Bagus Gede Widnyana Putra Ida Bagus Leo Mahadya Suta Ida Bagus Leo Mahadya Suta Ida Bagus Leo Mahadya Suta Ida Bagus Surya Paramarta IGAM Yoga Mahaputra Irvan Dinda Prakoso Irwansyah Cahya Irwansyah Cahya Adha L Iskandar, Adi Panca Saputra Isnan Murdiansyah IW Dani Pranata Jauzaa Maylia Suhendro Josep Geas Sapalatua Kadek Ary Budi Permana Kadek Ary Budi Permana Kadek Ary Budi Permana Kadek Ary Budi Permana Kheri Arionadi Shobirin Komang Agus Putra Kardiyasa Komang Ayu Triana Indah Komang Budiarta Komang Budiarta Komang Budiarta Komang Isabella Anasthasia Komang Nova Artawan Komang Oka Saputra Komang Sri Utami Lanang Bagus Amertha Lanang Bagus Amertha Lie Jasa Linawati Linawati Luh Gede Putri Suardani Luh Ria Atmarani M. Azman Maricar Made Dinda Pradnya Pramita Made Dinda Pradnya Pramita Made Pasek Agus Ariawan Made Pradnyana Ambara, Made Pradnyana Made Sri Indradewi Adnyana Manuh Artana Michael Tanduk Langi Londong Allo Minho Jo Minho Jo Minho Jo Muhammad Ridwan Satrio Murpratiwi, Santi Ika Naser Jawas Nengah Widiangga Gautama Ni Ketut Novia Nilasari Ni Komang Sri Julyantari Ni Komang Sukri Antariani Ni Luh Gede Pivin Suwirmayanti, S.Kom, MT, Ni Luh Gede Pivin Ni Luh Ratniasih, Ni Luh Ni Made Ananda Putri Pratiwi Ni Made Ari Lestari Ni Made Dwi Antari Ni Putu Sutramiani Ni Wayan Lusiani Ni Wayan Sri Ariyani Nurkholis - Nyoman Gede Yudiarta Nyoman Paramaita Nyoman Pramaita Nyoman Putra Sastra Nyoman Swastika Dharma Pande Made Sutawan Philipus Novenando Mamang Weking Purwania Ida Bagus Gede Putri Sintya Dewi Putri Suardani Putu Agung Ananta Wijaya Putu Angelina Widya Putu Arya Mertasana Putu Bagus Satria Paramartha Putu Risanti Iswardani Putu Wirya Kastawan R. Sapto Hendri Boedi Soesatyo Reni Surmayanti Ricky Aurelius Nutanto Diaz, Ricky Aurelius Rifky Lana Rahardian Risky Aswi R, Risky Rizky Muharram Julyanto Roekhudin, Roekhudin Rukmi Sari Hartati Rukmi Sari Hartati Tria Hikmah Fratiwi Vony Wahyunurani Wahyudin Wahyudin Wayan Gede Ariastina Wikan Pradnya Dana, Gde Y. Yuliati Yogiswara Dharma Putra Yogiswara Dharma Putra Yoni Yogiswara Yudhistira Bayu Perkasa Zulfachmi, Zulfachmi