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ANALISIS ANTRIAN PASIEN INSTALASI RAWAT JALAN POLIKLINIK LANTAI 1 DAN 2 RSUD CENGKARENG, JAKARTA Nadeak, Sanitoria; Sugito, Sugito; Suparti, Suparti
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (705.218 KB) | DOI: 10.14710/j.gauss.v5i1.11059

Abstract

The queue process associates with the arrival of the costumers of a service facility, waiting in a queue line when all waiters are busy, and finally left the facility after being served. Queuing phenomena can be found in public service facilities, such as in District General Hospital (RSUD) Cengkareng. The length of the registration procedure, consultation services for physicians, and waiting time for the pharmacy services, can influence the satisfaction of the patients of Outpatient Installation of RSUD Cengkareng. Therefore, it is necessary to have an appropriate queue model to get an effective service, balanced and efficient, that can reduce the long queues and waiting time. From the analysis, the queue model for the registration of the Workers Social Security Agency (BPJS) patient is (M /M/6):(GD/∞/∞) with the number of server is 6 counters and for the non BPJS patients is (M/M/2):(GD/∞/∞) with the number of server is 2 counters. The queue model for the psychiatrist clinic and anesthetic is (M/M/1):(GD/∞/∞) with the number of server is 1 counter. The queue model for the other Polyclinic is (M/M/c):(GD/∞/∞) with the number of server depends on the clinic itself.Keywords: Queue, Outpatient Installation, District General Hospital (RSUD) Cengkareng
PREDIKSI INFLASI BEBERAPA KOTA DI JAWA TENGAH TAHUN 2014 MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE (VAR) Utami, Tika Nur Resa; Rusgiyono, Agus; Sugito, Sugito
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.258 KB) | DOI: 10.14710/j.gauss.v4i4.10240

Abstract

Inflation is a situation where there is an increase in the general price level. Inflation for goods and services purchased by consumers is measured by changes in the Indeks Harga Konsumen (IHK). Determination of the amount, type and quality of commodities in the package of goods and services in the IHK is based on the Survey Biaya Hidup (SBH). In Central Java, there are only four cities covered in the implementation of SBH, namely Purwokerto, Solo, Semarang, and Tegal. It was the underlying researchers took the four cities. In this case, researchers taken for the period of 2009-2013. Inflation Purwokerto, Solo, Semarang, and Tegal is a multivariate time series  that show activity for a certain period. One method to analyze multivariate time series is Vector Autoregressive (VAR). VAR method is one of the multivariate time series analysis of variables that can be used to predict and assess the relationship between variables. Inflation researchers predict that by 2014 the four cities using VAR (1). Chosen VAR (1) is based on the results of some tests. VAR (1) have the optimal lag value, there is no correlation between the residual lag, and the value Root Mean Square Error (RMSE) is smaller than the other models.                                                                                      Keywords: Inflation, IHK, SBH, Multivariate Time Series, Forecasting, Vector Autoregressive (VAR).
PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN SUPPORT VECTOR REGRESSION (SVR) DENGAN ALGORITMA GRID SEARCH Septiningrum, Lutfia; Yasin, Hasbi; Sugito, Sugito
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (509.262 KB) | DOI: 10.14710/j.gauss.v4i2.8579

Abstract

The existence of capital market Indonesia is one of the important factors in the development of the national economy, proved to have many industries and companies that use these institutions as a medium to absorb investment and media to strengthen its financial position. Capital market Indonesia is an emerging market development is very vulnerable to global economic conditions and capital markets of the world. Prediction JCI (Jakarta Composite Index) is necessary to know the great value that will occur in the future so as investors can take the right policy. To predict in this study used a Support Vector Regression (SVR) method to find the hyperplane in the best regression function to predict the closing price of the JCI using a linear kernel function with output in the form of continuous data. Parameter selection cost and epsilon using a grid search algorithm combined with cross validation and obtained best cost 1 and best epsilon 0.1. While the criteria to measure the goodness of the model is MAPE (Mean Absolute Percentage Error) and R2 (Coefficient Determination). The results of this study showed that SVR with linear kernel function provides excellent accuracy in the prediction of JCI with R2 results on training data 98.4% with a MAPE 0.873% while the testing of data R2 90.9% with a MAPE 0.613%.Keywords: JCI, Support Vector Regression (SVR), Hyperplane, Kernel Linear, Grid Search Algorithm, Cross Validation, Accuracy
PENERAPAN METODE WEIGHTED PRODUCT (WP) DAN ELIMINATION ET CHOIX TRANDUISANT LA REALITÉ (ELECTRE) DENGAN PEMBOBOTAN ENTROPY MENGGUNAKAN GUI MATLAB (Studi Kasus: Pemilihan Hero Terkuat Arena of Valor) Sukanianto, Eko Adyan; Sugito, Sugito; Rahmawati, Rita
Jurnal Gaussian Vol 7, No 2 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1148.455 KB) | DOI: 10.14710/j.gauss.v7i2.26645

Abstract

Arena of Valor (AOV) is a mobile game published by Garena in Indonesia. There will be 5 players in each team, selecting a hero to play in the game. By selecting the strongest hero each role can help facilitate team to strategize the composition of heroes that will be used to achieve victory. Weighting each criteria and selecting the strongest hero also become a consideration to control the game to be stable and balanced by the developer. The alternatives are all hero from each role (Tank, Warrior, Assassin, Mage, Archer and Support), while the criterias are skill effect points, maximum HP (Health Points), physical attack, physical defense, movement speed and HP recovery every 5 seconds. In this study, the writer uses WP and ELECTRE methods to select the strongest hero with Entropy weighting method. This study produce a Matlab GUI that can be used to facilitate computational selection. The results show that the strongest hero in AOV are Grakk (Tank), Astrid (Warrior), Ormarr (Warrior), Murad (Warrior/Assassin), Lauriel (Mage/Assassin), The Joker (Archer) and Alice (Support). While the criteria with the highest weighting is the skill. Keywords: AOV, Garena Indonesia, WP, ELECTRE, Entropy, GUI Matlab
ANALISIS ANTRIAN PENGUNJUNG DAN KINERJA SISTEM DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KOTA SEMARANG Astrelita, Fahra Pracendi; Sugito, Sugito; Wuryandari, Triastuti
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (486.7 KB) | DOI: 10.14710/j.gauss.v4i4.10138

Abstract

Department of Population and Civil Registration (Dispendukcapil) has the duty of assistance in the field of population and civil registration. Civil registration services such as services related to birth, death, marriage, and divorce. As a service provider, Dispendukcapil of Semarang has the motto "No Day Without Service Quality Improvement". Queuing problem is that often occur and must be considered. The queue situation occurs because the number of visitors to a service facility exceeds the available capacity to perform such services. A system is always trying to serve visitors well in accordance with the rate of arrival of each visitor. Therefore please note the size of the system's performance on each section on service system. Dispendukcapil queuing system at Semarang city located on the Legalized, Change Data, Birth, Death, Divorce/Marriage, and Decision Act. Based on the results obtained and the analysis of models of queuing at the counter is Legalized (G/G/2):(GD/∞/∞), while the counter is Birth (G/G/3):(GD/∞/∞), the Change the counter Data, Death, Divorce / Marriage is (M/G/1):(GD/∞/∞) and Decision Deed is (G/G/1):(GD/∞/∞).  Keywords: Queuing System, Dispendukcapil, Dispendukcapil of Semarang, Legalized, Birth, Death, Divorce, Marriage.    
ANALISIS ANTRIAN PASIEN RAWAT INAP BERDASARKAN SPESIALISASI PENYAKIT DI RSUP Dr KARIADI SEMARANG Rahayu, Anisa Alfiani; Sugito, Sugito; Sudarno, Sudarno
Jurnal Gaussian Vol 2, No 4 (2013): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (307.147 KB) | DOI: 10.14710/j.gauss.v2i4.3766

Abstract

The arrival rate of inpatients at the Dr Kariadi Hospital very much in every day, either derived from poly outpatient and the ER (emergency room). With limited bed capacity, the hospital often refer patients to the hospital inpatient others who still have bed capacity. But many patients who do not want to refer to others hospitals and they will waiting for a inpatient ward. Therefore, it is necessary to determine the queuing system model according to the conditions and characteristics of the queue service facilities in Dr Kariadi hospital based specialization disease patients. Based on the analysis of data obtained for each specialization disease models queuing system that occurs in hospital based specialties Dr Kariadi hospital disease is (M / M / c): (GD / ∞ / ∞) and the model of the queue at the payment system is (M / M / 4): (GD / ∞ / ∞). Number of inpatient services by specialist have been effective because of the amount of each disease have many specialists. As for the payment / checkout number of officers who perform duties detailed breakdown of costs need to be added so that patients who come do not wait too long to get service.
ANALISIS REGRESI NONPARAMETRIK KERNEL MENGGUNAKAN METODE JACKKNIFE SAMPEL TERHAPUS-1 DAN SAMPEL TERHAPUS-2 (Studi Kasus: Pemodelan Tingkat Inflasi Terhadap Nilai Tukar Rupiah di Indonesia Periode 2004-2016) Putri, Agum Prafindhani; Santoso, Rukun; Sugito, Sugito
Jurnal Gaussian Vol 6, No 1 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (794.587 KB) | DOI: 10.14710/j.gauss.v6i1.14756

Abstract

Exchange rate is a conversion between currencies of a country to another country. Inflation can be defined as the rise of good and service’s level of price continually. The fluctuation of exchange rate is related to inflation, because inflation is the reflection of changes in the price level which happens in market and led to changes in level of money demand and supply. From the data distribution pattern which doesn’t show linearity relation, therefore the right modeling needs to be done using non-parametrical regression. Kernel Function which is used in non-parametrical component is Gaussian with optimal choice of bandwidth using the delete-1 Jackknife sample and the delete-2 Jackknife sample in Cross Validation (CV) method. This research using monthly data, 100 in sample data which taken from September 2014 until December 2012, while the number of out sample data used is 40 which taken from January 2013 until April 2014. Based on the analysis which had been done, the best kernel non-parametrical regression is the model using the delete-2 Jackknife sample because it produced the smallest Mean Absolute Percentage Error (MAPE) therefore it had better model accuracy evaluation. Keyword : Exchange Value, Non-parametrical Regression, Kernel, Jackknife Method, Cross Validation (CV)
PREDIKSI JUMLAH PENUMPANG KERETA API MENGGUNAKAN MODEL VARIASI KALENDER DENGAN DETEKSI OUTLIER (Studi Kasus : PT. Kereta Api Indonesia DAOP IV Semarang) Saputri, Ani Funtika; Hoyyi, Abdul; Sugito, Sugito
Jurnal Gaussian Vol 6, No 3 (2017): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (518.055 KB) | DOI: 10.14710/j.gauss.v6i3.19301

Abstract

Transportation is an inseparable and indispensable part of society in everyday life. Trains became one of the most popular public transportation, especially during the Eid. The shifting of the lunar month of Eid forms a pattern called calendar variation. The calendar variation model is a model that combines the dummy regression model with the ARIMA model. In time series models sometimes there are outliers that can affect the suitability of the model. So that modeling and forecasting method is done using model of calendar variation with outlier detection. Based on the analysis that has been done on the data of the number of passengers of Argo Bromo Anggrek railway, we get the ARIMA model ([11], 0, 1), Dt, Dt-2,t with the addition of 4 outliers as the best model and the resulted forecasting shows increase Railway passengers increase in the months leading up to Eid. Keywords: Train, Calendar Variations, Outlier Detection
ANALISIS ANTREAN BUS NONPATAS AKAP DAN AKDP JALUR TIMUR TERMINAL TIRTONADI KOTA SURAKARTA Sitomurang, Rosalina Aprilda; Sugito, Sugito; Mukid, Moch. Abdul
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.715 KB) | DOI: 10.14710/j.gauss.v7i3.26663

Abstract

The queuing system is a set of customers, services and a set of rules governing the arrival of its customers and services. Queue is a waiting phenomenon that is part of everyday human life. The queue is formed if the number of subscribers to be served exceeds the available service capacity. Queue phenomenon one of them seen in the queue nonpatas buses at Terminal Tirtonadi Surakarta. Nonpatas bus lanes studied include non-purpose buses Surabaya, Karanganyar, Wonogiri, Purwodadi and Pedesaan. The queue displant used is FIFO (First In First Out). For the five nonpatas bus lanes it meets steady state conditions because it has utility value less than 1. The selected model is a model that has the following 4 types of distributions: Erlang, Weibull, Gamma and Lognormal. The queue model generated for the five tracks (ERLA/ERLA/1):(GD/∞/∞) for Surabaya nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Karanganyar nonpatas buses, (GAMM/WEIB/1):(GD/∞/∞) for Wonogiri nonpatas buses, (ERLA/WEIB/1):(GD/∞/∞) for Purwodadi nonpatas buses, (WEIB/LOGN/1):(GD/∞/∞) for Pedesaan nonpatas buses. Based on the value of the system performance measure indicated that the five lines are queue system is good. Keywords: Beta, Erlang, FIFO, Gamma, Steady State Conditions, Lognormal, Queue Model, Queuing Systems, System Performance Measure, Weibull
ANALISIS ANTREAN BUS KOTA DI TERMINAL INDUK PURABAYA SURABAYA Priyambodo, Richy; Sugito, Sugito; Suparti, Suparti
Jurnal Gaussian Vol 1, No 1 (2012): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.313 KB) | DOI: 10.14710/j.gauss.v1i1.912

Abstract

Transportation is an important factor to grow the economy of a region. This is because the more smoothly transport then the faster the economy growth of a region. For that, Purabaya bus station always try to provide optimum service to avoid long queue. Queue process is a process of the coming of a customer to a service facility, then waiting in line (queue) when the officers busy, and leaving the place after getting the service. If the queue at Purabaya bus station is pretty much, it will reduce the amount of revenue generated by the transport service provider. Therefore, we need a model of the queue to optimize service to customers in Purabaya bus station. From the analysis, the best queuing models obtained on the service system in Purabaya bus station is (M/G/c): (GD/∞/∞) to service system at the postal arrival with 5 counters, service system for each bus line in passenger service post is (M/G/1): (GD/∞/∞), and (G/G/2): (GD/∞/∞) to service system at the postal departure.
Co-Authors Abdul Harris Abdul Hoyyi Abdul Rosyid Abu Suud, Abu Adin Ariyanti Dewi Aditya Mahatidanar Hidayat, Aditya Mahatidanar AFIFAH, ZAHRA Agum Prafindhani Putri, Agum Prafindhani Agung, Yosep Roro Agus Rusgiyono Agus Wijaya Agustina Ainiyah, Luluk Qurrotul Ajironi Akbar, M Ramadhan Akbari, Windusiwi Asih Akil, Azzahra Alan Prahutama Alan Prahutama AlFath, Ayatullah Muhammadin Alhail, Hadi Allsabah, M. Akbar Husein AllSabah, Muhammad Akbar Husein Aminah Aminah Amir Danis Ammara, Avilda Afrin Andek Prabowo Anggraini, Anggun Nur Anisa Alfiani Rahayu Anny Yanuriati Appin Purisky Redaputri Ardyes, Rizki Arham Arham, Arham Ari Gunawan Arif Widagdo Asmarida - Asrul Asrul Atika Resty Handani Ayu Nurdiana, Fitria AZHAR - Azis, Adek Cerah Kurnia AZMI, AZMI Bastian, Antoni Bhakti, M Aditya Wira Bilalodin Bilalodin Boli, Lusia Silfia Pulo Brigita P Manohara Buana, Lalang Cakra Budhi Waskito Budi Warsito Budiarti, Nugraheni Dwi Budiyanto Budiyanto Burstiando, Rizki Candra Silvia, Candra Caprityan, Revin D.M.T, Immaculata Dasrul Dasrul Dede Indra Setiabudi Defi Vianika Sri Ambarwati Defira, Citra Delli Lefiana, Delli Dewi Novitasari Dewi Safitri Dewi, Dora Silvia Dhantriviana, Indah Dhedhy Yuliawan Di Asih I Maruddani Dj, Herlinda Djohan Dwi Ispriyanti Dwinna Aliza Effendi, Ihsan Eka Lidiasari Elpalina, Srimutia Elyasa, Fatiya Rahmita Emirza Henderlan Harahap Erma Kusumawardani Ersita, Vika Erwin Erwin Etriwati E Fadilla, Irdo Fadrial Karmil Fahra Pracendi Astrelita, Fahra Pracendi Fakhrurrazi - Faqih, Ghazi Muhammad Faria Ruhana Fauziyyah, Fida Febrianty, Resty Ferdian, Riyan Filli Pratama Firdaus, Mokhamad FIRMANSYAH, ANDI Fitriana, Rizky Fitriani, Arifah Devi Fitta Ummaya Santi Gamal Kartono Gatot Priyanto Gunawan, Roni Hamdani Budiman harahap, zety shafitri Harmono, Setyo Hartabela, Dadang Hartono Hartono Haryadi - Hasan, Nur Aini Hasbi Yasin Hasby, Esa Pallewi Hati Wau, Kerinus Helmi, Habib Mustofa Hermanto Hermanto Hermawanto, Ifan Herrialfian - Hilmi, Mulkiah Husein Allsabah, Muhammad Akbar Ichsan, Onne Akbar Nur Ida Zulfida, Ida Idham Khalid Ika Maulita Indah Nurhayati Indra Permana Jati Indrawan, Surya Indria Tsani Hazhiah Ipung Permadi Irnani, Christina Irsyad, Alhafidz Jessica Anggun Safitri joko budi poernomo, joko budi Joko Sutrisno Karim, Iksan Khoirin, Lukman Khoirul Faizin Kholis, Muh. Nur Krismayasari, Damiyana Kurniawan, Wing Prasetya Kurniawati, Galuh Nurvinda Kustini Kustini Laba, Vinsensius Fereri Laksita, Anindya Linda Triana Luksmana, Roby Lusianti, Septyaning Lutfia Septiningrum M. Isa M. Nur Salim Made S, Desak Maghfiroh, Fatkhiyatul Maharani, Regina Ayu Mahuli, Jenda Ingan Majid, Dian Makmur, Ali Makmur, Ali Manap, Abdul Mangaraja Manurung Mardianto, Ardhi Marissa Fauzia Marlina, Amalia Dwi Maulana, Bintang Adrian Merlia Yustiti Merynda Indriyani Syafutri MESRA MESRA, MESRA MIFTAKHUL JANNAH Moch Abdul Mukid Moch. Abdul Mukid Moch. Mahsun Mohammad Firdaus, Mohammad Muhamad Mahfud, Muhamad Muhammad Al Kholif Muhammad Amirulloh, Tifan Muhammad Hasan Muhammad Zulhilmi Muslim Muslim Mutahajjid, Zul Azmi Nabila Latifa Hafizsha Nashrulloh, Moch. Haris Nastiti, Wahyuningsih Natalia Natalia Natasya, Ananda Nia Puspita Sari Nisa, Mukrimatun Nur Ahmad Muharram Nur Nahar Hutabarat Nurhidayah, Aulia Nurliana - NURLIANA NURLIANA Nuzul Asmilia Octavian Kusdardjanto, Fakhri Oesman, Nastiti Maharani Pangeran, Pangeran Pangestu, Adhi Permatasari, Nidya Phani, Chika Padila Pramitasari, Anisa Praptomo, Muhammad Nauval Prasetya, Genta Adi Pratama, Budiman Agung Pratiwi, Citra Prayogi, Wempy Puja Cikal Bangsa Puji Rahayu Puji Yanti Fauziah Pujiono Pujiono Pungut, Pungut Purwanto A T, Purwanto A Puspodari, Puspodari Puspodari, Puspodari Puspodari Putra, M Iqbal Syah Pratama Putra, Muhammad Uke Dwi Putra, Rendhitya Prima Putra, Rengga Khatulistiwa Putri Milenia, Erin Novela Rachim, Febyani Rafiqah, Thara N Rahmad Firnanda Rahmi Eka Putri Rahmi, Aisyah Rahmi, Ulfa Rajiman Rajiman Rajudin, Rajudin Ramadhan, Alga Anur Rasyida Ulfa Ratnawati, Rhenny Ratnawaty, Gervacia Jenny Razali Daud Razali Daud Razali Razali Reza Hanafi Lubis Richy Priyambodo Rieuwpassa, Jessica Athalia Rinidar - Rinidar Rinidar, Rinidar Rita Rahmawati Rivaldi, Mukhamad Hafiz Riyanto, Hariz Nur Aziz Rizky, Muhammad Yanuar Rizqullah, Afif Mu’tashim Rohmadiani, Linda Dwi Ronny Hasudungan Purba Rosidah, Umi Roslizawaty - Roslizawaty Roslizawaty, Roslizawaty Rosmaneliana, Dina Rukun Santoso Sadad, Imam Akmal Saleh, Khaerul Salma, Anugrah Rawiyah Samadi Samadi Sandi Andaryadi Sanitoria Nadeak, Sanitoria Santoso, Agung Dwi Trawasa Saputra, Yoga Saputri, Ani Funtika Saputro, Jessica Aulia Sari, Loren Ardana Sari, Titis Lukita Sari, Wahyu Eka Saskia, Dewi Saut Mardame Simamora Sa’adah, Ardiana Alifatus Sehah Sehah Sekianti, Atik Setyawan, Irwan Sinta Maulida Hapsari, Sinta Maulida Siregar, Dahrul Siregar, Dian Maya Sari Siti Julaeha, Siti Siti Komariyah Siti Rahmadhani Siregar Sitomurang, Rosalina Aprilda Slamet . Slamet Junaidi Sofia Naning Hertiana Solly Aryza Sri Ramadhani Subandowo, Marianus Sudarno Sudarno Sudjarwanto, Sudjarwanto Sukanianto, Eko Adyan Sukmana, Abdian Asgi Sulastri Irbayuni, Sulastri Sumarti Suryaningsih Suparti Suparti Supriyanto Supriyanto Susilowati Susilowati Susilowati Susy Sriwahyuni Sutismawati Sirait, Lincaria Sutriswanto, Sutriswanto Suwandi, Edy Suwitho, Suwitho Switarto, Bambang Syafaati, Naini Siseptya Syafruddin Syafruddin Syafutri, Merynda I Syah, Nada Yulian Syaifuddin Syaifuddin Syaiful, Friska Syari, Jajar Syinta Ramadhani Tamrin Tamrin Tarigan, Nelson Tarno Tarno Tatik Widiharih Teuku Reza Ferasyi Thedjo, Melawati Tia Zalia Btb Tika Nur Resa Utami, Tika Nur Resa Tina Miniawati Barusman Tri Wardani Widowati Triastuti Wuryandari Tristanti Tristanti Tristiana, Dwi Sari Triwardani Widowati Utami, Krisdiana Nur Vara Tassa Sutari Wahyuni, Sigma Waluyo, Subagio S wasis himawanto, wasis Wati, Dewi Ayu Trisno Wati, Serly Widya Weda, Weda Wibowo, Bagus Tri Widayanto, Muhammad Fajar Alamsyah Widiyana, Made Dimas Wihantoro Wihantoro Wirapratama, Bima Ilham Yahya, Rian Ainun Yarmaliza Yuciana Wilandari Yulfriwini, Yulfriwini Yulia Agnis Sutarno Yulingga Nanda Hanief Yumielda, Vivi Destri Yusak Maryunianta Yusnindar, Yusnindar Zainuddin - Zainuddin, Zainuddin Zamzami, Rumi Sahara Zaroh Irayani Zawawi, M. Anis Zidny Taqiyya Zuhrawati -