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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Manajemen dan Agribisnis FORUM STATISTIKA DAN KOMPUTASI Jurnal Ekonomi Pembangunan Indonesian Journal of Geography IJCCS (Indonesian Journal of Computing and Cybernetics Systems) ETIKONOMI EKSAKTA: Journal of Sciences and Data Analysis MATRIK: JURNAL MANAJEMEN, STRATEGI BISNIS, DAN KEWIRAUSAHAAN Media Statistika Sosiohumaniora Statistika Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Business and Entrepreneurship (IJBE) Jurnal Berkala Ilmu Perpustakaan dan Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Scientific Journal of Informatics Journal of Consumer Science Jurnal Ilmiah Arena Tekstil MIX : Jurnal Ilmiah Manajemen JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Asia-Pacific Management and Business Application MAJALAH ILMIAH GLOBE Journal of Consumer Sciences Matra Pembaruan: Jurnal Inovasi Kebijakan Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Informatika Pertanian CogITo Smart Journal Inovasi : Jurnal Ekonomi, Keuangan, dan Manajemen Indonesian Journal of Artificial Intelligence and Data Mining JEPA (Jurnal Ekonomi Pertanian dan Agribisnis) Albacore : Jurnal Penelitian Perikanan Laut BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Warta Penelitian Perhubungan Jambura Journal of Mathematics Journal of Humanities and Social Studies International Journal of Remote Sensing and Earth Sciences (IJReSES) Aptisi Transactions on Technopreneurship (ATT) STI Policy and Management Journal Jurnal Aplikasi Statistika & Komputasi Statistik TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Jurnal Administrasi dan Manajemen Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications Ecces: Economics, Social, and Development Studies Dinasti International Journal of Education Management and Social Science International Journal of Zakat (IJAZ) Inferensi InPrime: Indonesian Journal Of Pure And Applied Mathematics International Journal of Science, Engineering and Information Technology Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya Jurnal Pengabdian Masyarakat Indonesia Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SRIWIJAYA JOURNAL OF ENVIRONMENT Jurnal Natural Eduvest - Journal of Universal Studies Xplore: Journal of Statistics STATISTIKA Jurnal Informatika: Jurnal Pengembangan IT PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Jurnal Ekonomi dan Pembangunan Indonesia The Indonesian Journal of Computer Science Journal of Mathematics, Computation and Statistics (JMATHCOS) Journal of International Accounting, Taxation and Information Systems Indonesian Journal of Statistics and Its Applications Diophantine Journal of Mathematics and Its Applications Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Warta Penelitian Perhubungan The International Journal of Remote Sensing and Earth Sciences (IJReSES) Teknobuga : Jurnal Teknologi Busana dan Boga Scientific Contributions Oil and Gas

Bagus Sartono

Departmen Statistika Institut Pertanian Bogor Gedung IPB Dramaga, Jl. Meranti Wing 22 Lantai 2 Kampus IPB Darmaga Bogor (16680) Indonesia Phone: +62-811-118-508

Author-ID : 373849

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COMPARISON OF SARIMA, SVR, AND GA-SVR METHODS FOR FORECASTING THE NUMBER OF RAINY DAYS IN BENGKULU CITY Puspita, Novi; Afendi, Farit Mochamad; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The number of rainy days is a calculation of the rainy days that occur in one month. In recent years, there has been a decrease in rainy days in some parts of Indonesia. One of the areas at risk of quite a high decreasing number of rainy days is the Bengkulu City area. The decrease in the number of rainy days is one of the impacts caused by climate change. The community will feel the impact of climate change-related to the season, especially those working in the agricultural sector. In compiling the planting calendar, it is necessary to consider the seasons to estimate water availability. This study aimed to forecast the data on the number of rainy days in Bengkulu City in the period January 2000 to December 2020 using the Seasonal Autoregressive Integrated Moving Average (SARIMA), Support Vector Regression (SVR), and Genetic Algorithm Support Vector Regression (GA-SVR) methods. The criteria for selecting the best model used was Mean Absolute Deviation (MAD). The MAD value in the SARIMA method was 4,16, 5,07 in the SVR model, and 3,67 in the GA-SVR model. Based on these results, it can be concluded that the GA-SVR model is the best model for forecasting the number of rainy days in Bengkulu City.

RESTRICTED MAXIMUM LIKELIHOOD ESTIMATION FOR MULTIVARIATE LINEAR MIXED MODEL IN ANALYZING PISA DATA FOR INDONESIAN STUDENTS Santi, Vera Maya; Notodiputro, Khairil Anwar; Indahwati, Indahwati; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The Program for International Student Assessment (PISA), becomes one of the references or indicators used to assess the development of students' knowledge and skills in each member country of the Organization for Economic Cooperation and Development (OECD). The results of the PISA survey in 2018 placed Indonesia in the bottom 10, indicating that the implementation of the national education system has not been successful. This underlies the need for a more in-depth study of the factors that influence PISA data scores not only statistically qualitatively but also quantitatively which is still very rarely done. The data structure of the PISA survey results is complex, which involves multicollinearity, multivariate response variables, and random effects. Thus, it requires an appropriate statistical analysis method such as the multivariate mixed linear regression (MLMM) model. In this study, secondary data from the results of the 2018 PISA survey with Indonesian students as the smallest unit of observation were used as sample. School is used as an intercept random effect which is assumed to be normally distributed. Multicollinearity is overcome by selecting independent variables based on AIC and BIC values. Estimation of variance and random effect parameters was performed using the restricted maximum likelihood (REML) method. Based on the estimator of the variance of random effects for the response variables of mathematics, science, and reading literacy, it was obtained 1548.12, 1359.39, and 1082.48, respectively, which explains the significant effect of each school as a random effect on the three response variables.

FUNCTION GROUP SELECTION OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) SIGNIFICANT TO ANTIOXIDANTS USING OVERLAPPING GROUP LASSO kusnaeni, kusnaeni; Soleh, Agus M; Afendi, Farit M; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Functional groups of sembung leaf metabolites can be detected using FTIR spectrometry by looking at the spectrum's shape from specific peaks that indicate the type of functional group of a compound. There were 35 observations and 1866 explanatory variables (wavelength) in this study. The number of explanatory variables more than the number of observations is high-dimensional data. One method that can be used to analyze high-dimensional data is penalized regression. The overlapping group lasso method is a development of the group-based penalized regression method that can solve the problem of selecting variable groups and members of overlapping groups of variables. The results of selecting the variable groups using the overlapping group lasso method found that the functional groups that were significant for the antioxidants of sembung leaves were C=C Unstructured, CN amide, Polyphenol, Sio2.

ASSOCIATION RULES IN RANDOM FOREST FOR THE MOST INTERPRETABLE MODEL Ilma, Hafizah; Notodiputro, Khairil Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Random forest is one of the most popular ensemble methods and has many advantages. However, random forest is a "black-box" model, so the model is difficult to interpret. This study discusses the interpretation of random forest with association rules technique using rules extracted from each decision tree in the random forest model. This analysis involves simulation and empirical data, to determine the factors that affect the poverty status of households in Tasikmalaya. The empirical data was sourced from Badan Pusat Statistik (BPS), the National Socio-Economic Survey (SUSENAS) data for West Java Province in 2019. The results obtained are based on simulation data, the association rules technique can extract the set of rules that characterize the target variable. The application of interpretable random forest to empirical data shows that the rules that most distinguish the poverty status of households in Tasikmalaya are house wall materials and the main source of drinking water, house wall materials and cooking fuel, as well as house wall materials and motorcycle ownership.

COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA Suantari, Ni Gusti Ayu Putu Puteri; Fitrianto, Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1495-1502

Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). In the last few years, many classification approaches have been developed in machine learning, but only a few considered the presence of time-to-event variable. Random Survival Forest and Survival Support Vector Machine are machine learning approach which is a nonparametric classification method when dealing with large data and a response variable of survival time. Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. The data used in this study is generated data in the form of right-censored survival data. This study uses the RandomForestSRC and SurvivalSVM packages on R software. This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. Meanwhile, Random Survival Forest is a method that has the best performance for all conditions in mixed predictor variables scenario. Method, proportion of censored data and size of data are factors that affect the model performance.

MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA Meylisah, Eni; Rini, Dyah Setyo; Fransiska, Herlin; Agwil, Winalia; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1653-1662

When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users , the number of small and micro industries and the number of tourist villages n cluster 2, the amount of village tours . In cluster 3, the percentage of users of electricity and the percentage of villages that have mining and quarrying .

IMPROVING ACCURACY OF PREDICTION INTERVALS OF HOUSEHOLD INCOME USING QUANTILE REGRESSION FOREST AND SELECTION OF EXPLANATORY VARIABLES Asrirawan, Asrirawan; Notodiputro, Khairil Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp1915-1926

Quantile regression forest (QRF) is a non-parametric method for estimating the distribution function of response by using the random forest algorithm and constructing conditional quantile prediction intervals. However, if the explanatory factors (covariates) are highly correlated, the quantile regression forest's performance will decrease, resulting in low accuracy of prediction intervals for the outcome variable. The selection of explanatory variables in quantile regression forest is investigated and addressed in this paper, using several selection scenarios that consist of the full model, forward selection, LASSO, ridge regression, and random forest to improve the accuracy of household income data prediction. This data was obtained from National Labour Force Survey in 2021. The results indicate that the random forest method outperforms other methods for explanatory selection utilizing RMSE metrics. With regard to the criteria of average coverage value just above the 95% target and statistical test results, the RF-QRF and Forward-QRF methods outperform the QRF, LASSO-QRF, and Ridge-QRF methods for constructing prediction intervals.

GEOGRAPHICALLY WEIGHTED MACHINE LEARNING MODEL FOR ADDRESSING SPATIAL HETEROGENEITY OF PUBLIC HEALTH DEVELOPMENT INDEX IN JAVA ISLAND Suprayogi, Muhammad Azis; Sartono, Bagus; Notodiputro, Khairil Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2577-2588

Random Forest (RF) machine learning models have emerged as a prominent algorithm, addressing problems arising from the sole use of decision trees, such as overfitting and instability. However, conventional RF has global coverage that may need to capture spatial variations better. Based on the analysis of the level of public health development, the relationship between the level of health development and risk factors can vary spatially. We use a modified RF algorithm called Geographically Weighted Random Forest (GW-RF) to address this challenge. GW-RF, as a tree-based non-parametric machine learning model, can help explore and visualize relationships between the Public Health Development Index (PHDI) as response variables and factors that are indicators at the district level. GW-RF output is compared with global output, which is RF in 2018 using the percentage of the population with access to clean/decent water (X1), consumption of eggs and milk per capita per week (X2), number of healthcare facilities per 1000 people (X3), number of doctors per 1000 people (X4), pure participation rate ratio female/male (X5), percentage of households that have hand washing facilities with soap and water (X6) as independent variables. Our results show that the non-parametric GW-RF model shows high potential for explaining spatial heterogeneity and predicting PHDI versus a global model when including six major risk factors. However, some of these predictions mean little. Findings of spatial heterogeneity using GW-RF show the need to consider local factors in approaches to increasing PHDI values. Spatial analysis of PHDI provides valuable information for determining geographic targets for areas whose PHDI values need to be improved.

FACTORS AFFECTING INDONESIAN PADDY HARVEST FAILURE: A COMPARISON OF BETA REGRESSION, QUASI-BINOMIAL REGRESSION, AND BETA MIXED MODELS Kusumaningrum, Dian; Hidayat, Agus Sofian Eka; Notodiputro, Khairil Anwar; Kurnia, Anang; Sartono, Bagus; Sumertajaya, I Made
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2611-2622

The Paddy harvest failure rate is one of the key aspects in determining the total number of claims in a crop insurance policy. It is also an important factor indicating the fulfillment of targeted total production. Therefore, we proposed Beta Regression, Quasi Binomial Regression, and Beta Mixed Models which can be used to analyze significant variables affecting paddy harvest failure rates. Model selection and evaluations indicated that the Nested Beta Mixed Model is the best. Previous research has shown four significant fixed effect variables: drought, flood, pests, and disease risks. Pests and other types of risks also affect the variability of loss rate. All variables have positive effects, indicating higher values cause a higher possibility of a higher average harvest failure rate. High variability was shown for province, municipality, and farmers' random effects. Hence, to prevent a more significant loss rate, MoA should consider more intensive and innovative participatory activities in farmer groups to enhance good farming practices, especially for farmers who suffer from certain risks. These activities should also consider the local characteristics of each province or municipality. As for AUTP development and improvement, farmers with lower failure risks could be given a discounted premium to make it more appealing.

A COMPARISON OF RANDOM FOREST AND DOUBLE RANDOM FOREST: DROPOUT RATES OF MADRASAH STUDENTS IN INDONESIA Purwanto, Arie; Sartono, Bagus; Notodiputro, Khairil Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp227-236

Random forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem. The research was conducted by utilising Education Management Information System (EMIS) data, which is related to the incidence of school dropout. The data used consists of 2 data, namely MTs and MA dropout data. The initial testing procedure was carried out using the random forest algorithm for each data set, then the data was evaluated using the double random forest method. From this study, the underfitting case can be overcome well using the double random forest algorithm, while in the fit case, the difference in the goodness-of-fit value of the model is relatively the same. The results obtained show that MTs prioritise school quality more than MA, although family factors are more important at the MA level. Although the total number of factors used is basically the same, it should be noted that the two school levels have different relevance variables. It should be noted that no forecasting was done in this study given that the methodology used two different types of data.

Co-Authors -, Salsabila Aam Alamudi Abdul Aziz Nurussadad Abyan, Muhammad Fatih Achmad Fauzan Achsani, Noer Azham Adi Hadianto Adinna Astrianti Afendi, Farit M Agus M Soleh Agus M Soleh Agus M. Sholeh Agus Mohamad Soleh Agusta, Madania Tetiani Agwil, Winalia Aisyah, Nisa Nur Aji Hamim Wigena Akbar Rizki Akbar Rizki Akhilla, Kharismatul Zaenab Alfa Nugraha Pradana ALFIAN FUTUHUL HADI Alifviansyah, Kevin Alona Dwinata Alwinie, Ade Agusti Amanda, Nabila Tri Amatullah, Fida Fariha Amin, Toufiq Al Amir Abduljabbar Dalimunthe Anang Kurnia Andi Susanto Andrie Agustino Anggraeni, Kartika Novira Anggraini Sukmawati Ani Safitri Anik Djuraidah Anisa Nurizki Annisa Permata Sari, Annisa Permata Annissa Nur Fitria Fathina Anton Ferdiansyah Anwar Fajar Rizki Ardhani, Rizky Ardiansyah, Muhlis Arie Wahyu Wijayanto Arief Daryanto Arief Daryanto Arief Gusnanto Aris Yaman Aris Yaman Aristawidya, Rafika Aruddy Aruddy Aryasa, Komang Budi Asep Rusyana ASEP SAEFUDDIN Asfar Asrirawan, Asrirawan Audina, Delia Fitri Aulia Rizki Firdawanti Aunuddin Aunuddin Auzi Asfarian Ayu Sofia Azlam Nas Bagus Randhyartha Gumilar Bariq, Muhammad Shidqi Abdul Barokaturrizkia Ameliani Bayu Indrayana Bayu Pranata, Bayu Bayu Suseno Beny Mulyana Sukandar Billy Bimandra Adiputra Djaafara Bonar Marulitua Sinaga Budi Susetyo Budi Susetyo Bukhari, Ari Shobri Butar, Rupmana Br Cahya, Septa Dwi Carlya Agmis Aimandiga Cici Suhaeni Cici Suhaeni Cici Suhaeni Cintari, Nanda Putri Citra, Reza Felix Dani Al Mahkya Darwis Darwis Dede Dirgahayu Dede Dirgahayu Defri Ramadhan Ismana Deiby T Salaki Deni Achmad Soeboer Deri Siswara Desi Prabandari Kusuma Ningtyas Dessy Rotua Natalina Siahaan Dewi Margareth Lumbantoruan Dhanu Dhanu Saptowulan Dian Ayuningtyas Dian Handayani Dian Kusumaningrum Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Dwi Fitrianti Dwi Wahyu Triscowati Eko Ruddy Cahyadi Embay Rohaeti Endriani, Desy Erfiani Erfiani Erira, Salsa Rifda Erliza Noor Erwan Setiawan, Erwan Etis Sunandi EVI RAMADHANI EVITA PURNANINGRUM Fachry Abda El Rahman Fadhila Hijryani FAHREZAL ZUBEDI Fany Apriliani Farit M. Afendi Farit Mochamad Afendi Fauzi, Fatkhurokhman Ferdiansyah, Anton Ferdiansyah, Anton Fitri Mudia Sari Fitrianto, Anwar Frisca Rizki Ananda Galih Hedy Saputra Gerry Alfa Dito Ghiffary, Ghardapaty Ghaly Ginting, Victor Gumilar, Bagus Randhyartha Hanum Rachmawati Nur Hardiana Widyastuti Hari Wijayanto Hari Yanni, Meri Harianto Harianto Hartoyo Hartoyo Hartoyo Hazan Azhari Zainuddin Hendri Wijaya Hendria, Muhammad Herlin Fransiska Herlina Herlina Hidayat, Agus Sofian Eka Hidayat, Muhammad Hilman Dwi Anggana Hiola, Yani Prihantini I Made Sumertajaya I Wayan Mangku Idqan Fahmi Ilma, Hafizah Ilma, Meisyatul Ilmani, Erdanisa Aghnia Iman, Mutiara Nurul IMARA, FADIAH RETNO INA YATUL ULYA Indahwati Indonesian Journal of Statistics and Its Applications IJSA Intan Arassah, Fradha Irene Muflikh Nadhiroh Irfan Syauqi Beik Ismah, Ismah Ita Wulandari Itasia Dina Sulvianti Iwan Kurniawan Jaelani, Raditya Kamila, Sabrina Adnin Khairil Anwar Notodiputro Khairunnajah Khairunnajah Khairunnisa, Adlina Khikmah, Khusnia Nurul Kudang Boro Seminar Kusman Sadik Kusnaeni Kusnaeni, Kusnaeni Kusuma Ningtyas, Desi Prabandari La Surimi, La Laode Ahmad Sabil Leni Anggraini Susanti Lilik Noor Yuliati Limba, Syella Zignora Linda Karlina Sari Lisa Amelia Luky Adrianto Lukytawati Anggraeni M. Yunus Magfirrah, Indah Matualage, Dariani Megawati - Megawati Simanjuntak Meylisah, Eni Mohamad Agus Setiawan Muhammad Hendria Muhammad Ilham Abidin Muhammad Irfan Hanifiandi Kurnia Muhammad Nur Aidi Muhammad Subianto Muhammad Syafiq Muhammad Yusran Mukhamad Najib Murpraptomo, Saka Haditya Musthafa, Hafiz Syaikhul MY, Hadyanti Utami Nimmi Zulbainarni Nofrida Elly Zendrato Novian Tamara Nugraha, Adhiyatma Nur Aulia NUR HASANAH NURADILLA, SITI Nurfadilah, Khalilah Nurrahmaniah, Nurrahmaniah Oktaviani, Rina Pardomuan Robinson Sihombing Parwati Sofan, Parwati Pika Silvianti Popong Nurhayati Pratiwi, Windy Ayu Purnaningrum, Evita Purwanto, Arie Puspita, Novi Putra, I Gusti Ngurah Sentana Putri, Mega Ramatika Qalbi, Asyifah Rachma Fitriati Rahardi, Naufal Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rahma Dany Asyifa Rahman, Gusti Arviana Rahmatulloh, Febriandi Rais Rere Kautsar Resiloy, Unique Desyrre A. Rhendy K P Widiyanto Riantika, Ines Rina Oktaviani Rini, Dyah Setyo Riska Yulianti, Riska Riza Indriani Rakhmalia Rizal Bakri Rizka Rahmaida Rizqi, Tasya Anisah ROCHYATI ROCHYATI Roy Sembel Sachnaz Desta Oktarina salsa bila Saptowulan Sarah Putri Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Seta Baehera Setiabudi, Nur Andi Setiadi Djohar Setyowati, Silfiana Lis Sholeh, Agus M. Siregar, Indra Rivaldi Siskarossa Ika Oktora Sri Amaliya Suantari, Ni Gusti Ayu Putu Puteri Suhaeni, Cici Suhaeri, ⁠Bulan Cahyani Sukarna Sukarna Sunan, Muh. Suprayogi, Muhammad Azis Susanto, Andi Suseno Bayu Syam, Ummul Auliyah Syarip, Dodi Irawan Totong Martono Toufiq Al Amin Toufiq Al Amin Triscowati, Dwi Wahyu Tsabitah, Dhiya Ulayya Tsaqif, Denanda Aufadlan Ujang Sumarwan Ulfia, Ratu Risha Utami Dyah Syafitri Valentika, Nina Vera Maya Santi Virgie, Meriza Immanuela Wahida Ainun Mumtaza Wahyudi Setyo Wahyuni, Silvia Tri Waliulu, Megawati Zein Wawan Saputra Yani Nurhadryani Yanuari, Eka Dicky Darmawan Yenni Angraini Yoga Primanda Yopi Ariesia Ulfa Yudhianto, Rachmat Bintang Yuliani, Leny Zahra, Latifah Zaima Nurrusydah Zulhijrah Zulmi, Muhammad Indra

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