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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 JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Scientific Journal of Informatics Journal of Consumer Science CESS (Journal of Computer Engineering, System and 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 Knowledge Engineering and Data Science MAJALAH ILMIAH GLOBE 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 Jurnal Organisasi Dan Manajemen JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) 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 International Journal of Zakat (IJAZ) Indonesian Journal of Electrical Engineering and Computer Science 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 Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SRIWIJAYA JOURNAL OF ENVIRONMENT Jurnal Natural 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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Identifikasi Tema Perbincangan Masyarakat Tentang Vaksinasi Covid-19 di Media Sosial Cici Suhaeni; , Bagus Sartono
Diophantine Journal of Mathematics and Its Applications Vol. 1 No. 1 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/diophantine.v1i1.25216

Informasi mengenai vaksin covid-19 dan program vaksinasi pemerintah merupakan isu yang mendapat perhatian besar masyarakat dan menjadi perbincangan utama di media sosial, termasuk twitter. Beragam tema dan sudut pandang telah disampaikan oleh masyarakat, dan penelitian ini berupaya mengidentifikasi opini apa saja yang berkembang. Pengetahuan ini dapat menjadi masukan bagi pemerintah dan pemangku kepentingan lain untuk secara bersama membantu proses pemulihan dampak pandemi. Identifikasi opini masyarakat mengenai vaksin covid-19 dilakukan menggunakan metode text clustering terhadap tweets hasil crawling data di Twitter dalam kurun waktu 1 s.d 7 Agustus 2021. Hasil analisis menunjukkan terdapat enam tema besar yang menjadi isu perbincangan yaitu: (1) Kepercayaan terhadap efek vaksin, (2) keikutsertaan dalam vaksinasi untuk mencegah terpapar covid, (3) vaksinasi sebagai upaya herd immunity, (4) keampuhan vaksin melawan virus, (5) jenis-jenis vaksin (6) riset medis tentang.

Evaluation of Spatial Approaches of Poverty in East Java Agusta, Madania Tetiani; Sartono, Bagus; Djuraidah, Anik
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.7663

Geographically Weighted Regression (GWR) is the most frequently used for spatial modeling. GWR produces local model parameter estimates for each observed point. Unfortunately, GWR is known to be numerically unstable and can produce extreme coefficient estimates. Spatially Clustered Regression (SCR) and Spatially Constrained Clusterwise Regression (SCCR) are new approaches that combine cluster identification and regression estimation in one stage. This research evaluates these approaches to develop poverty alleviation in East Java with the largest number of poor people in rural areas as per March 2023 according to BPS. The response variable used is the percentage of poor families. While the explanatory variables used are the percentage of female heads of households, the percentage of non-electricity families, the average years of schooling, the percentage of home ownership, and the percentage of agricultural laborers. The results of GWR and K-Means produced three clusters in East Java, SCR produced four clusters in East Java, and SCCR produced three clusters in East Java. Based on the AIC value, the best approach is SCR with a value of 1,614. Based on its grouping, SCR is better in forming cluster with adjacent locations rather than GWR + K-Means and SCCR. The variables that significant to the percentage of poor families are the percentage of agricultural laborers, the percentage of home ownership, and the percentage of female heads of households.

Pemodelan Topik pada Komentar YouTube Arra: Komparasi LDA dan K-Means Menggunakan Fitur Leksikal dan Semantik Nuradilla, Siti; Kamila, Sabrina Adnin; Zahra, Latifah; Suhaeni, Cici; Sartono, Bagus
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8763

YouTube has become a platform for sharing content, including positive material and stereotypes that often trigger debates. One noteworthy phenomenon is the video of Arra, a toddler known for her remarkable communication skills. This uniqueness has drawn significant attention and sparked debates about the mismatch between her age and cognitive development. The diverse comments on Arra’s videos reflect sharply differing perspectives among netizens, making manual analysis highly challenging. Therefore, it is important to examine the topics discussed by netizens to understand the dominant issues emerging in these discussions. Through this approach, the public can gain insights, and parents may receive valuable input regarding child-rearing practices. The main objective of this study is to explore the effectiveness of the two methods and their combinations of text representations in identifying key topics within comments by comparing the coherence performance of the models. This research applies topic modeling to analyze comments using two primary approaches: Latent Dirichlet Allocation (LDA) and K-Means clustering. The study involves data collection through comment crawling, followed by text preprocessing and text representation using TF-IDF and GloVe embeddings. LDA and K-Means are then used to identify dominant topics appearing in the comments. The results show that LDA with TF-IDF achieved the highest coherence score of 0.662, although the resulting topics were still difficult to interpret due to overlap. Meanwhile, K-Means with GloVe 100D yielded a slightly lower coherence score of 0.6538 but outperformed in terms of interpretability. Therefore, K-Means with GloVe 100D is considered a more balanced approach in terms of both coherence and topic readability.

Data-Driven Insights Into Underdeveloped Regencies: SHAP-Based Explainable Artificial Intelligence Approach Oktora, Siskarossa Ika; Matualage, Dariani; Notodiputro, Khairil Anwar; Sartono, Bagus
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1399

Classification analysis in high-dimensional data presents significant challenges, particularly due to the presence of complex non-linear patterns that traditional methods, such as logistic regression, fail to capture effectively. This limitation is often reflected in relatively low model accuracy. One approach to addressing this issue is through machine learning-based classification methods, such as Random Forest and Support Vector Machine (SVM). While these models generally achieve higher accuracy than logistic regression, their black-box nature limits interpretability, making it difficult to explain their classification decisions. As machine learning models continue to advance, interpretability has become a crucial concern, especially in data-driven decision-making. Post-hoc explainable artificial intelligence (XAI) techniques offer a viable solution to enhance model transparency. This study applies SHAP to machine learning models to gain insights into the underdevelopment status of regencies in Indonesia. The results indicate that SVM outperforms both logistic regression and Random Forest. SHAP values estimated from SVM, using various permuted variable subsets, exhibit stability. Clustering analysis identifies five optimal clusters of underdeveloped regencies. Based on average SHAP values, underdevelopment alleviation strategies should focus on social factors (Cluster 1), infrastructure (Cluster 2), accessibility (Cluster 3), and a combination of infrastructure, accessibility, education, and healthcare (Cluster 4), while Cluster 5 requires improvements in accessibility and economic conditions.

SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java Setiawan, Erwan; Sartono, Bagus; Notodiputro, Khairil Anwar
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9933

Random Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance. There are several approaches to enhance RF performance when coping with data imbalance issues, such as using weighting and oversampling. This research explores the intervention of RF in addressing data imbalances, focusing on case studies of health problem classification in Java This study aims to develop models to analyze the health status of regions using RF, WRF, SMOTE-RF, and SMOTE-WRF methods. The objective is to compare the performance of these models and identify the best model for classifying DBK and Non-DBK categories in Java. The research results show that SMOTE-WRF is the most effective model in classifying DBK, achieving an accuracy level of 93.62%, sensitivity of 85.71%, precision of 75%, F-score of 80%, and AUC of 93.57%. The three key variables in the SMOTE-WRF model entail access to adequate sanitation, egg and milk consumption, and the number of doctors

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.

Co-Authors -, Salsabila Aam Alamudi Abdul Aziz Nurussadad Achmad Fauzan Achmad Fauzan, Achmad Achsani, Noer Azham Ade Agusti Alwinie Adi Hadianto, Adi Adinna Astrianti Afendi, Farit M Agus M Soleh Agus M Soleh Agus M. Sholeh Agus Mohamad Soleh Agusta, Madania Tetiani Agwil, Winalia Ain Fitri Basri Aji Hamim Wigena Akbar Rizki 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 Anggraini Sukmawati Ani Safitri Anik Djuraidah Anisa Nurizki Annisa Permata Sari Annissa Nur Fitria Fathina Anton Ferdiansyah Ardhani, Rizky Ardiansyah, Muhlis Arie Wahyu Wijayanto Arief Daryanto Arief Daryanto Arief Gusnanto Arif Imam Suroso Aris Yaman Aris Yaman Aristawidya, Rafika Aruddy Aruddy Asep Rusyana ASEP SAEFUDDIN Asfar Asrirawan, Asrirawan Aulia Rizki Firdawanti 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 Pranata, Bayu Bayu Suseno Beny Mulyana Sukandar Billy Bimandra Adiputra Djaafara Bonar Marulitua Sinaga Budi Susetyo Bukhari, Ari Shobri Cahya, Septa Dwi Carlya Agmis Aimandiga Cici Suhaeni Cici Suhaeni Cici Suhaeni Cintari, Nanda Putri Claudian Tikulimbong Tangdilomban Dani Al Mahkya Dede Dirgahayu Dede Dirgahayu Defri Ramadhan Ismana Deiby T Salaki Dela Gustiara Denanda Aufadlan Tsaqif Deni Achmad Soeboer Deri Siswara Desi Prabandari Kusuma Ningtyas Desi Prabandari Kusuma Ningtyas Dessy Rotua Natalina Siahaan Desy Endriani Dewi Margareth Lumbantoruan Dhanu Dhanu Saptowulan Dian Ayuningtyas Dian Handayani Dian Kusumaningrum Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Dwi Agustin Nuriani Sirodj Dwi Erzalianti Dwi Wahyu Triscowati Dyah Setyo Rini Eko Ruddy Cahyadi Embay Rohaeti Erfiani Erfiani Erliza Noor Erwan Setiawan, Erwan Etis Sunandi EVI RAMADHANI Evita Purnaningrum Fachry Abda El Rahman Fadhila Hijryani FAHREZAL ZUBEDI Farit M. Afendi Farit Mochamad Afendi Fatiya Hanifah Fauzi, Fatkhurokhman Fauziah, Nadira Aribah 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 Gusti Arviana Rahman Hanum Rachmawati Nur Hari Wijayanto Harianto Harianto Hartoyo Hartoyo Hartoyo Hazan Azhari Zainuddin Hazelita Dwi Rahmasari Hendri Wijaya Hendria, Muhammad Herlin Fransiska Herlina Herlina Hidayat, Agus Sofian Eka Hidayat, Muhammad Hilman Dwi Anggana I Gusti Ngurah Sentana Putra I Made Sumertajaya I Wayan Mangku Idqan Fahmi Ilma, Hafizah Ilma, Meisyatul Ilmani, Erdanisa Aghnia Iman, Mutiara Nurul INA YATUL ULYA Indahwati Indonesian Journal of Statistics and Its Applications IJSA Ines Riantika Irene Muflikh Nadhiroh Irfan Syauqi Beik Ismah, Ismah Itasia Dina Sulvianti Iwan Kurniawan Jaelani, Raditya Joice Junansi Tandirerung Kamila, Sabrina Adnin Kenny Masbagusdanta Khairil Anwar Notodiputro Khairunnajah Khairunnajah Khairunnisa, Adlina Kharismatul Zaenab Akhilla Khikmah, Khusnia Nurul Kinanti Rizky Pangestutik Kudang Boro Seminar Kusman Sadik Kusnaeni Kusnaeni, Kusnaeni La Surimi La Surimi, La Laode Ahmad Sabil Leni Anggraini Susanti Lilik Noor Yuliati Linda Karlina Sari Lisa Amelia Luh Putu Widya Adnyani Luky Adrianto Lukytawati Anggraeni M. Yunus Magfirrah, Indah Mardatunnisa Isnaini Matualage, Dariani Mega Maulina Mega Ramatika Putri Megawati - Megawati Simanjuntak Meri Hari Yanni Meylisah, Eni Mohamad Agus Setiawan Muh. Sunan Muhammad Hendria Muhammad Ilham Abidin Muhammad Irfan Hanifiandi Kurnia Muhammad Nur Aidi Muhammad Rizal Muhammad Subianto Muhammad Syafiq Muhammad Yusran Mukhamad Najib Murpraptomo, Saka Haditya MY, Hadyanti Utami Nimmi Zulbainarni Nisa Nur Aisyah Nofrida Elly Zendrato Novian Tamara Nugraha, Adhiyatma Nur Aulia NUR HASANAH NURADILLA, SITI Nurfadilah, Khalilah Oktaviani, Rina Pardomuan Robinson Sihombing Pika Silvianti Popong Nurhayati Pratiwi, Windy Ayu Purwanto, Arie Puspita, Novi Qalbi, Asyifah Rachma Fitriati Rahardi, Naufal Rahardiantoro, Septian Rahma Anisa Rahma Anisa Rahma Dany Asyifa Rahman, Gusti Arviana Rahmatulloh, Febriandi Rais Rere Kautsar Rhendy K P Widiyanto Rina Oktaviani Riska Yulianti, Riska Riza Indriani Rakhmalia Rizal Bakri Rizka Rahmaida Rizqi Annafi Muhadi Rizqi, Tasya Anisah ROCHYATI ROCHYATI Roy Sembel Rupmana Br Butar Sachnaz Desta Oktarina Saka Haditya Murpraptomo salsa bila Saptowulan Sarah Putri Sari, Jefita Resti Sentana Putra, I Gusti Ngurah Seta Baehera Setiadi Djohar Setyowati, Silfiana Lis Shalshabilla Shafa Sholeh, Agus M. Siregar, Indra Rivaldi Siskarossa Ika Oktora Siti Aisyah Suantari, Ni Gusti Ayu Putu Puteri Suhaeni, Cici Sukarna Sukarna Suprayogi, Muhammad Azis Susanto, Andi Suseno Bayu Syaifullah Yusuf Ramdhan Syam, Ummul Auliyah Syarip, Dodi Irawan Syella Zignora Limba Totong Martono Toufiq Al Amin Toufiq Al Amin Triscowati, Dwi Wahyu Tsabitah, Dhiya Ulayya Ujang Sumarwan Ulfia, Ratu Risha Unique Desyrre A. Resiloy Utami Dyah Syafitri Valentika, Nina Vera Maya Santi Wahida Ainun Mumtaza Wahyudi Setyo Wahyuni, Silvia Tri Waliulu, Megawati Zein Wawan Saputra Widiyanto, Rhendy K P Windi Pangesti Yani Prihantini Hiola Yanuari, Eka Dicky Darmawan Yenni Angraini Yoga Primanda Yopi Ariesia Ulfa Yudhianto, Rachmat Bintang Zahra, Latifah Zaima Nurrusydah Zulhijrah Zulmi, Muhammad Indra

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