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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
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PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE Riza Indriani Rakhmalia; Agus M Soleh; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.667

Abstract

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.
Improving Classification Model Performances using an Active Learning Method to Detect Hate Speech in Twitter: Peningkatan Kinerja Model Klasifikasi dengan Pembelajaran Aktif dalam Mendeteksi Ujaran Kebencian di Twitter Muhammad Ilham Abidin; Khairil Anwar Notodiputro; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p26-38

Abstract

Efforts from the police to address hate speech on social media such as Twitter will not be sufficient to rely solely on manual checks. Therefore, it is necessary to use statistical modelling like the classification model to detect hate speech automatically. Classification is a type of predictive modelling to produce accurate predictions based on labelled data. Generally, the available data are usually unlabelled implying that the labelling process needs to be done beforehand. Data labelling is time consuming, high cost, and often fails to produce correct labels. This research aims to improve the performances of classification models by adding a small amount of data through the so called active learning method. The results showed that there was no significant difference in the performances of logistic regression and naïve bayes classification models in detecting hate speech. However, the results also showed that adding data through the active learning method substantially improved the logistics regression performance in detecting hate speech when compared to data addition based on a simple random sampling method. Therefore, the performances of classification models in detecting hate speech on Twitter could be improved by using an active learning method.
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

Abstract

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

Abstract

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.
Optimizing Machine Learning for Daily Rainfall Prediction in Bogor: A Statistical Downscaling Approach Intan Arassah, Fradha; Sadik, Kusman; Sartono, Bagus; Sofan, Parwati
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.51307

Abstract

This study explores the use of machine learning models as a statistical downscaling technique to predict daily rainfall in Bogor, Indonesia. The general circulation model (GCM) is a leading tool for climate prediction, and this research applied a two-stage machine learning model to improve its predictions. The main objectives were to evaluate different GCM domains and handle missing data using two imputation approaches. The first stage involved constructing datasets with varying methods for addressing missing values, followed by the application of a support vector classification (SVC) model to classify rainy and non-rainy days. In the second stage, a recurrent neural network (RNN) model was developed to predict daily rainfall amounts. The results revealed that using random forest imputation for missing data enhanced model accuracy and reduced the root mean square error (RMSE). Among the different GCM domains, the 5 km resolution GCM data was the most accurate when compared to local station climatology. The SVC model, using a radial basis function kernel, achieved an impressive classification accuracy of 98.5%, while the RNN model achieved an RMSE of 16.19. These findings are valuable for improving rainfall predictions and can provide effective data-driven recommendations for disaster mitigation efforts in the region.
Digital Newsworthiness Scores Model Using a Combination of Unsupervised and Supervised Learning Approaches: Pemodelan Skor Kelayakan Berita Digital dengan Pendekatan Kombinasi Unsupervised dan Supervised Learning Citra, Reza Felix; Wigena, Aji Hamim; Sartono, Bagus
Indonesian Journal of Statistics and Applications Vol 9 No 1 (2025)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i1p86-99

Abstract

The rapid evolution of digital technology has transformed the media landscape, making news more accessible while also introducing challenges related to content quality and accuracy. The rise of misinformation and fake news has diminished public trust in traditional media. A method for evaluating the quality and potential impact of news articles prior to publication. By adapting credit risk scoring principles, a model was used to predict the suitability of news content based on factors such as title length, number of images, news category, and publication timing. A variable target was firstly formed using three clustering methods: K-Means, K-Modes, and K-Medoids. The results indicated that K-Means outperformed the other methods, leading us to use its outcomes for determining publication suitability. Subsequently, stepwise logistic regression was applied to implement the credit risk scoring approach, allowing for variable selection and assessment of importance. Ultimately, ten variables were identified to generate a newsworthiness score, with minimum and maximum scores of 997 and 1407, respectively. The average scores for articles deemed publishable and not publishable were 1137 and 1110. A cutoff score of 1123 was established based on these averages, categorizing 6708 articles (57.9%) as suitable for publication. These findings aim to assist media organizations in refining their content curation processes, thereby enhancing the overall quality of news consumption.
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

Abstract

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

Abstract

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.
Performance Comparison of Random Forest, Bagging, and CART Methods in Classifying Recipients of the Family Program in North Aceh Hari Yanni, Meri; Anwar Notodiputro, Khairil; Sartono, Bagus
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.5098

Abstract

Machine learning is a method in data mining, it is used to study large data patterns through classification methods including Random Forest, Bagging, and CART. The Random Forest method develops the Bagging technique and Decision Tree components (CART) in decision-making. The difference between RF and Bagging is the selection of random features in forming a decision tree. It is only found in RF. Bagging can improve performance, model stability, and reduce variance by forming many different models. The research aims to see the performance of the Random Forest, Bagging, and CART methods in classifying family recipient programs in North Aceh. The results show that the performance of the RF, Bagging, and CART classification methods using the SMOTE technique for handling unbalanced classes is better than before handling unbalanced data. The classification method is evaluated through each model's accuracy, sensitivity, specificity, precision, F1 score, and AUC values. The results show good performance with accuracy values of 90% Smote-RF and 86% Smote Bagging. The best performance was seen in the Smote-RF model which was obtained by tuning the Grid Search CV model parameters with k = 5 and repeat = 1 for a data set proportion of 90:10. This shows that the model can correctly predict all observations with an accuracy percentage of 90% with an average AUC value of 93.52%. On the other hand, the CART method has a very low accuracy value, so the model is less able to accurately predict all observations. Measurement of the level of importance of predictor variables that have the greatest influence in predicting recipient households is the floor area of the house, the number of household members aged 10 years and over, and the type of work of the head of the household.
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

Abstract

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
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 Dela Gustiara 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 Mardatunnisa Isnaini 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 Annafi Muhadi 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 Shalshabilla Shafa 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