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All Journal Jurnal Ilmu Pertanian Indonesia Jurnal Manajemen dan Agribisnis FORUM STATISTIKA DAN KOMPUTASI Jurnal Ekonomi Pembangunan 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 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 Matematika UNAND Variance : Journal of Statistics and Its Applications Ecces: Economics, Social, and Development Studies 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 IIJSE 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) Scientific Contribution Oil and Gas 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
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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IMPLEMENTATION OF THE GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY METHOD FOR FORECASTING THE STOCK RETURN OF PT LIPPO GENERAL INSURANCE TBK Bariq, Muhammad Shidqi Abdul; Sartono, Bagus; Sofia, Ayu
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page123-134

Abstract

The Indonesian capital market is one of the investment destinations for investors from developed countries. The development of Indonesia's economic conditions is considered good for investors in investing their funds. Financial sector shares are one of the sectors that has experienced development throughout this year. One of the seven stocks showing good growth is PT Lippo General Insurance Tbk (LPGI). The important thing that is the main concern of investors is the level of yield or return from a stock. Based on this, stock return forecasting analysis can be important information for investors. This research uses the GARCH method to forecast LPGI stock returns. The analysis results show that the best model for LPGI stock returns is ARIMA (2,0,0) GARCH (1,1) with a very small return value and a negative sign. Thus, these results provide information that the forecasting period is not the right time for investors to buy LPGI shares. However, investors who have bought LPGI shares and made a profit are advised to sell LPGI shares before the forecast period. The empirical evidence from this study demonstrates that the GARCH model can effectively capture the volatility pattern of LPGI stock returns in n financial market. This finding supports the application of GARCH in modeling return fluctuations in emerging markets.
A Study on Prediction Intervals Produced Using Quantile Regression Forest With and Without Variable Selection Megawati, Megawati; Sartono, Bagus; Oktarina, Sachnaz Desta
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 3 December 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i3.34392

Abstract

Quantile Regression Forest (QRF) is a method that utilizes the random forest algorithm to estimate the conditional distribution of response variables and form quantile prediction intervals. However, when there is a high correlation between covariates, QRF performance may decrease due to the multicollinearity effect, thereby reducing the accuracy of the prediction interval for the target variable. In linear models, multicollinearity must be addressed because it can cause large variances. This study contributes to enhancing the reliability of prediction intervals in correlated data through the integration of adaptive-LASSO with QRF. Specifically, it examines the role of variable selection by the adaptive LASSO method on the performance of the QRF prediction interval in the simulated data, and the best model obtained in the study is then applied to predict the interval in the productivity data of oil palm fresh fruit bunches. The results of the study show that variable selection is proven to produce coverage close to the target prediction interval. In addition, the QRF model with variable selection applied to the productivity data of oil palm fresh fruit bunches produces a good prediction interval.
CART and Random Forest Analysis on Graduation Status of Halu Oleo University Students Rahman, Gusti Arviana; Notodiputro, Khairil Anwar; Sartono, Bagus; Surimi, La
Inferensi Vol 8, No 3 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i3.23336

Abstract

Classification and Regression Tree (CART) is a popular classification method and it is used in various fields. The method is capable to be applied on various data conditions. An alternative method of CART is random forest. These two methods of classification were studied in this paper using graduation data of Halu Oleo University. This data was interesting due to the imbalance problem existed in the data. We compared several scenarios, namely the CART and Random Forest methods, Random Forest with oversampling, and Random Forest with undersampling. There were three explanatory variables considered in the model including Study Program, GPA, and TOEFL score. The results showed that the best method to classify the student’s graduation status at Halu Oleo University is Random Forest without handling imbalanced data, as it provided the highest sensitivity. This suggests that Random Forest, even without specific adjustments for data imbalance, can effectively capture the patterns in the data and provide accurate classifications, making it a robust choice for this dataset.
CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA Dwi Wahyu Triscowati; Bagus Sartono; Anang Kurnia; Dede Dirgahayu; Arie Wahyu Wijayanto
International Journal of Remote Sensing and Earth Sciences Vol. 16 No. 2 (2019)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2019.v16.a3217

Abstract

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.
Improving E-Service Quality of Indonesian Toll Road Application with Entrepreneurship Insights Setiawan, Mohamad Agus; Hartoyo, Hartoyo; Seminar, Kudang Boro; Sartono, Bagus; Fitriati, Rachma; Ginting, Victor
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 2 (2025): July
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i2.579

Abstract

Digital transformation in Indonesia toll road sector faces significant challenges related to information fragmentation among Toll Road Operators (BUJT), hindering the optimization of electronic service quality (E-Service Quality). This research aims to enhance E-Service Quality through the Soft Systems Methodology (SSM), integrated with Structural Equation Modeling-Partial Least Squares (SEM-PLS) analysis to understand key service dimensions. The findings indicate that integrating information across BUJTs, with a focus on informativeness, is critical to improving user satisfaction. The SSM-based conceptual model developed provides systemic solutions through policy integration, collaboration among BUJTs, and the development of an integrated information system. The assessment reveals that the proposed changes support toll road digital transformation and are culturally feasible. This study offers a strategic framework for improving toll road service quality, strengthening stakeholder collaboration, and creating a better user experience.
COMPARISON OF COPULA FAMILY (GAUSSIAN, ARCHIMEDEAN, AND REGRESSION) IN A CASE STUDY OF COMPOSITE STOCK PRICE INDEX ON INDONESIA STOCK EXCHANGE Darwis, Darwis; Sartono, Bagus; Yuliani, Leny
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0755-0768

Abstract

Stocks are one of the most popular financial market instruments. On the other hand, stocks are an investment instrument that is widely chosen by investors because stocks are able to provide attractive profit levels. Investment is an effort to postpone consumption in the present. Comparing copula families is crucial for selecting the model that best fits the observed data dependency structure. This helps produce more accurate analysis and more meaningful interpretations. This study analyzes different types of copula relationships using the Tau Kendall method, applying it to the movement of the Composite Stock Price Index (IHSG) on the Indonesia Stock Exchange (IDX). The data used are secondary monthly data of IHSG as a response variable, while the explanatory variables are inflation (%), exchange rate (Rp/USD), and interest rate (%) in 2010-2014. The results show the pattern of the relationship between IHSG and its macroeconomic factors on the IDX using copula parameter estimation with the Tau Kendall approach, with the largest log-likelihood fitting results showing a relationship pattern following the Gumbel copula, namely IHSG with inflation, interest rates with IHSG following the Clayton copula, and exchange rates following the Frank copula. Meanwhile, using the regression copula has better interpretation results compared to the Gaussian and Archimedean copula, with an MAPE value of 0.122 with a correlation of 70.63%.
POISSON MIXED MODELS WITH A BOOSTING APPROACH FOR THE ANALYSIS OF COUNT DATA Wulandari, Ita; Notodiputro, Khairil Anwar; Sartono, Bagus; Fitrianto, Anwar; Kurnia, Anang
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0815-0828

Abstract

Boosting is a powerful technique for enhancing predictive accuracy by iteratively reweighting observations, and is particularly effective in high-dimensional settings and for variable selection. While previous studies have demonstrated the advantages of integrating boosting with generalized linear mixed models (GLMMs) for binary outcomes, its application to count data within hierarchical frameworks remains limited. This study addresses that gap by extending boosting methods to count data through the development of a boosted Poisson mixed model (bPMM), a novel approach for small area estimation and variable selection in complex survey designs. The proposed model is applied to fertility data in the Indonesian provinces of Bali and East Nusa Tenggara, where the response variable is the number of live births and the predictors include twenty-eight socio-demographic covariates. Using the Akaike Information Criterion (AIC) for model selection, three significant variables were identified in Bali (Model 1), and one in East Nusa Tenggara (Model 2). The results demonstrate that bPMM not only improves variable selection in high-dimensional settings but also accommodates hierarchical structure in count data.
Studi Komparatif Metode Boosting Dalam Pengklasifikasian Penerima Bantuan Program Keluarga Harapan (PKH) Amatullah, Fida Fariha; MY, Hadyanti Utami; Rizqi, Tasya Anisah; Wahyuni, Silvia Tri; Sartono, Bagus; Firdawanti, Aulia Rizki
TELKA - Telekomunikasi Elektronika Komputasi dan Kontrol Vol 11, No 3 (2025): TELKA
Publisher : Jurusan Teknik Elektro UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/telka.v11n3.315-326

Abstract

Ensemble Learning adalah paradigma pembelajaran mesin dimana beberapa model (biasanya disebut "weak learners") dilatih untuk memecahkan masalah yang sama dan digabungkan untuk mendapatkan hasil yang lebih baik. Salah satu model Ensemble, yaitu model boosting. Beberapa metode boosting yang digunakan dalam penelitian ini, yaitu Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), dan CatBoost. Penelitian ini akan mengklasifikasikan Rumah Tangga (RT) yang menerima bantuan Program Keluarga Harapan (PKH). Pengklasifikasian PKH sangat penting dilakukan, karena saat ini pemberian PKH belum optimal dan masih banyak yang tidak tepat sasaran. Hasil penelitian menunjukkan bahwa metode LGBM menunjukkan performa terbaik ketika jumlah data latih berukuran besar, yaitu 90% dengan akurasi sebesar 67,97%, sedangkan untuk data latih kecil yaitu 60:40, LGBM memiliki performa yang kurang baik, dengan nilai balanced accuracy terendah dibandingkan metode boosting lainnya, yaitu sebesar 54,43%. Keunggulan LGBM ini disebabkan karena kemampuannya dalam mengelola data besar dan kompleks yang sesuai dengan karakteristik data sosial ekonomi rumah tangga penerima PKH. Dua fitur yang memiliki peran penting untuk pengklasifikasian PKH dalam model terbaik yaitu LGBM adalah faktor ekonomi dan jumlah anggota rumah tangga. Ensemble Learning is a machine learning paradigm in which multiple models (commonly referred to as "weak learners") are trained to solve the same problem and combined to achieve better results. One of the Ensemble models is the boosting model. Several boosting methods used in this study include Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), and CatBoost. This study aims to classify households (RT) that receive assistance from the Program Keluarga Harapan (PKH). The classification of PKH recipients is crucial because the distribution of PKH aid has not been optimal, with many cases of misallocation. The results of the study indicate that the LGBM method demonstrates the best performance when the latih dataset is large (90%), achieving an accuracy of 67.97%. However, when the latih dataset is small (60:40), LGBM performs poorly, recording the lowest balanced accuracy among the boosting methods, at 54.43%. The superiority of LGBM is attributed to its ability to handle large and complex data, which aligns with the socio-economic characteristics of PKH recipient households. Two key features that play a significant role in PKH classification using the best-performing model, LGBM, are economic factors and the number of household members.
Making Sense of Fashion Feedback : Comparing Two Popular Text Analysis Tools Muhammad Syafiq; Wawan Saputra; Carlya Agmis Aimandiga; Cici Suhaeni; Bagus Sartono; Gerry Alfa Dito
TEKNOBUGA: Jurnal Teknologi Busana dan Boga Vol. 13 No. 1 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/teknobuga.v13i1.25930

Abstract

The rapid expansion of the fashion industry, propelled by digital technology and e-commerce, has resulted in a significant volume of customer-generated reviews. These reviews serve as a valuable source for understanding customer satisfaction and behavior. This study aims to (1) analyze customer sentiment, (2) predict product recommendations, and (3) examine the relationship between sentiment classification and recommendation decisions using text embeddings from Word2Vec and GloVe. The research utilized over 23,000 fashion product reviews sourced from Kaggle. Text data were preprocessed and vectorized using Word2Vec and GloVe, followed by classification and prediction tasks using six machine learning models: Random Forest, SVM, Naïve Bayes, LSTM, Logistic Regression, and Gradient Boosting. The results revealed that Word2Vec consistently outperformed GloVe across all models and tasks, with the Word2Vec-LSTM combination achieving the highest accuracy of 87.35% and F1 score of 92.35% in imbalanced data scenarios. Correlation analysis also confirmed a strong and statistically significant relationship between sentiment and recommendation labels, with Spearman’s Rho of 0.8340 and Kendall’s Tau of 0.8120. These findings suggest that high-quality sentiment representation can effectively support product recommendation systems. This study contributes to the understanding of embedding effectiveness in fashion-related text analysis and opens avenues for hybrid and transformer-based representations in future research.
Co-Authors -, Salsabila Aam Alamudi Abdul Aziz Nurussadad 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 Aji Hamim Wigena 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 Arief Daryanto Arief Daryanto Arief Gusnanto Aris Yaman Aris Yaman Aristawidya, Rafika Aruddy Aruddy Asep Rusyana ASEP SAEFUDDIN Asfar Asrirawan, Asrirawan Aulia Rizki Firdawanti Aunuddin Aunuddin Auzi Asfarian 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 Bukhari, Ari Shobri 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 Dessy Rotua Natalina Siahaan Dewi Margareth Lumbantoruan Dhanu Dian Ayuningtyas Dian Handayani Dian Kusumaningrum Dito, Gerry Alfa Dwi Agustin Nuriani Sirodj Dwi Fitrianti Dwi Wahyu Triscowati 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 Fany Apriliani Faqih Udin dan Jono M. Munandar Meivita Amelia 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 Gustara, Muhammad 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 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 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 La Surimi, La Laode Ahmad Sabil Leni Anggraini Susanti Lilik Noor Yuliati Linda Karlina Sari 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 Nofrida Elly Zendrato Novian Tamara Nugraha, Adhiyatma Nur Aulia NUR HASANAH NURADILLA, SITI Nurfadilah, Khalilah Oktaviani, Rina Pardomuan Robinson Sihombing Parwati Sofan, Parwati Pika Silvianti Popong Nurhayati Pratiwi, Windy Ayu Purnaningrum, Evita Purwanto, Arie Puspanegara, Ladia 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 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 Setiadi Djohar Setyowati, Silfiana Lis Sholeh, Agus M. Siregar, Indra Rivaldi Siskarossa Ika Oktora Sofia, Ayu Sri Amaliya Suantari, Ni Gusti Ayu Putu Puteri Suhaeni, Cici Sukarna Sukarna 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 Wahida Ainun Mumtaza Wahyudi Setyo Wahyuni, Silvia Tri Waliulu, Megawati Zein Wawan Saputra Yanuari, Eka Dicky Darmawan Yenni Angraini Yoga Primanda Yopi Ariesia Ulfa Yudhianto, Rachmat Bintang Yuliani, Leny Zahra, Latifah Zaima Nurrusydah