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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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Effectiveness of SMOTE-ENN to Reduce Complexity in Classification Model Riantika, Ines; Sartono, Bagus; Anwar Notodiputro, Khairil
Indonesian Journal of Statistics and Applications Vol 8 No 1 (2024)
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.v8i1p70-82

Abstract

A failure to produce classification models with high performance might be caused by the dataset's characteristics, such as the between-class overlapping and the class imbalance. The higher the data complexity, the more complicated it is for the algorithm to find good models. Combining the issues of class imbalance and overlapping would make the problem more challenging. To deal with this problem, this research implemented a hybrid class-balancing technique named SMOTE-ENN. This technique adds observations to the minority class to balance the class frequencies. After that, it removes some observations to reduce the degree of overlapping. The research revealed that SMOTE-ENN succeeds in doing that. We employed a random forest method to evaluate it. In 28 out of 46 cases we investigated, the new datasets generated by SMOTE-ENN could produce models with higher accuracy.
Classification of Drinking Water Source Suitability in West Java Using XGBoost and Cluster Analysis Based on SHAP Values: Klasifikasi Kelayakan Sumber Air Minum di Jawa Barat Menggunakan XGBoost dan Analisis Klasterisasi Berdasarkan Nilai SHAP Sari, Annisa Permata; Billy; Tsaqif, Denanda Aufadlan; Sartono, Bagus; Firdawanti, Aulia Rizki
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
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.v8i2p202-214

Abstract

Water is essential for meeting the basic needs of living organisms. In Indonesia, ensuring safe and quality drinking water is crucial for public health. However, in some regions, particularly in West Java Province, people still rely on unsuitable water sources, which can negatively impact health. The classification of water source suitability can be achieved using machine learning, such as the Extreme Gradient Boosting (XGBoost) model. XGBoost with feature selection is effective in improving prediction accuracy and minimizing overfitting. This study evaluates the performance of the XGBoost model in classifying household drinking water sources in West Java and uses the K-Means algorithm for cluster SHAP values to identify key characteristics of households with safe drinking water. The results show that the XGBoost model, with an accuracy of 77.43% and an F1-Score of 80.17%, successfully classified 4187 households, with 2349 having safe drinking water and 1838 having unsuitable sources. SHAP value analysis identified location, water collection time, and monthly per capita expenditure as significant factors influencing water source suitability. Households with water sources inside the house's fence, a short water collection time, and high monthly per capita expenditure tend to have safe drinking water sources. There are 4 clusters formed, with cluster 1 and cluster 3 needing immediate quality of drinking water sources improvement with cluster 2 as an indicator of success. Cluster 4 consists of households with high expenditure, marking it as a potential household for the government to make water quality improvements.
Effect of Product Attributes, Promotions, and Motivation on Subsidized Home Buying Decisions Jaelani, Raditya; Yuliati, Lilik Noor; Sartono, Bagus
Jurnal Aplikasi Bisnis dan Manajemen Vol. 9 No. 3 (2023): JABM Vol. 9 No. 3, September 2023
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.9.3.819

Abstract

Subsidized housing is a government program with the aim of meeting the housing needs of low-income peoplewhere subsidies or housing financing assistance are in the form of houses with selling prices below market prices. However, companies that develop subsidized houses must now be able to survive during increasingly fierce business competition conditions and world economic conditions that are being shaken by the Covid-19 pandemic. People's purchasing power that has decreased due to the rules for "Stay at Home" has made all areas of the economy decrease. Sentra Hills Tenjo is one of the many subsidized housing estates developed by housing developers. This housing is under the auspices of PT Bogor Indonesia Depeloper as the developer. This housing is in Singabangsa, Tenjo District, Bogor, West Java. This housing offers two types of houses, namely type 24/66 and type 36/72. The purpose of this study is to identify the characteristics of buyers who have made subsidized home loan agreements in Perumahaan Sentra Hills Tenjo., Analyze the influence of product attributes, promotions, and motivations on the decision to buy subsidized houses in Sentra Hills Tenjo and Formulate managerial implications for increasing the number of subsidized home purchases in Sentra Hills Tenjo. Analysis using Structural Equation Modelling. The results showed that product, promotion and motivation variables influenced the decision to buy subsidized houses in Sentra Hills Tenjo Housing.
Does Subjective Well-Being and Perceived Organizational Can Support Startup Employees’ Performance During Hybrid Workforce Era? Iman, Mutiara Nurul; Sartono, Bagus; Sukmawati, Anggraini
Jurnal Aplikasi Bisnis dan Manajemen Vol. 9 No. 3 (2023): JABM Vol. 9 No. 3, September 2023
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.9.3.784

Abstract

The hybrid workforce is a combined work from an office and remote working implemented by many companies after the pandemic subsided. This study analyzed the subjective well-being and perceived organizational support in the hybrid workforce era on startup employees' performance in Indonesia. The total of the research samples was 110 samples, which the researcher used convenience sampling to obtain and SEM-PLS analysis to analyze the data. The results showed differences between permanent and temporary employees that stem from differences in perceptions between the two groups of employees. In the effect of perceived organizational support on subjective well-being, the researcher obtained the same results between temporary and permanent employees, in which perceived organizational support affected their well-being. The result obtained for the effect of subjective well-being on employee performance showed that it did not affect temporary employees but permanent employees. In the effect of perceived organizational support on employee performance, temporary employees have a more significant influence on permanent employees. In the influence of the hybrid workforce era on employee performance, the results obtained did not affect temporary but affect permanent employees. Keywords: permanent employees, performance management, remote working, SEM-PLS, temporary employees
Performance Analysis of Robust Functional Continuum Regression to Handle Outliers Ismah, Ismah; Erfiani, Erfiani; Wigena, Aji Hamim; Sartono, Bagus
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38928

Abstract

Robust functional continuum regression (RFCR) is an innovation as a development of functional continuum regression that can be applied to functional data and is resistant to outliers. The resistance of RFCR depends on the applied weighting function. This study aims to evaluate the RFCR performance to handle outliers. We propose the various weighting functions in this evaluation, i.e., Huber, Hampel, Ramsay, and Tukey (Bisquare), which do not eliminate or give zero weight to observed data identified as outliers. This contribution is essential to determining the appropriate RFCR method without eliminating the outlier data. The result shows that the RFCR performance with the Huber weighting function is better than the others, based on the goodness of fit, consisting of the root means square error of prediction (RMSEP), the correlation between the actual data and the model, and the mean absolute error (MAE).Keywords: functional data analysis; Huber weighted function; Hampel weighted function; Ramsay weighted function; Tukey (Bisquare) weighted function. AbstrakRegresi kontinum fungsional kekar (RFCR) merupakan inovasi yang merupakan pengembangan dari regresi kontinum fungsional yang dapat diaplikasikan pada data fungsional dan tahan terhadap outlier. Resistansi RFCR bergantung pada fungsi pembobotan. Penelitian ini bertujuan untuk mengevaluasi kinerja RFCR. Kami mengusulkan beberapa fungsi pembobotan dalam evaluasi tersebut, yaitu Huber, Hampel, Ramsay, dan Tukey (Bisquare), dengan tidak menghilangkan atau memberikan bobot nol pada data observasi yang teridentifikasi sebagai outlier. Kontribusi ini penting untuk menentukan metode RFCR yang tepat tanpa menghilangkan data outlier. Hasil menunjukkan bahwa kinerja RFCR dengan fungsi pembobotan Huber lebih baik dibandingkan fungsi pembobotan lain berdasarkan goodness of fit, yang terdiri dari root mean square error of prediksi (RMSEP), korelasi antara data aktual dan model, dan mean kesalahan absolut (MAE).Kata Kunci: analisis data fungsional; fungsi berbobot Huber; fungsi tertimbang Hampel; fungsi tertimbang Ramsay; fungsi berbobot Tukey (Bisquare). 2020MSC: 62J99, 62R10
A Comparative Study of Random Forest and Double Random Forest Models from View Points of Their Interpretability Khairunnisa, Adlina; Notodiputro, Khairil Anwar; Sartono, Bagus
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.48721

Abstract

Purpose: This study aims to compare the performance of ensemble trees such as Random Forest (RF) and Double Random Forest (DRF) from view points of interpretability of the models. Both models have strong predictive performance but the inner working of the models is not human understandable. Model interpretability is required to explain the relationship between the predictors and the response. We apply association rules to simplify the essence of the models.Methods: This study compares interpretability of RF and DRF using association rules. Each decision tree formed from each model is converted into if-then rules by following the path from root node to leaf nodes. The data was selected in such a way that they were underfit data. This is due to the fact that DRF has been shown by other researchers to overcome the underfitting problem faced by RF. A Simulation study has been conducted to evaluate the extracted rules from RF and DRF. The rules extracted from both models are compared in terms of model interpretability based on support and confidence values. Association rules may also be applied to identify the characteristics of poor people who are working in Yogyakarta.Result: The simulation results revealed that the interpretability of DRF outperformed RF especially in the case of modelling underfit data.  On the other hand, using empirical data we have been able to characterize the profile of poor people who are working in Yogyakarta based on the most frequent rules.Novelty: Research on interpretable DRF is still rare, especially the interpretation model using association rules. Previous studies focused only on interpreting the random forest model using association rules. In this study, the rules extracted from the random forest and double random forest models are compared based on the quality of the rules extracted.
Performance of LAD-LASSO and WLAD-LASSO on High Dimensional Regression in Handling Data Containing Outliers Cahya, Septa Dwi; Sartono, Bagus; Indahwati, Indahwati; Purnaningrum, Evita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.8968

Abstract

In several research areas, it is common to have a dataset with more explanatory variables than the number of observations, called high-dimensional data. This condition can lead to multicollinearity problem. The least absolute shrinkage and selection operator (LASSO) solves the problem by shrinking the estimated coefficient to zero so that it can simultaneously carry on the variable selection and the parameter estimation.  But LASSO performs poorly when the data contains some outliers in the response or explanatory variables. Robust methods have addressed this problem based on the least-absolute-deviation approach, such as LAD-LASSO and WLAD-LASSO. This current research aims to evaluate the performance of the LAD-LASSO and WLAD-LASSO methods on high-dimensional and low-dimensional data containing outliers. To evaluate the performance of these methods, the simulation study was conducted. The simulation study used three scenarios (without outliers, outliers on the response variable (5%, 10%, 15%), outliers both on the response and explanatory variables (5%, 10%, 15%)). We also used the Minimum Regularized Covariance Determinant (MRCD) estimator in calculating the weights on the WLAD-LASSO. The best method from this simulation then will be applied to sembung leaf extract data to identify antioxidant marker compounds in sembung leaf extract. The simulation results show that LAD-LASSO tends to be very tight in selecting, while LASSO tends to be too loose.  Meanwhile, WLAD-LASSO is in the middle of those two techniques and performs the best in identifying the important variables correctly. Even the existence of weights cause WLAD-LASSO more robust against the presence of outliers in the response and explanatory variables compared to LAD-LASSO. Furthermore, performance of these methods on high-dimensional data decrease compared to low-dimensional data. The performance of these methods also tends to decrease when the rate of outlier increases. The WLAD-LASSO was then implemented in actual data to find the compound of antioxidant markers in the sembung leaf extract. The compounds/formulas obtained are Umbelliferone, 12-Hydroxyjasmonic Acid, C22H14N8O2, and Acetyleugenol (with a prediction error is 0.133050). These compounds/formulas can be developed as natural antioxidants and have the potential to be developed as medicinal ingredients.
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.
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