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All Journal International Journal of Public Health Science (IJPHS) Jurnal Ilmu Pertanian Indonesia Jurnal Ekonomi Pembangunan EKSAKTA: Journal of Sciences and Data Analysis JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen Jurnal TIMES Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Kubik Journal of Accounting and Investment JURNAL KOLABORASI JIMKesmas (Jurnal Ilmiah Mahasiswa Kesehatan Masyarakat) Al-Jabar : Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Desimal: Jurnal Matematika Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Socioeconomics and Development Jurnal Informatika Universitas Pamulang J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Teorema: Teori dan Riset Matematika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Journal of Information System, Applied, Management, Accounting and Research Ecces: Economics, Social, and Development Studies Inferensi Journal of Data Science and Its Applications International Journal of Science, Engineering and Information Technology Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam SRIWIJAYA JOURNAL OF ENVIRONMENT MATHunesa: Jurnal Ilmiah Matematika VARIANSI: Journal of Statistics and Its Application on Teaching and Research Aceh International Journal of Science and Technology Jurnal Sains dan Informatika : Research of Science and Informatic STATISTIKA Scientific Journal of Informatics Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Fitrianto, Anwar
Departemen Statistika, Fakultas MIPA, IPB University, Bogor 16680
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Pemodelan Tingkat Kecanduan Games Online Menggunakan Regresi Logistik Ordinal Hidayah, Nur; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika Vol. 5 No. 1 (2024): MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/mathlocus.v5i1.4335

Abstract

Analisis regresi yang digunakan untuk memodelkan hubungan antara variabel prediktordan variabel respon yang berskala ordinal disebut regresi logistik ordinal. Data ini diperoleh darisurvei yang dilakukan oleh peneliti sebelumnya untuk mengukur tingkat kecanduan games onlinedengan menggunakan pemodelan matematika PEAR. Kecanduan games online menjadifenomena yang semakin mengkhawatirkan di era digital ini, dengan dampak negatif yangsignifikan pada aspek sosial, psikologis, dan akademik. Penelitian ini bertujuan untukmemodelkan model prediktif dengan mengukur tingkat kecanduan games online melalui aplikasiregresi logistik ordinal. Model ini mempertimbangkan beberapa variabel prediktor, yaitu umur,durasi bermain games, durasi bermain per hari, dan jenis games. Regresi logistik ordinaldigunakan karena variabel responnya, yaitu tingkat kecanduan bermain games, bersifat ordinaldan terdiri dari lebih dari dua kategori yang berurutan. Model ini menunjukkan akurasi sebesar92,5%, yang mengindikasikan kemampuan model dalam mengklasifikasikan tingkat kecanduanbermain games online dengan keandalan yang tinggi.
Analisis Regresi Logistik Biner untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Keterdeteksian Kasus Perceraian di Indonesia Timur (Maluku, Maluku Utara, dan Papua Barat) Waliulu, Megawati Zein; Indahwati; Fitrianto, Anwar; Erfiani; Muftih Alwi Aliu
MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika Vol. 5 No. 1 (2024): MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika
Publisher : Universitas Tidar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31002/mathlocus.v5i1.4339

Abstract

Mayoritas kabupaten/kota di Provinsi Maluku, Maluku Utara, dan Papua Barat tidak melaporkan kasus perceraian dengan persentase sebesar 53,6%. Sementara itu, 46,4% kabupaten/kota di provinsi tersebut melaporkan adanya kasus perceraian pada tahun 2023. Penelitian ini menggunakan metode regresi logistik biner yang bertujuan untuk memodelkan serta mengidentifikasi faktor-faktor yang mempengaruhi kasus perceraian di Indonesia Timur. Penelitian ini penting dilakukan untuk memahami dinamika sosial dan ekonomi di Indonesia Timur. Hasil penelitian menunjukkan bahwa model regresi logistik biner memiliki ketepatan prediksi sebesar 77,27% dengan peubah jumlah pulau (X3), jarak ke ibu kota (X4), dan luas kabupaten/kota (X5) memberikan pengaruh yang signifikan terhadap kasus keterdeteksian perceraian pada taraf nyata 90%.
Shear Wave Travel Time Prediction using Well Log Filtering and Machine Learning Siregar, Indra Rivaldi; Nugraha, Adhiyatma; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.29021

Abstract

Shear wave travel time (also known as Delta-T Shear and commonly abbreviated as DTS) is an important parameter in petroleum for exploration, production, and characterization of borehole stability. Direct measurement of DTS is often limited by high costs and a constraint of geography, making machine learning (ML) predictive approaches necessary. This study aims to explore the effectiveness of ML models in predicting DTS, emphasizing the importance of data preprocessing techniques to improve prediction accuracy. Preprocessing techniques include Yeo-Johnson transformation to handle non-normality, outlier elimination using z-score, and data smoothing using the Savitzky-Golay filter and median filter. Incorporating smoothing techniques can fill important gaps in some existing studies and may improve the performance of machine learning models in predicting DTS, particularly in situations with limited or noisy data. Four ML models were tested in this study, namely Linear Regression (LR), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), with performance evaluation based on metrics RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2 (coefficient of determination). The results showed that the RF model produced the best performance with RMSE of 9.41, MAE of 6.35, and R2 of 0.90 in scenarios with Yeo-Johnson transformation, outlier elimination, and smoothing techniques using a median filter with a window size of 5.
Comparison of Ordinal Logistic Regression and Geographically Weighted Ordinal Logistic Regression (GWOLR) in Predicting Stunting Prevalence among Indonesian Toddlers Setyowati, Silfiana Lis; Indahwati; Fitrianto, Anwar; Erfiani; Aliu, Muftih Alwi
Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Vol. 21 No. 2 (2024): Sainmatika : Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/sainmatika.v21i2.15416

Abstract

Ordinal logistic regression is a type of logistic regression used for response variables with an ordinal scale, containing two or more categories with levels between them. This method is an extension of logistic regression where the observed response variable is ordinal with a clear order. It addresses spatial effects that can cause variance heterogeneity and improve parameter estimation accuracy compared to logistic regression. Geographically Weighted Regression (GWR) is a statistical analysis technique designed to account for spatial heterogeneity. GWOLR is an extension of OLS and GWR models that incorporates spatial elements into regression with categorical variables. This study compares the effectiveness of OLR and GWOLR in analyzing stunting prevalence in toddlers. Comparing OLR and GWOLR can help assess the spatial impact on stunting prevalence. This analysis could reveal that certain regions have a higher tendency for stunting prevalence, while others might have lower tendencies, thus helping in understanding regional disparities. Toddler height is a key indicator of health and nutrition in early growth. The prevalence of stunting for toddlers, according to WHO, is categorized into four levels: low, moderate, high, and very high. The Ordinal Logistic Regression model is better suited for modeling toddler stunting prevalence in Indonesia than the GWORL model. The Ordinal Logistic Regression model and the GWOLR both have a classification accuracy of 85.7%, but the OLR model has a lower AIC value. The GWOLR model is not suitable for analyzing stunting prevalence among Indonesian toddlers due to the lack of spatial variability in the data. The Breusch-Pagan test results indicate that there is no spatial heterogeneity in the data on stunting prevalence among Indonesian toddlers, as the p-value is less than the significance level of 0.05. The prevalence of undernourished toddlers is the main factor influencing stunting among Indonesian toddlers.
Perbandingan Algoritma Klasterisasi dengan Principal Component Analysis pada Indikator Sosial Ekonomi Kesehatan Jawa Timur Hasanah, Uswatun; Fauziah, Monica Rahma; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11534

Abstract

K-Means dan K-Medoids digunakan untuk menilai indikator sosial ekonomi dan kesehatan di Provinsi Jawa Timur tahun 2023 melalui metode klasterisasi. Dengan menggunakan Principal Component Analysis (PCA) untuk mereduksi dimensi variabel, penelitian ini mengelompokkan wilayah berdasarkan karakteristik sosial ekonomi dan kesehatan. Data yang dianalisis termasuk angka harapan hidup, tingkat kemiskinan, pengangguran, dan akses ke layanan kesehatan. Kebaruan penelitian ini terletak pada kombinasi unik antara PCA dan K-Medoids untuk menghasilkan klaster yang lebih akurat dan robust terhadap outlier, dibandingkan metode yang biasanya hanya menggunakan satu teknik klasterisasi atau tidak melibatkan reduksi dimensi. Hasil penelitian menunjukkan bahwa K-Medoids dengan PCA menghasilkan klaster yang lebih koheren dan terpisah daripada K-Means, terutama dalam menangani outlier. Menurut metode Elbow dan Silhouette, empat hingga lima klaster adalah pilihan terbaik. PCA meningkatkan akurasi dan efisiensi klasterisasi dengan mengurangi kompleksitas data, yang menghasilkan klaster yang lebih baik Diharapkan temuan ini akan membantu pemerintah membuat kebijakan yang lebih baik untuk mengatasi ketimpangan kesehatan dan sosial ekonomi di Jawa Timur.   Kata kunci: Klasterisasi, Outlier, Principal Component Analysis (PCA)
Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning Aziza, Vivin Nur; Syafitri, Utami Dyah; Fitrianto, Anwar
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4052

Abstract

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
Analisis Pola Konvergensi Transpor Kelembapan Udara di Indonesia Bagian Barat Menggunakan K-Means dengan Pembobotan Statistik dan Hierarchical Shape-Based Clustering Pratiwi, Asri; Azis, Tukhfatur Rizmah; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i2.39753

Abstract

This study analyzes the convergence patterns of Vertically Integrated Moisture Transport (VIMT) in the western region of Indonesia using the K-Means method with statistical weighting and Hierarchical Shape-Based Clustering based on Dynamic Time Warping (DTW). Daily data on specific humidity, zonal wind speed, and meridional wind speed from 2020–2023 were used to calculate VIMT. Clustering methods were utilized to identify grouping patterns in moisture transport data. The results showed that moisture convergence significantly increased during the rainy season (November–February). Using the K-Means method, five clusters with clearer separations were obtained compared to the four clusters produced by the Hierarchical Clustering method. Performance evaluation using Silhouette and Calinski-Harabasz scores indicated that the K-Means method was superior, with scores of 0.37 and 104.88 compared to 0.13 and 96.34 for the Hierarchical method. This provides an understanding of the moisture transport patterns, serving as a reference for predicting weather and climate patterns, thereby supporting efforts to mitigate the impacts of extreme weather in Western Indonesia.
Perbandingan Metode K-Means dan OPTICS dalam Penggerombolan Kemiskinan Multidimensi di Indonesia Sari, Devi Permata; Rizqi, Tasya Anisah; Fitrianto, Anwar; M.S, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9, No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i2.39877

Abstract

Kemiskinan multidimensi tetap menjadi tantangan serius di Indonesia meskipun telah mengalami penurunan dalam beberapa tahun terakhir. Penelitian ini bertujuan menganalisis dan membandingkan pola kemiskinan multidimensi di 34 provinsi Indonesia menggunakan metode K-Means dan OPTICS Clustering. Data kemiskinan multidimensi yang digunakan mencakup aspek ekonomi, pendidikan, ketenagakerjaan, dan standar hidup dari Badan Pusat Statistik. Analisis statistik deskriptif mengungkapkan kesenjangan signifikan antar provinsi dalam berbagai dimensi kemiskinan, dengan korelasi tertinggi sebesar 0,4 antara dimensi pendidikan dan status ketenagakerjaan. K-Means Clustering mengidentifikasi 5 cluster provinsi dengan karakteristik beragam, menunjukkan adanya trade-off antara akses fasilitas dan tingkat kemiskinan. Sementara itu, OPTICS Clustering menghasilkan 2 cluster utama, dengan cluster 1 terdiri dari 24 provinsi yang memiliki kondisi cenderung homogen dan cluster 2 terdiri dari 7 provinsi dengan karakteristik yang berbeda secara signifikan. Perbandingan performa menunjukkan OPTICS unggul dengan nilai Silhouette Index dan WCSS yang lebih baik dibandingkan K-Means. Temuan ini memberikan kontribusi penting dalam analisis kemiskinan multidimensi di Indonesia dan dapat dimanfaatkan untuk merancang program pengentasan kemiskinan yang lebih terlokalisasi sesuai karakteristik masing-masing cluster.
VISUALIZATION AND MAPPING OF HOUSEHOLD HOUSING CONDITIONS IN WEST JAVA USING MULTIDIMENSIONAL SCALING Hafsah, Siti; Rifda Nida’ul Labibah; Anwar Fitrianto; Erfiani; L.M. Risman Dwi Jumansyah
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08201

Abstract

This study aims to map household housing conditions in West Java using the Multidimensional Scaling (MDS) approach. West Java, as the most populous province in Indonesia, faces significant challenges regarding housing inequalities, infrastructure access, and socio-economic disparities between urban and rural areas. These disparities necessitate a comprehensive and systematic approach to identify vulnerable regions and inform targeted policy interventions. Using data from the 2023 National Socio-Economic Survey (Susenas), this study analyzes five main groups of variables: basic needs, housing facilities and ownership, socio-economic status, access to services and infrastructure, and household demographics and welfare. The Multidimensional Scaling (MDS) technique is employed due to its capability to reduce complex, high-dimensional data into a two-dimensional representation, allowing clearer visualization of regional disparities and interrelationships among variables. MDS also facilitates robust model evaluation, ensuring high-quality mapping results. The MDS results reveal significant variations in household conditions, with urban areas such as Bekasi and Depok City showing better infrastructure access and welfare outcomes compared to rural areas like Cirebon and Sukabumi District. Evaluation of the MDS model indicates excellent performance, with STRESS values ranging from 0.042 to 0.083 and RSQ values between 0.993 and 0.999, demonstrating high accuracy. This study addresses a research gap where few studies have comprehensively mapped housing inequalities in large, diverse regions like West Java using advanced multidimensional techniques. The findings emphasize the importance of policies focusing on infrastructure development and equitable distribution of social assistance in underdeveloped regions to reduce regional disparities.
Clustering Time Series Forecasting Model for Grouping Provinces in Indonesia Based on Granulated Sugar Prices Amatullah, Fida Fariha; Ilmani, Erdanisa Aghnia; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L. M. Risman Dwi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8840

Abstract

Clustering time series is the process of organizing data into groups based on similarities in specific patterns. This research uses the prices of granulated sugar in each province of Indonesia. According to USDA reports, sugar consumption in Indonesia in 2023 reached 7.9 million tons. On April 26, 2024, the price of granulated sugar peaked in the Papua Mountains at Rp29,320 per kg, while the lowest price was recorded in the Riau Islands at Rp16,460 per kg. The research aims to cluster provinces based on the characteristics of granulated sugar prices and to use forecasting models for each group. Two groups were formed based on the price patterns of granulated sugar over time. The provinces of Papua and West Papua are in group 2, while the other 30 provinces are in group 1. The best model developed using the auto ARIMA method is ARIMA (2, 1, 0), with a MAPE value of 2.36% for cluster 1, and ARIMA (1, 1, 1), with a MAPE value of 2.59% for cluster 2. These values are less than 10%, indicating that the models built using the auto ARIMA method for clusters 1 and 2 are suitable for forecasting.
Co-Authors -, Salsabila A. A., Muftih Aam Alamudi Abd. Rahman Adeline Vinda Septiani Agung Tri Utomo Agus M Soleh Agus Mohamad Soleh Ahmad Syauqi Alfa Nugraha Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfi Indah Nurrizqi Alifviansyah, Kevin Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amalia Kholifatunnisa Amanda, Nabila Amatullah, Fida Fariha Amelia, Reni Amir Abduljabbar Dalimunthe Anadra, Rahmi Anang Kurnia Anang Kurnia Angelia, Riza Rahmah Anik Djuraidah Anisa Nurizki Annissa Nur Fitria Fathina Ardhani, Rizky Aristawidya, Rafika Askari, M. Aiman Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Azis, Tukhfatur Rizmah Aziza, Vivin Nur Bagus Sartono Budi Susetyo Bukhari, Ari Shobri Cahya Alkahfi Choon, Lai Ming Daswati, Oktaviyani Defri Ramadhan Ismana Deri Siswara Dessy Rotua Natalina Siahaan Dessy Siahaan Devi Permata Sari Dian Handayani Dwi Jumansyah, L.M. Risman Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani Fadilah, Anggita Rizky Fahira, Fani Farit M Affendi Farit M. Afendi Farit M. Afendi Farit Mochamad Afendi Fatimah Fatimah Fauziah, Monica Rahma Fulazzaky, Tahira Ghina Fauziah Gustiara, Dela Hari Wijayanto Harismahyanti A., Andi Hasnataeni, Yunia Hasnita Hasnita Heri Cahyono I Made Sumertajaya Ilham Azagi Ilmani, Erdanisa Aghnia Imam Hanafi Indah, Yunna Mentari Indahwati Indahwati Indahwati Indahwati, Indahwati Irsyifa Mayzela Afnan Irzaman, Irzaman Ismah, Ismah Isna Shofia Mubarokah Iswan Achlan Setiawan Iswati Ita Wulandari Jamaluddin Rabbani Harahap Jap Ee Jia Jia, Jap Ee Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Kapiluka, Kristuisno Martsuyanto Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnia N. K. Khusnia Nurul Khikmah Kriswan, Suliana Kusman Sadik L.M. Risman Dwi Jumansyah La Ode Abdul Rahman La Ode Abdul Rahman Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Manaf, Silmi Annisa Rizki Marshelle, Sean Megawati Megawati Muftih Alwi Aliu Muftih Alwi Aliu Muhadi, Rizqi Annafi Muhammad Irfan Hanifiandi Kurnia Muhammad Yusran mutiah, siti Nabila Ghoni Trisno Hidayatulloh Nadira Nisa Alwani Nashir, Husnun Nisa Nur Aisyah Novi Hidayat Pusponegoro Nugraha, Adhiyatma Nur Hidayah Nur Khamidah NURADILLA, SITI Nurizki, Anisa Pangestika, Dhita Elsha Pika Silvianti Pradnya Sri Rahayu Pratiwi, Nafisa Berliana Indah Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Mega Ramatika Putri, Oktaviani Aisyah Rafika Aufa Hasibuan Rahmatun Nisa, Rahmatun Rais Ramadhan, Syaifullah Yusuf Reka Agustia Astari Reni Amelia Reni Amelia Retna Nurwulan Riansyah, Boy Rifda Nida’ul Labibah Riska Yulianti, Riska Rizki Manaf, Silmi Anisa Rizki, Akbar Rizqi, Tasya Anisah Sachnaz Desta Oktarin salsa bila Sari, Jefita Resti Seta Baehera Setyowati, Silfiana Lis Siau Hui Mah Siau Man Mah Silmi Annisa Rizki Manaf Siregar, Indra Rivaldi Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Sofia Octaviana Sony Hartono Wijaya Suantari, Ni Gusti Ayu Putu Puteri Suliana Kriswan Tangke, Nabillah Rahmatiah Titin Agustina Titin Yuniarty Yuniarty Uswatun Hasanah Utami Dyah Syafitri Utami, Annisa Putri Vitona, Desi Vivin Nur Aziza Waliulu, Megawati Zein Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Waode, Yully Sofyah Winata, Hilma Mutiara Xin, Sim Hui Yenni Angraini Yudhianto, Rachmat Bintang Yuniarsyih R.A, Rizqi Dwi Yusuf, Fajar Athallah Zaenal, Mohamad Solehudin Zahid, Muhammad Farhan Zahra, Latifah Zein Rizky Santoso