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All Journal International Journal of Public Health Science (IJPHS) Jurnal Ilmu Pertanian Indonesia Jurnal Ekonomi Pembangunan 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 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 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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ANALISIS FAKTOR YANG MEMPENGARUHI INDEKS PEMBANGUNAN MANUSIA DI INDONESIA MENGGUNAKAN MODEL REGRESI LOGISTIK BINER Vitona, Desi; Erfiani, Erfiani; Indahwati, Indahwati; Fitrianto, Anwar; Aliu, Mufthi Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.634

Abstract

The primary tool for assessing the extent of human development progress in a country is the Human Development Index (HDI). There are three components of Indonesia's Human Development Index (HDI). The method used to characterize the quality of human existence is based on these foundational aspects of HDI. The three elements include the role of economic advancement in human progress, as well as health, knowledge, and a decent standard of living. The objective of this research is to conduct binary logistic regression modeling to identify the key aspects that influence the Human Development Index of Regencies and Cities in Indonesia. If the response variable is binary and the predictor factors consist of one or more continuous or categorical variables, binary logistic regression is the statistical technique used to model the categorical response variable. The research results indicate that the percentage of Life Expectancy (X1), Average Length of Schooling (X2), Expected Years of Schooling (X3), and Per Capita Expenditure (X4), both partially and simultaneously, are independent variables that have the most significant impact on HDI at a real level of α = 5%. A balanced accuracy rating of 91.83% was achieved from the model evaluation, indicating that the model is useful
MODEL KLASIFIKASI REGRESI LOGISTIK BINER UNTUK LAPORAN MASYARAKAT DI OMBUDSMAN REPUBLIK INDONESIA Daswati, Oktaviyani; Indahwati, Indahwati; Erfiani, Erfiani; Fitrianto, Anwar; Aliu, Muftih Alwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i2.702

Abstract

A classification model is needed to predict data into the right class according to the pattern of previous data. Binary Logistic Regression can be used in building classification models, even though the independent variables are categorical scale data. Through binary logistic regression, it can also be seen which category of independent variables influences the response variable. Public complaint reports at the Ombudsman of the Republic of Indonesia are classified into reports that found maladministration and not. The Binary Logistic Regression model with several categorical independent variables related to the public complaint reports data applied resulted in a classification model with an overall classification accuracy of 66.08% and a sensitivity of 75.31% in estimating the presence of maladministration findings in the submitted public complaint reports. Based on the 95% confidence level of the model, it is known that the factors that influence the occurrence of maladministration are the Group of Reportees, the Substance of the Report, the Method of Submission, the Request for Confidentiality, and the Location of the Inspection Office. This model can be used as a reference to reduce the incidence of maladministration cases in public service providers by focusing socialization and education on categories that have a real influence on each of these factors
PERBANDINGAN K-MEDOIDS DAN CLARA (Clustering Large Application) PADA DATA POPULASI TERNAK DI INDONESIA Ardhani, Rizky; Marshelle, Sean; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L. M. Risman Dwi
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.764

Abstract

This study compares the K-Medoids and CLARA (Clustering Large Application) methods for livestock population data in Indonesian districts and cities. Calculating the distance between points and objects in the data, K-Medoids is a method for clustering based on data points (medoids). A larger dataset is divided into several samples for comparison in CLARA, an extension of the K-Medoids approach. The CLARA method analysis results show that three clusters are the ideal number. The ideal number of clusters in a K-Medoids cluster analysis is two. The Silhouette Score (SS), Davis-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) are the metrics that are measured. The evaluation of the comparison results shows that the CLARA method has an SS value of 0.66, while K-Medoids has an SS value of 0.62. The comparison of the CLARA and K-Medoids approaches yielded DBI values of 1.38 and 1.92, respectively, and 197.54 and 132.73 for CHI. The findings indicate that, in comparison to the K-Medoids approach, the SS value for the CLARA method is closer to 1, and that the CHI value derived from the CLARA method is likewise greater. The K-Medoids approach has a higher DBI value than the CLARA method, where a lower DBI value denotes superior performance. The CLARA approach is the most effective way to do cluster analysis on livestock population data in Indonesian districts and cities, according to the findings.
PENERAPAN K-MODES DALAM KLASTERISASI KABUPATEN/KOTA DI JAWA BARAT BERDASARKAN INDIKATOR INFRASTRUKTUR Rahman, Abd.; Anadra, Rahmi; Fitrianto , Anwar; Erfiani, Erfiani; Dwi Jumansyah, L.M. Risman
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 3 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik
Publisher : LPPM Universitas Bina Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46306/lb.v5i3.787

Abstract

Clustering is a statistical method used to group data based on certain similar characteristics, particularly in the context of complex and diverse data. This study aims to cluster districts/cities in West Java Province based on infrastructure indicators, namely access to clean water, sanitation, electricity, and energy, using the K-Modes clustering method. The data used is categorical data sourced from SUSENAS West Java 2023. The cluster analysis resulted in four distinct clusters, each representing significant differences in infrastructure characteristics across regions. The first cluster consists of 8 regions, the second cluster includes 7 regions, the third cluster consists of 1 region, and the fourth cluster contains 11 regions. These characteristic differences among clusters indicate infrastructure disparities that need to be addressed in planning more equitable development to improve the quality of life of people in West Java
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.
Co-Authors 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 Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amalia Kholifatunnisa Amanda, Nabila Amatullah, Fida Fariha Amelia, Reni Amir Abduljabbar Dalimunthe Anadra, Rahmi Anang Kurnia Anang Kurnia Anik Djuraidah Anisa Nurizki Annisa Putri Utami Annissa Nur Fitria Fathina Ardhani, Rizky Aristawidya, Rafika Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Azis, Tukhfatur Rizmah Aziza, Vivin Nur Bagus Sartono Budi Susetyo Budi Susetyo Budi Susetyo Budi Susetyo Bukhari, Ari Shobri Cahya Alkahfi 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 Erfiani Erfiani Erfiani Erfiani Fadilah, Anggita Rizky Fajar Athallah Yusuf Farit M Affendi 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 Jamaluddin Rabbani Harahap Jap Ee Jia Jia, Jap Ee Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnia N. K. Khusnia Nurul Khikmah Kriswan, Suliana Kusman Sadik L.M. Risman Dwi Jumansyah L.M. Risman Dwi Jumansyah La Ode Abdul Rahman La Ode Abdul Rahman Lai Ming Choon Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Marshelle, Sean Megawati Megawati Mohamad Solehudin Zaenal Muftih Alwi Aliu Muftih Alwi Aliu Muhadi, Rizqi Annafi Muhammad Farhan Zahid Muhammad Irfan Hanifiandi Kurnia mutiah, siti Nabila Ghoni Trisno Hidayatulloh Nadira Nisa Alwani Nafisa Berliana Indah Pratiwi Nashir, Husnun Nisa Nur Aisyah Novi Hidayat Pusponegoro Nugraha, Adhiyatma Nur Hidayah Nur Khamidah Pangestika, Dhita Elsha Pika Silvianti Pika Silvianti Pradnya Sri Rahayu Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Oktaviani Aisyah Rachmat Bintang Yudhianto Rafika Aufa Hasibuan Rahmatun Nisa, Rahmatun Rais 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 Seta Baehera Setyowati, Silfiana Lis Siau Hui Mah Siau Man Mah Silmi Annisa Rizki Manaf 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 Tahira Fulazzaky Titin Agustina Titin Yuniarty Yuniarty Uswatun Hasanah Utami Dyah Syafitri 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 Wan Zuki Azman Wan Muhamad Waode, Yully Sofyah Winata, Hilma Mutiara Xin, Sim Hui Yenni Angraini Yuniarsyih R.A, Rizqi Dwi Zein Rizky Santoso