Article Per Year (5 Year)
p-Index From 2021 - 2026
6.948
P-Index
This Author published in this journals
All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika Kubik KEUNIS Sinkron : Jurnal dan Penelitian Teknik Informatika Syntax Literate: Jurnal Ilmiah Indonesia BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Teorema: Teori dan Riset Matematika Krea-TIF: Jurnal Teknik Informatika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Saintifik : Jurnal Matematika, Sains, dan Pembelajarannya Jambura Journal of Mathematics Inferensi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Riset dan Inovasi Pembelajaran PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) VARIANSI: Journal of Statistics and Its Application on Teaching and Research Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan Limits: Journal of Mathematics and Its Applications
Erfiani Erfiani
Pogram Studi Statistika Dan Sains Data, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 70 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 70 Documents
Search

 1   2   3   4   5 
Analisis Kualitas Aplikasi Mobile JKN dan Layanan Care-Center terhadap Kepuasan Peserta JKN-KIS dengan Metode PLS-SEM Made Agung Prebawa Parama Artha; Aji Hamim Wigena; Erfiani Erfiani
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.373 KB) | DOI: 10.36418/syntax-literate.v7i4.6735

Abstract

Sains data (data science) telah menjadi perhatian ahli statistika dunia akhir-akhir ini. Dunia industri, bisnis dan ilmu pengetahuan sangat memerlukan ilmu ini untuk melakukan analisis data maupun prediksi untuk perkembangan bisnis, industri maupun bidang - bidang lain di masa depan. Penelitian ini bertujuan untuk mengetahui pengaruh kualitas aplikasi Mobile JKN dan Layanan Care-Center terhadap kepuasan peserta JKN-KIS dengan menggunakan metode PLS-SEM. Teknik pengumpulan data menggunakan kuisioner dengan jumlah responden sebanyak 1.389 orang. Penelitian ini menggunakan pendekatan Partial Least Square-Strucural Equation Modelling (PLS-SEM) yang dapat memprediksi hubungan kausalitas variabel laten eksogen dengan variabel laten endogen. Hasil yang diperoleh dari penelitian ini yaitu nilai loading factor dari semua indikator sudah memenuhi kriteria melebihi dari angka 0.7 atau sering digunakan batas 0,5 sebagai batasan minimal dari nilai loading factor (Kock, 2020). Inner model yang diperoleh pada data dengan taraf nyata 5%: Kepuasan Peserta = 0.38 Kualitas Aplikasi + 0.31 Kualitas Layanan CareCenter +z. Ini artinya Peubah kualitas aplikasi berpengaruh signifikan pada taraf nyata 5% dengan nilai koefisen sebesar 0.38, sedangkan peubah kualitas layanan CareCenter berpengaruh signifikan dengan nilai koefisien sebesar 0.31. Nilai t-statistik untuk peubah Kualitas aplikasi terhadap Kepuasan Peserta sebesar 11,297 dan peubah Kualitas Layanan Care-Center terhadap Kepuasan Peserta sebesar 9,256. Hal ini menunjukkan nilai t-statistik lebih besar dari t-tabel yang artinya Kualitas Aplikasi Mobile JKN dan Layanan Care-Center berpengaruh terhadap Kepuasan Peserta.
The Comparison between Ordinal Logistic Regression and Random Forest Ordinal in Identifying the Factors Causing Diabetes Mellitus Assyifa Lala Pratiwi Hamid; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 2: August 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i2.20289

Abstract

Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which is higher than the ordinal random forest. The four most important variables causing diabetes are body mass index, hypertension, age, and cholesterol.
Agglomerative Nesting Cluster Analyst in Mapping District/City Health Facilities in West Java Province Nadira Nisa Alwani; Megawati Megawati; Anwar Fitrianto; Erfiani Erfiani; Alfa Nugraha Pradana
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32043

Abstract

The use of Hierarchical Clustering is used to group districts or cities in West Java according to the number of health facilities, distance to health facilities and population density using Agglomerative Nesting (AGNES). Clustering in this study utilizes complete linkage clustering. The elbow method produces two optimal clusters which are then validated with the sillhoute coefficient and Calinski-Harabasz. In this study, there are 27 variables in the form of health facilities spread across 27 regencies/cities in West Java in 2021. The results of the cluster analysis formed in this study are 18 districts/cities in cluster  one and 9 districts/cities in cluster two
PENERAPAN MULTI-CLUSTERING DALAM PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA BARAT BERDASARKAN INDEKS DESA MEMBANGUN Nur Khamidah; Reka Agustia Astari; Anwar Fitrianto; Erfiani Erfiani; Alfa Nugraha Pradana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 4 No. 3 (2023): 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.v4i3.459

Abstract

Cluster analysis is a statistical learning technique that aims to uncover hidden patterns in data by grouping it based on known explanatory variables. In multi-clustering algorithms, similar data in different sub-dimensions of categories are initially grouped separately and then re-categorized based on the obtained clusters, resulting in a more exploratory grouping. This research aims to conduct exploratory analysis of regencies/cities in West Java based on the Village Development Index (Indeks Desa Membangun/IDM), which consists of three sub-dimensions: Social Resilience Index, Economic Index, and Environmental Index. It also aims to observe how the regencies/cities in West Java are grouped based on these indices using a multi-clustering algorithm with KMeans for each sub-dimension. From the exploration and analysis results, regencies/cities are clustered based on the three sub-dimensions. Additionally, recommendations are obtained suggesting that the equal distribution of educational facilities, addressing crime rates, improving economic infrastructure, and enhancing environmental quality should be priorities for the government of West Java province
PENGGEROMBOLAN KECAMATAN DI PROVINSI JAWA BARAT BERDASARKAN AKSES PENDIDIKAN MENENGAH ATAS (SMA-SEDERAJAT) DENGAN K-PROTOTYPES Sofia Octaviana; Ahmad Syauqi; Anwar Fitrianto; Erfiani Erfiani; Alfa Nugraha Pradana
Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Vol. 5 No. 1 (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.v5i1.478

Abstract

Education is an important element for the Indonesian nation and must be felt by all citizens. The availability of educational facilities is important for the realization of overall educational equality for the Indonesian people. The aim of this research is to group sub-districts in West Java Province according to their level of access to Senior High School (SHS-equivalent). The data included in this study comprises both numerical and categorical variables, which were obtained from the 2021 Village Potential Data Collection (PODES) conducted by the Central Statistics Agency. A cluster analysis method that can be used to group objects based on numerical and categorical data is K-Prototypes. The results of the grouping divide the data into 2 groups, where the first group has the characteristics of an urban subdistrict, the topographic area is plain, access to the nearest high school is very easy, and has an average number of high school and equivalent schools of 22 schools per subdistrict, and has an average distance to the nearest high school of 1,86 km. Meanwhile, the second group has the characteristics of subdistricts with rural areas, topography in the form of slopes, easy access to the nearest high school, and has an average number of high schools of 7 per subdistrict, and the average distance to the nearest high school is 4,06 km. The second group is sub-districts that need to be given special attention because they have relatively fewer high schools and the distance to the nearest high school is further
Metode Machine Learning-Based Univariate Time Series Imputation Method untuk Estimasi Nilai Hilang pada Data Non-Stasioner Dini Ramadhani; Agus Mohamad Soleh; Erfiani Erfiani
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 1 (2024): SEPTEMBER 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i1.36468

Abstract

Handling missing values in time series data is crucial because they can disrupt data analysis and interpretation. Sequentially missing values in time series often pose a more complex challenge compared to randomly missing values. One of the promising recent methods is Machine Learning-Based Univariate Time Series Imputation (MLBUI), although it is still not widely used and its accessibility is limited. MLBUI employs Random Forest Regression (RFR) and Support Vector Regression (SVR) algorithms. This study evaluates the performance of MLBUI in addressing missing data scenarios in non-stationary univariate time series data. The data used in this research is the average temperature data from Bogor Regency. The missing data scenarios considered include rates of 6%, 10%, and 14%. Besides MLBUI, five other comparison methods are used: Kalman StructTS, Kalman Auto-ARIMA, Spline Interpolation, Stine Interpolation, and Moving Average. The results show that MLBUI performs poorly for non-stationary data, although the obtained Mean Absolute Percentage Error (MAPE) is below 10%.
Perbandingan Metode Klastering K-Means dan DBSCAN dalam Identifikasi Kelompok Rumah Tangga Berdasarkan Fasilitas Sosial Ekonomi di Jawa Barat Mutiah, Siti; Hasnataeni, Yunia; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
Teorema: Teori dan Riset Matematika Vol 9, No 2 (2024): September
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/teorema.v9i2.16290

Abstract

Penelitian ini bertujuan untuk membandingkan efektivitas dua metode klastering, yaitu K-Means dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN), dalam mengelompokkan rumah tangga berdasarkan karakteristik sosial ekonomi di Jawa Barat. Perbandingan kedua metode ini penting karena masing-masing metode memiliki kelebihan dan keterbatasan yang berbeda, K-Means unggul dalam menangani data dengan klaster yang lebih seragam, sedangkan DBSCAN lebih fleksibel dalam mengelola outlier dan klaster tidak teratur yang sering muncul dalam data sosial ekonomi. Data yang digunakan meliputi empat kategori: Fasilitas Rumah Tangga, Ketersediaan dan Kualitas Air, Bantuan Sosial dan Ekonomi, serta Kesejahteraan Ekonomi. Hasil analisis menunjukkan ketimpangan dalam akses fasilitas, air bersih, dan bantuan sosial ekonomi di berbagai wilayah, di mana wilayah seperti Bandung dan Garut lebih unggul dibanding Indramayu dan Cirebon. Motode terbaik dilihat dari nilai silhouette tertinggi. Metode K-Means menghasilkan segmentasi yang lebih terstruktur dengan skor silhouette 0,69, menunjukkan performa yang baik dalam mengelompokkan data dengan karakteristik yang lebih seragam. Sebaliknya, metode DBSCAN, yang lebih fleksibel dalam menangani outlier, menghasilkan 7 klaster dengan 248 noise points dan skor silhouette yang lebih rendah yaitu 0,398, mengindikasikan struktur klaster yang kurang kuat. Perbandingan kedua metode ini relevan dalam konteks klastering rumah tangga di Jawa Barat, di mana K-Means lebih efektif untuk wilayah dengan akses fasilitas yang seragam, sedangkan DBSCAN lebih baik dalam menangkap variasi yang tidak beraturan dan outlier. Penjelasan perbandingan kedua metode ini telah diperinci lebih lanjut untuk mencakup bagaimana variasi akses sosial ekonomi di berbagai wilayah memengaruhi efektivitas masing-masing metode sehingga memberikan pemahaman yang lebih mendalam tentang keunggulan dan keterbatasan keduanya dalam menangani heterogenitas data social ekonomi di Jawa Barat.Kata kunci: DBSCAN, K-Means, Klastering, Susenas
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
Eksplorasi dan Klasifikasi K-NN Terhadap Kejadian Luar Biasa Diare di Jawa Barat Fulazzaky, Tahira; Waode, Yully Sofyah; Fitrianto, Anwar; Erfiani, Erfiani; Pradana, Alfa Nugraha
Techno.Com Vol. 22 No. 4 (2023): November 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i4.9281

Abstract

Tujuan dari penelitian ini adalah untuk mengkaji bagaimana kualitas air dan sanitasi mempengaruhi Kejadian Luar Biasa (KLB) Diare di Provinsi Jawa Barat, Indonesia, menggunakan data Pendataan Potensi Desa (PODES) tahun 2021. Diare merupakan permasalahan serius dalam kesehatan masyarakat Indonesia, terutama pada kelompok anak balita, dan salah satu faktor penyebab utamanya adalah rendahnya kualitas air dan sanitasi. Dalam konteks penelitian ini, kami menerapkan metode algoritma K-Nearest Neighbors (K-NN) untuk mengklasifikasikan wilayah-wilayah yang mengalami KLB Diare. Hasil eksplorasi data menunjukkan variasi yang signifikan dalam jumlah kasus diare di sejumlah kabupaten dan kota yang tersebar di wilayah Jawa Barat. Untuk menangani masalah ketidakseimbangan data, kami menerapkan teknik Pengurangan Acak (Random Under Sampling), Penambahan Acak (Random Over Sampling), dan Synthetic Minority Oversampling Technique (SMOTE).Hasil analisis menunjukkan bahwa model K-NN dengan penggunaan metode SMOTE menghasilkan tingkat akurasi tertinggi, yaitu sebesar 71.28%. Meskipun demikian, nilai F1 score untuk semua model cenderung rendah, yang mengindikasikan adanya tantangan dalam mengklasifikasikan wilayah-wilayah dengan KLB Diare. Penelitian ini memberikan wawasan yang penting mengenai korelasi antara kualitas air, sanitasi, dan KLB Diare di Jawa Barat, serta mengidentifikasi wilayah-wilayah yang memerlukan perhatian lebih dalam upaya pencegahan dan pengendalian penyakit diare. Hasil ini dapat digunakan sebagai dasar untuk merancang program-program kesehatan yang lebih efektif di daerah-daerah dengan tingkat insiden diare yang tinggi. Kata kunci: Algoritma K-Nearest Neighbors (K-NN), SMOTE, Ketidakseimbangan data dan teknik pengambilan sampel ulang, Kualitas air dan sanitasi, Program pencegahan dan pengendalian diare.
PERBANDINGAN ANALISIS REGRESI LOGISTIK BINER DAN NAÏVE BAYES CLASSIFIER UNTUK MEMPREDIKSI FAKTOR RESIKO DIABETES Aristawidya, Rafika; Indahwati, Indahwati; Erfiani, Erfiani; Fitrianto, Anwar; A. A., Muftih
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.v5i2.617

Abstract

Diabetes is a global health problem that is increasing in prevalence worldwide. This study compares the performance of two data analysis methods, namely binary logistic regression and naïve bayes classifier in predicting diabetes risk. This study aims to identify factors that significantly affect diabetes risk and classify diabetes risk using binary logistic regression, then compare the classification with the naive bayes classifier algorithm. Binary logistic regression models the relationship between independent predictor variables and binary dependent variables, while naïve bayes classifier uses the assumption of independence between variables. In this study, both methods were evaluated based on accuracy, sensitivity, specificity and positive predictive value. The results show that the factors that influence the risk of diabetes are Age, Gender, Polyuria, Polydipsia, Genital thrush, Itching, Irritability, and Partial paresis. Furthermore, the binary logistic regression results have a higher classification accuracy (92.31%) compared to the naïve bayes classifier (84.61%). Therefore, binary logistic regression was identified as the best method to predict diabetes risk in the context of this study
Co-Authors . Aunuddin A. A., Muftih Abd. Rahman Abqorunnisa, Farah Agung Tri Utomo Agus Mohamad Soleh Ahmad Khairul Reza Ahmad Nur Rohman Ahmad Syauqi Aji Hamim Wigena Alamanda, Dinda Aprilia Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amatullah, Fida Fariha Amelia, Reni Aminah Aminah Anadra, Rahmi Anang Kurnia Anik Djuraidah Anissa Tsalsabila Ardhani, Rizky Arini Annisa Adi Aristawidya, Rafika ASEP SAEFUDDIN Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Aunuddin . Aunuddin Aunuddin Azis, Tukhfatur Rizmah Bagus Sartono Bartho Sihombing Bimawan Sudarmoko Budi Susetyo Daswati, Oktaviyani Daulay, Nurmai Syaroh Deti Anggraeni Ekawati Dian Kusumaningrum Dini Ramadhani Dwi Jumansyah, L.M. Risman Dwi Putri Kurniasari Fanny Amalia Farit M Afendi Farly Shabahul Khairi Fatimah Fatimah Fauziah, Monica Rahma Fitrianto, Anwar Freza Riana Fulazzaky, Tahira Hamim Wigena, Aji Hari Wijayanto Harismahyanti A., Andi Hasnataeni, Yunia Herlin Fransiska Hilda Zaikarina I Made Sumertajaya Ihsan, Muhammad Taufik Ilmani, Erdanisa Aghnia Indah, Yunna Mentari Indahwati Irzaman, Irzaman Ismah, Ismah Julianti, Elisa D Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Khikmah, Khusnia Nurul Khusnia Nurul Khikmah Lestari, Nila Made Agung Prebawa Parama Artha Mahfuz Hudori Marshelle, Sean Megawati Megawati Misrika, Dahlia Mohammad Masjkur Muggy David Cristian Ginzel Muhammad Nur Aidi mutiah, siti Nadira Nisa Alwani Nenden Rahayu Puspitasari Novitri Novitri Nugraha, Adhiyatma Nur Khamidah Nurul Fadhilah Pardomuan Robinson Sihombing Qalbi, Asyifah R, Arifuddin Rahmatun Nisa, Rahmatun Ramadhani, Dini Ratih Dwi Septiani Reka Agustia Astari Reni Amelia Retno Dwi Jayanti Rika Rachmawati Riska Asri Pertiwi Siregar, Indra Rivaldi Sofia Octaviana Tetinia Gulo Tiara, Yesan Umam Hidayaturrohman Uswatun Hasanah Utami Dyah Syafitri Vitona, Desi Waode, Yully Sofyah Wati, Wahyuni Kencana Weisha, Ghea Wigena, Aji Wijaya, Ferdian Bangkit Winda Chairani Mastuti Windi D.Y Putri Yulia Christina Yuniar Istiqomah Zaima Nurrusydah