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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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Penggerombolan Desa di Jawa Barat Berdasarkan Daerah Rawan Bencana Defri Ramadhan Ismana; Seta Baehera; Anwar Fitrianto; Bagus Sartono; Sachnaz Desta Oktarina
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06210

Abstract

Indonesia is one of the countries that has a large potential for natural disasters. Indonesia's position at the confluence of 4 continental plates makes the potential for earthquakes even greater. The tropical climate with 2 seasons makes changes in weather, temperature and wind direction quite extreme. These climatic conditions combined with the relatively diverse surface and rock topography conditions, these conditions can cause several bad consequences for the community such as hydrometeorological disasters such as floods, landslides, forest fires, and droughts. Particularly in West Java province, natural disasters that have occurred include: landslides, droughts, cyclones/typhoons, tidal waves, fires, volcanic eruptions, tsunamis, and other disasters. The purpose of this study was to cluster villages in the West Java region based on the level of disaster-prone in 2018. The research was carried out using K-Prototypes clustering and testing evaluation using the silhouette coefficient. The results showed that the optimal number of clusters in this study was nine clusters. These clusters can be distinguished based on the disaster category and the characteristics of the area.
Analisis Karakteristik Keberadaan Perbankan di Nusa Tenggara Barat Terhadap Kondisi Perekonomian Daerah Menggunakan K-Means Clustering Anisa Nurizki; Muhammad Irfan Hanifiandi Kurnia; Anwar Fitrianto; Bagus Sartono; Sachnaz Desta Oktarina; Dian Handayani
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06211

Abstract

In certain areas, there are still many people who have to travel long distances to access some banks. Difficult mobility is considered to hinder business activities. The West Nusa Tenggara (NTB) Province is one of the favorite travel destinations for some foreigner tourists as well as domestic tourists because of its natural beauty and cultural diversity. the existence of some banks in the NTB Province , is important to facilitate the circulation of money. For this reason, this study aims to analyze the existence of some banks in the NTB Province and the condition of mobility in accessing themto regional economic conditions by applying K-Means clustering. Our results show that there are two clusters, , where the cluster 2 is an urban area and a tourist area. It has charactersitic has a GDP greater than cluster 1.
Regresi Ordinal Logit dan Probit pada Faktor Kesejahteraan Rumah Tangga Petani Tanaman Pangan di Provinsi Sulawesi Tenggara Titin Yuniarty Yuniarty; Erfiani; Indahwati; Anwar Fitrianto; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 6 No 2 (2022): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.06216

Abstract

Sektor pertanian memiliki sumbangsih besar dalam perekonomian Indonesia, tetapi menjadi sektor yang seringkali diidentikkan dengan kemiskinan. Hingga saat ini, pembangunan sektor pertanian belum mampu sepenuhnya meningkatkan kesejahteraan rumah tangga petani. Provinsi Sulawesi Tenggara selama 2013-2021 tercatat memiliki Indeks Nilai Tukar Petani (NTP) untuk subsektor tanaman pangan, stabil di bawah 100. Indeks NTP di bawah 100 menunjukkan bahwa kesejahteraan petani belum begitu baik. Penelitian ini bertujuan menentukan faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara. Status kesejahteraan merupakan peubah respon berskala ordinal dengan tiga kategori yaitu miskin, rentan miskin, dan tidak miskin. Metode regresi yang sesuai untuk peubah respon berskala ordinal adalah regresi ordinal, dengan beberapa kemungkinan fungsi hubung. Dalam kajian ini menggunakan fungsi hubung logit dan probit. Hasil analisis regresi menunjukkan bahwa umur kepala rumah tangga (X3), kepemilikan telepon seluler (X6), sumber penghasilan utama rumah tangga (X9), akses terhadap kredit usahatani (X11), dan keberadaan jaminan sosial rumah tangga (X13) berpengaruh positif dan signifikan terhadap status kesejahteraan rumah tangga petani tanaman pangan, sedangkan jumlah anggota rumah tangga (X4) dan usia kawin pertama (X5) berpengaruh negatif dan signifikan. Dengan membandingkan nilai R2 dan balanced accuracy model logit dan probit, disimpulkan bahwa model logit lebih baik dalam mengidentifikasi faktor determinan kesejahteraan rumah tangga petani tanaman pangan di Provinsi Sulawesi Tenggara daripada model probit.
Regency Clusterization Based on Village Characteristics to Increase the Human Development Index (IPM) in Papua Province Rais; Amir Abduljabbar Dalimunthe; Anwar Fitrianto; Bagus Sartono; Sachnaz Desta Oktarina
Jurnal Ekonomi Pembangunan Vol. 20 No. 02 (2022): Jurnal Ekonomi Pembangunan
Publisher : Pusat Pengkajian Ekonomi dan Kebijakan Publik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jep.v21i02.22911

Abstract

Inequality in the Human Development Index (IPM) in Papua Province amid the disbursement of development funds needs to be studied adequately so that the policies and programs that have been planned can be more directed and on target. For this reason, research is needed that can map the priority needs of each district in Papua Province by identifying regional characteristics, namely villages. By using Cluster Analysis and Factor Analysis, the results of this research show that 4 district clusters in Papua Province were formed with different priority focuses on increasing the HDI. The main focus of the district HDI improvement priorities in Papua Province is divided into three through factor analysis: the infrastructure-telecommunication factor, the sanitation-economic factor, and the health-education factor. Each cluster is generally still dominated by districts with a low HDI category. The main obstacle to increasing HDI in Papua Province is the transportation and telecommunications infrastructure factor. Local governments are expected to be able to formulate human development programs and policies concerning the priority needs of each district as a result of this research.
Synthetic Minority Oversampling Technique Pada Model Logit dan Probit Status Pengangguran Terdidik Fatimah Fatimah; Anwar Fitrianto; Indahwati Indahwati; Erfiani Erfiani; Khusnia Nurul Khikmah
Jambura Journal of Mathematics Vol 5, No 1: February 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjom.v5i1.17050

Abstract

Educated unemployment is caused by a misalignment of educational development planning and employment development, resulting in underemployed graduates from various educational institutions. Unemployment data in DKI Jakarta shows an unequal class. Unbalanced data is a severe problem of modeling because it can cause prediction errors that affect the accuracy of the resulting model. Using SMOTE to handle unbalanced data will likely increase the model’s accuracy. This study aims to find the best model for identifying the factors influencing the status of educated unemployment using logit and probit models and handling unbalanced data using SMOTE. The results showed that the independent variables that affect the status of educated unemployment in the logit and probit models are the same: age group and participation in training. The independent variables that affect the status of educated unemployment in the logit and probit models with SMOTE are also the same: age group, marital status, and participation in training. Unbalanced data handling using SMOTE can increase the balanced accuracy value significantly. Balanced accuracy values for the logit and probit models with SMOTE are higher than the logit and probit models without SMOTE. The logit model with SMOTE is the best because it has the highest balanced accuracy value compared to other models. According to the logit model with SMOTE, the educated unemployed in DKI Jakarta are young and have never married. There is a need for the government to play a role in improving the quality of educational institutions in producing graduates who meet company qualifications and can be hired by employers. Unemployed people who have attended the training, despite having a higher education, may also become unemployed. The training provided has not been able to reduce the unemployment rate. As a result, the government should be able to provide training to improve entrepreneurship skills while also providing capital in the form of business loans to reduce educated unemployment.
Comparing Outlier Detection Methods using Boxplot Generalized Extreme Studentized Deviate and Sequential Fences Anwar Fitrianto; Wan Zuki Azman Wan Muhamad; Suliana Kriswan; Budi Susetyo
Aceh International Journal of Science and Technology Vol 11, No 1 (2022): April 2022
Publisher : Graduate Program of Syiah Kuala University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (410.433 KB) | DOI: 10.13170/aijst.11.1.23809

Abstract

Outliers identification is essential in data analysis since it can make wrong inferential statistics. This study aimed to compare the performance of Boxplot, Generalized Extreme Studentized Deviate (Generalized ESD), and Sequential Fences method in identifying outliers. A published dataset was used in the study. Based on preliminary outlier identification, the data did not contain outliers. Each outlier detection method's performance was evaluated by contaminating the original data with few outliers. The contaminations were conducted by replacing the two smallest and largest observations with outliers. The analysis was conducted using SAS version 9.2 for both original and contaminated data. We found that Sequential Fences have outstanding performance in identifying outliers compared to Boxplot and Generalized ESD.
Variable Importance Kesehatan dan Pendidikan dalam Pembentukan IPM dengan Algoritme Machine Learning Cahya Alkahfi; Zein Rizky Santoso; Anwar Fitrianto; Sachnaz Desta Oktarin
SAINS DAN INFORMATIKA : RESEARCH OF SCIENCE AND INFORMATIC Vol. 8 No. 2 (2022): Jurnal Sains dan Informatika : Research of Science and Informatic
Publisher : Lembaga Layanan Pendidikan Tinggi (LLDIKTI) Wilayah X

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.312 KB)

Abstract

Pembelajaran mesin adalah bidang studi yang menggunakan algoritma komputasi untuk mengubah data empiris menjadi model yang dapat digunakan. Pada penelitian ini akan membandingkan lima pembelajaran mesin supervised, yaitu forward selection, LASSO, random forest, gradient boosting, dan extra trees dengan studi kasus mengetahui faktor-faktor infrastruktur kesehatan dan pendidikan di tingkat desa/kelurahan yang mempengaruhi skor IPM kabupaten/kota di Pulau Jawa. Pada penentuan variable importance, metode forward-selection dan LASSO menggunakan nilai absolut koefisien regresi, sedangkan random forest, extra trees dan gradient boosting menggunakan nilai Mean Decrease in Impurity (MDI). Metode bootstrap akan diterapkan pada semua metode pembelajaran mesin dengan tujuan untuk memperluas ruang sampel dan menghasilkan indikator yang lebih akurat. Berdasarkan hasil pemodelan dari lima pembelajaran mesin, jumlah dokter dan dokter gigi per 1000 penduduk merupakan faktor yang paling mempengaruhi besaran nilai IPM di Pulau Jawa karena memiliki koefisien tertinggi atau nilai MDI terbesar. Extra Trees merupakan pembelajaran mesin supervised terbaik karena menghasilkan nilai RMSE yang paling kecil serta interval yang juga lebih pendek dibandingkan model lainnya.
Ensemble learning with imbalanced data handling in the early detection of capital markets Putri Auliana Rifqi Mukhlashin; Anwar Fitrianto; Agus M Soleh; Wan Zuki Azman Wan Muhamad
Journal of Accounting and Investment Vol 24, No 2: May 2023
Publisher : Universitas Muhammadiyah Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jai.v24i2.17970

Abstract

Research aims: This study aims to create an early detection model to predict events in the Indonesian capital market.Design/Methodology/Approach: A quantitative study comparing ensemble learning models with imbalanced data handling detected early capital market events. This study used five ensemble learning models—Random Forest, ExtraTrees, CatBoost, XGBoost, and LightGBM—to detect early events in the Indonesian capital market by handling imbalanced data, such as under sampling (RUS), oversampling (SMOTE, SMOTE-Broder, ADASYN), and over-under sampling (SMOTE-Tomek, SMOTE-ENN), weighted (class weight). Global and regional stock markets, commodities, exchange rates, technical indicators, sectoral indices, JCI leaders, MSCI, net buys of foreign stocks, national securities, and national share ownership all predicted the lowest return of Crisis Management Protocol (CMP) binary responses.Research findings: Hyperparameters and thresholds were tuned to produce the optimum model. The best model had the highest G-mean. ExtraTrees with SMOTE-ENN predicted the highest number of one-day events, with a G-Mean of 96.88%. LightGBM with SMOTE handling best predicted five-day events with an 89.21% G-Mean. With a G-Mean of 89.49%, CatBoost with SMOTE-Border handling was the best for a 15-day event. In addition, LightGBM with SMOTE-Tomek handling and 68.02% G-Mean was best for 30-day events. Further, performance evaluation scores decreased with increased prediction time.Theoretical contribution/Originality: This work relates more imbalance handling methods and ensemble learning to capital market early detection cases.Practitioner/Policy implication: Capital markets can indicate economic stability. Maintaining capital market efficacy and economic value requires a system to detect pressure.Research limitation/Implication: This study used ensemble learning models to predict capital market events 1, 5, 15, and 30 days ahead, assuming Indonesian working days. The model's forecast results are expected to be utilized to monitor the capital market and take precautions.
Comparing Several Missing Data Estimation Methods in Linear Regression;Real Data Example and A Simulation Study Anwar Fitrianto; Jap Ee Jia; Budi Susetyo; La Ode Abdul Rahman
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 7, No 4 (2023): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/ca.v7i4.20548

Abstract

Analysis on incomplete could lead to biased estimation when using standard statistical procedure since it ignores the missing observations. The disadvantage of ignoring missing data is that the researcher might not have enough data to conduct an analysis. The main objective of the study is to compare the performance between listwise deletion (LD), mean substitution (MS) and multiple imputation (MI) method in estimating parameters. The performance will be measured through bias, standard error and 95% confidence interval of interested estimates for handling missing data with 10% missing observations. A complete empirical data set was used and assumed as population data. Ten percent of total observations in the population ere set as missing arbitrarily by generating random numbers from a uniform distribution,  . Then, bias of parameter estimates and confidence interval of parameter estimates are calculated to compare the three methods. A Monte Carlo simulation was carried out to know the properties of missing data and investigated using simulated random numbers. Simulation of 1000 sampled data with 20, 50, and 100 observations and each sample is set to have 10% missing observations. Standard statistical analyses are run for each missing data and get the average of parameter estimates to calculate the bias and standard error of parameter estimates for every missing data method. The analysis was conducted by using SAS version 9.2. It was found that the MI method provided the smallest bias and standard error of parameter estimates and a narrower confidence interval compared to the LD and MS methods Meanwhile, the LD method gives a smaller bias of parameter estimates and standard error for small sample size of missing data. And, MS method is strongly recommended not to use for handling missing data because it will result in large bias and standard error of parameter estimates.
Pemodelan Regresi Logistik Ordinal Backward dengan Imputasi K-Nearest Neighbour pada Indeks Pembangunan Manusia di Indonesia Tahun 2021 Muftih Alwi Aliu; Anwar Fitrianto; Erfiani; Indahwati; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07105

Abstract

The human development index (HDI) is one of the important things to note in Indonesia today. The growth of HDI in Indonesia in 2021 is not evenly distributed in all regencies/cities and has high disparities. This study aims to find out the description of HDI data, get the best model to determine the factors that significantly affect the HDI of regencies/cities in Indonesia in 2021 and identify the classification accuracy results of the best model. The independent variables used in this study are average years of schooling, open unemployment rate, population growth rate, population density, percentage of poor people and sex ratio. The independent variables in this study contained missing values, so they were handled using k-nearest neighbour (KNN) imputation and continued modelling using ordinal logistic regression using the backward elimination technique to obtain significant factors. The results showed that the proportion of the low HDI category was 4.28%, the medium HDI category was 48.64%, and the high HDI category was 47.08%. Based on logistic regression modeling using backward elimination which has the smallest AIC value of 293.387, a model with independent variables of average years of schooling (X1), population density (X4), percentage of poor people (X5) and sex ratio (X6) is a variable that significantly affects the HDI of regencies/cities in Indonesia in 2021. The accuracy value of the classification accuracy of training data and test data from the ordinal logistic regression model of HDI of regencies/cities in Indonesia in 2021 is 83.46% and 86.61%, respectively, which means that the model is good for prediction.
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