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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Public Health Science (IJPHS) 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 Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) 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 MUST: Journal of Mathematics Education, Science and Technology 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 JURNAL PENDIDIKAN TAMBUSAI Teorema: Teori dan Riset Matematika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jambura Journal of Mathematics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 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 Journal of Mathematics, Computation and Statistics (JMATHCOS) Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Info Kesehatan

Fitrianto, Anwar

Departemen Statistika, Fakultas MIPA, IPB University, Bogor 16680

Author-ID : 2752567

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Logistic regression model for identifying factors affecting hospitalization of children with pneumonia Fitrianto, Anwar; Wan Muhamad, Wan Zuki Azman
Al-Jabar: Jurnal Pendidikan Matematika Vol 13 No 2 (2022): Al-Jabar: Jurnal Pendidikan Matematika
Publisher : Universitas Islam Raden Intan Lampung, INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ajpm.v13i2.10641

Pneumonia is a lung infection that could happen in babies, children, adults and older people. However, pneumonia in infants and older adults is more serious. Several studies found that infants are more likely to get pneumonia if they live in low-income families. The study aimed to identify factors that cause children to be hospitalized for pneumonia. The binary logistic regression analysis was performed to build a full model regardless of the significance of the variables. The forward selection approach was used to select the significant variables. It was found that the age of the mother, cigarette smoked by the mother during pregnancy, duration (in months) of the children on solid food, and the age when the child had pneumonia with the p-value of 0.0009, 0.0010, 0.0003 and less than 0.0001, respectively. The odds ratio of mother's age, cigarette smoked by mother during pregnancy, how many months the child on solid food, and children’s age when they had pneumonia are 0.69, 6.22, 0.40 and 0.60, respectively.

Statistical integration in excise supervision: Multinomial logistic regression for detecting risk factors of tobacco factory excise violations Riansyah, Boy; Nisa Nur Aisyah; Anwar Fitrianto; Aam Alamudi
Desimal: Jurnal Matematika Vol. 8 No. 2 (2025): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/xxvpmv08

Excise tax serves as a fiscal instrument used by the government to control the consumption of goods with negative externalities for society and the environment, such as tobacco and alcoholic beverages. In Indonesia, tobacco excise remains the largest contributor to national excise revenue, amounting to IDR 216.9 trillion in 2024, or approximately 95.8% of the total. Monitoring violations in the tobacco excise sector is crucial to safeguarding state revenue and ensuring regulatory effectiveness. In the context of classifying violations with more than two categories, multinomial logistic regression is an appropriate statistical method for analyzing the influence of independent variables on the probability of each violation type. This study aims to classify types of excise violations based on internal characteristics of tobacco manufacturers using multinomial logistic regression. The data were obtained from enforcement documentation in 2023 by the Directorate General of Customs and Excise, with four categories of violations serving as the response variable. The issue of class imbalance was addressed by comparing oversampling and weighting techniques. Evaluation results indicate that oversampling produced superior model performance. Partially, variables such as business entity type, asset ownership status, and company age significantly influenced the likelihood of specific violations. Companies with non-permanent asset ownership and complex organizational structures tend to have a higher risk of non-compliance. These findings underscore the importance of implementing risk-based supervision that considers operational profiles as key indicators of potential violations.

KOMPARASI TEKNIK UNDERSAMPLING DAN OVERSAMPLING PADA REGRESI LOGISTIK BINER DALAM MENDUGA FAKTOR DETERMINAN BERHENTI MEROKOK PENDUDUK LANJUT USIA Amelia, Reni; Indahwati; Erfiani; Fitrianto , Anwar; Rizki, Akbar
Jurnal TIMES Vol 10 No 2 (2021): Jurnal TIMES
Publisher : STMIK TIME

Teknik resampling adalah salah satu teknik pre-processing untuk menyeimbangkan distribusi data sehingga mengurangi efek distribusi kelas atau kategori yang tidak seimbang. Teknik resampling yang biasa digunakan adalah random oversampling dan random undersampling. Dalam penelitian ini, random oversampling digunakan untuk menyeimbangkan data dengan cara oversampling secara acak pada kelas minoritas (penduduk lansia yang berhenti merokok). Random undersampling digunakan untuk menyeimbangkan data dengan cara undersampling (mengeliminasi) secara acak kelas mayoritas (penduduk lansia yang masih merokok). Data yang telah diproses dengan resampling selanjutnya dilakukan pemodelan dengan model regresi logistik biner. Model regresi logistik biner dengan random undersampling merupakan model terbaik karena memiliki balanced accuracy terbesar. Peubah yang signifikan memengaruhi berhenti merokok adalah pendidikan, pekerjaan, akses internet, dan usia lansia.

PENDEKATAN GEOGRAPHICALLY WEIGHTED ZERO INFLATED POISSON REGRESSION (GWZIPR) DENGAN PEMBOBOT FIXED BISQUARE KERNEL PADA KASUS DIFTERI DI INDONESIA Ismah, Ismah; Sumertajaya, I Made; Djuraidah, Anik; Fitrianto, Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 1 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheria

COMPARISONS BETWEEN ROBUST REGRESSION APPROACHES IN THE PRESENCE OF OUTLIERS AND HIGH LEVERAGE POINTS Fitrianto, Anwar; Xin, Sim Hui
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

The study aimed to compare a few robust approaches in linear regression in the presence of outlier and high leverage points. Ordinary least square (OLS) estimation of parameters is the most basic approach practiced widely in regression analysis. However, some fundamental assumptions must be fulfilled to provide good parameter estimates for the OLS estimation. The error term in the regression model must be identically and independently comes from a Normal distribution. The failure to fulfill the assumptions will result in a poor estimation of parameters. The violation of assumptions may occur due to the presence of unusual observations (which is known as outliers or high leverage points. Even in the case of only one single extreme value appearing in the set of data, the result of the OLS estimation will be affected. The parameter estimates may become bias and unreliable if the data contains outlier or high leverage point. In order to solve the consequences due to unusual observations, robust regression is suggested to help in reducing the effect of unusual observation to the result of estimation. There are four types of robust regression estimations practiced in this paper: M estimation, LTS estimation, S estimation, and MM estimation, respectively. Comparisons of the result among different types of robust estimator and the classical least square estimator have been carried out. M estimation works well when the data is only contaminated in response variable. But in the case of presence of high leverage point, M estimation cannot perform well.

OUTLIER DETECTION ON HIGH DIMENSIONAL DATA USING MINIMUM VECTOR VARIANCE (MVV) A., Andi Harismahyanti; Indahwati, Indahwati; Fitrianto, Anwar; Erfiani, Erfiani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 3 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

High-dimensional data can occur in actual cases where the variable p is larger than the number of observations n. The problem that often occurs when adding data dimensions indicates that the data points will approach an outlier. Outliers are part of observations that do not follow the data distribution pattern and are located far from the data center. The existence of outliers needs to be detected because it can lead to deviations from the analysis results. One of the methods used to detect outliers is the Mahalanobis distance. To obtain a robust Mahalanobis distance, the Minimum Vector Variance (MVV) method is used. This study will compare the MVV method with the classical Mahalanobis distance method in detecting outliers in non-invasive blood glucose level data, both at p>n and n>p. The test results show that the MVV method is better for n>p. MVV shows more effective results in identifying the minimum data group and outlier data points than the classical method.

OUTLIER IDENTIFICATION ON PENALIZED SPLINE REGRESSION MODELING FOR POVERTY GAP INDEX IN JAVA Fadilah, Anggita Rizky; Fitrianto, Anwar; Sumertajaya, I Made
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Java is one of the islands in Indonesia which has good establishment acceleration. Even though economic growth was good, poverty is still a serious problem. Three of six provinces, including DI Yogyakarta, Central Java, and East Java still have poverty rates above national rates in March 2020. This problem indicates that an imbalance in poverty happens between those regions. Several regions have extreme conditions or known as outliers. Besides that, poverty gap data have a complex pattern so modeling using a non-parametric approach is suitable. This study aims to build an appropriate model to support the success of poverty alleviation in Java and the identification of outliers was carried out using an adjusted boxplot. The best-penalized regression spline model for Poverty Gap Index in Java Island was obtained by Generalized minimum Cross-Validation (GCV) using optimum smoothing parameter (λ) 0,12 and knot combination (1, 2, 4, 1, 5, 3, and 1) for seven predictor variables. The result shows that penalized spline regression model has a higher R2 than OLS regression. The R2 is obtained 69,10%, so the model is feasible to explain the variability of the poverty gap in Java. Moreover, based on the outliers’ identification shows a dependency between outlier in data and residual because some districts/cities are identified as outliers in both.

SUBDISTRICT CLUSTERING IN WEST JAVA PROVINCE BASED ON DISEASE INCIDENCE OF JKN PARTICIPANTS PRIMARY SERVICES Nashir, Husnun; Kurnia, Anang; Fitrianto, Anwar
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

One of the efforts that can be done to optimize health services and the distribution of facilities and infrastructure efficiently in a wide scope is by profiling and clustering areas in the province of West Java to the scope of sub-districts that have similar characteristics of disease category. The methods that will be compared to get the best clustering are hierarchical clustering and ensemble clustering. The data used as the object of research is the BPJS Kesehatan capitation primary service sample data for the 2017-2018 period. Some of the important variables used include: primary disease diagnosis data (ICD-10) of patients at the puskesmas, service time, type of visit, and location of service sub-district. This study uses several evaluation metrics Silhouette coefficient, Dunn index, Davies-Bouldin index, and C-index to determine the optimal number of clusters formed. In addition, descriptive analysis and visualization of the clustering results are also used as considerations in selecting the optimal cluster. Based on the evaluation results, the optimal method is hierarchical clustering with complete linkage. This method produces three clusters: cluster 1 consists of 5 sub-districts that have a high/dominant mean value in almost all disease categories, cluster 2 consists of 26 sub-districts that have a medium mean value, and cluster 3 consists of 589 sub-districts that have a low mean value. Most of the members of clusters 1 and 2 are sub-districts located in the districts/cities around the national capital (DKI Jakarta) and the provincial capital (Bandung) while the members of cluster 3 are mostly sub-districts located in suburban districts/cities or far from the central government.

COMPARATIVE STUDY OF SURVIVAL SUPPORT VECTOR MACHINE AND RANDOM SURVIVAL FOREST IN SURVIVAL DATA Suantari, Ni Gusti Ayu Putu Puteri; Fitrianto, Anwar; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1495-1502

Survival analysis is a statistical procedure in analyzing data with the response variable is time until an event occurs (time-to-event). In the last few years, many classification approaches have been developed in machine learning, but only a few considered the presence of time-to-event variable. Random Survival Forest and Survival Support Vector Machine are machine learning approach which is a nonparametric classification method when dealing with large data and a response variable of survival time. Random Survival Forest is tree based method that using boostrapping algorithm, and Survival Support Vector Machine using hybrid approaches between regression and ranking constrain. The data used in this study is generated data in the form of right-censored survival data. This study uses the RandomForestSRC and SurvivalSVM packages on R software. This study aimed to compare the performance of the Survival Support Vector Machine and Random Survival Forest methods using simulation studies. Simulation results on right-censored survival data using binary predictor variables scenario indicate that the Survival Support Vector Machine (SSVM) method with Radial Basic Function Kernel (RBF Kernel) has the best model performance on data with small volumes, whereas when the data volume becomes larger, the method that has the best performance is Survival Support Vector Machine using Additive Kernel. Meanwhile, Random Survival Forest is a method that has the best performance for all conditions in mixed predictor variables scenario. Method, proportion of censored data and size of data are factors that affect the model performance.

Penentuan Lama Waktu Optimal pada Pengukuran Glukosa Darah Noninvasif Fitrianto, Anwar; Erfiani, Erfiani; Nisa, Rahmatun
JST (Jurnal Sains dan Teknologi) Vol. 11 No. 1 (2022)
Publisher : Universitas Pendidikan Ganesha

Pengukuran kadar glukosa darah menggunakan metode invasif, yaitu melukai bagian tubuh, seperti jari, merupakan metode yang kurang disukai oleh sebagian besar masyarakat. Tujuan penelitian ini untuk mengembangkan teknologi berupa alat pengukur kadar glukosa darah noninvasif. Alat ini menggunakan prinsip kerja spektroskopi inframerah. Oleh karena itu, lama waktu pengukuran menjadi hal yang harus dipertimbangkan. Keoptimalan lama waktu pengukuran diperlukan agar proses pemeriksaan kadar glukosa darah efisien dan bisa merekam seluruh informasi. Tujuan penelitian ini adalah menentukan lama waktu optimal pada alat pengukur kadar glukosa darah noninvasif. Data yang digunakan merupakan data primer hasil pengukuran kadar glukosa darah dari tiga responden. Data tersebut dianalisis menggunakan metode eksplorasi dan regresi linier. Hasil pemodelan dengan persamaan , lama waktu optimal tersebut berada pada waktu perlakuan sebesar 1700 ms dengan menggunakan metode gradien pada kurva. Maka, lama waktu tersebut secara umum dikatakan sebagai waktu yang sangat singkat dalam dalam melakukan pengukuran glukosa dalam darah secara noninfasif.

Co-Authors -, Salsabila A. A., Muftih Aam Alamudi Abd. Rahman Adeline Vinda Septiani Agus M Soleh Agus Mohamad Soleh Ahmad Syauqi Aimandiga, Carlya Agmis Aji Hamim Wigena 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 Amri Luthfi Najih Anadra, Rahmi Anang Kurnia Anang Kurnia Andi Harismahyanti A. Angelia, Riza Rahmah Anik Djuraidah Anisa Nurizki Annissa Nur Fitria Fathina Ardhani, Rizky Arifa, Panji Lokajaya Aristawidya, Rafika Asri Pratiwi Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Az-Zahra, Putri Nisrina 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 Destriana Aulia Rifaldi Devi Permata Sari Devi Permata Sari Dian Handayani Dwi Jumansyah, L.M. Risman Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani M.S Fadilah, Anggita Rizky Fahira, Fani Farit M Affendi Farit M. Afendi Farit Mochamad Afendi Fatimah Fatimah Fauziah, Monica Rahma Febriati, Baiq Nina Fulazzaky, Tahira Ghina Fauziah Gustiara, Dela Hari Wijayanto Hasnataeni, Yunia Hasnita Hasnita Heri Cahyono I Gusti Ngurah Sentana Putra 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 Kamila, Sabrina Adnin Kevin Alifviansyah 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 Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Maisa Salsabila Marshelle, Sean Megawati Megawati Muftih Alwi Aliu Muftih Alwi Aliu Muh Akbar Idris Muh. Sunan Muhadi, Rizqi Annafi Muhammad Farhan Zahid Muhammad Irfan Hanifiandi Kurnia Muhammad Rizky Nurhambali Muhammad Syafiq Muhammad Yusran mutiah, siti Mutmainah, Zamrah Nabila Fida Millati 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 Pika Silvianti Pika Silvianti Pradnya Sri Rahayu Pratiwi, Nafisa Berliana Indah Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Mega Ramatika Rachmat Bintang Yudhianto Rachmat Bintang Yudhianto Rafika Aufa Hasibuan Rahmasari, Hazelita Dwi Rahmatun Nisa, Rahmatun Rais Ramadhan, Syaifullah Yusuf Reka Agustia Astari Reni Amelia Reni Amelia Retna Nurwulan Reyuli Andespa Riansyah, Boy Rifda Nida’ul Labibah Riska Yulianti, Riska Rizaldi Boer Rizki Manaf, Silmi Anisa Rizki, Akbar Rizqi, Tasya Anisah Rupmana Butar Butar Sachnaz Desta Oktarin salsa bila Sari, Jefita Resti 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 Sundari, Marta Tangdilomban, Claudian Tikulimbong Tangke, Nabillah Rahmatiah Tasya Anisah Rizqi Titin Yuniarty Yuniarty Tukhfatur Rizmah Azis Unique DA Resiloy Uswatun Hasanah Utami Dyah Syafitri Utami, Annisa Putri Utomo, Agung Tri Vitona, Desi Vivin Nur Aziza Wahda, Aisya Wina 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 Widyawati, Amalia Safira Winata, Hilma Mutiara Xin, Sim Hui Yenni Angraini Yudhianto, Rachmat Bintang Yuniarsyih R.A, Rizqi Dwi Yusuf, Fajar Athallah Zaenal, Mohamad Solehudin Zahra, Latifah Zein Rizky Santoso

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