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All Journal International Journal of Public Health Science (IJPHS) Jurnal Ilmu Pertanian Indonesia Jurnal Ekonomi Pembangunan EKSAKTA: Journal of Sciences and Data Analysis JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen Jurnal TIMES Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika 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 Journal of Applied Food Technology 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 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 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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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.

Application of Propensity Score Matching for Analyzing Factors Contributing to Pre-Diabetes Putri, Oktaviani Aisyah; Fitrianto, Anwar; Alamudi, Aam
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): 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/cauchy.v10i2.32754

Inappropriate comparisons between control and treatment groups can be caused by overlapping factors, usually called confounders. Propensity score methods help reduce bias from measured confounding by summarizing the distribution of multiple measured confounders into a single score, based on the probability of receiving treatment. This study applies binary logistic regression to estimate propensity scores and identify risk factors that significantly influence complications in fasting blood glucose levels. Nearest Neighbor Matching (NNM) is used with various caliper and score orders to determine the most effective combination in reducing bias. The results show that gender becomes a confounding variable. Both the order of propensity scores and caliper selection affect the outcome of the matching process. Matching with a random order and caliper yields the best result, with 99,93 percent reduction bias. The significance of the average treatment effect for treated (ATT), all condition order with caliper indicates that gender have a positive relationship and significantly affects fasting blood glucose levels. Also, based on the matching results with the best combination, it indicates that age, academic position, structural position, education level, and lecturer performance do not influence abnormal fasting blood sugar (FBS).

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.

Analisis Pola Konvergensi Transpor Kelembapan Udara di Indonesia Bagian Barat Menggunakan K-Means dengan Pembobotan Statistik dan Hierarchical Shape-Based Clustering Pratiwi, Asri; Azis, Tukhfatur Rizmah; Fitrianto, Anwar; Erfiani, Erfiani; Jumansyah, L.M. Risman Dwi
KUBIK Vol 9 No 2 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

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

This study analyzes the convergence patterns of Vertically Integrated Moisture Transport (VIMT) in the western region of Indonesia using the K-Means method with statistical weighting and Hierarchical Shape-Based Clustering based on Dynamic Time Warping (DTW). Daily data on specific humidity, zonal wind speed, and meridional wind speed from 2020â€“2023 were used to calculate VIMT. Clustering methods were utilized to identify grouping patterns in moisture transport data. The results showed that moisture convergence significantly increased during the rainy season (Novemberâ€“February). Using the K-Means method, five clusters with clearer separations were obtained compared to the four clusters produced by the Hierarchical Clustering method. Performance evaluation using Silhouette and Calinski-Harabasz scores indicated that the K-Means method was superior, with scores of 0.37 and 104.88 compared to 0.13 and 96.34 for the Hierarchical method. This provides an understanding of the moisture transport patterns, serving as a reference for predicting weather and climate patterns, thereby supporting efforts to mitigate the impacts of extreme weather in Western Indonesia.

Image Classification of Rice Leaf Diseases with KNN Based Model using Stratified-KCV Rizqi, Tasya Anisah; Anwar Fitrianto; Kusman Sadik
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6590

Rice is a staple food for people in the world, especially Indonesia. The rice harvest decreased in 2023, reducing harvest productivity and causing losses for farmers. Rice cultivation is often affected by diseases that hinder rice harvests. SKCV is a resampling method that performs more accurately because it can ensure that class frequencies are maintained. RGB and VGG16 are image processing methods that extract images into numerics. RGB image extraction is done by taking the average value of the red, green, and blue layers while VGG16 image extraction is done by taking the value of visual pattern features such as edges, textures, and object shapes. In this study, rice leaf diseases were classified using KNN-based models, including KNN, WKNN, CDNN, and ECDNN. This classification was performed to determine which method had better performance using SKCV and comparing the results of RGB and VGG16 image extraction. This classification also produces a comparison of SKCV and KCV results to determine the best resampling performance. The results of the analysis that have been carried out show that the ECDNN method produces the highest accuracy of 81.20% in classifying rice leaf diseases using SKCV with VGG16 extraction followed by CDNN and WKNN each at 68.80%, and KNN at 56.20% while RGB extraction only produces an accuracy of 43.8% using ECDNN and CDNN, 56.20% using WKNN, and 50% using KNN. The results of this rice leaf diseases classification analysis are expected to help farmers in increasing rice production in Indonesia.

Optimasi Hyperparameter Model Klasifikasi Citra untuk Daging Sapi dan Babi Menggunakan Convolutional Neural Networks -, Salsabila; Anwar Fitrianto; Bagus Sartono
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 6, ISSUE 2, October 2025
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol6.iss2.art6

Deep learning classification network in one case, has different classification capabilities than the network in another. The classification method of deep learning using CNN has specific hyperparameters that can be adjusted to have good performance. These hyperparameters include the number of convolutional layers, the number of neurons in the convolutional and fully connected layers, kernel size, and activation functions. Deep Learning uses experimental principles in finding the best hyperparameter in various cases. The model architecture can be determined by choosing a different design. This research uses pork and beef images as the data for classification using CNN. The abstract textures of beef and pork may make it difficult for the CNN classification model to distinguish between them. Hence, 32 combinations of five hyperparameters were compared. It was found that these hyperparameters affect the model's performance. The best model has obtained 98,7% accuracy that uses 20 neurons both layers of the convolution was, kernel size of 5 × 5, ReLU activation function, and two fully connected layers with dropout 0.7 as a method of overfitting prevention. A significant difference also occurs in the application of the activation function, in which ReLU has a better performance than tanh function to increase the model's prediction.

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 Alfiryal, Naufalia Alifviansyah, Kevin Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amalia Kholifatunnisa Amanda, Nabila Amatullah, Fida Fariha Amelia, Reni Amir Abduljabbar Dalimunthe Anadra, Rahmi Anang Kurnia Anang Kurnia Angelia, Riza Rahmah Anik Djuraidah Anisa Nurizki Annissa Nur Fitria Fathina Ardhani, Rizky Arifa, Panji Lokajaya Aristawidya, Rafika Askari, M. Aiman Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Azis, Tukhfatur Rizmah Aziza, Vivin Nur Bagus Sartono Budi Susetyo Bukhari, Ari Shobri Cahya Alkahfi Choon, Lai Ming Daswati, Oktaviyani Defri Ramadhan Ismana Deri Siswara Dessy Rotua Natalina Siahaan Dessy Siahaan Devi Permata Sari Dian Handayani Dwi Jumansyah, L.M. Risman Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani Fadilah, Anggita Rizky Fahira, Fani Farit M Affendi Farit M. Afendi Farit M. Afendi Farit Mochamad Afendi Fatimah Fatimah Fauziah, Monica Rahma Febriati, Baiq Nina Fulazzaky, Tahira Ghina Fauziah Gustiara, Dela Hari Wijayanto Harismahyanti A., Andi Hasnataeni, Yunia Hasnita Hasnita Heri Cahyono I Made Sumertajaya Ilham Azagi Ilmani, Erdanisa Aghnia Imam Hanafi Indah, Yunna Mentari Indahwati Indahwati Indahwati Indahwati, Indahwati Irsyifa Mayzela Afnan Irzaman, Irzaman Ismah, Ismah Isna Shofia Mubarokah Iswan Achlan Setiawan Iswati Ita Wulandari Jamaluddin Rabbani Harahap Jap Ee Jia Jia, Jap Ee Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Kamila, Sabrina Adnin Kapiluka, Kristuisno Martsuyanto Kevin Alifviansyah Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnia N. K. Khusnia Nurul Khikmah Kriswan, Suliana Kusman Sadik L.M. Risman Dwi Jumansyah La Ode Abdul Rahman La Ode Abdul Rahman Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Maisa Salsabila Manaf, Silmi Annisa Rizki Marshelle, Sean Megawati Megawati Muftih Alwi Aliu Muftih Alwi Aliu Muh Akbar Idris Muh. Sunan Muhadi, Rizqi Annafi Muhammad Irfan Hanifiandi Kurnia Muhammad Syafiq Muhammad Yusran mutiah, siti 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 Pradnya Sri Rahayu Prasetya, I Putu Gde Inov Bagus Pratiwi, Nafisa Berliana Indah Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Mega Ramatika Putri, Oktaviani Aisyah 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 Sachnaz Desta Oktarin salsa bila Sari, Jefita Resti Seta Baehera Setyowati, Silfiana Lis Siau Hui Mah Siau Man Mah Silmi Annisa Rizki Manaf Siregar, Indra Rivaldi Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Sofia Octaviana Sony Hartono Wijaya Suantari, Ni Gusti Ayu Putu Puteri Suliana Kriswan Sundari, Marta Tangke, Nabillah Rahmatiah Titin Agustina Titin Yuniarty Yuniarty 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 Wawan Saputra 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 Zahid, Muhammad Farhan Zahra, Latifah Zein Rizky Santoso

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