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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATEMATIKA JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ilmu Sosial dan Humaniora CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Matematika dan Sains Journal of Information Systems Engineering and Business Intelligence Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Syntax Literate: Jurnal Ilmiah Indonesia MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Cakrawala: Jurnal Litbang Kebijakan PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Aplikasi Statistika & Komputasi Statistik JP2M (Jurnal Pendidikan dan Pembelajaran Matematika) G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Jurnal Layanan Masyarakat (Journal of Public Service) Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Economic Development Analysis Journal Jurnal Keselamatan Transportasi Jalan Limits: Journal of Mathematics and Its Applications
Toha Saifudin
Departement Of Mathematics, Faculty Of Science And Technology, Airlangga University
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PERBANDINGAN PENDEKATAN DATA PANEL UNIVARIAT DAN PANEL SUR DALAM PEMODELAN STUNTING, WASTING, DAN UNDERWEIGHT DI INDONESIA Teguh Susanto; Toha Saifudin; Nur Chamidah
Seminar Nasional Hasil Riset dan Pengabdian Vol. 7 (2025): Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Ke 7 Tahun 2025
Publisher : LPPM Universitas PGRI Adi Buana

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Abstract

Indonesia berkomitmen untuk mewujudkan Sustainable Development Goals khususnya Zero Hunger 2030. Penelitian ini bertujuan untuk mengevaluasi efisiensi komparatif dan konsistensi struktural antara model regresi data panel univariat dengan model multivariat Panel Seemingly Unrelated Regression dalam memodelkan kasus stunting, wasting, dan underweight pada periode 2007–2023 di Indonesia. Pemilihan model Panel SUR didasarkan pada hasil uji diagnostik yang menunjukkan adanya korelasi signifikan antar error term pada ketiga persamaan (p < 0,001). Metode estimasi yang digunakan adalah FGLS dua arah. Hasil penelitian menunjukkan bahwa model univariat menghasilkan anomali tanda koefisien, di mana variabel berat badan lahir rendah (BBLR) berhubungan negatif dengan wasting, yang bertentangan dengan teori biologis. Sebaliknya, model Panel SUR melalui estimasi simultan berhasil memperbaiki arah hubungan tersebut menjadi positif dan meningkatkan nilai koefisien determinasi (R²) pada persamaan wasting secara signifikan. Selain itu, hasil evaluasi efisiensi berdasarkan Mean Square Error (MSE) menunjukkan bahwa model Panel SUR memberikan estimasi yang lebih efisien (MSE lebih rendah dibandingkan model univariat). Secara keseluruhan, temuan ini menunjukkan bahwa model Panel SUR lebih tepat digunakan untuk analisis sistem malnutrisi karena menawarkan konsistensi parameter yang lebih baik dan efisiensi statistik yang lebih tinggi, sehingga memberikan dasar yang lebih kuat bagi perumusan kebijakan gizi terpadu di Indonesia.
Pemodelan Kasus Tuberkulosis di Jawa Tengah dengan Geographically Weighted Negative Binomial Regression Andini Putri Mediani; Toha Saifudin; Nur Chamidah
Limits: Journal of Mathematics and Its Applications Vol. 21 No. 3 (2024): Limits: Journal of Mathematics and Its Applications Volume 21 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Tuberkulosis (TB) dianggap sebagai permasalahan kesehatan global yang utama karena menjadi salah satu penyakit menular yang mematikan di seluruh dunia. World Health Organization (WHO) mengategorikan sebanyak 30 negara di dunia dengan beban tinggi kasus TB dengan Negara Indonesia menempati peringkat kedua dalam kategori beban tinggi tersebut. Salah satu provinsi dengan penderita terbanyak kasus TB adalah Provinsi Jawa Tengah. Banyaknya penderita TB di Kabupaten Jawa Tengah menunjukkan bahwa terdapat faktor-faktor yang memengaruhi tingginya kasus TB, sehingga perlu dilakukan analisis secara statistik untuk mengetahui penyebab terjadinya permasalahan tersebut sekaligus mendukung tercapainya target yang berkaitan dengan target SDGs pada poin 3.3, yaitu untuk mengakhiri epidemi TB. Pada jumlah kasus TB yang berupa data diskrit, regresi Poisson merupakan metode yang sesuai untuk memodelkan data diskrit dengan asumsi ekuidispersi yang harus terpenuhi. Namun, untuk kasus TB di Jawa Tengah asumsi tersebut tidak terpenuhi, dengan kata lain terdapat overdispersi. Overdispersi dapat ditangani dengan regresi Binomial Negatif, tetapi dengan mempertimbangkan faktor spasial metode yang sesuai untuk digunakan adalah Geographically Weighted Negative Binomial Regression (GWNBR). Hasil diperoleh fungsi pembobot untuk GWNBR adalah Fixed Gaussian dengan nilai CV terkecil 4427790. Pemodelan dengan GWNBR lebih baik dalam memodelkan jika dibandingkan dengan regresi global. Hal ini diperkuat oleh nilai AIC terkecil, yakni 370,14 sehingga permasalahan overdispersi sudah teratasi. Kemudian, variabel yang berpengaruh signifikan pada setiap kabupaten dan kota di Jawa Tengah adalah persentase rumah tangga yang memiliki sumber air minum layak, jumlah tenaga kesehatan, rasio jenis kelamin, dan jumlah penduduk usia produktif dengan besar pengaruh yang berbedabeda.
Prediksi Jumlah Penumpang Kereta Api Stasiun Surabaya Gubeng dengan Metode Monte Carlo Angga Kusuma Bayu Viargo; Toha Saifudin; Nur Chamidah
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 3 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 3 Edisi No
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Jumlah penumpang kereta api di Indonesia kembali mengalami peningkatan semenjak masa pandemi. Salah satu stasiun yang mengalami peningkatan penumpang adalah Stasiun Surabaya Gubeng. Penelitian ini bertujuan untuk mendapatkan hasil prediksi jumlah penumpang harian kereta api di Stasiun Surabaya Gubeng menggunakan metode Monte Carlo dengan pembangkit bilangan acak yang berbeda. Metode Monte Carlo merupakan metode yang menginterpretasikan hasil ketidakpastian probabilitas dari suatu proses dan menyimulasikan nilai frekuensi secara stokastik dari segala kemungkinan hasil. Pembangkit bilangan acak yang digunakan yaitu; multiplicative, mixed, dan random uniform . Tingkat keakuratan dari hasil penelitian dihitung berdasarkan nilai Mean Absolute Percentage Error (MAPE). Data dalam penelitian ini merupakan data time series diambil dari tanggal 16 Mei 2022 hingga 2 Oktober 2022 sebanyak 140 hari. Data dibagi menjadi tujuh kelompok berdasarkan nama hari sebanyak 20 data untuk setiap kelompok. Prediksi dilakukan menggunakan Monte Carlo diperoleh rata-rata nilai MAPE outsample dari setiap kelompok hari yaitu; hari Senin sebesar 25,25%, hari Selasa sebesar 16,74%, hari Rabu sebesar 17,73%, hari Kamis sebesar 3,32%, hari Jumat sebesar 12,36%, hari Sabtu sebesar 4,88%, dan hari Minggu sebesar 2,62%. Kesimpulan akhir diperoleh bahwa hasil prediksi sangat akurat terjadi pada hari Kamis, Sabtu dan Minggu.
SPATIAL MODELING OF CHILD MALNUTRITION IN INDONESIA USING GEOGRAPHICALLY WEIGHTED MULTIVARIATE REGRESSION (GWMR) Teguh Susanto; Toha Saifudin; Nur Chamidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp1837-1854

Abstract

In Indonesia aspires to become a developed nation by 2045, with one of its key pillars being the improvement of human resource quality through the achievement of Sustainable Development Goal (SDG) 2: ending hunger and ensuring access to adequate nutrition. However, the prevalence of stunting, wasting, and underweight among children under five remains a critical challenge that hampers these efforts. This study aims to simultaneously analyze the determinants influencing these three forms of malnutrition among Indonesian children by incorporating spatial aspects through the Geographically Weighted Multivariate Regression (GWMR) approach. The analysis employs nine predictor variables representing socioeconomic, demographic, and environmental factors across all provinces in Indonesia. The findings reveal that Complete Basic Immunization, Knowledge of Stunting Prevention, and Lower-Middle Economic Status consistently have significant effects on stunting and underweight. Meanwhile, Complete Basic Immunization and Complementary Feeding Practices play major roles in influencing wasting across provinces.Spatial analysis highlights varying patterns of determinants across regions. Western Indonesia (Java, Sumatra, and western Kalimantan) is more influenced by community behavior (mothers without a MCH Book,Children receiving complete basic immunizations receiving and children recheived complementary feeding), access to adequate sanitation, and lower-middle economic status. In contrast, Eastern Indonesia (Maluku and Papua) is more affected by structural conditions such as preterm births, low immunization coverage, knowledge of stunting prevention, and economic limitations. Central Indonesia demonstrates a more complex and varied combination of influencing factors. Furthermore, the GWMR model exhibits substantially better performance compared to the global (multivariate linear regression) model, as indicated by a significantly lower AIC value (Global AIC = 287.537; GWMR AIC = 44.956). These findings underscore the importance of spatially adaptive and decentralized nutrition policies to ensure more targeted and context-specific interventions.
Advanced inferential statistics and data mining for chlorophyll distribution clustering Felix Reba; Toha Saifudin; Rimuljo Hendradi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2081-2091

Abstract

This study proposes an integrated statistical framework to analyze chlorophyll distribution in marine environments by combining probability distribution modeling, goodness-of-fit (GoF) evaluation, and machine learning-based clustering. Eight probability distribution models—half normal, inverse Gaussian, Rician, Birnbaum–Saunders, Nakagami, extreme value, t location-scale, and stable—were evaluated using observational chlorophyll-a data from the Copernicus Marine Service. Model performance was assessed through the Kolmogorov–Smirnov (KS) and Anderson Darling (AD) GoF tests, along with five statistical information criteria. The results indicate that the inverse Gaussian and extreme value distributions consistently offered the best statistical fit and ecological relevance across varying sample sizes. Clustering analysis, performed using the k-means algorithm and validated via the silhouette index, further confirmed the robustness of these two models in forming stable and well-separated clusters. In contrast, the half-normal distribution showed poor performance and instability, especially with smaller sample sizes. The proposed taxonomy and spatial visualizations enable empirical classification of model behavior and support integration into real-time marine decision support systems (DSS) for ecosystem monitoring. Overall, the study contributes to the development of accurate, data-driven analytical tools that aid sustainable marine resource management, aligned with sustainable development goal (SDG) 14 on marine ecosystem protection.
TRAINING ON EARLY STUNTING DETECTION USING WEB AND R-SHINY APPLICATIONS FOR COMMUNITY HEALTH WORKERS (POSYANDU) IN THE SONGGON COMMUNITY HEALTH CENTER CATCHMENT AREA, BANYUWANGI REGENCY Nur Chamidah; Ardi Kurniawan; Toha Saifudin; Raaulia Gita Nafsi; Mia Khoirunnisa; Fa’iqotus Zuqna Dwi Syauqie; Dwika Maya Harsanti; Verina Tita Nabila; Naufal Ramadhan Al Akhwal Siregar
Jurnal Layanan Masyarakat (Journal of Public Services) Vol. 10 No. 1 (2026): JURNAL LAYANAN MASYARAKAT
Publisher : Universitas Airlangga

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Abstract

Stunting is a condition that reflects the nutritional status of toddlers and serves as a crucial indicator for monitoring their growth and development. The prevalence of stunting in East Java Province was recorded at 19.2% in 2022, indicating that the province still faces serious challenges requiring sustained intervention. However, monitoring efforts at the local level, particularly within the Songgon Public Health Center (Puskesmas) working area, still encounter technical obstacles such as inconsistent and inaccurate nutritional data recording systems, which risk compromising the validity of early detection. To address these issues, this community service activity aimed to equip Posyandu cadres with nutritional knowledge and technical skills in utilizing a Web-based and R-Shiny early detection application. The application allows users to input toddler anthropometric data (Weight-for-Age, Height-for-Age, and BMI-for-Age) and automatically generates growth charts based on reference standards. It also integrates National Identification Number (NIK) inputs to ensure data validity and prevent duplication. The activity was conducted on August 2, 2025, involving 49 cadres from the Songgon Health Center working area. Evaluation results showed a significant increase in competence, marked by a higher average post-test score (90.204) compared to the pre-test score (77.007), with a paired t-test p-value of 0.000. Participants' satisfaction levels were also categorized as excellent, with average scores exceeding 85 across all indicators. Through intensive mentoring and an accurate local data-driven approach, this program is expected to serve as an adaptive, modern community service model that can be replicated to accelerate stunting reduction.
Nonlinear Ordinal Logistic Regression and Multivariate Adaptive Regression Splines (NORL-MARS) for Prediction of Diabetes Mellitus Risk Any Tsalasatul Fitriyah; Maylita Hasyim; Nur Chamidah; Toha Saifudin; Vita Fibriyani
ZERO: Jurnal Sains, Matematika dan Terapan Vol 10, No 1 (2026): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v10i1.28733

Abstract

Diabetes Mellitus (DM) is a high-risk metabolic disease with increasing prevalence in Indonesia, requiring an effective classification model based on significant risk factors. This study uses Nonparametric Ordinal Logistic Regression based on the Multivariate Adaptive Regression Spline estimator (NOLR-MARS). Unlike conventional parametric ordinal regression, this model does not assume a fixed functional pattern but rather determines the form of the relationship based on data patterns through basis functions, making it more flexible in handling complex predictor variable interactions. Using 664 records from the Non-Alcoholic Fatty Liver Disease (NAFLD) cohort, we explore the relationship between metabolic factors, included age, sex, Body Mass Index (BMI), LDL cholesterol, and hypertension—and DM risk. This NOLR-MARS integration addresses the nonlinear relationship while maintaining the ordinal nature of DM stages, a combination often overlooked in traditional models. Based on Generalized Cross Validation (GCV) selection, the best model achieved 74.92% accuracy for in-sample data and 80.30% for out-sample data. Furthermore, a sensitivity of 70% and a specificity of 92.86% were obtained for stage 2 DM. Factors such as age, BMI, LDL cholesterol, and hypertension significantly influenced DM status. The results showed that the NORL-MARS model had good predictive performance. The novelty of this study lies in the integration of the MARS estimator into an ordinal logistic regression framework for more granular DM risk assessment. Although this model shows potential as a screening tool in high-risk metabolic cohorts, further clinical application requires external validation to ensure broader generalizability.
Bayes estimation of a two-parameter exponential distribution and its implementation Ardi Kurniawan; Johanna Tania Victory; Toha Saifudin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 6: December 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i6.26015

Abstract

Life test data analysis is a statistical method used to analyze time data until a certain event occurs. If the life test data is produced after the experiment has been running for a set amount of time, the life time data may be type I censored data. When conducting observations for survival analysis, it is anticipated that the data would conform to a specific probability distribution. Meanwhile, to determine the characteristics of a population, parameter estimation is carried out. The purpose of this study is to use the linear exponential loss function method to derive parameter estimators from the exponential distribution of two parameters on type I censored data. The prior distribution used is a non-informative prior with the determination technique using the Jeffrey’s method. Based on the research results that have been obtained, application is carried out on real data. This data is data on the length of time employees have worked before they experienced attrition with a censorship limit based on age, namely 58 years, obtained from the Kaggle.com website. Based on the estimation results, the average length of work for employees is 6.29427 years. This shows that employees tend to experience attrition after working for a relatively long period of time.
Local Polynomial Estimator in The Nonparametric Model of Inflation in Indonesia Abdul Aziz; Nur Chamidah; Toha Saifudin
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.27625

Abstract

Inflation is a general and continuous increase in prices of goods and services over a certain period.  Nonparametric regression analysis can be used to model inflation data that does not form a particular pattern. This study applies a local polynomial nonparametric method to model the rate of change rate in the inflation over a period considering two factors influencing inflation: the rate of change in the BI interest rate and the rate of change rate in the money supply from the previous period. The bivariate local polynomial method estimates the nonparametric regression function by considering the optimum Gaussian kernel bandwidth and polynomial order using the Taylor series expansion and WLS estimator. The optimal local polynomial nonparametric regression model was obtained based on a minimum GCV value of  0.015108 with two optimum Gaussian kernel bandwidth values of 0.1 and 0.03 in polynomial order of 1. The best model had a MAPE value of 3.45%, showing that all the prediction models were highly accurate. The benefits gained are additional information and consideration for determining monetary policy, especially inflation in Indonesia, by determining the BI interest rate and money supply.
ANALYSIS OF FACTORS AFFECTING PNEUMONIA IN INDONESIAN TODDLERS USING NONPARAMETRIC REGRESSION WITH LEAST SQUARE SPLINE AND FOURIER SERIES METHODS Toha Saifudin; Suliyanto Suliyanto; Nabila Nurdin; Bryan Given Christiano Ginzel; Sabrina Salsa Oktavia; Jovansha Ariyawan; Mohammad Noufal Ubadah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0087-0104

Abstract

Pneumonia is the leading cause of death among children under five, with the highest prevalence in Indonesia found in West Papua Province (75%) and the lowest in North Sulawesi (0.3%). This study aims to analyze the factors influencing the prevalence of pneumonia in Indonesian toddlers using nonparametric regression approach by comparing Least Square Spline (LS-Spline) and Fourier Series. Data sourced from the Indonesian Ministry of Health website, consisting of 34 provinces in Indonesia in 2023, with one response variable (Y) and five predictor variables (X). The analyzed factors include the coverage of vitamin A supplementation, malnutrition rates, low birth weight prevalence, measles immunization coverage, and exclusive breastfeeding rates. The analysis was conducted by modeling with nonparametric Least Square Spline regression using up to three optimal knot points, then performing analysis using nonparametric regression with the Fourier series approach. The two methods were compared based on GCV and R², with the best model having lower GCV and higher R². The results showed that LS-Spline was better than Fourier Series, with a GCV value of 233.16 and a coefficient of determination of 92.5%. The findings reveal that the relationships between predictor factors and pneumonia prevalence are nonlinear, with varying influence patterns across different variable ranges. These results indicate that LS-Spline has a strong ability to explain data variability. The Fourier series is limited in this study because it is best suited for periodic data, unlike pneumonia data and its causal factors which do not show such patterns. The weakness of the Fourier Series in this study lies in its suitability for periodic data, while pneumonia cases and their causal factors do not follow such patterns. This study offers insights into health policy making to reduce pneumonia cases, improve their lives, in line with the SDGs target on Good Health and Well-being.
Co-Authors Abdul Aziz Aditya Syarifudin Akbar Aflaha, Nabila Shafa Aini Divayanti Arrofah Aisharezka, Mutiara Aisyah, Arlisya Shafwan Al Hasri, Ilham Maulana Alfi Nur Nitasari Alfredi Yoani Alpandi, Gaos Tipki Ameliatul 'Iffah Ana, Elly Andini Putri Mediani Angga Kusuma Bayu Viargo Angga Kusuma Bayu Viargo Aniq Atiqi Any Tsalasatul Fitriyah Ardi Kurniawan Ardi Kurniawan Ariani, Fildzah Tri Januar Aulia, Niswa Faizah Auliyah, Nina Ayuning Dwis Cahyasari Azis, Aurelia Islami Azizah, Khansa Belindha Ayu Ardhani Bryan Given Christiano Ginzel Chaerobby Fakhri Fauzaan Purwoko Christopher Andreas Dewanti, Maria Setya Dewanty, Sanda Insania Diah Puspita Ningrum Dita Amelia Dita Amelia Dita Amelia, Dita Doni Muhammad Fauzi Dwika Maya Harsanti Easyfa Wieldyanisa, Ezha Elly Pusporani Erfiana Erfiana Fachriza Yosa Pratama Faiza, Atikah Fajrina, Sofia Falasifah, Sabrina Fatmawati Fatmawati Fauziah, Nathania Fa’iqotus Zuqna Dwi Syauqie Felix Reba Fina Insyiroh Firmansyah, Mochamad FIRMANSYAH, MOCHAMMAD Fitriana Nur Afifa Fitriani, Mubadi'ul Fortunata, Regina Gaos Tipki Alpandi Gaos Tipki Alpandi Hardiansyah, Fernanda Rizky Hasyim, Maylita Herdianto, Muhammad Hendra Ika Purnamasari Ilma Amira Rahmayanti Indrasta, Irma Ayu Insania Dewanty, Sanda Isryad Yoga Adyatma Johanna Tania Victory Jovansha Ariyawan Khairian, Farhan Aldan Kholidiyah, Azizatul Leni Sartika Panjaitan Lensa Rosdiana Safitri M. Fariz Fadillah Mardianto Maelcardino Christopher Justin Mahadesyawardani, Arinda Maharani, Prima Makhbubah, Karina Rubita Marisa Rifada Marpaung, Josua Ronaldo Davico Marshanda Aprilia Marwanda, Nadia Dwi Mediani, Andini Putri Mia Khoirunnisa Mochamad Firmansyah Mochamad Rasyid Aditya Putra Mohammad Noufal Ubadah Muhammad Rosyid Ridho Az Zuhro Mutiara Aisharezka Nabila Nurdin Nahar, Muhammad Hafidzuddin Naufal Ramadhan Al Akhwal Siregar Naura, Sheila Sevira Asteriska Novianti, Dita Aris Nugraha, Galuh Cahya Nur Chamidah Nur Chamidah Nur chamnidah Nur Rahmah Miftakhul Jannah Nurrohmah, Zidni 'Ilmatun Panjaitan, Leni Sartika Puspasari, Laili Raaulia Gita Nafsi Rahayu, Rizky Dwi Kurnia Ramadhani, Azzah Nazhifa Wina Ramadhanty, Devira Thania Ramadhina, Fidela Sahda Ilona Recylia, Rien Rimuljo Hendradi Risky Wahyuningsih Sa'idah, Andini Sabrina Salsa Oktavia Safitri, Lensa Rosdiana Salma Bethari Andjani Sumarto Salsabila, Fatiha Nadia Sa’idah Zahrotul Jannah Sediono, Sediono Sentosa, Martha Ayu Setyawan, Muhammad Daffa Bintang Shalwa Oktavrilia Kusuma Siagian, Kimberly Maserati Siti Maghfirotul Ulyah Sri Wahyuningsih Sugha Faiz Al Maula Suliyanto Suliyanto Suliyanto Syaugi Sungkar, Salman Teguh Susanto Teguh Susanto Tiani Wahyu Utami Titania Faisha Purnama Trisa, Nadya Lovita Hana Valida, Hanny Verina Tita Nabila Victory, Johanna Tania VITA FIBRIYANI Wahyuli, Diana Widyawati, Ayu Wieldyanisa, Ezha Easyfa Wulandari, Indana Zulfa Yan Dwi