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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Ilmiah Kursor BIOTIK: Jurnal Ilmiah Biologi Teknologi dan Kependidikan Jurnal Ilmiah Pangabdhi Journal of Natural Sciences and Mathematics Research Jurnal Ekonomi dan Bisnis Islam Jurnal Pengukuran Psikologi dan Pendidikan Indonesia (JP3I) Jurnal Sains Matematika dan Statistika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Matematika: MANTIK MUST: Journal of Mathematics Education, Science and Technology BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zeta - Math Journal J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Mathvision : Jurnal Matematika Journal of Islamic Monetary Economics and Finance Variance : Journal of Statistics and Its Applications Jurnal Varian Publica: Jurnal Pemikiran Administrasi Negara Jurnal ABDINUS : Jurnal Pengabdian Nusantara G-Tech : Jurnal Teknologi Terapan Inferensi Contemporary Mathematics and Applications (ConMathA) ESTIMASI: Journal of Statistics and Its Application Didaktika: Jurnal Pemikiran Pendidikan JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Journal of Mathematics Education and Science Tensor: Pure and Applied Mathematics Journal Jurnal Jeumpa Biofaal Journal Jurnal Pengabdian kepada Masyarakat Indonesia (JPKMI) Islamiconomic: Jurnal Ekonomi Islam Journal of Health and Nutrition Research Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Journal of Scientific Research, Education, and Technology Journal Focus Action of Research Mathematic (Factor M) Jurnal Statistika dan Komputasi (STATKOM) Jurnal Pengabdian Masyarakat Bangsa Eksponensial Jurnal Agroteknologi Merdeka Pasuruan Limits: Journal of Mathematics and Its Applications
M. Fariz Fadillah Mardianto
Department Of Mathematics, Faculty Of Science And Technology, Universitas Airlangga, Jl. Dr. Ir. H. Soekarno, Mulyorejo, Kec. Mulyorejo, Kota Surabaya, Jawa Timur 60115
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Prediction of Divorce Data in Pamekasan District Based on Comparison of Exponential Smoothing and Moving Average Yudistira, Ira; Romlah, Siti; Yulianto, Tony; faisol, Faisol; Mardianto, M.Fariz Fadillah
Tensor: Pure and Applied Mathematics Journal Vol 5 No 2 (2024): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol5iss2pp67-78

Abstract

Divorce is a form of breakdown in domestic or marital relationships which is characterized by separation. Based on the Indonesian Statistics report, the number of divorce cases in Indonesia will reach 516,334 cases in 2022. This number is up 15.31% compared to the previous year of 447,743 cases. East Java is ranked second as the province with the highest divorce cases, namely 102,065 cases throughout 2022. To know the development of divorce in the future, forecasting is needed to determine when an event will occur, an increase in the divorce rate, so that we can prepare what will be done to overcome the spike. the divorce rate. In this research, the methods used to predict the number of divorce cases in Pamekasan Regency are the Exponential Smoothing and Moving Average methods. single exponential smoothing method for both divorce lawsuits and divorce divorces with MAD values ​​= 10.40539 and 15.3366868, MSE = 449.0276211 and 181.0038, MAPE = 22.1859129 and 23.84152 and SE values ​​= 21.57911661 and 13, 70064 with a value of α=0.12 for contested divorce and α=0.26 for talak divorce.
Analisis Keputusan Hedging pada Bank Non-Syariah di Indonesia Menggunakan Model Regresi Logit Biner Data Panel dengan Efek Acak Koesnadi, Grace Lucyana; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
Jurnal Sains Matematika dan Statistika Vol 11, No 1 (2025): JSMS Januari 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i1.33886

Abstract

Volatilitas pasar global yang semakin tinggi telah menjadi tantangan besar bagi sektor perbankan di Indonesia, khususnya dalam menghadapi fluktuasi nilai tukar rupiah. Dalam mengatasi risiko ini, strategi hedging menjadi langkah penting untuk menjaga stabilitas keuangan. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi keputusan hedging pada bank non-syariah di Indonesia, seperti leverage, likuiditas, profitabilitas, ukuran perusahaan, dan peluang pertumbuhan. Dengan menggunakan regresi logit biner pada data panel dengan efek acak, penelitian ini memanfaatkan data sekunder dari laporan keuangan tahunan bank non-syariah yang terdaftar di Bursa Efek Indonesia (BEI) untuk periode 2020-2022. Hasil analisis menunjukkan bahwa leverage dan ukuran perusahaan memiliki pengaruh signifikan terhadap keputusan hedging, sedangkan likuiditas dan peluang pertumbuhan menunjukkan pengaruh yang bervariasi. Penelitian ini memberikan wawasan penting terkait pengelolaan risiko nilai tukar yang strategis untuk memperkuat stabilitas keuangan sektor perbankan non-syariah di Indonesia, serta mendukung pengambilan keputusan yang lebih akurat dalam mitigasi risiko keuangan.
Analisis Korespondensi Hasil Produksi Budidaya Perikanan Berdasarkan Jenis Budidaya dan Pembagian Wilayah di Indonesia Abdillah, Adrian Wahyu; Marthabakti, CitraWani; Budijono, Gabriella Agnes; Wulandari, Indana Zulfa; Amelia, Dita; Mardianto, M. Fariz Fadillah; Ana, Elly
Jurnal Sains Matematika dan Statistika Vol 11, No 1 (2025): JSMS Januari 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i1.27913

Abstract

Indonesia dikenal sebagai negara maritim karena mayoritas wilayahnya terdiri dari perairan sehingga sektor perikanan menjadi bagian integral dari kehidupan dan ekonomi masyarakat Indonesia. Produk perikanan menjadi salah satu komoditas ekspor utama Indonesia. Adanya perbedaan faktor geografis dan topografis di berbagai wilayah Indonesia berpengaruh terhadap jenis budidaya yang paling cocok pada keberhasilan budidaya perikanan. Oleh karena itu, penelitian menganalisis kecenderungan dari jenis budidaya perikanan dengan wilayah Indonesia secara geografis. Hasil pencatatan dari Produksi Budidaya Perikanan Menurut Provinsi dan Jenis Budidaya pada tahun 2021 digunakan sebagai data sekunder yang akan dianalisis. Pendekatan statikstika yang dipilih yaitu analisis korespondensi dengan jenis budidaya perikanan dan pembagian wilayah Indonesia sebagai variabel analisis. Sebelum dilakukan analisis korespondensi, diperlukan uji independensi yang hasilnya adalah terdapat keterkaitan yang nyata antar kedua variabel. Dari hasil analisis korespondensi diperoleh bahwa jenis budidaya jaring apung tawar, jaring apung laut, tambak intensif, tambak semi intensif, kolam air tenang, kolam air deras, dan minapadi sawah lebih cenderung dikembangkan di wilayah barat. Sedangkan jenis budidaya jaring tancap tawar, tambak sederhana. karamba, dan rumput laut lebih cenderung dikembangkan di wilayah tengah. Dan jenis budidaya laut lainnya lebih cenderung dikembangkan di wilayah timur Indonesia. Dari hasil ini, para pelaku produksi perikanan budidaya dapat menggunakannya sebagai acuan dalam memilih jenis budidaya yang tepat sehingga hasil produksi dapat lebih maksimal.
Sentiment Analysis of Suicide on X Using Support Vector Machine and Naive Bayes Classifier Algorithms Mardianto, M. Fariz Fadillah; Pratama, Bagas Shata; Audilla, Marfa; Pusporani, Elly
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 1 (2025): February 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i1.23742

Abstract

Background: The World Health Organization (WHO) defines health as a state of physical, mental, and social well-being, not just the absence of disease. Mental health, essential for overall well-being, is often neglected, leading to disorders like depression, a major cause of suicide. In Indonesia, suicide cases have surged, with 971 reported from January to October 2023. Objective: This study aims to analyze public sentiment regarding the rise in suicide cases in Indonesia using sentiment analysis methods, specifically Support Vector Machine (SVM) and Naive Bayes Classifier (NBC). The findings are expected to raise public awareness and provide policy recommendations to support mental health initiatives. Methods: One method used to understand public perception regarding the issue of suicide is text mining. This research employs text mining techniques with the Support Vector Machine (SVM) and Naive Bayes Classifier algorithms to analyze public sentiment related to suicide cases in Indonesia. Data was collected from tweets on social media platform X using crawling methods with snscrape and Python, totaling 1,175 tweets. Results: The results indicate that the Linear SVM model achieved higher accuracy than Naive Bayes in classifying tweet sentiments, with an accuracy rate of 80%. Conclusion: The SVM algorithm with a linear kernel achieved 80% accuracy and an identical ROC-AUC score. Word cloud visualization highlighted terms like "kill," "self," "depression," and "stress" as key negative sentiments. This study aims to raise public awareness and support better mental health policies in Indonesia.
Optimizing Brain Tumor MRI Classification with Transfer Learning: A Performance Comparison of Pre-Trained CNN Models Mardianto, M. Fariz Fadillah; Pusporani, Elly; Salsabila, Fatiha Nadia; Nitasari, Alfi Nur; Lu’lu’a, Na’imatul
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.87377

Abstract

This study aims to classify brain MRI images into several types of brain tumors using the Convolutional Neural Network (CNN) approach with transfer learning. This method has the advantage of processing complex images in a shorter time than conventional CNN approaches. In this study, the data used was a public database from Kaggle, which consisted of four categories: glioma, meningioma, no tumor, and pituitary. Before entering the transfer learning process, data augmentation is carried out on the training data. Four pre-trained CNN models were used: VGG19, ResNet50, InceptionV3, and DenseNet121. The four models compared their ability to classify MRI images with several evaluation metrics: accuracy, precision, recall, and F1 score. The results of the performance comparison of the four pre-trained models show that the ResNet50 is the best model, with an accuracy of 98%. Meanwhile, VGG19, DenseNet121, and InceptionV3 produce 97%, 96%, and 95% accuracy, respectively. The ResNet50 architecture demonstrated superior performance in brain tumor classification, achieving 98% accuracy. It can be attributed to its residual learning structure, which efficiently manages complex MRI features.  Further research should concentrate on larger, more diverse datasets and advanced preprocessing techniques to enhance model generalizability.
COMPARISON FORECASTING BETWEEN SINGULAR SPECTRUM ANALYSIS AND LOCAL LINEAR METHOD FOR SHIP ACCIDENT SEARCH AND RESCUE OPERATIONS IN INDONESIA Recylia, Rien; Saifudin, Toha; Chamidah, Nur; Mardianto, M. Fariz Fadillah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1329-1340

Abstract

As a maritime country strategically located along the world's leading transportation routes, Indonesia often faces increased ship accidents. Based on the Basarnas Statistics Book, ship accidents handled by Basarnas from 2021 to 2023 increased by 3%. This condition requires an effective forecasting method to carry out SAR operations to predict ship accidents in the Indonesian region in the future and assess the readiness and needs of Basarnas resources. This study compares the forecasting results obtained using the Singular Spectrum Analysis (SSA) and the Local Linear methods. Both methods do not require parametric assumptions. The data used in this study are divided into training data and test data. This data is secondary data obtained from the Basarnas Statistics Book. The training data in this study is the number of SAR operations from January 2021 to December 2022, while the testing data is from January 2023 to December 2023. From the analysis results, it is known that the method with the smallest MAPE is the Local Linear method with a MAPE of test data of 18.67% (good forecasting category), optimal bandwidth (h) = 4.299, and CV (h) = 231.39 where bandwidth is used to determine the level of smoothness of the estimate, while the CV (h) value is used to select the optimal bandwidth that minimizes the estimation error. At the same time, the SSA method has a MAPE of 40.27% (fair forecasting category). This shows that the Local Linear method provides a more accurate forecast of the number of SAR operations related to ship accidents in Indonesia. This research contributes to the SDGs to make Basarnas an effective and accountable institution and improve the planning and decision-making process in SAR operations through accurate forecasting research is relevant to accurate forecasting.
DETERMINANTS OF INDONESIAN CONVENTIONAL AND ISLAMIC BANK DEPOSITOR TRUST DURING THE COVID-19 PANDEMIC Cahyono, Eko Fajar; Rani, Lina Nugraha; Mardianto, M. Fariz Fadillah
Journal of Islamic Monetary Economics and Finance Vol. 7 (2021): Special issue 1: Islamic Economy and Finance in times of Covid-19 Pandemic
Publisher : Bank Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21098/jimf.v7i1.1352

Abstract

Depositor trust plays an essential role in the banking sector. The main objective of this study is to test several factors that significantly affect depositors’ confidence in conventional and Islamic banks in Indonesia during the COVID-19 pandemic. We conducted qualitative research with a sample of 217 customers who had a minimum of two bank accounts, one conventional, and one Islamic. In a questionnaire, customers were asked their opinions related to indicators of the variables studied, such as depositor trust, and their perceptions of inflation, conventional bank interest, the equivalent yield rate of Islamic banks, and industry perception Productivity Index. The results of the questionnaire were analysed using the partial least squares (PLS) method. The PLS analysis results show that the indicators related to conventional bank interest and the equivalent yield rate of Islamic banks significantly affected depositors’ trust and hands. In other words, customers were influenced when making bank deposits by the factors related to conventional bank interest and the equivalent yield rate of Islamic banks. The external aspect of the industrial production index based on the PLS test had a significant effect on depositors’ trust in both types of bank. In contrast, the external factor of inflation did not significantly affect depositors’ trust in either conventional or Islamic banks. Therefore, based on the PLS-SEM results, conclusions can be drawn regarding the factors influencing depositor trust.
Prediction of Dow Jones Index, US Inflation, and Interest Rate with Kernel Estimator and Vector Error Correction Model Mardianto, M. Fariz Fadillah; Syahzaqi, Idruz; Permana, Made Riyo Ary; Makhbubah, Karina Rubita; Vanisa, Davina Shafa; Afifa, Fitriana Nur
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.28460

Abstract

The Dow Jones Industrial Average (DJIA) is the oldest running U.S. stock market index, established by Dow Jones & Company under Charles Dow. Comprising thirty major publicly traded companies, the DJIA is a key indicator of macroeconomic health, reflecting investor confidence and economic stability. This study applies a quantitative research approach to forecast DJIA stock prices, inflation, and U.S. interest rates using time series analysis. Two forecasting methods are compared: Vector Error Correction Model (VECM) and Kernel regression. VECM, a parametric approach, estimates both short- and long-term relationships among economic variables, while Kernel regression, a nonparametric technique, effectively captures complex, nonlinear relationships without strict model assumptions. The results indicate that the Gaussian Kernel method provides the most accurate predictions, achieving a Mean Absolute Percentage Error (MAPE) of 5.72%. The analysis also shows that despite annual fluctuations, the DJIA has exhibited a steady growth trend from 2009 to 2024, with both its starting and ending prices increasing over time. This research is significant for investors, policymakers, and financial analysts, offering insights into market trends and economic indicators. By providing a reliable forecasting model, it aids in better decision-making regarding stock market investments and economic policies.
A Comparison of Multivariate Adaptive Regression Spline and Spline Nonparametric Regression on Life Expectancy in Indonesia Pratama, Bagas Shata; Suliyanto, Suliyanto; Mardianto, M. Fariz Fadillah; Sediono, Sediono
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.29413

Abstract

Life expectancy is a key indicator of a population’s overall health and well-being. It also reflects the effectiveness of government efforts in improving public welfare. Despite various initiatives by both the government and society to improve life expectancy in Indonesia, significant disparities remain. This quantitative study aims to support these efforts by analyzing factors influencing life expectancy in Indonesia using data from the Indonesian Central Agency of Statistics (BPS) in 2023. A comparative analysis was conducted using two methods: Multivariate Adaptive Regression Spline (MARS) and Spline Nonparametric Regression. The results show that the MARS model outperforms the Spline model, achieving a lower Mean Squared Error (MSE) of 1.183 and a higher R-Square of 82.7%. Key variables significantly influencing life expectancy include access to decent housing, access to safe drinking water, per capita expenditure, and the Gini ratio. The findings not only confirm the presence of complex interactions among predictor variables effectively captured by the MARS method, but also contribute to the existing literature by emphasizing the importance of socioeconomic determinants in health outcomes. From a policy perspective, the results suggest that government strategies should prioritize improving access to basic needs and reducing inequality. These insights can guide targeted, data-driven interventions aimed at enhancing life expectancy in Indonesia.
Application of Support Vector Regression in Time Series Analysis of Dior Stock Prices Sari, Adma Novita; Zuleika, Talitha; Mardianto, M. Fariz Fadillah; Pusporani, Elly
Zeta - Math Journal Vol 10 No 1 (2025): May
Publisher : Universitas Islam Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31102/zeta.2025.10.1.51-60

Abstract

Christian Dior (Dior) is a multinational company focusing on luxury goods, including fashion products, cosmetics, and accessories. In 2020–2024, Dior's share price will experience significant fluctuations influenced by financial performance, global market trends, etc. These fluctuations require investors to implement appropriate strategies to minimize the risk of losses and support sustainable economic growth. This step aligns with goal 8 of the Sustainable Development Goals (SDGs), emphasizing the importance of sustainable economic growth through investment and infrastructure development for economic prosperity. One of the effective methods for modeling and predicting stock prices is Support Vector Regression (SVR). By applying SVR using the Radial Basis Function (RBF) kernel, this study shows that the model can generate predictions with a MAPE value of 2.5864% on the test data. The SVR method is expected to provide accurate predictions, making it a helpful tool for investors and market analysts to make better investment decisions.
Co-Authors Abdilah, Nurullah Asep Abdillah, Adrian Wahyu Adma Novita Sari Adnan Syawal Adilaha Sadikin Adriansyah, Muhammad Haykal Afifa, Fitriana Nur Aflaha, Nabila Shafa Agnes Happy Julianto Agustiansyah, Lucky Dita Ahmad Saddam Hussein Ain, Dzuria Hilma Qurotu Aini Divayanti Arrofah Aldawiyah, Najwa Khoir Aldawiyah, Najwa Khoir Alfredi Yoani Aliffia, Netha Almira Sophie Syamsudin Alya Rahma Inneztiana Amalia, Nadinta Kasih Amalia, Rica Ana, Elly Andi Vania Ghalliyah Putrie Andri Tri Cahyono Andriani, Putu Eka Anggriawan, Muhammad Rizal Anggriawan, Muhammad Rizal Annisa Putri Nayumi Antonio Nikolas Manuel Bonar Simamora Antonio Nikolas Manuel Bonar Simamora Anwari, Anwari Apidianti, Sari Pratiwi Aprilia Prastyaningrum Ardi Kurniawan Ardi Kurniawan Ariyawan, Jovansha Arum Eka Ismiranda Putri Astuti, Aprillia Audilla, Marfa Aulia Ramadhanti Aulia, Niswa Faizah Ayu Safitri Ayuning Dwis Cahyasari Ayuning Dwis Cahyasari Azzah Nazhifa Wina Ramadhani Bimo Okta Syahputra Bintang Alyaa Sabila Br Pangaribuan, Fani Agustina Budijono, Gabriella Agnes Chaerobby Fakhri Fauzaan Purwoko Chairunnisa, Nurul Rizky Christopher Andreas Citra Imama Cynthia Anggelyn Siburian Darmawan, Kezia Eunike Davina Shafa Vanisa Deshinta Arrova Dewi Devayanti Anugerahing Husada Dewanty, Sanda Insania Dewi, Berlianti Alisa Dewi, Deshinta Arrova Disty Ridha Hastuti Dita Amelia Dita Amelia Dita Amelia, Dita Doni Muhammad Fauzi Dwiyanto, Adelia Sukma Dyah Rohma Wati Efan Yudha Winata Eko Fajar Cahyono, David Kaluge Elly Ana Elly Ana Elly Ana Elly Ana Elly Anna Elly Pusporani Elok Zubaidah Eris Tri Kurniawati Erlina Anggraini Erlina Anggraini, Erlina Faisol Faisol Faisol, Faisol Faizun, Nurin Fajar Hidayanto, Fajar Faradilla Harianto Farah Fauziah Putri Farizi, Muhammad Fikry Al Fauzan, Muhammad Hafid Fauzi, Doni Muhammad Febriyani, Eka Riche Fernanda Desmak Pertiwi Firda Aulia Pratiwi Firman Rezaldi Fitri, Marfa Audilla Fitria Eka Resti Wijayanti Fitrianingsih, Eka Rani Fitriyani, Mubadi’ul Fortunata, Regina Ghasani, Anisah Nabilah Girsang, Anne Vinella Grace Lucyana Koesnadi Haq, Affan Fayzul Hari Hariadi, Hari Hasanah, Sarmiatul Helda Urbhani Rosa Hermawan, Mohamad David Humaira, Edla Putri I Kadek Pasek Kusuma Adi Putra I Nyoman Budiantara Immanuel Alexander Sirait Indrasta, Irma Ayu Inneztiana, Alya Rahma Ira Yudistira Isna Nurul Izza Amalia Junaedi, Candra Karina Rubita Makhbubah Karina Tri Handayani Koesnadi, Grace Lucyana Koesnadi, Grace Lucyana Kresna Oktafianto Kurnia, Rizky Dwi Kusuma, Shalwa Oktavia Kusumasari Kartika Hima Darmayanti Kuzairi Leni Halimatusyadiah Lu'lu'a, Na'imatul Lu’lu’a, Na’imatul M. Nabil Saputra Mahadesyawardani, Arinda Makhbubah, Karina Rubita Mamdudah, Siti Marbun, Barnabas Anthony Philbert Marcel Laverda Subiyanto Marcel Laverda Subiyanto Marcelena Vicky Galena Maria Setya Dewanti Maritha, Vevi Marthabakti, CitraWani Maulidya, Utsna Rosalin Meliyawati Meliyawati Mochammad Baihaqi Mochammad Imron Awalludin Muhammad Andry Muhammad Faizal Fathurrohim Muhammad Faizhal Fathurrohim Muhammad Fikry Al Farizi Muhammad Luthfi Muhammad Rizaldy Baihaqi Muhammad Rosyid Ridho Az Zuhro Muhammad Walid Jumlat Mu’jijah Mu’jijah Na'imatul Lu'lu'a Nabila Angel Nafisha Nadia Dwi Marwanda Naufal Ramadhan Al Akhwal Siregar Nauvaldy, Muhammad Netha Aliffia Nitasari, Alfi Nur Noer Azizah Nur Chamidah Nurdin, Nabila Nurfitriyah, Luluk Nurmaulawati, Rina Nurrohmah, Zidni ‘Ilmatun Nurul M’rifatil Laila Nurvadilah, Eva Pambudi, Daffa Satrio Pamungkas, Barolym Tri Panjaitan5, Leni Sartika Permana, Made Riyo Ary Pratama, Bagas Shata Pratama, Fachriza Yosa Pratiwi, Firda Aulia Prayitno Prayitno Pressylia Aluisina Putri Widyangga Previan, Anggara Teguh Purba, Gaby Valenia Rosa Putra, Mochamad Rasyid Aditya Putra, Mochamad Rasyid Aditya Putri Fardha Asa Oktavia Hans Putri Masyita Qomaryah Putri, Asyifa Charmadya Putri, Farah Fauziah Putri, Ferdiana Friska Rahmana Putri, Larisa Mutiara Putrie, Andi Vania Ghalliyah Putu Eka Andriani Rachma Hikmaya Rahmada, Indrastanto Oktodian Rahmi Fadhillah, Fitri Raka Andriawan Ramadhan, Achmad Wahyu Ramadhani, Maulana Syah Putra Ramadhanty, Devira Thania Rani, Lina Nugraha Rasyid, Mochamad Recylia, Rien Reswara, Aqil Azmi Reynaldy Aries Ariyanto Reza Febrian Nugroho Rezaldi, Firman Rica Amalia Rohman, Naylur Romadhoni, M. Suma Firman Romadhoni, Moh Suma Firman Rosyida Widadina Ulya Rosyida Widadina Ulya Sadikin, Adnan Syawal Adilaha Safitri , Endang Safitri, Endang Sahidah, Sahidah Sakinah Priandi Salsabila, Fatiha Nadia Sanda Insania Dewanty Sari, Adma Novita Sa’idah Zahrotul Jannah Sa’idah, Andini Sediono, Sediono Setyaji, Diyan Yunanto Shafira Renianti, Fayza Sholiha, Anisatus Siagian, Kimberly Maserati Sifriyani, Sifriyani Sihite, Rivaldi Sihombing, Abednego Simamora, Antonio Nikolas Manuel Bonar Siregar, Naufal Ramadhan Al Akhwal Siti Maghfirotul Ulyah Siti Maghfrotul Ulyah Siti Romlah Sofia Andika Nur Fajrina Steven Soewignjo Sukardi Sugeng Rahmad Sulaiman, Faizah Jauhar Suliyanto Suliyanto Suliyanto Suliyanto Suryono, Alda Fuadiyah Swastika Oktavia Syahfitri, Nabila Syahzaqi, Idruz Tagawa, Dustin Nathanael Tanjung, Siti Aisiyah Tika Widiastuti Toha Saifudin Tony Yulianto Ucu Wandi Somantri Ukhrowi, Putri Usman Setiawan Vanisa, Davina Shafa Wibawa, Yoga Setya Widyangga, Pressylia Aluisina Putri Wijayanti Wijayanti Wulandari, Indana Zulfa Yenny, Ratna Fitry Yudistira, Ira Yuliana Kolo Yusuf, Bima Sakti Putra Yuwinani, Iin Zah, Alfian Iqbal Zahrani, Vista Vanadya Zalfaa Nur Amalia Zhafirab, Azizah Atsariyyah Zuleika, Talitha Zuleika, Talitha