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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Karya Pendidikan Matematika Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Fourier Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika Jurnal Ilmiah Pendidikan dan Pembelajaran B-Dent, Jurnal Kedokteran Gigi Universitas Baiturrahmah EIGEN MATHEMATICS JOURNAL Variance : Journal of Statistics and Its Applications Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Square : Journal of Mathematics and Mathematics Education Community Development Journal: Jurnal Pengabdian Masyarakat ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Soeropati: Journal of Community Service Journal of Intelligent Computing and Health Informatics (JICHI) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS LOSARI: Jurnal Pengabdian Kepada Masyarakat JURNAL INOVASI DAN PENGABDIAN MASYARAKAT INDONESIA Tepis Wiring : Jurnal Pengabdian Masyarakat Journal Focus Action of Research Mathematic (Factor M) Jurnal Statistika dan Komputasi (STATKOM) Journal of Data Insights Jurnal Statistika dan Sains Data Prosiding Seminar Nasional Unimus Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal Statistika Industri dan Komputasi Journal of Mathematics, Computation and Statistics (JMATHCOS) Emerging Statistics and Data Science Journal Amalgamasi: Journal of Mathematics and Applications Data Science Insights RAGAM: Journal of Statistics and Its Application
M. Al Haris
Department Of Statistics, Universitas Muhammadiyah Semarang, Semarang, Indonesia
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Implementasi Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) untuk Memprediksi Curah Hujan di Kota Semarang Ermawati, Asti; Amrullah, Ahmad; Huda, Khoirul; Haris, M. Al
Jurnal Statistika dan Komputasi Vol. 3 No. 2 (2024): Jurnal Statistika dan Komputasi
Publisher : Universitas Nahdlatul Ulama Sunan Giri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/statkom.v3i2.3224

Abstract

Background: Rainfall is one of the important factors that has a significant impact on various aspects of life, especially in urban areas such as Semarang. Significant fluctuations in rainfall can cause flooding, which negatively impacts infrastructure, agriculture, health and well-being of the community. Therefore, accurate rainfall forecasting is essential to support informed decision-making. Objective: The purpose of this study is to identify and build an optimal SARIMA model for rainfall forecasting in Semarang City. Methods: This study used the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to analyze the monthly rainfall data of Semarang City for the period 2017-2022, because it was able to handle seasonal patterns in the time series data. The best model is determined based on the Akaike Information Criterion (AIC) value, while the accuracy of the prediction is measured using the Mean Absolute Percentage Error (MAPE) value. Results: Based on the results of the analysis, the best SARIMA model was SARIMA (1,1,0) (0,1,0)12 because it produced the smallest AIC value (121.67) and MAPE of 41.59%. This model is used to predict rainfall from January 2023 to December 2025. Conclusion: The SARIMA (1,1,0) (0,1,0)12 model is the best model for rainfall forecasting in Semarang City. The results of this study support previous studies that state that the SARIMA method is effective for rainfall data that have high fluctuations and extreme values.
FORECASTING NICKEL PRICES WITH THE AUTOMATIC CLUSTERING FUZZY TIME SERIES MARKOV APPROACH Haris, M. Al; Sari, Wulan; Fauzi, Fatkhurokhman; Sam'an, Muhammad
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/barekengvol19iss2pp1237-1250

Abstract

Nickel was a critical raw material used in a wide range of industries. The price movement of nickel tends to fluctuate and remain uncertain due to market conditions varying over time. Therefore, forecasting nickel prices was essential to understanding future price movements. In this study, we applied the automatic clustering fuzzy time series Markov chain method. The automatic clustering algorithm generates multiple intervals and fuzzy relations. Subsequently, forecasting was based on these fuzzy relations and a Markov chain transition probability matrix involving three stages to enhance forecast accuracy. We use monthly closing futures nickel price data from January 2009 to May 2024. The accuracy of the forecasting model was measured using the mean absolute percentage error (MAPE). The analysis showed that implementing the automatic clustering fuzzy time series Markov chain method results in excellent forecasting accuracy, with a MAPE value of 1.76% (equivalent to 98.24% accuracy). The predicted nickel price for June 2024 was US$ 19,608.5.
GEOGRAPHICALLY WEIGHTED GENERALIZED POISSON REGRESSION AND GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION MODELING ON PROPERTY CRIME CASES IN CENTRAL JAVA Arum, Prizka Rismawati; Gautama, Rahmad Putra; Haris, M. Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1469-1484

Abstract

Property crime in Indonesia remains one of the most prevalent categories of crime across various regions of the country. This category encompasses a range of criminal acts, including theft, illegal appropriation of goods, robbery, motor vehicle theft, arson, and property damage. One of the commonly used regression analysis methods is Poisson regression. The assumption violation of overdispersion in Poisson regression is often found in property crime data in Central Java. This study also considers spatial aspects, depicting local regional characteristics and the integration of local and global variables. Therefore, this study employs Geographically Weighted Generalized Poisson Regression (GWGPR) and Geographically Weighted Negative Binomial Regression (GWNBR) methods with Adaptive Bisquare Kernel weighting. The aim of this research is to develop a model for each district/city in Central Java using Adaptive Bisquare Kernel weighting, thus providing a more accurate representation of the factors influencing property crime in each region. The AIC value criterion of 411.3652 indicates that the GWNBR method is the most suitable for modeling the number of property crime cases in each district/city in Central Java compared to Poisson regression, negative binomial regression, and GWGPR methods.
CLUSTERING OF DISTRICTS IN CENTRAL JAVA ACCORDING TO PEOPLE'S WELFARE INDICATORS USING WARD'S METHOD Purwanto, Dannu; Pratama, Rizky Adi; Lein, Raymond Bolly; Prastyo, Ikwan; Haris, M. Al
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page73-82

Abstract

One of the main goals of development activities carried out by every country was to improve people's welfare. Community welfare was a situation where citizens could fulfill and adequately fulfill their material and spiritual needs. The poverty rate of Central Java Province was recorded: out of a total population of 37.03 million people, around 3,831.44 thousand people were poor. The population density of Central Java Province reaches 1,120 people per km2, the third largest number of poor people in Indonesia. This study aimed to group regencies/cities in Central Java based on the characteristics of the community welfare indicators. The indicators used in this study were the Open Unemployment Rate (UR), Labor Force Participation Rate (LFPR), Poverty, Human Development Index (HDI), and District Minimum Wage (DMW). The method used in this research was Ward's Agglomerative Hierarchical Clustering. The final results concluded that the best number of clusters formed was 6 clusters. The first cluster consists of 13 Regencies/Cities, the second cluster consists of 8 Regencies/Cities, the third cluster consists of 3 Regencies/Cities, the fourth cluster consists of 1 Regency/City, the fifth cluster consists of 5 Regencies/Cities, the sixth cluster consisting of 5 Regencies/Cities.
Peramalan dan Permodelan Volatilitas Harga Penutupan Crypto Tether dengan Metode GARCH pada Periode Januari - Juni 2024: Peramalan dan Permodelan Volatilitas Harga Penutupan Crypto Tether dengan Metode GARCH pada Periode Januari - Juni 2024 Syaharani, Nabbila Dyah; Khikman, Muhammad Alvaro; Wahid, Siti Nurasriyanti; Watur, Annisa Cahyaningrum; Amri, Ihsan Fathoni; HARIS, M. AL
Emerging Statistics and Data Science Journal Vol. 2 No. 3 (2024): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol2.iss.3.art29

Abstract

Penelitian ini bertujuan untuk memodelkan dan meramalkan volatilitas harga penutupan cryptocurrency Tether (USDT) menggunakan metode Generalized Autoregressive Conditional Heteroscedasticity (GARCH) pada periode Januari - Juni 2024. Data diperoleh dari platform investing.com. Metode GARCH digunakan karena volatilitas tinggi dalam harga cryptocurrency. Hasil analisis menunjukkan bahwa harga penutupan Tether memiliki rata-rata sebesar 1.000016 dengan standar deviasi 0.000446812. Uji Augmented Dickey-Fuller (ADF) menunjukkan bahwa data harga penutupan sudah stasioner. Model Autoregressive Moving Average (ARMA) digunakan untuk mendukung model GARCH, dan model ARIMA terbaik yang ditemukan adalah ARMA (1,0). Uji signifikansi parameter, uji normalitas, dan uji autokorelasi menunjukkan bahwa model tersebut valid untuk prediksi. Model GARCH digunakan untuk mengestimasi volatilitas dan hasilnya menunjukkan bahwa model ini mampu menangani fluktuasi dan heteroskedastisitas dalam data. MAPE GARCH terbaik yang ditemukan sebesar 0.0264701, menunjukkan bahwa model ini sangat akurat dalam meramalkan volatilitas harga penutupan Tether. Penelitian ini memberikan panduan bagi investor dalam mengelola risiko dan mengoptimalkan return investasi di pasar cryptocurrency.
PREDIKSI RATA-RATA KELEMBAPAN MENGGUNAKAN METODE SARIMAX DENGAN RATA-RATA TEMPERATUR SEBAGAI VARIABEL EXOGENOUS Amri, Ihsan Fathoni; Sari, Selvi Ana Windia; Kinanta, Ailsha Syafa; Haris, M. Al; Sidqi, Isnaeni Miftahul; Choirudin, Mochamad Fahmi
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 2 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv3i02pp93-106

Abstract

Rata-rata kelembapandi Indonesia memiliki variansi yang bergantung pada lokasi dan musim. Kelembapanjuga bisa bervariasisepanjang hari, dengan puncak kelembapanbiasanya terjadi pada pagi hari dan menurun pada siang hari sebelum meningkat kembali pada malam hari. Penelitian ini bertujuan untuk memprediksi rata-rata kelembapan di Stasiun Klimatologi Jawa Tengah dengan menggunakan metode SARIMAX (Seasonal Autoregressive Integrated Moving Average with ExogenousVariables).Metode SARIMAX dipilih karena memiliki kemampuan dalammenangani data time series yang memliki komponen musiman dan melibatkan variabel exogenous. Rata-rata temperature digunakan sebagai variabel exogenouskarena adanya korelasi yang signifikan antara rata-rata temperature danrata-ratakelembapan. Data rata-rata kelembapandan temperature diambil dari catatan harian pada periode yang digunakan dalam penelitian. Model SARIMAX kemudian dikembangkan dengan parameter yang dioptimalkan melalui proses iteratif untuk mencapaitingkat akurasi prediksi yang maksimal. Hasil penelitian menunjukkan bahwa model SARIMAX (1, 1, 1)(1, 1, 1)4 dengan nilai AIC sebesar 323,89dan nilai MAPE sebesar 2,863913mampu memberikan prediksi yang cukup akurat terhadap rata-rata Kelembapandi Stasiun Klimatologi Jawa Tengah,denganerror prediksi paling rendah. Model ini dapat memprediksi rata-rata kelembapan selama 8 hari kedepan. Temuan ini dapat membantu dalam perencanaan dan pengelolaan berbagai sektor kegiatan di wiliyah tersebut.
The Impact of Implementing the Independent Curriculum on Elementary School Students' Learning Outcomes Fisabilillah, Muh. Irodat; Ahmadi; Supriadin; Ridwanulhaq, Alfina Fauziah; Masudah, Nurhidayatul; Nur, Indah Manfaati; Haris, M. Al; Amri, Saeful
Jurnal Ilmiah Pendidikan dan Pembelajaran Vol. 9 No. 1 (2025): March
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jipp.v9i1.91548

Abstract

The Independent Curriculum is an essential component in Muhammadiyah Elementary Schools in Semarang, supporting the spirit of learning that has developed over time. However, there is an imbalance in student achievement scores between phases that require data-based tracking. This study aims to evaluate the effectiveness of the Independent Curriculum in improving students' cognitive understanding using the factorial design method. This type of research is quantitative descriptive. The research population is Muhammadiyah Elementary Schools in the independent school category, with a sample of four schools selected using the two-stage cluster random sampling technique. Data collection techniques are observation and questionnaires. The application of analysis methods includes descriptive and inferential statistics. The research results show that the implementation of the Independent Curriculum can significantly improve students' cognitive understanding, as reflected in the increase in the average student achievement scores between the 2022/2023 and 2023/2024 academic years. In addition, the analysis of the elementary school phase shows that the Independent Curriculum can support the development of student competencies in stages according to educational needs in each phase, thereby improving the quality of learning.
FORECASTING THE NUMBER OF FOREIGN TOURISM IN BALI USING THE HYBRID HOLT-WINTERS-ARTIFICIAL NEURAL NETWORK METHOD Haris, M. Al; Himmaturrohmah, Laily; Nur, Indah Manfaati; Ayomi, Nun Maulida Suci
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1027-1038

Abstract

Bali was one of the destinations frequently visited by tourists because it had natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 had increased, but there had been a very significant decrease in 2020. Forecasting the number of tourists coming to Bali in the future was needed to provide input or recommendations to the government and business people in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used was the Holt-Winters method. The Holt-Winters method was part of Exponential Smoothing which is based on smoothing stationary, trend and seasonal elements. However, the Holt-Winters method can only capture linear patterns, so a method was needed that can capture non-linear patterns. The Artificial Neural Network method was proposed to overcome the shortcomings of the Holt-Winters Method. This research was focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists was the Hybrid Holt-Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by the MAD value of 0.036684, MSE 0.01098698 and MAPE 6.30417%.
FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME Arum, Prizka Rismawati; Indriani, Anita Retno; Haris, M Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1183-1192

Abstract

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city
FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD Wasono, Rochdi; Fitri, Yulia; Haris, M. Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0427-0436

Abstract

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.
Co-Authors Abdul Ghufron Abidah, Khansa Ni'mal Abimanyu Arya Ramadhan Ach Ridoi Alambara Adhwaningrum, Arullah Salsabila Agata Dwi Putri Putri Agi Khoerunnisa Ahmad Jundi Ismail AHMADI Ainurrafiq Dawam Ainurrofiah, Safira Al Aghni Naufalia Albertus Dion Sarah Ali Imron Ali Imron Alia Permata Alwan Fadlurohman Alya Febriyani Amalia Jihan Syafiqoh Amin Samiasih Amri, Saeful Amrullah, Ahmad Amrullah, Setiawan Anggoro, Vernanda Kresna Anis Priyanti Anne Mutiara Wardani Ardana Setiawan, Deftha Ariska Fitriyana Ningrum Arya Praditya Arya, Abimanyu Asriyanti Sawiah Adam Astuti, Sofi Anggi Asyfani, Yusrisma Aulia Dewi Gustiarni Aulia Fadhli Boer Ayesha Nayla Salsadella Ayomi, Nun Maulida Suci Ayu Wulandari Ayuda Nur Sukmawati Azzahrani, Rahma Dewi Bahaudin, Muhammad Barlian, Seftia Amelia Rizki Bunga Ayuningrum Choirudin, Mochamad Fahmi Choirunnisa Hasna Nisa Cika Awani Ayuwida Dannu Purwanto danu priambodo Dea Zahra Khairunnisa Devina Nadifa Nur Aulia Diani, Nandini Lova Dimar Pangestika Sari Dwi Purnomo Putro Dzeaulfath, Muhammad Eko Andy Purnomo Elfina Latifah Safira Eny Winaryati Eny Winaryati Ermawati, Asti Erna Julia Nanga Evida Oktaviana Fabiola, Gwenda Fadhilah Azzahra Fadillah, Muhammad Reza Faninda Aidina Fitri Fathir Naufal Hasan Fatkhurrokhman Fauzi Fauzi, Fatkhurokhman Fazia Risnita Widiyana Febrianti, Fatika Lovina Febryana Dilla Setyaningrum Firdatul Fahria Firdaus, Falah Tinton Firochul Masichah Fisabilillah, Muh. Irodat Fitri Anjani Fitri Diana Musa Fitria Fatichatul Hidayah Gautama, Rahmad Putra Hafiza Abas Haris, M Al Haris, M. Al Havinka Angel Salsabilla Havinka Angel Salsabilla Heppy Nur Asavia Ginasputri Heppy Nur Asavia Ginasputri Herculianus Rowa Dawi Hidayat, Muhamad Arif Hilma Hanna Mahanna Haqq Himmaturrohmah, Laily Husna, Rizqa El Iffah Norma Hidayati Ihsan Fathoni Ihsan Fathoni Amri Ikhwanudin, Muhamad Ilham Khairul Anam Imelya Susianti Inayah Pangestu, Eka Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indriani, Anita Retno Irawan, Alfian Chandra Isnaini Maulida Iva Aurellia Khalif Jesicha Arsusma Kaia Raissa Akmalia Kaia Raissa Akmalia Kamilah Citra Chumairoh Kamilah Citra Khumairoh Khansa' Ni'mal 'Abidah Khikman, Muhammad Alvaro Khoirul Huda Kinanta, Ailsha Syafa Latisa Alifa Maura Lea Angelina Lein, Raymond Bolly Linda Puspitasari Lydia Nur Sa'adah Lydia Nur Sa'adah Lydia Nur Sa'adah Mandala Adikara Sencoko Marsela Ayu Irdiana Masudah, Nurhidayatul Miftakhiyah Fazza Baita Miftakhul Haris Miftakhurizki Mochamad Hasyim Mualim Tahari Mufidatul Ulya Muhammad Alvaro Khikman Muhammad Bahaudin Muhammad Hali Mukron Muhammad Najwan Kamil Muhammad Rifqy Ardiansyah Muhammad Saifuddin Nur Multiyaningrum, Riska Nadia Khoirunnafisa Salma Nandini Lova Diani Nasyiatul Izzah Nikmah Handayani Ninu, Maria Febronia Nufita Nurohmah Nugroho, Muhammad Dimas Alfian Nur Arifah, Miftah Nurfuad, Khilmi Nurhidajah Nurmalita, Rahma Nurmawati Ainun Hidayana Okiyanto, Rizal Pandiriyan, Muhammad Tegar Prastiwi, Harvina Sindy Prastyo, Ikwan Pratama, Rifin Fadilla Pratama, Rizky Adi Priambodo, Danu Prissy Nusaiba Yulisa Prizka Rismawati Arum Purnama, Estyaningsi Puspitasari, Linda Putra, Septian Malik Putri Wahyu Muharamah Putri, Melfia Verahma R.A Qonita Syalsabilla Handayani RA. Qonita Syalsabilla Handayani Rahma Nurmalita Rahma Safira Raka Nurhaq Mulya Hartanto Ramadhan, Abimanyu Arya Ramadhan, Wulan Nur Rangga Sa'adillah SAP Rendi Andika Putra Revika Inta Nur Kholifah Ridwanulhaq, Alfina Fauziah Riska Multiyaningrum Riska Multiyaningrum Riska Multiyaningrum Rochdi Wasono Rochdi Wasono Rochdi Wasono Ryan Mahardika Saeful Amri Saeful Amri Salmah Salmah Salsabila Dhea Sintya Salsabila Rahma Anisa Salwa Salsabila, Galuh Sam'an, Muhammad Sanmas, Safril Ahmadi Saputri, Atika Dwi Sari, Selvi Ana Windia Septi Winda Utami Septia, Siti Fajar Sesotyaning Harum Prabuningrat Shinta Amaria Sidqi, Isnaeni Miftahul Siswahyudianto Siti Hamidah Ardhy Siti Nurhalisa siti wulandari Suci Izzati Suci Laeliyah Suci Mega Puji Lestari Suherdi, Andri Sulistiya, Indah Sulistiyani, Dwi Sunday Emmanuel Fadugba Supriadin Supriadin Supriadin Supriadin Syafina Amira Firdaus Syaharani, Nabbila Dyah Tiani Wahyu Utami Tresiani Yunitasari Tri zahrotun Wahyuningsih Ulinuha, Samikoh Utami, Rossy Prima Nada Utiningtyas, Almas Rizki Velia Arni Widyasari Wahid, Siti Nurasriyanti Wahyuningsih, Andria Watur, Annisa Cahyaningrum Widiyanti, Karin Dita Wulan Sari Wulan Sari, Wulan Yan Nazala Bisoumi Yolan Triky Yulia Nur Kumala Yulianita, Tanti