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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Statistika Universitas Muhammadiyah Semarang 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 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 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 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 Jurnal Statistika dan Komputasi (STATKOM) Journal of Data Insights 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 RAGAM: Journal of Statistics and Its Application
M. Al Haris
Departement Of Statistics, Universitas Muhammadiyah Semarang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Implementasi Teknologi Ramah Lingkungan untuk Menunjang Sektor Pertanian di Desa Margohayu Karangawen Demak M. Al Haris; Dannu Purwanto; Ali Imron; RA. Qonita Syalsabilla Handayani; Arya Praditya
LOSARI: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 2 (2024): Desember 2024
Publisher : LOSARI DIGITAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53860/losari.v6i2.349

Abstract

Margohayu village is one of the regions in the Karangawen Subdistrict, Demak Regency, Central Java. Desa Margohayu has a population of 8,056, with the majority working as farmers. Currently, the irrigation of rice fields in Desa Margohayu still relies on fossil fuel-based energy, which gradually depletes and has environmental consequences. However, Desa Margohayu has significant potential to establish a self-sustaining energy system by harnessing solar energy. Sunlight can be converted into electricity through solar panels to power water pumps. Therefore, the Community Service Team from Universitas Muhammadiyah Semarang proposes an environmentally friendly and sustainable technology implementation in the agricultural sector through the Margo Mulyo Farmer Group in Desa Margohayu. The goal is to reduce the negative impact of fossil fuel usage that has been prevalent. The results of this initiative show that the Margo Mulyo Farmer Group gains knowledge and skills related to solar energy utilization. The implementation of this technology is expected to reduce agricultural costs, particularly in the irrigation process.
Pengelompokkan Wilayah Banjir di Jawa Tengah untuk Mitigasi Banjir Menggunakan Pendekatan K-Medoids Sanmas, Safril Ahmadi; Nurmalita, Rahma; Sulistiyani, Dwi; 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.v3i2.3223

Abstract

Background: Flooding is a natural event that can occur at any time, often resulting in fatalities and significant material losses. Mapping flood zones in Central Java based on flood occurrences is crucial for optimizing disaster management. Clustering approaches are highly relevant and potential methods for tackling flood mitigation challenges in Central Java. Objective: To map flood zoning in Central Java using the optimal K-Medoids method based on the Silhouette Coefficient. Methods: This study uses the K-Medoids method for Clustering analysis because it is more resistant to outliers. Unlike K-Means, K-Medoids selects the medoid as the cluster center, making it more stable against extreme values. The data used was obtained from the Dinas PUSDATARU of Central Java Province regarding flood events in the region from October 1, 2022, to March 2023. Results: The K-Medoids method with k=2 produced the highest Silhouette Coefficient of 0.83748, Clustering 34 districts/cities with low flood occurrences and 1 district/city with high flood occurrences. This model evaluation supports the planning of disaster mitigation policies that focus more on flood-prone areas. Conclusion: There are two groups of districts/cities based on flood occurrence levels. The high Silhouette Coefficient value indicates a good cluster structure.
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 the Volatility of Tuna Fish Prices in North Sumatra using the ARCH Method in the Period January - April 2024 Multiyaningrum, Riska; Amri, Ihsan Fathoni; Haris, M. Al; Salsabilla, Havinka Angel; Ginasputri, Heppy Nur Asavia; Sintya, Salsabila Dhea
Eigen Mathematics Journal Vol 7 No 2 (2024): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i2.236

Abstract

Tuna (Euthynnus affinis) is one of the most important fisheries commodities in Indonesia with significant economic value, especially in its contribution to fisheries export revenue. However, the price of tuna experiences significant fluctuations that can affect local and national economic stability. This study analyzes the daily price fluctuations of tuna in the North Sumatra market from January 1, 2024 to April 29, 2024 using a time series analysis approach. Daily price data were collected and analyzed to identify existing price patterns and volatility. The Autoregressive Conditional Heteroskedasticity (ARCH) model was selected to address the heteroscedasticity in the data, which suggests that the volatility of tuna prices can be well predicted based on past price behavior. The analysis steps include identifying the optimal ARCH model using the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF), as well as testing parameter significance and normality assumptions to validate the model fit. The results show that the ARMA (1,0,0) model is the optimal one to model the price volatility of yellow tuna with the MAPE obtained of 2.382. compared to the ARMA-ARCH method with the MAPE value obtained of 2,747. Because it still contains heteroskedasticity effects, even though the results are good, the prediction results do not closely match the original data. The model is effective in improving price forecasting accuracy, which is important to support decision-making in risk management and economic planning in the fisheries sector. The findings contribute to understanding the dynamics of the yellowtail market and optimizing strategies for fisheries management.
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.
Co-Authors Abdul Ghufron Abidah, Khansa Ni'mal Abimanyu Arya Ramadhan Adhwaningrum, Arullah Salsabila Agi Khoerunnisa AHMADI Ainurrofiah, Safira Alambara, Ach Ridoi Ali Imron Ali Imron Alwan Fadlurohman Alya Febriyani Amalia Jihan Syafiqoh Amin Samiasih Amri, Ihsan Fathoni Amri, Saeful Amrullah, Ahmad Amrullah, Setiawan Andy Purnomo, Eko Angelina, Lea Anggoro, Vernanda Kresna Anne Mutiara Wardani Ariska Fitriyana Ningrum Arsusma, Jesicha Arya Praditya Arya, Abimanyu Astuti, Sofi Anggi Asyfani, Yusrisma athoni Amri, Ihsan F Aulia Dewi Gustiarni Aulia Fadhli Boer Ayesha Nayla Salsadella Ayomi, Nun Maulida Suci Ayu Wulandari Azzahrani, Rahma Dewi Barlian, Seftia Amelia Rizki Bunga Ayuningrum Choirudin, Mochamad Fahmi Cika Awani Ayuwida Dannu Purwanto Devina Nadifa Nur Aulia Diani, Nandini Lova Dzeaulfath, Muhammad Eny Winaryati Eny Winaryati Ermawati, Asti Evida Oktaviana Fabiola, Gwenda Fadhilah Azzahra Fadillah, Muhammad Reza Fauzi, Fatkhurokhman Fauzi, Fatkhurrokhman Fazia Risnita Widiyana Fazza Baita, Miftakhiyah Febrianti, Fatika Lovina Firdatul Fahria Firdaus, Falah Tinton Fisabilillah, Muh. Irodat Fitri Anjani Gautama, Rahmad Putra Ginasputri, Heppy Nur Asavia Haris, M Al Haris, M. Al Havinka Angel Salsabilla 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 Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indriani, Anita Retno Inta Nur Kholifah, Revika Irawan, Alfian Chandra Izzah, Nasyiatul Kaia Raissa Akmalia Khikman, Muhammad Alvaro Khoirul Huda Kholifah , Revika Inta Nur Kinanta, Ailsha Syafa Latisa Alifa Maura Lein, Raymond Bolly Linda Puspitasari Mandala Adikara Sencoko Marsela Ayu Irdiana Masichah, Firochul Masudah, Nurhidayatul Miftakhul Haris Miftakhurizki Mochamad Hasyim Mualim Tahari Mufidatul Ulya Muhammad Hali Mukron Muhammad Rifqy Ardiansyah Muhammad Saifuddin Nur Multiyaningrum, Riska Musa, Fitri Diana Nadia Khoirunnafisa Salma Nandini Lova Diani Nikmah Handayani Ninu, Maria Febronia Nugroho, Muhammad Dimas Alfian Nur, Rachmat Kahfiwan Nurfuad, Khilmi Nurhalisa, Siti Nurhidajah Nurmalita, Rahma Nurohmah, Nufita Okiyanto, Rizal Pandiriyan, Muhammad Tegar Permata, Alia Prastiwi, Harvina Sindy Prastyo, Ikwan Pratama, Rifin Fadilla Pratama, Rizky Adi Priambodo, Danu Prissy Nusaiba Yulisa Prizka Rismawati Arum Purnama, Estyaningsi Purnomo Putro, Dwi Puspitasari, Linda Putra, Septian Malik Putri Wahyu Muharamah Putri, Agata Dwi Putri Putri, Melfia Verahma RA. Qonita Syalsabilla Handayani Rahma Nurmalita Ramadhan, Abimanyu Arya Ramadhan, Wulan Nur Rangga Sa'adillah SAP Ridwanulhaq, Alfina Fauziah Rochdi Wasono Rochdi Wasono Ryan Mahardika Sa'adah , Lydia Nur Safira, Elfina Latifah Safira, Rahma Salma, Nadia Khoirunnafisa Salsabila Rahma Anisa Salsabilla, Havinka Angel Sam'an, Muhammad Sanmas, Safril Ahmadi Saputri, Atika Dwi Sarah, Albertus Dion Sari, Selvi Ana Windia Sawiah Adam, Asriyanti Septi Winda Utami Septia, Siti Fajar Sesotyaning Harum Prabuningrat Shinta Amaria Sidqi, Isnaeni Miftahul Sintya, Salsabila Dhea Siti Hamidah Ardhy Suci Laeliyah Suci Mega Puji Lestari Suherdi, Andri Sulistiya, Indah Sulistiyani, Dwi 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 Wahid, Siti Nurasriyanti Wahyuningsih, Andria Watur, Annisa Cahyaningrum Widiyanti, Karin Dita Widyasari, Velia Arni Wulan Sari Wulan Sari, Wulan Yolan Triky Yulia Nur Kumala Yulianita, Tanti