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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
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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K-Nearest Neighbor (KNN) Method for Weather Data Prediction: Penerapan Metode K-Nearest Neighbour (KNN) Untuk Prediksi Data Cuaca Agata Dwi Putri Putri; M. Al Haris; Fatkhurokhman Fauzi; Saeful Amri
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.214

Abstract

The weather tends to change frequently every day, so weather forecasts are made to be used as an early warning if sudden weather changes occur. By forecasting the weather, losses can be minimized and people are alert to carry out outdoor activities. From this problem, the K-Nearest Neighbor (KNN) method was applied. This method is expected to provide accurate and efficient information to obtain weather predictions for existing conditions. The data used is secondary data. After conducting research on training data (old data) amounting to 80% and test data (new data) amounting to 20%. The accuracy results from the testing data predictions are 75% with a value of k = 8.
Analysis Autocorrelation Spatial on Amount Fundraising at LAZISMU Semarang City Using Moran's Index: Analisis Autokorelasi Spasial pada Jumlah Penghimpunan Dana di LAZISMU Kota Semarang Menggunakan Indeks Moran Choirunnisa Hasna Nisa; Khansa' Ni'mal 'Abidah; M. Al Haris; Fatkhurokhman Fauzi
Journal of Data Insights Vol 3 No 2 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i2.314

Abstract

Institution Zakat and Infaq Collectors And Sed e kah Muhammadiyah (LAZISMU) , has role important in gather And distribute funds activity social use help communities in need . L AZISMU Semarang City in general special focus on management funds at the level city , with not quite enough answer gather And allocate funds from public to humanitarian programs like help education , health , and help social research​ This aim For increase effectiveness collection funds Institution Zakat, Infaq , and Charity Collectors Alms Muhammadiyah in Semarang City. With apply approach spatial , research This analyze pattern distribution geographical donors , potential donations , and characteristics economy as well as demographics in each sub-district . Methodology study involving spatial data collection and analysis statistics . Results study This expected can give contribution on understanding scientific related zakat- based management spatial And become guidelines for institution similar in optimize collection And allocation funds .
Forecasting Honda Car Retail Sales Using the Seasonal Autoregressive Integrated Moving Average Method: Peramalan Penjualan Retail Mobil Honda Menggunakan Metode Seasonal Autoregressive Integrated Moving Average Lea Angelina; Alia Permata; Jesicha Arsusma; Firochul Masichah; M. Al Haris; Ihsan Fathoni Amri
Journal of Data Insights Vol 3 No 1 (2025): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v3i1.416

Abstract

This article discusses the forecasting of Honda car retail sales using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. The study aims to forecast Honda car retail sales for the upcoming year. Various SARIMA models have been tested to determine the best model, and the results show that the SARIMA (1,1,0)(1,1,1)¹² model provides the lowest Mean Absolute Percentage Error (MAPE) among all tested models, which is 17,74%. Therefore, this model was chosen for forecasting sales over the next 12 months. The forecast results are expected to assist management in making optimal decisions regarding stock and marketing, as well as significantly enhancing operational efficiency and customer satisfaction in the future.
K-Nearest Neighbor Algorithm in Classification of Stunting Detection Dataset: Algoritma K-Nearest Neighbor dalam Klasifikasi Dataset Deteksi Stunting Lea Angelina; Saeful Amri; M Al Haris; Rochdi Wasono; Erna Julia Nanga; Faninda Aidina Fitri
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.752

Abstract

Stunting is a nutritional problem that can affect children's physical growth and cognitive development and has a long-term impact on the quality of future generations. Early detection of stunting is crucial to enable timely and effective interventions. As technology advances, machine learning algorithms such as K-Nearest Neighbors (KNN) offer potential solutions to improve the accuracy of stunting risk classification. This study aims to design a classification model based on the K-Nearest Neighbors (KNN) algorithm in the early detection of stunting risk in toddlers. This research uses the 2024 stunting dataset obtained from Kaggle. The data is analyzed through the stages of cleaning, transformation, and division into training and testing data. The KNN model was tested with various K values to determine the optimal value. The results showed that the KNN model with a value of K=8 resulted in an accuracy of 93.80%, F1-Score of 93.65%, precision of 93.63%, and recall of 93.79%. This shows that KNN is reliable in classifying the nutritional status of toddlers and can be applied in stunting prevention efforts using more accurate data. This research contributes to developing machine learning-based classification systems that can support decision-making in public health programs, especially in reducing stunting rates.
Survival Analysis Using Kaplan-Meier and Cox Regression in Hypertension Patients at Kefamenanu Regional Hospital Muhammad Alvaro Khikman; Riska Multiyaningrum; Revika Inta Nur Kholifah; Lydia Nur Sa'adah; Elfina Latifah Safira; Albertus Dion Sarah; Ihsan Fathoni Amri; M. Al Haris
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.270

Abstract

Hypertension is a chronic disease with a steadily increasing global prevalence and is one of the leading causes of serious complications. Indonesia is among the countries with a high prevalence of hypertension, necessitating an understanding of the factors influencing patient treatment duration to enhance the effectiveness of healthcare services. This study aims to analyze differences in the survival rates of hypertensive patients at Kefamenanu Hospital based on gender. The Kaplan-Meier method was used to estimate patient survival rates, while Cox Proportional Hazards regression was used to evaluate the influence of gender on survival time. The Kaplan-Meier analysis results showed that female patients had a higher probability of survival than male patients during hospitalization. However, the Cox Proportional Hazards regression analysis indicated that this difference was not statistically significant. These findings suggest that while there are differences in survival patterns, gender is not the primary determinant of the duration of care for hypertensive patients. The results of this study are expected to provide input for hospitals in designing more effective care strategies that focus on other factors that may influence patient survival time.
Forecasting the Rupiah exchange rate against the US Dollar using the LSTM algorithm Riska Multiyaningrum; Herculianus Rowa Dawi; Raka Nurhaq Mulya Hartanto; M. Al Haris; Ihsan Fathoni Amri
Journal Focus Action of Research Mathematic (Factor M) Vol. 8 No. 2 (2025): December 2025
Publisher : Universitas Islam Negeri (UIN) Syekh Wasil Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/f_m.v8i2.6530

Abstract

Exchange rates are a vital indicator of an economy's balance. The fluctuations of Indonesia's currency, the rupiah, against the USD influenced trade patterns, investment, and both monetary and fiscal policy. Exchange rate fluctuations affect international trade, investment, inflation, and overall economic stability. The high volatility of the Rupiah against the USD, driven by macroeconomic and monetary factors, has a significant impact on national economic policy, necessitating research that utilizes the latest data and adaptive models. To capture the nonlinear and complicated behavior of exchange rates, an advanced methodology for forecasting is needed. This journal utilizes the Long Short-Term Memory (LSTM) neural network model to forecast the exchange rate of the rupiah towards the dollar from March 1, 2022, up to February 28, 2025, in daily data. The data used in this research are sourced from www.bi.go.id, which provides the official daily exchange rate of USD to IDR. The Long Short-Term Memory method was chosen for modeling long-term dependencies within time series. After normalization, an 80/20 split is performed for training and testing on the dataset. The network runs optimization using three hidden layers with 50 neurons each and a batch size of 32 for 200 epochs. The optimal configuration, achieved through experimental trials, consisted of two hidden layers with 50 neurons, a batch size of 32, and 200 epochs. This is manifest in the fact that LSTM effectively captures movements in exchange rates, with an RMSE of 0.6226 and a MAPE of 0.3031%. This degree of accuracy enables the model to inform economic policy decisions based on data.
Pemetaan Daerah Rawan Bencana di Pulau Sulawesi menggunakan Metode Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Havinka Angel Salsabilla; Nandini Lova Diani; Abimanyu Arya Ramadhan; M. Al Haris
Indonesian Journal of Applied Statistics Vol 8, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v8i2.106040

Abstract

Indonesia terletak pada pertemuan tiga lempeng tektonik aktif sehingga memiliki tingkat kerawanan yang tinggi terhadap bencana alam seperti gempa bumi, banjir, letusan gunung api, dan tanah longsor. Pulau Sulawesi merupakan salah satu wilayah dengan aktivitas seismik dan hidrometeorologi yang tinggi, sehingga identifikasi daerah rawan bencana menjadi penting dalam upaya pengurangan risiko dan perencanaan mitigasi yang efektif. Penelitian ini bertujuan untuk memetakan daerah rawan bencana di Pulau Sulawesi menggunakan algoritma Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DBSCAN merupakan metode klasterisasi berbasis kepadatan yang mampu mengidentifikasi pola spasial tanpa harus menentukan jumlah klaster di awal serta dapat mendeteksi data pencilan (outlier). Data yang digunakan adalah data sekunder dari Badan Nasional Penanggulangan Bencana (BNPB) tahun 2020–2024 yang mencakup kejadian bencana di seluruh kabupaten/kota di Pulau Sulawesi. Variabel yang dianalisis meliputi frekuensi kejadian banjir, tanah longsor, cuaca ekstrem, kekeringan, gempa bumi, letusan gunung api, dan gelombang pasang. Sebelum proses klasterisasi, data dinormalisasi menggunakan metode Min–Max. Hasil terbaik diperoleh pada parameter ε = 0,28 dan MinPts = 5, yang menghasilkan dua klaster utama dan satu kelompok noise. Klaster 1 menunjukkan wilayah dengan tingkat kejadian bencana tertinggi, terutama banjir, tanah longsor, dan cuaca ekstrem. Klaster 0 mencakup wilayah dengan intensitas bencana sedang, sedangkan kelompok noise terdiri atas wilayah dengan tingkat kejadian bencana yang rendah atau pola bencana yang tidak jelas. Penerapan algoritma DBSCAN terbukti efektif dalam pemetaan kerawanan bencana karena mampu menangani distribusi spasial yang tidak merata serta mengungkap pola tersembunyi. Hasil penelitian ini diharapkan dapat menjadi dasar dalam pengembangan strategi mitigasi bencana yang lebih terarah. Penelitian selanjutnya disarankan untuk menambahkan indikator kerentanan sosial-ekonomi serta memperluas cakupan data.Kata kunci: DBSCAN; Sulawesi; Klasterisasi Spasial; Pemetaan Bencana; Mitigasi RisikoIndonesia is located at the confluence of three active tectonic plates, making it highly vulnerable to natural disasters such as earthquakes, floods, volcanic eruptions, and landslides. Sulawesi Island is one of the regions with the highest seismic and hydro-meteorological activity in Indonesia, so identifying its disaster-prone areas is crucial for effective risk reduction and mitigation planning. This study aims to map disaster-prone areas in Sulawesi Island using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a density-based clustering method that is able to identify spatial patterns without determining the number of clusters from the start, as well as detect outlier data. The data used is secondary data from National Disaster Management Authority (BNPB) for 2020–2024 covering disaster events in all districts/cities in Sulawesi. The variables analyzed include the frequency of floods, landslides, extreme weather, droughts, earthquakes, volcanic eruptions, and tidal waves. The data was normalized using the Min-Max method before the clustering process. The best results were obtained at parameters ε = 0.28 and MinPts = 5, resulting in two main clusters and one noise group. Cluster 1 shows areas with the highest disaster occurrences, especially floods, landslides, and extreme weather. Cluster 0 includes areas with moderate disaster intensity, while the noise group consists of areas with low or unclear disaster patterns. The application of DBSCAN has proven effective for disaster vulnerability because it is able to handle uneven spatial distribution and reveal hidden patterns. These results are expected to be the basis for developing more targeted disaster mitigation strategies. Further research is recommended to add socio-economic vulnerability indicators and expand data coverage.Keywords: DBSCAN; Sulawesi; Spatial Clustering; Disaster Mapping; Risk Mitigation 
Infografis Dampak Pandemi Covid-19 sebagai Upaya Edukasi Pemberdayaan Masyarakat Desa Katonsari Kecamatan Demak Prizka Rismawati Arum; Eko Andy Purnomo; Ali Imron; M. Al Haris; Fatkhurrokhman Fauzi; Ach Ridoi Alambara
Jurnal Pengabdian Masyarakat: Tipis Wiring Vol 1 No 2 (2022): Tepis Wiring: Jurnal Pengabdian Masyarakat
Publisher : Fakultas Ekonomi dan Bisnis Unversitas Islam Raden Rahmat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/tepiswiring.v1i2.2978

Abstract

Based on observations in the Katonsari Village, Demak District, Demak Regency, the delivery of information about Covid-19 and its impacts is still traditional, namely using text, tables or diagrams that are less attractive. Katonsari Village apparatus must have innovations in conveying information about Covid-19 and its impacts, such as new methods or media for conveying information. One of them is through infographics. Through the training, mentoring, mentoring and coaching that will be carried out, it is hoped that it will be able to make the officials of Katonsari Village, Demak District, Demak Regency more professional in utilizing IT to convey information on the impact of Covid-19 to the public through infographics. So that it can increase the awareness of the people of Katonsari Village in preventing Covid-19. This program consists of several stages which include delivering conceptual material on infographics, compiling data on the impact of Covid-19 into IT-based information, especially infographics, and socializing the results of infographics to the public.
Evaluation of Changes in Alveolar Bone Height Following Removable Orthodontic Appliances at Unimus Dental Hospital Dea Zahra Khairunnisa; Dimar Pangestika Sari; Ayuda Nur Sukmawati; M. Al Haris
B-Dent: Jurnal Kedokteran Gigi Universitas Baiturrahmah Vol. 13 No. 1 (2026): Vol 13 No 1 (Juni 2026)
Publisher : Universitas Baiturrahmah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Introduction: Malocclusion is a condition characterized by misalignment of the teeth and improper jaw alignment, which can lead to impaired chewing function. One type of orthodontic appliance that can be used in the treatment of malocclusion is the removable orthodontic appliance. This appliance offers the advantage of being easily removed and reinserted by the patient, thereby helping to maintain better oral hygiene, and carries a lower risk of root resorption and periodontal complications compared to fixed orthodontic appliances. Research on changes in alveolar bone height has largely focused on the use of fixed orthodontic appliances, while studies on removable orthodontic appliances remain limited. Aim: This study aims to evaluate the differences in alveolar bone height before and after the use of removable orthodontic appliances in patients at Unimus Dental Hospital. Methods: This study applied an analytical observational approach with a cross-sectional design. The study was conducted by measuring the alveolar bone height on panoramic radiographs before and after the use of removable orthodontic appliances on 10 teeth in 17 subjects using ImageJ® software. Result: The results of both the paired sample t-test and the Wilcoxon signed rank tests showed p-values greater than 0.05. Conclusion: There was no difference in alveolar bone height before and after the use of removable orthodontic appliances in patients at the Unimus Dental Hospital Keywords: alveolar bone, bone resorption, removable orthodontic appliances
Forecasting Rice Prices in Indonesia Using a Hybrid HWES-MLP Time Series Prediction Model Supriadin Supriadin; M. Al Haris; Saeful Amri; Hafiza Abas; Sunday Emmanuel Fadugba
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35445

Abstract

Rice is the main staple food for the majority of the Indonesian population. However, the fluctuation in rice prices and future uncertainty emphasize the importance of forecasting rice prices, thus requiring a forecasting model capable of providing accurate predictions. Various previous forecasting methods have been limited in capturing the combination of linear and non-linear patterns in rice price data, spurring the need for a more comprehensive hybrid approach. This research applies a quantitative approach by utilizing secondary data sourced from publications of the Central Statistics Agency (BPS) of Indonesia. This study aims to forecast rice prices in Indonesia using a hybrid approach combining Holt–Winters Exponential Smoothing (HWES) with Multilayer Perceptron (MLP). The hybrid model is designed to overcome the limitations of the Holt-Winters Exponential Smoothing method, which can only capture linear patterns such as trend and seasonality, by adding the Multilayer Perceptron method to capture non-linear patterns that cannot be handled by the linear approach. The dataset comprises monthly rice prices in Indonesia from January 2010 to December 2024, while the period of January–December 2025 is used as the prediction period. The data analysis process was carried out using the software R-Studio and Minitab, which provide a variety of features to support time series modeling. The results indicate that the most effective method for forecasting rice prices in Indonesia is the Hybrid Holt Winters Exponential Smoothing (α = 0.5; β = 0.3; γ = 0.3)-Multilayer Perceptron (12-12-1), which achieved the highest accuracy with a MSE of 9666.12, a RMSE of 310.9117, and a MAPE of 1.9949%. This finding indicates that the Hybrid HWES-MLP approach is highly capable of capturing rice price data patterns. Thus, this model holds significant potential to be utilized as a benchmark supporting government policy in maintaining rice price stability, market intervention, and optimizing the management of national rice reserves stock.
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