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All Journal VALENSI Media Statistika Statistika CAUCHY: Jurnal Matematika Murni dan Aplikasi Journal of the Indonesian Mathematical Society Kubik International Conference on Coastal and Delta Areas Jurnal Fourier Desimal: Jurnal Matematika Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Buletin Sumber Daya Geologi JTAM (Jurnal Teori dan Aplikasi Matematika) Limits: Journal of Mathematics and Its Applications Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jurnal Matematika UNAND Kumawula: Jurnal Pengabdian Kepada Masyarakat InPrime: Indonesian Journal Of Pure And Applied Mathematics KUBIK: Jurnal Publikasi Ilmiah Matematika Pattimura Proceeding : Conference of Science and Technology Jurnal Matematika Integratif Matematika STATISTIKA Limits: Journal of Mathematics and Its Applications
Budi Nurani Ruchjana
Departemen Matematika, Fakultas MIPA, Universitas Padjadjaran, Indonesia
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COVID-19 PROJECTIONS ON JAVA AND BALI ISLANDS INVOLVING VACCINATION AND TESTING INTERVENTIONS USING VARI-X MODEL Ibrahim, Riza Andrian; Ruchjana, Budi Nurani
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/barekengvol17iss2pp0837-0846

Abstract

The Indonesian government implemented the policy of increasing vaccination and testing of Covid-19 for travel from or to the Java and Bali Islands to reduce the Covid-19 projected spread in there. As participation in these efforts, this study aims to project the Covid-19 spread measured by the active case rates by involving the intervention of vaccination and testing of Covid-19 in the two islands. Projections are performed using a vector of autoregression integrated with the exogenous variables (VARI-X) model. This model is used because it can simultaneously project the Covid-19 spread in the two islands by involving interventions of vaccination and testing of Covid-19 as exogenous variables. The most suitable model obtained is VARI-X (4, 2, 0). The mean-absolute-percentage error (MAPE) of the model for the Java and Bali Islands is 5.3027% and 3.0301%, respectively. Based on the MAPE value, the model is very accurate for projecting the future Covid-19 spread on the two islands. This accuracy can be seen practically from the Covid-19 spread projection results in the next four days, which are very close to the actual data. This research is expected to help the Indonesian government project the spread of Covid-19 on the Java and Bali Islands.
INTEGRATED OF WEB APPLICATION RSHINY FOR MARKOV CHAIN AND ITS APPLICATION TO THE DAILY CASES OF COVID-19 IN WEST SUMATERA Monika, Putri; Ruchjana, Budi Nurani; Parmikanti, Kankan; Abdullah, Atje Setiawan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2397-2410

Abstract

Discrete-time of Markov chains, starting now referred to as Markov chains, have been widely used by previous researchers in predicting the phenomenon. The predictions were made by manual calculations and using separate software, including Maple, Matlab, and Microsoft Excel. The analysis takes a relatively long time, especially in calculating the number of transitions from each state. This research built an integrated R script for the Markov chain based on the web application RShiny to quickly, easily, and accurately predict a phenomenon. The Markov chain integrated R script is built via command-command to predict the day-n distribution with the n-step distribution and long-term probability using a stationary distribution. The RShiny web application built is limited to state two and three. The integrated web application RShiny for the Markov chain is used to predict the daily cases of COVID-19 in West Sumatra. Based on the analysis carried out in predicting the daily cases of COVID-19 in West Sumatra from March 26, 2020, to October 20, 2020, for the next three days and in the long term, the results show that there is a 51.2% probability of an increase in COVID-19 cases, a 43% probability that cases will decrease, and 5.8% chance of stagnant cases
COMBINATION OF ETHNOMATHEMATICS AND THE MOZART EFFECT TO IMPROVE PROBLEM-SOLVING SKILLS AND MATHEMATICAL DISPOSITION Kusuma, Dianne Amor; Ruchjana, Budi Nurani; Widodo, Sri Adi; Dwipriyoko, Estiyan
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1155-1166

Abstract

The background of this research is that student learning outcomes in analytical geometry lecture during the transition from pandemic to Covid-19 endemic are still low, which is due to a lack of student interest in learning, and they are still accustomed to online learning, thus having an impact on their low problem-solving skills and mathematical disposition. This research aims to determine to what extent the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition from pandemic to endemic COVID-19, so the research is important to do. The implementation of ethnomathematics and the Mozart effect in mathematics learning is unique because it is a combination of learning approaches that have never been used before in Indonesia and other countries. The research method used was a quasi-experimental non-equivalent control group design because this research was experimental and sample determination was not carried out randomly, but using purposive sampling technique on the second-semester students of the mathematics undergraduate program, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran. The instruments used in this study were problem-solving skills test, mathematical disposition scale, and students’ attitude questionnaire toward learning with the implementation of ethnomathematics and the Mozart effect. The results showed that: (1) problem-solving skills of students who received learning by implementing ethnomathematics and the Mozart effect are better than students who achieved direct instruction; (2) mathematical disposition of students who received learning by implementing ethnomathematics and the Mozart effect is better than students who achieved direct instruction; and (3) students are interested and motivated to learn mathematics by implementing ethnomathematics and the Mozart effect. This research concludes that the implementation of ethnomathematics and the Mozart effect can improve students' problem-solving skills and mathematical disposition in analytical geometry lecture during the transition period from the pandemic to endemic COVID-19. It can be seen from good average post test scores achieved by students.
APPLICATION OF THE PRINCIPAL COMPONENT ANALYSIS-VECTOR AUTOREGRESSIVE INTEGRATED (PCA-VARI) MODEL TO FORECASTING ECONOMIC GROWTH IN INDONESIA Al Madani, Aulia Rahman; Najwa, Sandrina; Ruchjana, Budi Nurani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2301-2316

Abstract

Indonesia's economic growth has undergone significant fluctuations in recent years, driven by global shocks such as the 2020 COVID-19 pandemic, the 2013 taper tantrum, and the 2022 global energy crisis. These events underscore the urgent need for more accurate and robust forecasting models to support economic stability and policymaking. This study applies the Principal Component Analysis-Vector Autoregressive Integrated (PCA-VARI) model to forecast economic growth in Indonesia. PCA reduces seven economic variables into two principal components for ten years (2012-2022). The results show that the first component (PC1) shows the highest correlation with the variables of Money Supply, BI Rate, and Foreign Exchange Reserves, which reflect monetary policy and financial stability. Meanwhile, the second component (PC2) is highly correlated to the GDP Index, Exchange Rate, and Inflation variables, which reflect macroeconomic conditions. VARI, as a non-stationary multivariate time series model, is used to model the relationship between these components, with the third-order lag selected as the optimal lag based on the Akaike Information Criterion (AIC), Hannan-Quinn Criterion (HQ), and Final Prediction Error (FPE) values. The results show that the PCA-VARI(3) model is able to provide highly accurate forecasting with a MAPE of 1.21% for PC1 and 1.34% for PC2, and has met all the necessary model assumptions.
Integrating Spatial Autoregressive Exogenous with Ordinary Kriging for Improved Rainfall Prediction in Java: Enhancing Accuracy with Climate Variables and Spatial Autocorrelation Najwa, Sandrina; Pratiwi, Dhanti Aurilia; Ahdian, Muhammad Rhafi; Indriani, Ayu; Mindra, I Gede Nyoman; Falah, Annisa Nur; Ruchjana, Budi Nurani
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 7 No. 1 (2025)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v7i1.42070

Abstract

Indonesia is a tropical country with high rainfall influenced by its archipelagic geography and phenomena like El Niño and La Niña. According to the Meteorology, Climatology, and Geophysics Agency (BMKG), La Niña can increase Indonesia's monthly rainfall by 20-40% above normal. Despite numerous existing spatial interpolation methods, there remains a significant research gap in accurately predicting rainfall at unsampled locations, specifically when considering both spatial autocorrelation and multiple climate variables simultaneously. This research proposes Spatial Autoregressive Exogenous Kriging (SAR-X Kriging), a novel hybrid approach that integrates the SAR-X model with Ordinary Kriging to enhance rainfall prediction accuracy. Unlike conventional methods, SAR-X Kriging explicitly captures both spatial dependence and the influence of external climate factors, improving predictive performance. SAR-X Kriging first models spatial dependencies between locations and incorporates exogenous climate variables (surface pressure, air temperature, humidity, wind speed, and solar radiation) to enhance prediction accuracy. It also applies kriging for spatial interpolation. This method was chosen for its robustness in capturing spatial dependence and external influences. The analysis revealed significant spatial dependence across districts/cities in Java Island based on the Moran's Index test. The best SAR-X model, utilizing air temperature and wind speed as exogenous variables, achieved a p-value of 6.0352 × 10-9. Predictions using SAR-X Kriging yielded the lowest Mean Absolute Percentage Error (MAPE) of 3.82%, outperforming the standalone SAR-X method MAPE 4.68% and the Ordinary Kriging method MAPE 3.86%. Practically, these results provide reliable rainfall predictions, enabling better climate-informed decision-making in water resource management, agricultural planning, and flood prevention strategies in Java.Keywords: climate; rainfall; MAPE; SAR-X; SAR-X Kriging. AbstrakIndonesia merupakan negara tropis dengan curah hujan tinggi yang dipengaruhi oleh kondisi geografis kepulauan serta fenomena alam seperti El Niño dan La Niña. Menurut Badan Meteorologi, Klimatologi, dan Geofisika (BMKG), La Niña mampu meningkatkan curah hujan bulanan Indonesia hingga 20-40% di atas normal. Meskipun terdapat berbagai metode interpolasi spasial yang telah dikembangkan, masih terdapat kesenjangan penelitian dalam menghasilkan prediksi curah hujan secara akurat di lokasi yang tidak tersampel, terutama ketika mempertimbangkan secara bersamaan ketergantungan spasial serta pengaruh dari berbagai variabel iklim. Penelitian ini mengusulkan metode bernama Spatial Autoregressive Exogenous Kriging (SAR-X Kriging), sebuah pendekatan hybrid baru yang mengintegrasikan model SAR-X dengan metode Ordinary Kriging untuk meningkatkan akurasi prediksi curah hujan. Tidak seperti metode konvensional, SAR-X Kriging secara eksplisit menangkap ketergantungan spasial serta pengaruh faktor iklim eksternal, sehingga meningkatkan kinerja prediktif. SAR-X Kriging bekerja dengan memodelkan terlebih dahulu ketergantungan spasial antar lokasi, kemudian memasukkan variabel eksogen berupa tekanan permukaan, suhu udara, kelembaban, kecepatan angin, dan radiasi matahari untuk meningkatkan akurasi prediksi, serta terakhir menerapkan teknik kriging untuk interpolasi spasial. Metode ini dipilih karena mampu menangkap secara lebih baik ketergantungan spasial sekaligus pengaruh variabel eksternal dibandingkan metode konvensional. Hasil analisis menunjukkan adanya ketergantungan spasial yang signifikan antar kabupaten/kota di Pulau Jawa berdasarkan uji Moran’s Index. Model SAR-X terbaik diperoleh dengan variabel suhu udara dan kecepatan angin, mencapai nilai p-value sebesar 6.0352 × 10-9. Prediksi menggunakan SAR-X Kriging menghasilkan Mean Absolute Percentage Error (MAPE) sebesar 3,82%, mengungguli metode SAR-X yaitu MAPE 4,68% dan metode Ordinary Kriging yaitu MAPE 3,86%. Secara praktis, hasil ini dapat meningkatkan kualitas prediksi curah hujan yang bermanfaat dalam pengelolaan sumber daya air, perencanaan pertanian, serta strategi mitigasi banjir di Pulau Jawa.Kata Kunci: curah hujan; iklim; MAPE; SAR-X; SAR-X Kriging. 2020MSC: 62H11, 86A32
Model Autoregressive Moving Average (ARMA) untuk Peramalan Tingkat Inflasi di Indonesia Khoirunnisa Rohadatul Aisy Muslihin; Budi Nurani Ruchjana
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 2 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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

Abstract

Salah satu faktor yang mempengarui pertumbuhan perekonomian suatu negara adalah besarnya tingkat inflasi. Pentingnya menjaga kestabilan tingkat inflasi dikarenakan adanya pengaruh negatif terhadap kondisi sosial dan ekonomi negara yang diakibatkan oleh tingkat inflasi yang tinggi dan tidak stabil. Oleh karena itu peramalan dapat dilakukan sebagai salah satu upaya menjaga kestabilan tingkat inflasi. Penelitian ini membahas mengenai penggunakan model deret waktu Autoregressive Moving Average (ARMA) dalam meramalkan tingkat inflasi di Indonesia. Data tingkat inflasi dianalisis untuk menentukan model yang terbaik untuk peramalan. Dengan menggunakan data bulanan tingkat inflasi di Indonesia dari Januari 2016 sampai Desember 2021, diperoleh model terbaik yaitu model ARMA(3,3) berdasarkan nilai Akaike Information Criterion terkecil. Hasil analisis menunjukkan bahwa tingkat inflasi pada bulan Januari 2022 hingga Maret 2022 berada di sekitar 0,2%. Pola grafik hasil prediksi mengikuti pola data aktual sehingga model ARMA(3,3) baik untuk digunakan.
Prediksi Harga Saham Syariah menggunakan Bidirectional Long Short Term Memory (BiLSTM) dan Algoritma Grid Search Puteri, Dian Islamiaty; Darmawan, Gumgum; Ruchjana, Budi Nurani
Jambura Journal of Mathematics Vol 6, No 1: February 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i1.23297

Abstract

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.
Penerapan Perangkat Lunak RStudio untuk Penaksiran Parameter Model Spatial Autoregressive Salsabil, Tsuroyya; Kusuma, Dianne Amor; Ruchjana, Budi Nurani
KUBIK Vol 8 No 1 (2023): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v8i1.30037

Abstract

Research and analysis that are not only based on time (temporal) but also on space (spatial) require tools in the form of software to ensure that the data analysis and processing yield good, fast, and accurate results. One of the software tools that can be used for this purpose is RStudio software. The advantages of RStudio include being open-source software (OSS), which can be used freely without cost, and it has many packages and functions that can facilitate data processing. One of the spatial-based analyses is spatial data analysis. The structure within RStudio allows users to call functions related to spatial data analysis, perform computations with sparse matrices (matrices with many zero values), such as spatial weight matrices, estimation of spatial model parameters, and so on. This research examines the application of RStudio software in estimating the parameters of a first-order Spatial Autoregressive (SAR) model using the Maximum Likelihood Estimation (MLE) method on the data of the designation of Intangible Cultural Heritage (ICH) in Indonesia. Based on the results of applying RStudio software, a first-order SAR model with a Queen contiguity weight matrix for the categories of Traditional Customs, Rituals, and Celebrations (TCRC) and Performing Arts (PA) with the minimum Akaike Information Criterion (AIC) value and maximum pseudo- value was obtained for predicting the designation data of ICH in Indonesia. The application of RStudio software to the first-order SAR model for the designation data of ICH in Indonesia speeds up and simplifies calculations, making it suitable as a recommendation for relevant agencies such as the Department of Culture, Tourism, Youth, and Sports (Disbudparpora). 
Penerapan Model Geographically Weighted Regression pada Data Penetapan Warisan Budaya Takbenda di Indonesia Pratomo, Firdaus Ryan; Kusuma, Dianne Amor; Ruchjana, Budi Nurani
KUBIK Vol 9 No 1 (2024): KUBIK: Jurnal Publikasi Ilmiah Matematika
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kubik.v9i1.33492

Abstract

Intangible Cultural Heritage (WBTb) determination data in Indonesia is a cultural investment that needs to be preserved. One of the efforts to preserve WBTb is to determine the cultural preservation factors that influence the WBTb determination data in Indonesia. These factors include Percentage of Population Watching Performances/Art Exhibitions (PPWP), Percentage of Population Using Regional Languages (PPURL), and Percentage of Households Using Traditional Products (PHUTP). However, the different cultural wealth in each province results in spatial heterogeneity, resulting in differences in the determination of cultural preservation factors in each province. This determination can be done with the Geographically Weighted Regression (GWR) model. This study aims to apply the GWR model with Fix Gaussian Kernel, Fix Bisquare Kernel, and Fix Tricube Kernel weighting to determine cultural preservation factors in WBTb determination data in Indonesia so that it can be known what cultural preservation factors are most influential in each region. The research findings show the existence of spatial heterogeneity only in the category of WBTb designation data for Performing Arts (PA) and Oral Expression Tradition (OET), as well as different GWR models in each province that reflect differences in cultural preservation factors. Evaluation with the coefficient of determination shows that the GWR model with the Fix Gaussian Kernel weighting function is the best model for the PA category. 
Penerapan Model Seasonal Autoregressive Integrated Moving Average (SARIMA) dalam Peramalan Curah Hujan di Kabupaten Bandung Barat nadhira, valda azka; Ruchjana, Budi Nurani; Parmikanti, Kankan
KUBIK Vol 10 No 1 (2025): IN PRESS
Publisher : Jurusan Matematika, Fakultas Sains dan Teknologi, UIN Sunan Gunung Djati Bandung

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Abstract

The expansion of the Kabupaten Bandung, namely Kabupaten Bandung Barat (KBB) is located in hilly and lowland areas. Rainfall in Kabupaten Bandung Barat has an impact on the productivity and performance of key sectors, such as agriculture, plantations and tourism. Low rainfall can lead prolonged dry seasons and result in drought. Conversely, extreme rainfall can also have negative impacts, such as causing soil erosion and potentially affecting the appeal and smooth operation of tourist destinations. Therefore, rainfall forecasting is needed in making appropriate policies, especially regarding the impacts of rainfall changes in KBB. The Seasonal Autoregressive Integrated Moving Average (SARIMA) method is applied in this study to forecast rainfall in KBB. The aims of this research are to estimate the parameters of the SARIMA model using the Maximum Likelihood Estimation (MLE) method and to apply the SARIMA method in forecasting rainfall in KBB, particularly during the December-January-February (DJF) period. The results of the analysis show that the SARIMA model can be applied to forecast rainfall in KBB. The best SARIMA model obtained ARIMA(2,1,0)(0,0,1)3 with a MAPE value 17,80%, which indicates an accurate forecasting criterion. Keywords: SARIMA, MLE, Rainfall.
Co-Authors Ahdian, Muhammad Rhafi Ahmad Fawaid Ridwan Akmaliah, Syifani Al Fataa W Haq Al Madani, Aulia R. Al Madani, Aulia Rahman Alawiyah, Mutik Almeira Tsanawafa Anggraeni A Ani Pertiwi Annisa Alma Yunia Annisa Nur Falah, Annisa Nur Annisafiya, Nadira Arisya Maulina Bowo Asep Kurnia Permadi Asep Kurnia Permadi Asri Yuniar Asrirawan Atika Tresna Arianto Atje Setiawan Abdullah Auliyazhafira, Shabira A. Ayu Indriani Ayun Sri Rahmani Bambang Suhandi Bambang Suhandi Bowo, Arisya Maulina Chotimah, Husnul Dedi Rosadi Delvi Rutania Prama Desiyanti, Armalia Devi Munandar, Devi Devi Yanti, Devi Diah Chaerani Dian Islamiaty Puteri Dianne Amor Kusuma Dicky Muslim Dwipriyoko, Estiyan Eddy Hermawan Emah Suryamah Emah Suryamah, Emah Endang Rusyaman Endang Soeryana Hasbullah Fadhilah, Dila Nur Fajriatus Sholihah Falah, Annisa N. Gumgum Darmawan Gumgum Darmawan Hamim Tsalis Soblia Hardianto A Hendarmawan Hendarmawan Hendarmawan Hendarmawan, Hendarmawan Hera Khoirunnisa Husein Hernadi Bahti I Gede Nyoman Mindra I Gede Nyoman Mindra Jaya I Gede Nyoman Mindra Jaya Ibrahim, Riza Andrian Iin Irianingsih Kaerudin, Nandira Putri Kankan Parmikanti Kartika Sari Khafsah Joebaedi Khoirunnisa Rohadatul Aisy Muslihin Khoirunnisa Rohadatul Aisy Muslihin Kusuma, Dianne Amor Lucy Fitria Dewi Mahrudinda Mahrudinda Maryanto Rompon Mindra, I Gede Nyoman Monika, Putri Muhamad Sobari Muhammad Herlambang Prakasa Yudha Muthalib A nadhira, valda azka Najwa, Sandrina Nauli, Theresia S. Novi - Saputri Nur Hamid NUR HAMID Nurdeni, Nurdeni Nurul Gusriani, Nurul Permana, Pandu Permatasari, Noverlina Putri Pratiwi, Dhanti Aurilia Pratomo, Firdaus Ryan Puteri, Dian Islamiaty Putri Monika Putri Monika Putri, Fariza A. Putri, Salsabila Eka Resa Septiani Pontoh Rizka Pradita Prasetya Rizki Apriva Hidayana Salsabil, Tsuroyya Salsabila Salsabila Setialaksana, Wirawan - Shailla Rustiana Sobari, Muhamad Soetikno, Christophorus Sri Adi Widodo Sri Indra Maiyanti Suhandi, Bambang Sutawanir Darwis Tarigan, Wenny Srimeinda Tegar Bratasena WKM Tilas Notapiri Toni Toharudin Tsanawafa, Almeira Tsuroyya Salsabil Tubagus Robbi Megantara Viona Prisyella Balqis Vivian Wilhelmina Vivian Wilhelmina WKM, Tegar Bratasena Yunia, Annisa Alma Zahra, Nabila Zulfa Hidayah Satria Putri