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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Lingua Jurnal Bahasa dan Sastra PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Seminar Nasional Informatika (SEMNASIF) Widyariset Indonesian Journal of Science and Technology Jurnal Sains Matematika dan Statistika Al-Jabar : Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization JURNAL SIMETRIK Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK MAJALAH ILMIAH GLOBE Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review SALINGKA Jurnal Matematika UNAND Building of Informatics, Technology and Science Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science InPrime: Indonesian Journal Of Pure And Applied Mathematics Journal of Innovation Information Technology and Application (JINITA) Widyariset Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Journal of Mathematics: Theory and Applications Jurnal Pijar MIPA Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Al-Aqlu : Jurnal Matematika, Teknik dan Sains Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications Majalah Ilmiah Bahasa dan Sastra International Journal of Computing Science and Applied Mathematics-IJCSAM
Sri Nurdiati
School Of Data Science, Mathematics, And Informatics, IPB University, Jl. Raya Darmaga Kampus IPB, Bogor, West Jawa, 16680, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Veterinary Other

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Dynamic optimization using long short-term memory and genetic algorithms for predicting marine data Mukhlis Mukhlis; Indra Jaya; Sri Nurdiati; Karlisa Priandana; Irman Hermadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2826-2837

Abstract

This study aims to develop an accurate and efficient ocean data prediction model to tackle the challenges posed by climate change and complex oceanographic dynamics. The main goal is to use long short-term memory (LSTM) networks along with genetic algorithms (GA) to predict four key ocean factors at once: sea surface temperature (SST), sea surface height (SSH), sea surface salinity (SSS), and chlorophyll-a (Chl-a). An experimental quantitative approach is employed, utilizing satellite data from the Banda Sea region. This approach involves time series modeling using LSTM, which is optimized by GA for hyperparameters such as the number of neurons and batch size. The results show that the combined LSTM-GA model greatly improves prediction accuracy and successfully identifies seasonal trends and irregular changes in all variables, even when there is a lot of noise. Tests reveal that the optimal configuration varies for each variable, and the GA optimization process can expedite model convergence by as little as 10 epochs. These findings underscore the effectiveness of integrating evolutionary techniques in training deep learning (DL) models for ocean data. The implications of this research include potential applications in adaptive ocean monitoring systems, early warning initiatives, and data-driven planning in marine resource management.
Sentiment Analysis of Indonesia’s Free Nutritious Meal Program on X Using SVM and Random Forest Ferdy Aliansyah Hasyim; Talenta Parfaibya Mahenindra; Lilis Sriwahyuni; Alika Azka Shapira; Wigawijayanti Wigawijayanti; Nadhifa Zahra Ghaisani; Mirlan Sujana; Sri Nurdiati; Mohamad Khoirun Najib
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40717

Abstract

The Free Nutritious Meal (Makan Bergizi Gratis/MBG) Program was introduced to address stunting in Indonesia, yet its implementation has sparked diverse public debate. This study aims to map public perception on social media X and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in sentiment classification. Utilizing a large-scale dataset of 7,452 tweets collected via stratified random sampling from January to October 2025, this research applies TF-IDF feature extraction and SMOTE data balancing. The analysis reveals that positive sentiment dominates at 47.62%, while negative sentiment accounts for 39.8\%, and neutral for 12.57%. In model comparison, SVM without SMOTE achieved the best performance with 80.66% accuracy and an F1-Score of 79.79%, outperforming Random Forest, which only reached a maximum accuracy of 72.23% after SMOTE application. These findings provide an objective overview of MBG policy acceptance and methodological insights into the effectiveness of SVM in handling high-dimensional text data.
Short- and Long-Run Relationships Between Observed and Model 1 Output Rainfall Data in Majalengka Regency Sri Nurdiati; Mohamad Khoirun Najib; Fathia Rahmaisty
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.40902

Abstract

This study examines the short-run and long-run relationships between observed monthly rainfall and CMIP6 climate model projections in Majalengka Regency, Indonesia. Monthly rainfall observations from the BMKG Kertajati Meteorological Station are analyzed using the Autoregressive Distributed Lag (ARDL) framework, which enables simultaneous assessment of short-term dynamics and long-term equilibrium relationships. Stationarity and cointegration are evaluated using the Augmented Dickey–Fuller test and ARDL bounds testing, respectively, while model performance is assessed through out-of-sample validation for the period 2015–2017 under three CMIP6 emission scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The results indicate a positive and statistically significant short-run relationship between observed rainfall and CMIP6 projections across all scenarios, suggesting that climate models capture local scale monthly rainfall variability reasonably well. In contrast, the long-run relationship is weak and negative, highlighting limitations in representing long-term local rainfall dynamics. Model performance is highest under the low-emission SSP1-2.6 scenario and decreases under higher-emission scenarios. These findings suggest that CMIP6 outputs are more reliable for short-term rainfall analysis than for long-term local assessments without bias correction or downscaling.
Analysis of Drought Characteristics in West Java Based on Return Period of Consecutive Dry Days Amanda Nabila; Sri Nurdiati; I Gusti Putu Purnaba; Mohammad Khoirun Najib
Jambura Geoscience Review Vol 8, No 1 (2026): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jgeosrev.v8i1.32484

Abstract

Drought is one of the climate change phenomena that must be faced every year in some regions in Indonesia. West Java is a region that often experiences drought in Indonesia. Prolonged droughts are routinely experienced in some areas of West Java, while shorter periods of drought occur between rainfall events in several other regions of West Java. The characteristics of drought in West Java can be analyzed using one of the climate indicators, Consecutive Dry Days (CDD), based on the calculation of the return period of the climate indicator. Therefore, this study aims to analyze the characteristics of drought in West Java based on the calculation of the return period of the parametric distribution function by the CDD. Graph comparison and the Anderson-Darling test were used to estimate the parametric distribution function. Hourly ERA5-Land precipitation (1981–2022) was aggregated to daily totals; annual CDD was defined as the longest run of days with rainfall 1 mm, and return periods were computed using cut-off levels at the 75%, 85%, and 95% quantiles of the regional CDD distribution to map recurrence potential across cities and regencies. Based on the study's results, most of the CDD data in the West Java region have the fittest parametric distribution, namely the inverse Gaussian distribution, followed by the generalized extreme values, Weibull, and lognormal distributions. Further return period analysis shows that the area with the shortest return period to drought so that extreme drought often occurs, is the Indramayu Regency area. In that case, the areas with the longest drought return period are Bogor Regency, Bogor City, and Tasikmalaya City. These findings provide a distribution-based quantification of spatial drought recurrence in West Java to support early-warning and water-resources planning.
Heterogeneous Correlation Mapping between Rainfall Variability in Lake Toba and Indian Ocean Sea Surface Temperature Mohamad Khoirun Najib; Sri Nurdiati
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 1 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i1.7665

Abstract

Rainfall variability in the Lake Toba watershed of North Sumatra is influenced by large-scale ocean–atmosphere in-teractions, particularly those involving sea surface temperatures (SST) in the Indian Ocean. This study applies Heterogeneous Correlation Mapping (HCM) to examine the spatially varying relationship between monthly rainfall at 13 meteorological stations and SST over the Indian Ocean warm pool (5°S–10°N, 60°E–80°E) during 1981–2014. Singular Value Decomposition (SVD) is employed to extract dominant coupled modes of SST–rainfall variability. Results indicate that a six-month lag yields the strongest coupling, with the leading mode explaining 88.5% of the total variance. A clear spatial heterogeneity is observed: stations such as Lumban Julu and Silaen exhibit stronger SST–rainfall correlations, while others show weaker responses, likely due to topographic and local climatic modulation. These findings underscore the importance of accounting for spatial and temporal structures in hydroclimatic teleconnection analysis and offer insights for improving seasonal rainfall prediction in mountainous tropical regions
Penerapan Finite Element Method (FEM) Persamaan Difusi Pada Ruangan Yang Memiliki Air Conditioner Aufa Ghifada; Lizzilmi Syarifatuz Zaimah; Iftar Hendry; Farah Annisa Tri Sundari; Dezvini Muthmainnati Vidia; Naura Dalta Indriyani; Mochamad Tito Julianto; Sri Nurdiati; Mohamad Khoirun Najib; Syukri Arif Rafhida
Techno.Com Vol. 25 No. 2 (2026): May 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i2.15990

Abstract

Kenyamanan termal merupakan salah satu aspek penting dalam perancangan bangunan karena kondisi suhu berpengaruh langsung terhadap produktivitas dan kenyamanan penghuni ruangan. Salah satu sistem yang umum digunakan untuk mengatur suhu ruangan adalah air conditioner (AC). Namun demikian, distribusi suhu di dalam ruangan tidak langsung menjadi seragam ketika AC dinyalakan. Penelitian ini bertujuan untuk menganalisis distribusi suhu pada ruangan ber-AC menggunakan metode elemen hingga (Finite Element Method). Fenomena fisik dimodelkan menggunakan persamaan panas dua dimensi yang menggambarkan proses difusi suhu terhadap ruang dan waktu. Domain simulasi merepresentasikan denah rumah sederhana yang terdiri dari beberapa ruangan dengan sumber pendingin berada di ruang tengah. Model matematis yang digunakan melibatkan kondisi awal suhu ruangan sebesar 25°C serta kondisi batas yang merepresentasikan sumber pendingin dan dinding ruangan yang terisolasi. Simulasi numerik dilakukan menggunakan perangkat lunak Wolfram Mathematica untuk memperoleh distribusi suhu terhadap waktu. Hasil simulasi menunjukkan bahwa pendinginan awalnya terjadi di sekitar sumber AC kemudian secara bertahap menyebar ke bagian ruangan lainnya. Keberadaan dinding dan sekat ruangan mempengaruhi pola difusi suhu sehingga menghasilkan laju pendinginan yang berbeda pada setiap ruangan. Seiring berjalannya waktu, sistem mendekati kondisi tunak di mana distribusi suhu menjadi lebih seragam. Hasil penelitian ini menunjukkan bahwa metode elemen hingga efektif digunakan untuk memodelkan penyebaran suhu pada ruangan dengan geometri yang kompleks.   Kata Kunci - Distribusi Suhu, Metode Elemen Hingga, Pendingin Ruangan, Persamaan Panas, Simulasi Numerik
Long Short Term Memory-Based Marine Data Prediction with Pearson Correlation Mukhlis Mukhlis; Indra Jaya; Sri Nurdiati; Karlisa Priandana; Irman Hermadi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 1 (2025): Maret 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i1.10731

Abstract

Marine data prediction plays a vital role in supporting decision-making in the field of marine environment and resources. However, the complexity of marine data, which is nonlinear and dynamic, is a significant challenge in producing accurate predictions. This study aims to explore the role of Long Short-Term Memory (LSTM) models in computer systems to predict marine data, focusing on Pearson Correlation analysis. The methods applied include collecting historical marine data, implementing LSTM models for prediction, and evaluating performance using metrics such as Mean Absolute Error (MAE). In addition, Pearson Correlation analysis is used to understand the relationship between variables in marine data. The results show that the LSTM model is able to produce predictions with a low error rate with a composition of training data and testing data of 80:20, resulting in Sea Surface Temperature (SST) = 0.0053, Sea Surface Salinity (SSS) = 0.0026, sea Surface Height (SSH) = 0.0061 and CHL-a = 0.0002 and shows a significant relationship between variables through Multivariate correlation analysis. This research contributes to the development of marine data-based prediction systems and provides implications for the world of marine resource research and management.
Handling Missing Values using Weighted Linear Combination of KNN-SVD: A Case Study of Rainfall Data in West Java Rizkian Agung Jamaesa; Sri Nurdiati; Elis Khatizah; Mohamad Khoirun Najib; Lilis Sri Wahyuni
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 3 (2026): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i3.36708

Abstract

This study is an experimental and comparative quantitative research that evaluates missing value imputation methods for daily rainfall data in West Java. Rainfall data are crucial for environmental policies, particularly in flood control and water resource management. Daily rainfall records from five BMKG stations in West Java were used in this study. Although these stations provide accurate data through direct measurement, missing values often occur due to human error or equipment problems. To solve this, we introduce an integrated imputation method that combines K-Nearest Neighbors (KNN) and Singular Value Decomposition (SVD) with a Weighted Linear Combination (WLC) approach. This method represents a significant improvement over the single-model imputation methods employed in earlier research. We split the dataset into training and testing sets using five different ratios (95:5%, 90:10%, 80:20%, 70:30%, and 64:40%) to test the model's performance. We measured effectiveness using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results show that the combined KNN–SVD method outperforms KNN or SVD alone in all cases. The best results were obtained from the 95:5% split, with the lowest MAE and RMSE values of 7.35 and 13.22, respectively. These results suggest that the integrated KNN–SVD imputation model enhances the reliability of rainfall datasets, thereby improving climate information for hydrological studies, disaster risk reduction, and policy-making in West Java.
Random Forest-Based Multiclass Classification of Pestalotiopsis sp. Disease Severity in Rubber Plants Using UAV Multispectral Data. Solikin; Yeni Herdiyeni; Annisa; Lilik Budi Prasetyo; Tri Rapani Febbiyanti; Imas Sukaesih Sitanggang; Sri Nurdiati
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3310

Abstract

Pestalotiopsis sp.-induced leaf-fall disease threatens rubber (Hevea brasiliensis) productivity by reducing canopy density, degrading chlorophyll, and impairing photosynthetic capacity. This study developed an exploratory multiclass framework to classify disease severity in rubber plantations using UAV multispectral data and a Random Forest (RF) algorithm. The dataset comprised 144 ground-truth canopy-level samples labeled into four ordinal severity classes, L1 (mild), L2 (moderate), L3 (severe), and L4 (very severe), and 1760 additional UAV-derived observations for spatial inference. Spectral and structural predictors included the Leaf Chlorophyll Index (LCI), Normalized Difference Red Edge (NDRE), density_LCI, and density_NDRE, collected across three rubber clones, BPM 24, GT 1, and RRIC 100. Model development used an 80:20 stratified train–test split, stratified and nested cross-validation, class weighting, and SMOTE applied only within training folds to reduce imbalance and leakage. Performance was evaluated using accuracy, class-wise precision–recall–F1, and the multiclass Matthews Correlation Coefficient (MCC). Hyperparameter tuning (n_estimators = 150, max_depth = 7, min_samples_split = 9) increased MCC from 0.143 to 0.547 on independent test data and 0.586 under the all-labeled-data scenario, indicating moderate agreement. LCI emerged as the dominant predictor, supporting the physiological relevance of chlorophyll-sensitive red-edge information. However, low recall for L2 and unstable L4 performance reflected class imbalance, limited labeled samples, and overlapping spectral responses. The framework provides a more informative severity stratification than binary detection, but further ground-truth expansion, ordinal or cost-sensitive modeling, comparative benchmarking, and multi-season validation are required before operational deployment.
Time-varying Distribution Analysis for Rainfall and Air Temperature Data in Jakarta in Response to Future Climate Change Suci Nur Setyawati; Sri Nurdiati; I Wayan Mangku; Mohamad Khoirun Najib
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.32780

Abstract

AbstractIndonesia is vulnerable to climate change (rainfall and air temperature), which can increase the chances of climatic disasters. An organized risk analysis is a strategic plan to minimize the impact. The purpose of this research is to estimate time-varying distribution parameters for normal, generalized extreme value (GEV), and lognormal distributions using fminsearch and MLE algorithms on rainfall and air temperature data in Jakarta, as well as visualize and analyze the best time-varying distribution. The maximum likelihood estimation (MLE) method is used for stationary distribution parameter estimation. The fminsearch algorithm is used for stationary and nonstationary distribution parameter estimation. The highest difference value of stationary distribution parameter results from both methods is 5.3768 mm for rainfall data and 0.2670°C for air temperature data. The results of the best distribution based on the AIC value are the 3-parameter lognormal distribution for rainfall data and the 4-parameter GEV distribution for air temperature data. Over time, the variance of rainfall increases, and the average air temperature increases with a fixed variance.
Co-Authors AA Gede Rai Gunawan Aaron August Vincent Soelaiman Abisha, Nicholas Ade Irawan Ade Irawan Agah D. Garnadi Agung Widyo Utomo Agus Buono Aldri Frinaldi Alifah, Nayla Nur Alifah, Rifdah Nur Alika Azka Shapira Amalia, Rizki Nurul Amanah, Ayu Amanda Nabila Anak Agung Gede Rai Gunawan Andriani, Rizka D. Annisa Annisa Annisa Permata Sari, Annisa Permata Antika, Ester Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardiyani, Evi Aufa Ghifada Aulia Rizki Firdawanti Ayu Amanah Aziz, Muhammad Farhan Azizah Aulia Firdhasari Bib Paruhum Silalahi Blante, Trianty Putri Cece Sumantri Chairunisa, Ghevira David Vijanarco Martal Deni Suwardhi DEWI RAHMAWATI Dezvini Muthmainnati Vidia Edi Santosa Ekaputri, Dhea Endar Hasafah Nugrahani Eragilang Muhammad Hastapatria Ester Antika Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Faiza Mayla Sabita Fajar Delli Wihartiko Farah Annisa Tri Sundari Fathia Rahmaisty Fatmawati, Linda Leni Fauzan, Muhammad Daryl Ferdy Aliansyah Hasyim Foky Michelin Ginting, Dini Tri Putri Br Hanief, Hafzal Hany Savitry Harley Dearmanson Girsang Hasafah Nugrahani, Endar Heliza Rahmania Hatta, Heliza Rahmania Hendri Irwandi Henny Nuraini Henriyansah Herlambang, Karen Hilmi, Kautsar I Gusti Putu Purnaba I Wayan Mangku Iftar Hendry Imas Sukaesih Sitanggang Imni, Salsabila F. Indra Jaya Irman Hermadi Irman Hermadi Irmanida Batubara Jauhari, Muhammad Fakhri Karlisa Priandana Kasiyah M. Junus Kautsar Hilmi Khairuna Putri Gunawan Khatizah, Elis Khoerunnisa, Nazwa Komariah . Lana Syakina Lilik Budi Prasetyo LILIK BUDIPRASETYO Lilis Sri Wahyuni Lilis Sriwahyuni Linda Leni Fatmawati Lizzilmi Syarifatuz Zaimah M. Syamsul Maarif Maliha Qonita Maman Turjaman Marimin Marimin Mas’oed, Teduh W. Maulia, Syammira Dhifa Mirlan Sujana Mirza Farhan Azhari Mochamad Tito Julianto Mohamad Khoirun Najib Mohammad Khoirun Najib Mualim Arya Ilyas Wiradinata Muhamad Adzka Rizkia Muhamad Syukur Muhammad Adam Tripranoto Muhammad Faza Hanifan Muhammad Fikri Isnaini Muhammad Ilyas Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Mukhlis Mukhlis Mukhlis Mukhlis Muliawan Sebastian, Denny Nadhifa Zahra Ghaisani Nadhira Maulida Hayani Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad K. Najib, Mohamad Khoirun Najwaa Alifya Azka Nandika Safiqri Naura Dalta Indriyani Nerissa Patrice Manuella NGAKAN KOMANG KUTHA ARDHANA Nicholas Abisha Niswati, Za'imatun Noval Nur Fallahi, Putri Afia Nur Nabila Nurwegiono, Muhammad Nuzhatun Nazria Pandu Septiawan Pratama, Yoga Abdi Prihasuti Harsani Putri, Renda S. P. Rachma Fauziah Krismayanti Rafhida, Syukri Arif Rahma Alya Zahrani Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rika Kusumawati Rizkian Agung Jamaesa Rohimahastuti, Fadillah Roma Rio Simbolon Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Septian Dhimas Shelvie Nidya Neyman Sitanggang, Imas S. Solikin Solikin Sony Hartono Wijaya Sopaheluwakan, Ardhasena Sri Hartati Sri Mulatsih Srihadi Agungpriyono Sriwahyuni, Lilis Suci Nur Setyawati SUHARINI, YUSTINA SRI Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Syukri Arif Rafhida Syukri Arif Rafhida Talenta Parfaibya Mahenindra Tri Rapani Febbiyanti Tri Rapani Febbiyanti Trianty Putri Blante Triwulandari, Raden Roro Carissa Usmany, Rendy Valentdio, Ruben Harry Verry Riyanto Vicky Zilvan Wigawijayanti Wigawijayanti Wisnu Ananta Kusuma Yandra Arkeman Yasin Yusuf Yeni Herdiyeni Yoga Abdi Pratama Zelisha Pitriatuz Zahra Fauzi