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All Journal International Journal of Public Health Science (IJPHS) Jurnal Ilmu Pertanian Indonesia Jurnal Ekonomi Pembangunan EKSAKTA: Journal of Sciences and Data Analysis JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi JAM : Jurnal Aplikasi Manajemen Jurnal TIMES Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Kubik Journal of Accounting and Investment JURNAL KOLABORASI JIMKesmas (Jurnal Ilmiah Mahasiswa Kesehatan Masyarakat) Al-Jabar : Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Desimal: Jurnal Matematika MUST: Journal of Mathematics Education, Science and Technology Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Socioeconomics and Development Jurnal Informatika Universitas Pamulang Journal of Applied Food Technology J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Teorema: Teori dan Riset Matematika Sainmatika: Jurnal Ilmiah Matematika dan Ilmu Pengetahuan Alam Jambura Journal of Mathematics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) ComTech: Computer, Mathematics and Engineering Applications Journal of Information System, Applied, Management, Accounting and Research Ecces: Economics, Social, and Development Studies Inferensi Journal of Data Science and Its Applications International Journal of Science, Engineering and Information Technology Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Jurnal Statistika dan Aplikasinya KUBIK: Jurnal Publikasi Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi MATH LOCUS: Jurnal Riset dan Inovasi Pendidikan Matematika PROFETIK: Jurnal Mahasiswa Pendidikan Agama Islam SRIWIJAYA JOURNAL OF ENVIRONMENT MATHunesa: Jurnal Ilmiah Matematika VARIANSI: Journal of Statistics and Its Application on Teaching and Research Aceh International Journal of Science and Technology Jurnal Sains dan Informatika : Research of Science and Informatic STATISTIKA Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Jurnal Pendidikan Progresif Indonesian Journal of Statistics and Its Applications Jurnal Info Kesehatan
Fitrianto, Anwar
Departemen Statistika, Fakultas MIPA, IPB University, Bogor 16680
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Market Segmentation of Smartphones in Tokopedia Using Fuzzy C-Means Clustering Reyuli Andespa; Muh. Sunan; Maisa Salsabila; Anwar Fitrianto; Kevin Alifviansyah
Jurnal Pendidikan Tambusai Vol. 9 No. 3 (2025): Desember
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Penelitian ini bertujuan untuk menganalisis segmentasi pasar smartphone pada platform e-commerce Tokopedia dengan menggunakan algoritma Fuzzy C-Means (FCM). Dataset diperoleh melalui proses web scraping terhadap 428 produk smartphone, yang mencakup variabel harga, rating, volume penjualan, nama toko, dan lokasi penjual. Tahapan analisis meliputi data cleaning, eksplorasi deskriptif, penentuan jumlah klaster optimal menggunakan metode Elbow, serta penerapan algoritma FCM untuk membentuk segmen pasar yang homogen. Hasil penelitian mengidentifikasi tiga klaster optimal, yaitu: Budget, Mid-range, dan Premium. Segmen Budget terdiri dari 337 produk dengan rata-rata harga Rp1.623.426, rating rata-rata 4,81, dan volume penjualan rata-rata 301 unit. Segmen Mid-range mencakup 20 produk dengan rata-rata harga Rp3.462.007, rating 2,25, dan penjualan 22 unit. Sementara itu, segmen Premium berisi 102 produk dengan rata-rata harga Rp6.434.597, rating 4,92, dan penjualan 201 unit. Temuan ini menunjukkan bahwa konsumen Tokopedia cenderung lebih menyukai smartphone yang terjangkau namun tetap berkualitas, sementara segmen Mid-range menghadapi tantangan dalam hal positioning dan daya saing pasar.
Multivariate Exploration of Food Security in the Sulampua Region Identification of Clusters and Dominant Dimensions of Food Security Saputra, Wawan; Alfiryal, Naufalia; Prasetya, I Putu Gde Inov Bagus; Fitrianto, Anwar; Alifviansyah, Kevin
Journal of Applied Food Technology Vol 12, No 2 (2025)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17728/jaft.29754

Abstract

Food security is a strategic issue closely related to economic development, community welfare, and the achievement of sustainable development goals. The Food Security Index (FSI) is an important instrument for measuring food security conditions at the provincial and district/city levels. However, FSI performance in Indonesia still shows regional disparities, particularly in Sulawesi, Maluku, and Papua (Sulampua), which tend to have low scores. This study aims to explore patterns of food security and vulnerability in Sulampua through multivariate analysis and regional clustering using K-Means and K-Medoids (PAM) methods. The analysis begins with Principal Component Analysis (PCA) to reduce the dimensionality of FSI indicators and identify dominant factors contributing to data variation. The PCA results show that the first three components explain more than 77% of the variance, with dominant factors including poverty, food expenditure, basic infrastructure access, as well as health and nutrition indicators. The clustering analysis produces two main groups: cluster 1, which includes the majority of districts/cities in Sulawesi and Maluku with relatively better food security, and cluster 2, consisting of 16 districts/cities in Papua with significant food insecurity. Cluster validity evaluation indicates that the K-Medoids method performs better than K-Means, being more robust to outliers and producing more consistent cluster separation. This study contributes to the literature by providing multivariate visual exploration and regional classification based on FSI indicators, which can serve as a basis for formulating more targeted food security policies in the Sulampua region.
PERBANDINGAN K-MEANS DAN K-MEDOIDS DALAM PENGELOMPOKKAN KOMODITAS EKSPOR INDUSTRI DI INDONESIA Arifa, Panji Lokajaya; Rahmasari, Hazelita Dwi; Aimandiga, Carlya Agmis; Fitrianto, Anwar; Yudhianto, Rachmat Bintang
MUST: Journal of Mathematics Education, Science and Technology Vol 10 No 2 (2025): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v10i2.28661

Abstract

International trade plays a crucial role in Indonesia's economic growth, particularly through industrial commodity exports. However, its heavy dependence on a few key commodities makes it vulnerable to global market fluctuations. This study aims to explore trends in industrial commodity export values ​​and compare the performance of cluster methods in grouping commodities based on their value patterns. The research data used are monthly export values ​​from 2022 to mid-2025, sourced from the Central Statistics Agency (BPS). The analytical methods used include trend exploration and cluster analysis with K-Means and K-Medoids using Dynamic Time Warping (DTW) distance. The results of the export value trend exploration indicate that palm oil dominates industrial export value, while other commodities tend to have stable patterns at medium to low values. Evaluation of clustering results using K-Means and K-Medoids each obtained 3 clusters indicating that K-Medoids provided the best performance by obtaining a Silhouette Score of 0.1577 and a Davies-Bouldin Index (DBI) of 1.7990. This value is better than K-Means which obtained a Silhouette Score of 0.1493 and a DBI of 2.3037 indicating that the method is less than optimal in separating clusters. This finding explains that K-Medoids is more robust against outliers and is able to provide more representative groupings. So it can provide a deeper understanding of commodity grouping patterns and contribute to providing export policy recommendations to reduce dependence on primary commodities and increase the export competitiveness of Indonesian industrial products.
Augmentasi GPT-4o dan Fine-Tuning IndoBERT untuk Analisis Sentimen Publik pada Isu Reshuffle Menteri Keuangan Kamila, Sabrina Adnin; Wahda, Aisya Wina; Febriati, Baiq Nina; Anwar Fitrianto; Rachmat Bintang Yudhianto
MUST: Journal of Mathematics Education, Science and Technology Vol 10 No 2 (2025): DECEMBER
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/must.v10i2.28707

Abstract

Reshuffle Menteri Keuangan pada tahun 2025 memicu perhatian publik luas dan menghasilkan dinamika opini di media sosial, khususnya X. Opini publik yang terekam dalam bentuk teks bersifat masif, real-time, dan tidak terstruktur, sehingga menghadirkan tantangan analisis karena penggunaan bahasa informal serta distribusi kelas sentimen yang tidak seimbang. Penelitian ini bertujuan untuk mengidentifikasi sentimen publik terkait reshuffle Menteri Keuangan dengan memanfaatkan integrasi GPT-4o dan IndoBERT. GPT-4o digunakan sebagai instrumen augmentasi data untuk memperkaya kelas minoritas, sedangkan IndoBERT berperan sebagai model klasifikasi sentimen yang dioptimalkan untuk bahasa Indonesia. Hasil penelitian menunjukkan bahwa pendekatan ini mampu meningkatkan kualitas representasi data dan stabilitas klasifikasi. Model IndoBERT yang dilatih dengan data hasil augmentasi mencapai akurasi 86% dan macro-F1 sebesar 0,86, dengan performa terbaik pada kelas negatif (F1=0,88), disusul positif (F1=0,86) dan netral (F1=0,83). Temuan ini menegaskan bahwa integrasi GPT-4o dan IndoBERT efektif dalam mengatasi imbalanced data serta meningkatkan keandalan analisis sentimen berbahasa Indonesia. Penelitian ini tidak hanya memperkaya literatur analisis teks di Indonesia, tetapi juga memberikan kontribusi praktis bagi pembuat kebijakan dan media dalam memahami opini publik secara lebih proporsional.
Performance of Multivariate Missing Data Imputation Methods on Climate Data Widyawati, Amalia Safira; Fitrianto, Anwar; Silvianti, Pika
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11316

Abstract

Climate data plays an important role in various aspects of life. However, missing data is often found, which can interfere with data processing and reduce the quality of analysis. Therefore, appropriate handling methods are needed to ensure that the analysis results remain valid. This study aims to compare the performance of several imputation methods for missing multivariate data based on the identification of actual missing data patterns, and to determine the appropriate imputation method based on the mechanism of missing data. This study also aims to apply the best method to data with actual missing data patterns to assess its effect on descriptive statistical changes required for further climatological analysis. The methods used include monthly averages, missRanger, k-Nearest Neighbor (k-NN), and Iterative Robust-Model Imputation (IRMI). The missing data information was obtained from Global Surface Summary of the Day (GSOD) data, namely temperature, precipitation, humidity, pressure, and wind speed variables with a daily frequency for 11 years, with a missing data proportion of 11.4%. The missing data patterns were then applied to relatively complete NASA Power data to evaluate the imputation results. The results show that IRMI is less capable of handling extreme missing data conditions, namely 17 completely missing rows. In contrast, k-NN, missRanger, and monthly averages provided better results in both extreme and non-extreme conditions. Of the four methods, monthly averages were chosen because they were able to overcome missing data while maintaining multivariate structure with 58% on sMAPE and 2.64% on relative difference.
Optimizing Currency Circulation Forecasts in Indonesia: A Hybrid Prophet- Long Short Term Memory Model with Hyperparameter Tuning Vivin Nur Aziza; Utami Dyah Syafitri; Anwar Fitrianto
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4052

Abstract

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.
Household Climate Resilience Index and Its Determinants: An Empirical Study in DKI Jakarta Sundari, Marta; Sadik, Kusman; Wigena, Aji Hamim; Fitrianto, Anwar; Boer, Rizaldi
Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Vol 16 No 2 (2026): Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (JPSL)
Publisher : Pusat Penelitian Lingkungan Hidup, IPB (PPLH-IPB) dan Program Studi Pengelolaan Sumberdaya Alam dan Lingkungan, IPB (PS. PSL, SPs. IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jpsl.16.2.162

Abstract

Climate change has intensified environmental pressures in urban coastal areas, particularly in DKI Jakarta, where recurrent flooding, tidal inundation, and heat extremes threaten urban sustainability. This study developed a Household Climate Resilience Index (HCRI) to assess the resilience of urban households to climate-related hazards using a robust principal analysis (RPCA) framework. The analysis was based on household survey data from 221 respondents across 17 urban villages in Jakarta, encompassing four resilience dimensions: exposure, sensitivity, incremental adaptation, and transformational adaptation. RPCA with a minimum covariance determinant estimator was applied to minimize the influence of outliers and ensure stable component estimation. The results reveal clear spatial heterogeneity in resilience, characterized by a distinct north–south gradient: northern coastal areas such as Kamal, Koja, and Pluit show the lowest resilience due to high flood exposure and land subsidence, whereas central and southern areas exhibit stronger adaptive capacity. The key determinants of resilience include flood frequency, household education levels, per-family expenditure, and proactive adaptation behaviors. The Kendall correlation test (τ = 0.518, p = 0.015) confirmed a significant positive association between flood occurrence and low resilience levels. The developed HCRI provides a robust, data-driven framework to support targeted climate adaptation policies and urban resilience planning in Jakarta, Indonesia. HCRI outputs, together with the identified key determinants (flood frequency, education, per-family expenditure, and proactive adaptation), can guide the prioritization of urban environmental management and adaptation investments in the most vulnerable urban villages, including drainage upgrading, land subsidence control, and coastal protection.
Clustering of Central Java Districts Based on Educational Indicators: A Comparison of K-Means and Hierarchical Methods Muhammad Syafiq; Nabila Fida Millati; Muh Akbar Idris; Anwar Fitrianto; Kevin Alifviansyah; Erfiani Erfiani
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/xen35m31

Abstract

This study aims to cluster districts and municipalities in Central Java based on educational indicators and to compare the clustering performance of K-Means and Hierarchical methods. The analysis uses secondary data from the Statistical Publication of Education in Central Java Province 2024, covering eight indicators related to educational facilities, participation, and attainment. The data were standardized, explored using descriptive statistics, and analyzed using K-Means and Hierarchical clustering methods. The evaluation results show that both methods produced broadly comparable clustering structures. However, Hierarchical Clustering demonstrated slightly stronger performance in terms of cluster separation and compactness, with a higher Silhouette Index (0,591) and Dunn Index (0,320) and a lower Davies–Bouldin Index (0,501) compared with K-Means (SI 0,584, Dunn 0,225, DBI 0,562). Meanwhile, K-Means produced a more balanced partition and a higher Calinski–Harabasz Index (48,63) than Hierarchical Clustering (44,30). The clustering results reveal a clear pattern of educational disparities across the region. A small group consisting of Sukoharjo Regency and the cities of Semarang, Surakarta, Salatiga, and Magelang forms a higher-performing cluster characterized by stronger educational indicators, while most rural districts belong to a lower-performing group. These findings indicate that educational disparities in Central Java remain spatially concentrated and highlight the need for targeted policies to strengthen educational investment and improve progression to higher levels of education in less developed districts.
CLASSIFICATION OF CARDIOVASCULAR AND CHRONIC RESPIRATORY DISEASES UTILIZING ENSEMBLE MODELS WITH DATA EXPLORATION TECHNIQUES I Gusti Ngurah Sentana Putra; Amri Luthfi Najih; Unique DA Resiloy; Rachmat Bintang Yudhianto; Erfiani Erfiani; Anwar Fitrianto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9311

Abstract

Non-communicable diseases, especially cardiovascular and chronic respiratory conditions, contribute significantly to Indonesia’s healthcare burden and BPJS expenditure. Health claim data often suffer from class imbalance, multicollinearity, and outliers that impair model accuracy. This study evaluates the impact of essential data exploration techniques such as winsorizing, correlation and VIF analysis, variable selection, and SMOTE on the performance of ensemble classifiers. The dataset comprises 497,439 BPJS health insurance claims from 2022, including 27 predictors (14 numerical and 13 categorical). Two data pipelines were compared: one without preprocessing and another incorporating systematic data exploration. Five ensemble models were tested, namely Decision Tree, Extra Trees, Random Forest, XGBoost, and LightGBM. Model performance was assessed using F1-score, balanced accuracy, and G-mean across 20 stratified cross-validations. The results show that preprocessing substantially improves classification fairness and accuracy. Bagging models, particularly Random Forest, achieved the highest improvement, with balanced accuracy and G-mean increasing from around 0.93 to 0.99. Boosting models showed modest gains. These findings highlight that rigorous data exploration enhances ensemble classifier performance, enabling more reliable disease classification and supporting fairer, data-driven decision-making in BPJS health management.
KAJIAN EKSPLORASI TENTANG POLA KESEJAHTERAAN MULTIDIMENSI DI JAWA BARAT MENGGUNAKAN ANALISIS GEROMBOL Az-Zahra, Putri Nisrina; Tangdilomban, Claudian Tikulimbong; Mutmainah, Zamrah; Fitrianto, Anwar; Alifviansyah, Kevin; Erfiani, Erfiani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9307

Abstract

Kesejahteraan multidimensi mencerminkan kualitas hidup yang melampaui indikator tunggal seperti IPM. Penelitian ini berfokus pada eksplorasi dan visualisasi pola kesejahteraan multidimensi di Jawa Barat menggunakan algoritma K-Means dan HDBSCAN. Data Susenas Maret 2024 mencakup 12 variabel dalam empat dimensi: pendidikan, kesehatan, ekonomi, dan fasilitas rumah tangga. Reduksi dimensi dilakukan dengan PCA sebelum clustering. Hasil menunjukkan HDBSCAN lebih optimal dibandingkan K-Means, dengan Silhouette Score 0,558, Calinski-Harabasz Index 41,584, dan Davies-Bouldin Index 0,603. Visualisasi cluster mengungkap ketimpangan antarwilayah, di mana daerah perkotaan cenderung lebih sejahtera, sedangkan pedesaan dan pinggiran menunjukkan variasi yang lebih beragam.
Co-Authors -, Salsabila A. A., Muftih Aam Alamudi Abd. Rahman Adeline Vinda Septiani Agus M Soleh Agus Mohamad Soleh Ahmad Syauqi Aimandiga, Carlya Agmis Aji Hamim Wigena Alfa Nugraha Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfa Nugraha Pradana Alfi Indah Nurrizqi Alfiryal, Naufalia Alifviansyah, Kevin Aliu, Mufthi Alwi ALIU, MUFTIH ALWI Amalia Kholifatunnisa Amanda, Nabila Amatullah, Fida Fariha Amelia, Reni Amir Abduljabbar Dalimunthe Amri Luthfi Najih Anadra, Rahmi Anang Kurnia Anang Kurnia Angelia, Riza Rahmah Anik Djuraidah Anisa Nurizki Annissa Nur Fitria Fathina Ardhani, Rizky Arifa, Panji Lokajaya Aristawidya, Rafika Askari, M. Aiman Asri Pratiwi, Asri Assyifa Lala Pratiwi Hamid Az-Zahra, Putri Nisrina Azis, Tukhfatur Rizmah Aziza, Vivin Nur Bagus Sartono Budi Susetyo Bukhari, Ari Shobri Cahya Alkahfi Choon, Lai Ming Daswati, Oktaviyani Defri Ramadhan Ismana Deri Siswara Dessy Rotua Natalina Siahaan Dessy Siahaan Devi Permata Sari Dian Handayani Dwi Jumansyah, L.M. Risman Erfiani Erfiani Erfiani Erfiani Erfiani Erfiani Fadilah, Anggita Rizky Fahira, Fani Farit M Affendi Farit M. Afendi Farit M. Afendi Farit Mochamad Afendi Fatimah Fatimah Fauziah, Monica Rahma Febriati, Baiq Nina Fulazzaky, Tahira Ghina Fauziah Gustiara, Dela Hari Wijayanto Harismahyanti A., Andi Hasnataeni, Yunia Hasnita Hasnita Heri Cahyono I Gusti Ngurah Sentana Putra I Made Sumertajaya Ilham Azagi Ilmani, Erdanisa Aghnia Imam Hanafi Indah, Yunna Mentari Indahwati Indahwati Indahwati Indahwati, Indahwati Irsyifa Mayzela Afnan Irzaman, Irzaman Ismah, Ismah Isna Shofia Mubarokah Iswan Achlan Setiawan Iswati Ita Wulandari Jamaluddin Rabbani Harahap Jap Ee Jia Jia, Jap Ee Jumansyah, L. M. Risman Dwi Jumansyah, L.M. Risman Dwi Kamila, Sabrina Adnin Kapiluka, Kristuisno Martsuyanto Kevin Alifviansyah Khairil Anwar Notodiputro Khikmah, Khusnia Nurul Khusnia N. K. Khusnia Nurul Khikmah Kriswan, Suliana Kusman Sadik L.M. Risman Dwi Jumansyah La Ode Abdul Rahman La Ode Abdul Rahman Linganathan, Punitha lmam Hanafi M. Aiman Askari M.S, Erfiani Maisa Salsabila Manaf, Silmi Annisa Rizki Marshelle, Sean Megawati Megawati Muftih Alwi Aliu Muftih Alwi Aliu Muh Akbar Idris Muh. Sunan Muhadi, Rizqi Annafi Muhammad Irfan Hanifiandi Kurnia Muhammad Syafiq Muhammad Yusran mutiah, siti Mutmainah, Zamrah Nabila Fida Millati Nabila Ghoni Trisno Hidayatulloh Nadira Nisa Alwani Nashir, Husnun Nisa Nur Aisyah Novi Hidayat Pusponegoro Nugraha, Adhiyatma Nur Hidayah Nur Khamidah NURADILLA, SITI Nurizki, Anisa Pangestika, Dhita Elsha Pika Silvianti Pradnya Sri Rahayu Prasetya, I Putu Gde Inov Bagus Pratiwi, Nafisa Berliana Indah Punitha Linganathan Putri Auliana Rifqi Mukhlashin Putri, Mega Ramatika Putri, Oktaviani Aisyah Rachmat Bintang Yudhianto Rachmat Bintang Yudhianto Rafika Aufa Hasibuan Rahmasari, Hazelita Dwi Rahmatun Nisa, Rahmatun Rais Ramadhan, Syaifullah Yusuf Reka Agustia Astari Reni Amelia Reni Amelia Retna Nurwulan Reyuli Andespa Riansyah, Boy Rifda Nida’ul Labibah Riska Yulianti, Riska Rizaldi Boer Rizki Manaf, Silmi Anisa Rizki, Akbar Rizqi, Tasya Anisah Sachnaz Desta Oktarin salsa bila Sari, Jefita Resti Seta Baehera Setyowati, Silfiana Lis Siau Hui Mah Siau Man Mah Silmi Annisa Rizki Manaf Siregar, Indra Rivaldi Siti Hafsah Siti Hasanah Siti Nur Azizah, Siti Nur Sofia Octaviana Sony Hartono Wijaya Suantari, Ni Gusti Ayu Putu Puteri Suliana Kriswan Sundari, Marta Tangdilomban, Claudian Tikulimbong Tangke, Nabillah Rahmatiah Titin Agustina Titin Yuniarty Yuniarty Unique DA Resiloy Uswatun Hasanah Utami Dyah Syafitri Utami, Annisa Putri Utomo, Agung Tri Vitona, Desi Vivin Nur Aziza Wahda, Aisya Wina Waliulu, Megawati Zein Wan Muhamad, Wan Zuki Azman Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Wan Zuki Azman Wan Muhamad Waode, Yully Sofyah Wawan Saputra Widyawati, Amalia Safira Winata, Hilma Mutiara Xin, Sim Hui Yenni Angraini Yudhianto, Rachmat Bintang Yuniarsyih R.A, Rizqi Dwi Yusuf, Fajar Athallah Zaenal, Mohamad Solehudin Zahid, Muhammad Farhan Zahra, Latifah Zein Rizky Santoso