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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) HAYATI Journal of Biosciences Jurnal Pengolahan Hasil Perikanan Indonesia FORUM STATISTIKA DAN KOMPUTASI Indonesian Journal of Geography Media Statistika JURNAL KIMIA SAINS DAN APLIKASI Jurnal Manajemen Teknologi CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmu Komputer dan Agri-Informatika The Journal of Pure and Applied Chemistry Research JUITA : Jurnal Informatika Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Knowledge Engineering and Data Science Jurnal Matematika Sains dan Teknologi Syntax Literate: Jurnal Ilmiah Indonesia Indonesian Journal of Artificial Intelligence and Data Mining BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Chemistry JTAM (Jurnal Teori dan Aplikasi Matematika) Cetta: Jurnal Ilmu Pendidikan Martabat: Jurnal Perempuan dan Anak MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Zero : Jurnal Sains, Matematika, dan Terapan Jurnal Ilmiah Ecosystem Jambura Journal of Mathematics Jurnal Samudra Ekonomi dan Bisnis Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Inferensi InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Statistika dan Aplikasinya Enthusiastic : International Journal of Applied Statistics and Data Science Xplore: Journal of Statistics Molekul: Jurnal Ilmiah Kimia Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education
Utami Dyah Syafitri
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, IPB University, Jl. Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia
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K-Means Clustering Application of Open ‎Unemployment in 2020 Caused by COVID-19 in West Java Province Ardiansyah, M. Ficky Haris; Amany, Nurfatimah; Anugrah, Cahya Ireno; Syafitri, Utami Dyah
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 4 Issue 1, April 2024
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol4.iss1.art1

Abstract

West Java was the province with the highest unemployed rate during the COVID-19 pandemic. Significant increase of open ‎unemployment rate in West Java negatively impacts the national income. This study aims to apply the ‎clustering method using the k-means algorithm to determine priority clusters in West Java ‎Province by looking at the number of clusters in West Java’s city and the main characteristic of ‎each cluster. The clustering was conducted utilizing a k-means clustering algorithm which is grouping data based on similar ‎characteristics. The clustering results were evaluated using silhouette method. The results indicated that ‎two clusters were optimal. The clustering process using the k-means method showed that there were three clusters distinguishing the open unemployment rate during the pandemic in West Java Province in 2020. Cluster 1 ‎had a fairly low open unemployment rate due to the stalled service sector and low minimum city wage. ‎Cluster 2 had a high open unemployment rate due to the service sector and high minimum city wage. ‎Cluster 3 had medium open unemployment rate due to the service sector and also medium minimum city ‎wage. It suggests that cluster 2 is a priority cluster in dealing with the open unemployment rate.‎
Strategi Pengembangan Yayasan Seri Amal Pasca Pandemi Covid-19 : Studi Kasus SMA Cahaya Medan dan SMA ST. Petrus Sidikalang Gandaputra Simbolon, Andreas Nicholas; Fahmi, Idqan; Syafitri, Utami Dyah
Cetta: Jurnal Ilmu Pendidikan Vol 7 No 1 (2024)
Publisher : Jayapangus Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37329/cetta.v7i1.2932

Abstract

The COVID-19 pandemic has necessitated profound changes in the field of education, triggering a shift from conventional to online learning. This research delves into the impact of the implementation of online learning systems on students' academic performance in two high schools, namely SMA Putri Cahaya Medan and SMA Santo Petrus Sidikalang, under the auspices of the Yayasan Seri Amal. Through biplot analysis and the Analytical Hierarchy Process (AHP) approach, this study identifies patterns of changes in student grades and develops strategic school development strategies. The findings indicate that although there have been no significant changes in student grades, development strategies focused on staff training, improvement of facilities and technology, and external collaborations—especially with alumni and relevant institutions—are key to maintaining competitiveness and enhancing the quality of education. The conclusions and recommendations of this research provide guidance for school management and stakeholders to design adaptive strategic measures amid the evolving dynamics of education.
Strategi Pengembangan Yayasan Seri Amal Pasca Pandemi COVID-19 Studi Kasus: SMA Cahaya Medan & SMA St. Petrus Sidikalang Simbolon, Andreas Nicholas Gandaputra; Fahmi, Idqan; Syafitri, Utami Dyah
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36418/syntax-literate.v9i8.15879

Abstract

Penelitian ini bertujuan untuk: (1) Menganalisis faktor lingkungan internal dan eksternal yang berpengaruh pada SMA Cahaya Medan dan SMA St. Petrus Sidikalang, (2) Merumuskan alternatif strategi pengembangan yang dapat dipakai oleh kedua sekolah, dan (3) Menentukan serta merekomendasikan strategi bisnis yang tepat bagi Yayasan Seri Amal dalam menghadapi persaingan. Metode yang digunakan adalah analisis faktor lingkungan internal (IFE) dan eksternal (EFE) serta Analytical Hierarchy Process (AHP). Hasil penelitian menunjukkan bahwa faktor dominan yang mempengaruhi SMA Cahaya Medan dari faktor internal adalah kurangnya SDM, sedangkan faktor eksternal adalah regenerasi sekolah dan potensi pangsa pasar lebih besar ke luar kota. Untuk SMA St. Petrus Sidikalang, faktor internal yang paling berpengaruh adalah akreditasi A dan penggunaan LMS, sementara faktor eksternal adalah minimnya kompetitor SMA swasta di daerah tersebut. Strategi pengembangan yang direkomendasikan untuk SMA Cahaya Medan meliputi pelatihan SDM, peningkatan kerjasama dengan berbagai pihak, dan penggabungan pembelajaran daring dengan luring. Untuk SMA St. Petrus Sidikalang, strategi meliputi meningkatkan kerjasama dengan instansi di bidang olahraga dan seni, pembangunan fasilitas pendukung, serta pengembangan desain pembelajaran yang unggul dan terukur. Prioritas strategi pengembangan untuk SMA Cahaya Medan adalah pelatihan SDM dan penggabungan pembelajaran daring dengan luring, sedangkan untuk SMA St. Petrus Sidikalang adalah kerjasama dengan instansi dalam pengembangan kurikulum serta prestasi akademik dan non-akademik. Penelitian ini memberikan rekomendasi strategis yang dapat meningkatkan daya saing dan kualitas pendidikan di kedua sekolah tersebut.
The Role of Human Resource Risk on Employee Performance in The Hybrid Workforce Era Putri, Thasya; Syafitri, Utami Dyah; Sukmawati, Anggraini
Jurnal Aplikasi Bisnis dan Manajemen Vol. 9 No. 2 (2023): JABM Vol. 9 No. 2, Mei 2023
Publisher : School of Business, Bogor Agricultural University (SB-IPB)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17358/jabm.9.2.386

Abstract

The COVID-19 pandemic has brought about significant changes, particularly in work systems and patterns. The adoption of a hybrid work model allows employees to work remotely based on individually or collectively negotiated work arrangements. However, implementing a hybrid workforce presents several challenges, such as communication issues, varying levels of technological proficiency among employees, irregular working hours, and the potential for internal team problems. By implementing a hybrid work system in Pegadaian Co., employees are exposed to risks from both financial and non-financial aspects. This study aims to identify employee characteristics and analyze the impact of the hybrid workforce era and human resource risks on employee performance. The research uses the descriptive analysis method and SEM-PLS. The results show that the hybrid workforce era has a positive effect on employee performance, demonstrating that employees feel more productive when working in the office. These findings suggest that Pegadaian Co. should provide a comfortable and practical office environment to facilitate employee performance. Conversely, the study finds that human resource risks do not significantly influence employee performance. The selection process is identified as a minor factor within the HR risk variable. The company's selection process prioritizes effectiveness and efficiency by focusing on recruitment needs and objectives. Keywords: flexible working arrangement, hybrid workforce, working from office, Pegadaian, employee performance
Multilevel Regression Analysis on Graduate Student Grade Point Average Riswan, Riswan; Dyah Syafitri, Utami; Nur Aidi, Muhammad
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 1 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i1.3969

Abstract

Abstract:Multilevel regression is one of the methods used to analyze hierarchical data structures. One case of data with a hierarchical structure is the cumulative grade point average (GPA) data for students each semester (level one) which is nested within students (level two), and nested within faculties (level three). This study produced the three best three-level regression models: the multilevel regression model, the multilevel regression model with natural logarithmic transformation, and the multilevel binary logistic regression model. The multilevel regression model and the multilevel regression model with natural logarithmic transformation at a significant level of 5%, have the same variables that affect student GPA scores, including semesters, credits, gender, scholarships, and marital status with the same interaction effect, namely semester interactions with scholarships. In addition, the ICC values by the two models are also the same which explains that 91% of the total diversity of student GPA comes from the student level and 8% comes from the faculty level. For the multilevel binary logistic regression model, all explanatory variables affect GPA without involving interaction between levels. Abstrak:Regresi multilevel merupakan salah satu metode yang digunakan untuk menganalisis struktur data hirarkhi. Salah satu kasus data dengan struktur hirarki adalah data indeks prestasi kumulatif (IPK) mahasiswa tiap semester (level satu) yang tersarang dalam mahasiswa (level dua), tersarang dalam fakultas (level tiga). Dalam penelitian ini menghasilkan tiga model regresi tiga level terbaik yaitu model regresi multilevel, model regresi multilevel dengan transformasi logaritma natural, dan model regresi logistik biner multlevel. Model regresi multilevel dan model regresi multilevel dengan transformasi logaritma natural pada taraf nyata 5%, memiliki peubah sama yang berpengaruh terhadap nilai IPK mahasiswa antara lain semester, SKS, jenis kelamin, beasiswa, dan status nikah dengan pengaruh interaksi yang sama yaitu interaksi semester dengan beasiswa. Selain itu, nilai ICC oleh kedua model tersebut juga sama yang menjelaskan bahwa 91% total keragaman IPK mahasiswa berasal dari level mahasiswa dan 8% berasal dari level fakultas.  Untuk model regresi logistik biner multilevel semua peubah penjelas berpengaruh terhadap IPK tetapi tanpa melibatkan interaksi antar level.
A-Optimal Pada Mixture Amount Design Dengan Modifikasi Rancangan Petak Terbagi Menggunakan Algoritma Point-Exchange Sari, Mutia Dwi Permata; Syafitri, Utami Dyah; Djuraidah, Anik
Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Vol. 12 No. 2 (2024): Al-Khwarizmi : Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam
Publisher : Prodi Pendidikan Matematika FTIK IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/jpmipa.v12i2.4072

Abstract

Abstract:A Mixture Amount Experiment (MAE) is a design that consists of a mixture variable and the total amount variable. In practice, the composition of the mixture is run on each total amount of mixture, which consequently cannot be completely randomized, so that a split-plot design approach is needed. This research aims to develop an algorithm to find a A-Optimal design for a mixture amount experiment with a modified split-plot design. A-Optimal design is seeking a design in which minimizing the covariance of the model parameter. The study case of this research involved three ingredients and two total amounts of mixtures with different constraints. In this research, the whole plot factor is the total amount of mixtures, while the subplot factor is the composition of the mixture. The A-Optimal design was generated based on the Second-Order Scheefe model. To Construct the A-optimal design, we used the point exchange algorithm. The result from this algorithm produced an optimum composition in each total amount of mixture. Abstrak:Rancangan Jumlah Campuran (MAE) terdiri dari komponen campuran dan jumlah total. Dalam prakteknya, komposisi campuran dijalankan pada setiap jumlah total campuran, akibatnya tidak dapat diacak sempurna, sehingga diperlukan pendekatan rancangan petak terbagi. Penelitian ini bertujuan untuk mengembangkan suatu algoritma untuk menemukan rancangan dengan kriteria A-Optimal untuk percobaan jumlah campuran dengan menggunakan modifikasi rancangan petak terbagi. Rancangan A-Optimal mencari rancangan yang meminimalkan kovarian parameter model. Studi kasus penelitian ini terdiri dari tiga bahan dan dua jumlah total campuran yang berbeda. Dalam penelitian ini, factor petak utama adalah jumlah total campuran, sedangkan faktor anak petak adalah komposisi campuran. rancangan A-Optimal dihasilkan berdasarkan model Second-Order Scheefe. Untuk Membangun rancangan A-optimal, menggunakan pendekatan algoritma point-exchange. Hasil dari algoritma ini menghasilkan komposisi optimum pada setiap jumlah total campuran.
N-Level Structural Equation Models (nSEM): The Effect of Sample Size on the Parameter Estimation in Latent Random-Intercept Model Eminita, Viarti; Saefuddin, Asep; Sadik, Kusman; Syafitri, Utami Dyah
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
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.v6i1.38914

Abstract

Multilevel Structural Equation Modeling (MSEM) is claimed to address hierarchical data structures and latent response variables, but it becomes unstable with an increasing number of levels. N-Level SEM (nSEM) is an SEM framework designed to handle a growing number of levels in the model. The nSEM framework uses the Maximum Likelihood Estimation (MLE) method for parameter estimation, which requires a large sample size and correct model specification. Therefore, it is essential to consider the necessary minimal sample size to ensure accurate and efficient parameter estimation in the nSEM model. This study examined how sample size affects the performance of parameter estimators in nSEM models. We propose a method to evaluate the effect of many environments to estimate the results of factor loadings and environmental variance produced by the model. In addition, we also assess the impact of environment size on the estimation results of factor loadings and individual variance. The results were then applied to actual data on student mathematics learning motivation in Depok. The findings show that neither the number of environments nor the size of the environment affects the performance of fixed parameter estimation in the nSEM model. nSEM indicates excellent performance in estimating environmental variance at level 2 when the number of environments increases. Conversely, increasing the size of the environment worsens the performance of estimating individual variance parameters. Overall, the nSEM framework for the latent random-intercept (LatenRI) model performs well with increasing sample sizes. The application data on LatenRI models show almost similar estimation results.Keywords: hierarchical data; latent random intercept model; multilevel structural equation modeling; n-level structural equation modeling.AbstrakMultilevel Structural Equation Modeling (MSEM) diklaim dapat mengatasi struktur data hierarki dan variabel respons laten, namun menjadi tidak stabil dengan bertambahnya jumlah level. N-Level SEM (nSEM) adalah kerangka kerja SEM yang dirancang untuk menangani semakin banyak level dalam model. Kerangka kerja nSEM menggunakan metode Maximum Likelihood Estimation (MLE) untuk estimasi parameter, yang memerlukan ukuran sampel yang besar dan spesifikasi model yang benar. Oleh karena itu, penting untuk mempertimbangkan ukuran sampel minimal yang diperlukan untuk memastikan estimasi parameter yang akurat dan efisien dalam model nSEM. Studi ini menguji bagaimana ukuran sampel mempengaruhi kinerja penduga parameter dalam model nSEM. Kami mengusulkan metode untuk mengevaluasi pengaruh banyak lingkungan dalam memperkirakan hasil factor loadings  dan varians lingkungan yang dihasilkan oleh model. Selain itu, kami juga menilai dampak ukuran lingkungan terhadap hasil estimasi factor loadings dan varians individu. Hasilnya kemudian diterapkan pada data aktual motivasi belajar matematika siswa di Depok. Hasil menunjukkan bahwa baik jumlah lingkungan maupun ukuran lingkungan tidak mempengaruhi kinerja estimasi parameter tetap pada model nSEM. nSEM menunjukkan kinerja yang sangat baik dalam memperkirakan varians lingkungan pada level 2 ketika jumlah lingkungan meningkat. Sebaliknya, peningkatan ukuran lingkungan akan memperburuk kinerja pendugaan parameter varians individu. Secara keseluruhan, kerangka nSEM untuk model intersepsi acak laten (LatenRI) bekerja dengan baik dengan meningkatnya ukuran sampel. Data penerapan model LatenRI menunjukkan hasil estimasi yang hampir serupa.Kata Kunci: data hirarki; model intersep acak laten; model persamaan structural multilevel; model persamaan structural n-level. 2020MSC: 62D99
Comparing Self-Paced Ensemble and RUSBoost for Imbalanced Poverty Classification in West Java Setiabudi, Nur Andi; Sartono, Bagus; Syafitri, Utami Dyah; Aryasa, Komang Budi
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p218-229

Abstract

Class imbalance remains a major challenge in classification modelling that frequently leads to biased predictive models. This study aimed to compare two ensemble techniques based on an undersampling approach, namely Self-Paced Ensemble and RUSBoost, for handling imbalanced classification in poverty identification in West Java. The results suggested that RUSBoost consistently outperformed Self-Paced Ensemble across the most critical metrics. It showed better balance in classification outcomes. When the objective is to maximize the identification of poor households, the default threshold in the RUSBoost model was prefered. On the other hand, if precision is prioritized due to limited resources, the Youden Index threshold offers a better alternative. Given the overall evaluation metrics, RUSBoost with the default threshold was suggested as the most reliable and well-balanced option among the compared models for classifying poor households in West Java under imbalanced data condition
Integrating Support Vector Regression and Kriging in Spatial Interpolation of Statistical Seismicity Parameters Sirodj, Dwi Agustin Nuriani; Aidi, Muhammad Nur; Sartono, Bagus; Syafitri, Utami Dyah; Pranata, Bayu
Indonesian Journal of Geography Vol 57, No 3 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.102153

Abstract

Spatial interpolation methods, such as Inverse Distance Weighting (IDW) and kriging, are commonly used in various fields. In Kriging method, semivariogram fitting is an important step, where empirical data are used to derive a theoretical model. However, when the known theoretical semivariogram model does not provide a satisfactory fit, the bias in the estimated values is increased. To address this limitation, Support Vector Regression (SVR) can be used to model the empirical semivariogram with a machine-learning method. This method has been applied in ordinary kriging interpolation for semivariogram fitting to estimate parameters related to the potential occurrence of earthquake. Specifically, the calculated parameters, based on the Gutenberg-Richter law, include the seismic activity (a-value) and rock fragility (b-value) in the Sumatera region. The results showed that SVR can model the empirical semivariogram better than the theoretical. The integration of SVR-Ordinary Kriging provides the best performance compared to other methods, such as IDW, with the smallest RMSEP values for both the b-value and a-value measuring 0.1378 and 0.7423, respectively. Aceh and Mentawai Islands tend to show low a and b values, suggesting that these areas are more vulnerable to earthquake with large magnitudes.
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.
Co-Authors Aam Alamudi Abdul Rohman Abdul Rohman Agus Mohamad Soleh Agustin Faradila Aidi, Muhammad Nur Aji Hamim Wigena Akbar Rizki Alfi Hudatul Karomah ALIU, MUFTIH ALWI Amany, Nurfatimah Anang Kurnia Andrew Donda Munthe Anggrahini, Ervina Dwi Anggraini Sukmawati Anik Djuraidah Anissa Permatasari Antonio Kautsar Anugrah, Cahya Ireno Ardiansyah, M. Ficky Haris Aryasa, Komang Budi ASEP SAEFUDDIN Auliya Ilmiawati Aziza, Vivin Nur Azkiya, Azka Al Baehera, Seta Bagus Sartono Bambang - Riyanto Bambang Prajogo Eko Wardoyo Bambang Riyanto Bartho Sihombing Bayu Pranata, Bayu Budi Susetyo Christin Halim Cici Suhaeni Dea Amelia, Dea Dwi Agustin Nuriani Sirodj Dwi Putri Kurniasari Eka Dewi Pertiwi Eka Winarni Sapitri Eminita, Viarti Endina Fatihah Yasmin Erfiani Erfiani Erfiani, Erfiani Erlinda Widya Widjanarko Ernawati, Fitrah Eti Rohaeti Evita Choiriyah Fachry Abda El Rahman Fadhila Hijryani FAHREZAL ZUBEDI Farit M Afendi Fatimah, Zahra Nurul Fitrianto, Anwar Gandaputra Simbolon, Andreas Nicholas Hari Wijayanto I Made Sumertajaya Idqan Fahmi Immatul Ulya Indahwati Indonesian Journal of Statistics and Its Applications IJSA Indradewa, Rhian Intan Lukiswati Irmanida Batubara Irzaman, Irzaman Isti Rochayati Izzati, Mumpuni Nur Joko Santoso Jumansyah, L. M. Risman Dwi Khairil Anwar Notodiputro Kusman Sadik Laradea Marifni Lidiasari, Melisa Lismayani Usman M. Iqbal M. Rafi Meilania, Gusti Tasya Mohamad Rafi Mohamad Rafi Mohamad Rafi Mohammad Masjkur Muhamad Insanu Muhammad Bachri Amran Muhammad Nur Aidi Muhammad Nursid Mulianto Raharjo Muslim, Muhammad Irfai Muthahari, Wadudi Nanik Siti Aminah Nariswari Karina Dewi Ni Kadek Manik Dewantari Noer Endah Islami Nofrida Elly Zendrato Novia Yustika Tri Lestari. YR Nur Aidi, Muhammad Nurhajawarsi Nurhajawarsi Nursifa Mawadah Putri, Thasya R, Arifuddin Rifki Husnul Khuluk Ririn Fara Afriani Riswan Riswan Sanusi, Ratna Nur Mustika Sari, Mutia Dwi Permata Septaningsih, Dewi Anggraini Setiabudi, Nur Andi Setyowati, Silfiana Lis Sifa Awalul Fikriah Simbolon, Andreas Nicholas Gandaputra Siwi Haryu Pramesti Soleh, Agus M Soni Yadi Mulyadi Sony Hartono Wijaya Sri Sulastri Syam, Ummul Auliyah Syifa Muflihah Tania Amalia Darsono Topan . Ruspayandi Triyani Oktaria Vega, Iliana Patricia Vivin Nur Aziza Weisha, Ghea Wini - Trilaksani Wulan Tri Wahyuni Yenni Angraini Yuan Millafanti Yuni Suci Kurniawati Yuniar Istiqomah