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All Journal Media Statistika BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan Jurnal Edukasi dan Penelitian Informatika (JEPIN) JMPM: Jurnal Matematika dan Pendidikan Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Sarwahita : Jurnal Pengabdian Kepada Masyarakat Insan Cita Jurnal Pengabdian Kepada Masyarakat JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi
Martha, Shantika
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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PENERAPAN ALGORITMA K-MEDOIDS DENGAN OPTIMASI GAP STATISTICS DALAM PENGELOMPOKAN DAERAH RAWAN KRIMINALITAS DI INDONESIA Dyaherawati, Oktavia; Martha, Shantika; Imro’ah, Nurfitri
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 1 (2025): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v14i1.91906

Abstract

Kriminalitas atau tindak kejahatan adalah setiap perbuatan yang melanggar hukum pidana. Informasi terkait banyaknya tindak kejahatan yang terjadi sangat dibutuhkan oleh masyarakat dan penegak hukum. Penelitian ini bertujuan untuk mengelompokkan daerah rawan kriminalitas pada provinsi di Indonesia menggunakan algoritma K-Medoids dengan optimasi Gap Statistics. Algoritma K-Medoids merupakan metode analisis cluster dengan menggunakan perwakilan dari objek sebagai pusat cluster. Penentuan jumlah cluster teraik pada metode ini masih belum memiliki dasar teori yang jelas, sehingga diperlukan pendekatan untuk mengidentifikasi jumlah cluster optimal. Gap statistics merupakan salah satu pendekatan terbaik untuk menentukan jumlah cluster optimal dengan membangkitkan data acak dalam penentuan jumlah kelompok optimum. Data yang digunakan merupakan data sekunder yang didapatkan dari publikasi Badan Pusat Statistik yaitu Statistik Kriminal 2023 yang berisi data jumlah kriminalitas menurut jenis kejahatan dan kepolisian daerah tahun 2022. Penelitian ini berfokus untuk membentuk kelompok yang berisi provinsi dengan jarak terdekat berdasarkan karakteristik dari kriminalitas menggunakan analisis cluster. Berdasarkan analisis yang telah dilakukan, jumlah cluster optimal yang terbentuk berjumlah empat cluster dengan nilai gap statistics yang diperoleh sebesar 0,65. Cluster 1 dikategorikan sebagai daerah sangat rawan kriminalitas yang terdiri dari tiga provinsi, Cluster 2 dikategorikan sebagai daerah rawan kriminalitas yang terdiri dari dua provinsi, Cluster 3 dikategorikan sebagai daerah cukup rawan kriminalitas yang terdiri dari tujuh provinsi, dan Cluster 4 dikategorikan sebagai daerah tidak rawan kriminalitas yang terdiri dari 22 provinsi.  Kata Kunci : kriminalitas, analisis cluster, cluster optimal, outlier.
PENENTUAN JUMLAH CLUSTER OPTIMUM PADA PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR PENDIDIKAN TINGKAT SMA Rahmadanti, Putri; Martha, Shantika; Satyahadewi, Neva
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 2 (2025): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v14i2.92351

Abstract

Pendidikan merupakan aspek penting terkait dengan sumber daya manusia dan tentunya memiliki peran penting dalam upaya meningkatkan pembangunan nasional. Pada dasarnya pemerataan pendidikan sangat krusial untuk meningkatkan pendidikan di Indonesia. Indonesia yang terdiri dari banyak provinsi dengan beragam kondisi menjadikan perlu dilakukan pengelompokan terhadap pendidikan pada tiap daerah. Analisis cluster ialah teknik analisis statistik multivariat yang melakukan pengelompokan berdasarkan atas kesamaan karakteristik antar tiap objeknya. Tujuan penelitian ini adalah penentuan jumlah cluster optimum dengan metode Silhouette Coefficient untuk mengelompokkan provinsi di Indonesia berdasarkan indikator pendidikan tingkat SMA/sederajat. Data indikator pendidikan tingkat SMA/sederajat yang terdiri dari 11 variabel dari 34 provinsi di Indonesia digunakan dalam penelitian ini. Tahapan penelitian ini meliputi pengelompokan dengan menggunakan metode Ward dan dilanjutkan dengan proses penentuan jumlah cluster optimum dengan metode Silhouette Coefficient. Berdasarkan analisis yang telah dilakukan dengan mengelompokkan tingkat pendidikan di Indonesia, diperoleh cluster optimum berdasarkan indikator pendidikan tingkat SMA/sederajat, yaitu berjumlah 3 cluster dengan nilai Silhouette Coefficient yaitu bernilai 0,248. Cluster ke-1 merupakan cluster dengan tingkat pendidikan tinggi terdiri dari 8 provinsi yang didominasi dengan provinsi yang berada di Pulau Jawa. Kemudian cluster ke-2 sebagai cluster dengan tingkat pendidikan sedang terdiri dari 25 provinsi yang merupakan cluster terbesar yang mewakili sebagian besar wilayah di Indonesia. Serta cluster ke-3 sebagai cluster dengan tingkat pendidikan rendah hanya terdiri dari Provinsi Papua yang menunjukkan kesenjangan pendidikan yang signifikan.  Kata Kunci : Jarak Euclidean, Metode Ward, Silhouette Coefficient, Z-Score.
SELEKSI PENERIMAAN BEASISWA MENGGUNAKAN METODE EVALUATION BASED DISTANCE FROM AVERAGE SOLUTION (EDAS) (Studi Kasus: Beasiswa KIP Kuliah Merdeka di Universitas Tanjungpura) Clarenda Siboro, Viren Marcellya; Martha, Shantika; Imro’ah, Nurfitri
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 2 (2025): Bimaster : Buletin Ilmiah Matematika, Statistika dan Terapannya
Publisher : FMIPA Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/bbimst.v14i2.91910

Abstract

Beasiswa Kartu Indonesia Pintar (KIP) Kuliah Merdeka adalah program yang dirancang oleh pemerintah untuk memberikan dukungan finansial dalam bentuk pembiayaan pendidikan serta biaya hidup penerima bantuan beasiswa. Tujuan dilakukan penelitian ini yaitu untuk mengimplementasikan metode Sistem Pendukung Keputusan yaitu metode Evaluation Based Distance From Average Solution (EDAS) dalam proses pengambilan keputusan terkait seleksi penerima beasiswa KIP Kuliah Merdeka di Universitas Tanjungpura. EDAS merupakan metode yang digunakan untuk menentukan alternatif optimal yang dipilih berdasarkan perhitungan jarak masing-masing alternatif dari nilai optimal. Metode tersebut memiliki keunggulan dalam melakukan pemeringkatan dengan menghitung Average Solution (AV) sehingga hasil yang didapatkan lebih akurat. Penelitian ini menggunakan data beasiswa KIP Kuliah Merdeka tahun 2023 yang diperoleh dari Biro Akademik dan Kemahasiswaan (BAK) Universitas Tanjungpura. Kriteria yang digunakan yaitu pekerjaan ayah (C1), penghasilan ayah (C2), pekerjaan ibu (C3), penghasilan ibu (C4), dan jumlah tanggungan (C5). Proses analisis dimulai dengan menghitung Average Solution, Positive Distance Average, Negative Distance Average, Sum of Positive Distance, Sum of Negative Distance, Normalize Sum of Positif Distance, Normalize Sum of Negative Diatance, dan Appraisal Score. Berdasarkan hasil analisis alternatif terbaik yang layak dipilih dan menerima bantuan beasiswa KIP Kuliah Merdeka yaitu A210   dengan nilai appraisal score 1. Nilai akurasi yang diperoleh dari hasil analisis menggunakan metode EDAS sebesar 46,69% yang tergolong ke dalam kategori gagal. Hal tersebut disebabkan oleh sedikit atribut yang digunakan dan proses penentuan beasiswa dilakukan oleh pihak BAK dengan mempertimbangkan beberapa kriteria lainnya.  Kata Kunci : Pemeringkatan, Average Solution, Akurasi.
PERFORMANCE EVALUATION OF THE INDF.JK STOCK PRICE MOVEMENT PREDICTION MODEL USING RANDOM FOREST METHOD WITH GRID SEARCH CROSS VALIDATION OPTIMIZATION Zaria, Della; Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp2155-2168

Abstract

Investment in financial instruments in Indonesia has shown significant growth over time, with stocks often being the first choice for investors to invest money. Unfortunately, deciding to buy and sell stocks is not easy. When determining the right time to buy or sell stocks, volatile stock price movements and losses caused by wrong decisions are investors' problems. Thus, it is essential to analyze stock price movement predictions. This study aims to evaluate the prediction model's performance for PT Indofood Sukses Makmur Tbk (INDF.JK) stock price movement in the next 30 days to reduce the risk of possible losses and help the decision-making process. We used the Random Forest method and Grid Search Cross Validation (CV) optimization to form the model. The data used is the closing price of INDF.JK stock for the period January 2, 2014, to December 29, 2023, from Yahoo Finance, which is processed into eight types of stock technical indicators, namely SMA_5, SMA_10, SMA_15, SMA_30, EMA_9, MACD, MACD_Signal, and RSI. The research pipeline includes descriptive statistics, preprocessing, feature and target variables determination, data split, model formation without and with optimization, testing accompanied by performance evaluation, and comparison of the formed model. The results show that the prediction model of INDF. JK's stock price movement in the next 30 days has excellent performance, proven accurate by 90.8% with the application of Random Forest and Grid Search CV. The Random Forest prediction model with Grid Search CV optimization has better performance indicators than the Random Forest model without Grid Search CV optimization, which is shown by the increase of all indicator values. The relative Strength Index is the variable with the best performance for the prediction model. It can be used as the primary consideration for investors when deciding on the buying and selling process of INDF.JK stock in the next 30 days.
Peningkatan Keterampilan Analisis Data Bagi Fungsional BPS di Kalimantan Barat Melalui Pelatihan SEM dengan AMOS Martha, Shantika; Andani, Wirda; Sulistianingsih, Evy; Debataraja, Naomi Nessyana; Imro'ah, Nurfitri; Satyahadewi, Neva; Tamtama, Ray; Perdana, Hendra; Kusnandar, Dadan
Bahasa Indonesia Vol 22 No 01 (2025): Sarwahita : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/sarwahita.221.9

Abstract

This Community Service activity is a form of cooperation between Statistics Study Program FMIPA UNTAN and BPS through training activities. The purpose of this PKM is to provide knowledge and insight to BPS functional employees about SEM (Structural Equation Modeling) using AMOS. This activities were carried out on Monday, August 14, 2023 in the Vicon room of the West Kalimantan provincial BPS office with 32 participants attending. The results of this training activity are expected to be applied by BPS functional employees in processing and analyzing data as research needs and work related to statistical data. The level of success in this training was measured through pre-test, post-test and participant satisfaction survey. A wilcoxon test was conducted with α = 0.05 and the result was p-value smaller than 0.01. So that the p-value < α which means rejecting H0 and it can be concluded that the average pretest score < average posttest score. In other words, the post-test results increased significantly, which means that the participants' abilities increased after the training. Based on the participant satisfaction survey, the results showed that all participants (100%) had never used AMOS software before. Overall, participants were satisfied (61.5%) and very satisfied (38.5%) with the training because they could increase their knowledge and the training materials delivered were in accordance with their needs, easy to understand and interesting, could be applied easily, and were delivered in order and systematically.   Abstrak Kegiatan Pengabdian Kepada Masyarakat (PKM) ini merupakan salah satu wujud kerjasama Prodi Statistika FMIPA UNTAN dengan BPS melalui kegiatan pelatihan. Adapun tujuan PKM ini yaitu memberikan pengetahuan dan wawasan kepada pegawai fungsional BPS tentang teknik pengolahan dan analisis data SEM (Structural Equation Modelling) dengan menggunakan AMOS. Kegiatan PKM dilaksanakan pada hari Senin, 14 Agustus 2023 di ruang Vicon kantor BPS prov Kalbar dengan jumlah peserta yang hadir 32 orang. Hasil dari kegiatan pelatihan ini diharapkan dapat diterapkan oleh pegawai fungsional BPS dalam mengolah dan menganalisis data sebagai kebutuhan penelitian maupun pekerjaan yang berhubungan dengan data statistika. Tingkat keberhasilan pada pelatihan ini diukur melalui pemberian pretest, posttest dan survey kepuasan peserta. Dilakukan uji beda menggunakan uji wilcoxon dengan α = 0.05 dan didapatkan hasil yaitu berupa p-value lebih kecil dari 0.01. Sehingga p-value < α yang berarti tolak H0 dan dapat disimpulkan rata-rata nilai pretest < rata-rata nilai posttest. Dengan kata lain hasil posttest meningkat secara signifikan yang artinya kemampuan peserta meningkat setelah dilaksanakan pelatihan. Berdasarkan survey kepuasan peserta didapatkan hasil ternyata semua peserta (100%) belum pernah menggunakan software AMOS sebelum pelatihan. Secara keseluruhan peserta merasa puas (61,5%) dan sangat puas (38,5%) mengikuti pelatihan karena dapat menambah pengetahuan serta materi pelatihan yang disampaikan sesuai dengan kebutuhan, mudah dipahami dan menarik, dapat diterapkan dengan mudah, dan disampaikan dengan urut dan sistematis.
PENERAPAN METODE GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) PADA KASUS KEMISKINAN DI INDONESIA Martha, Shantika; Yundari, Yundari; Rizki, Setyo Wira; Tamtama, Ray
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 2 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (392.215 KB) | DOI: 10.30598/barekengvol15iss2pp241-248

Abstract

To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate. Keywords: fixed effect model, exponential adaptive kernel.
RISK ANALYSIS OF GOOGL & AMZN STOCK CALL OPTIONS USING DELTA GAMMA THETA NORMAL APPROACH Umiati, Wiji; Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1879-1888

Abstract

Stocks, as investment products, tend to carry risks due to fluctuations. The tendency of stock prices to rise over time leads investors to opt for call options, which are one of the derivative investment products. However, call options are influenced by several factors that can pose risks and have nonlinear dependence on market risk factors. Therefore, methods are needed to measure the risk of call options, such as Delta Normal Value at Risk and Delta Gamma Normal Value at Risk. Delta and Gamma are part of Option Greeks, parameters that measure the sensitivity of options to various factors used in determining option prices with the Black-Scholes model. This study uses an approach with the addition of Theta, which can measure the sensitivity of options to time. This study aims to analyze Value at Risk with the Delta Gamma Theta Normal approach for call options on Google (GOOGL) and Amazon (AMZN) stocks. The analysis uses closing stock price data from September 7, 2022, to September 7, 2023, and three in-the-money and out-of-the-money call option prices. The study begins by collecting closing stock prices and call option contract components, testing the normality of stock returns, calculating volatility, , Delta, Gamma, and Theta, then calculating the Value at Risk. Based on the analysis, it is found that GOOGL and AMZN call options have a Value at Risk of $0.89588 and $0.92760, respectively, at a 99% confidence level with a strike price of $120. Furthermore, based on the comparison of Value at Risk between in-the-money and out-of-the-money call options, it can be concluded that out-of-the-money call options tend to have larger estimated losses.
CLUSTER MAPPING OF HOTSPOTS USING KERNEL DENSITY ESTIMATION IN WEST KALIMANTAN Cahyani, Cristy Framedia; Kusnandar, Dadan; Debataraja, Naomi Nessyana; Martha, Shantika
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2353-2362

Abstract

Forest and land fires pose a recurring concern every year in Indonesia, often taking place in West Kalimantan Province, particularly during the dry season. This study aims to use the Kernel Density Estimation (KDE) to categorize the data of the hotspots in the province of West Kalimantan according to their density and to map the cluster level of the fire risks in the region. The data utilized in this study are secondary data obtained from the images of the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument, which are available on firms.modaps.eosdis.nasa.gov and provided by NASA. The data focuses on hotspots dispersed across West Kalimantan province during 2020. The variables examined in the study were the confidence level (≥80%) of forest and land fire hotspots, the distance from each point to the nearest river, and the distance from each point to the nearest road. The kernel density estimation method with a Gaussian kernel function yielded clustering results into three distinct groups according to their vulnerability levels. Low vulnerability areas comprise Cluster 1, which consists of 127 points or 50.97% of the total hotspots. Medium vulnerability areas belong to Cluster 2, which has 47 points or 30.32% of the total. Cluster 3 includes high vulnerability locations, consisting of 29 points or 18.71% of the total. The most susceptible areas to forest and land fires are located within the Ketapang regency.
AN EXAMINATION OF THE GREEN STOCK PORTFOLIO IN CONNECTION WITH THE 2024 INDONESIAN REPUBLIC PRESIDENTIAL GENERAL ELECTION Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda; Agustono, Hendri; Pebriyandi, Rifki; Gunawan, Risky; Maharani, Cinta Priscillia
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/barekengvol19iss4pp2543-2556

Abstract

The presidential election of the Republic of Indonesia occurs on a frequency of once every five years. The present work investigated the impact of the 2024 Presidential Election on the performance of the optimal stock portfolio constructed by K-Means Clustering during the first phase of stock selection. Subsequently, the portfolio will be evaluated using two distinct approaches, namely Mean Absolute Deviation (MAD) and Mean-Variance Efficient Portfolio (MVEP). Both techniques were employed to construct several portfolios throughout three time periods: before the Presidential Election (13 August 2023 to 13 February 2024) and after the Presidential Election (15 February to 15 April 2024 and 20 April 2024 to 20 May 2024). This was done by implementing a mechanism to manage the allocation of shares in order to optimize the portfolio. The analyzed data is historical data on daily green stock closing prices indexed on the SRI-KEHATI index. A portfolio was constructed and subsequently evaluated for its performance using the Sharpe Index. The findings of this study suggest that the upcoming 2024 general election for the presidency of the Republic of Indonesia had a favorable impact on the Indonesian capital market, particularly for stocks that are indexed by SRI-KEHATI. This criterion was proposed based on the observation that the average Sharpe ratio index for Period II and Period III exceeds the average Sharpe ratio index for Period I (prior to the election day). The most optimal portfolio examined in this study was the MVEP portfolio, mostly composed of assets in the primary consumer products industry, with a Sharpe ratio of 0.53586. Furthermore, the performance of portfolios in period III (after the election result release) was far superior to that of other portfolios examined in previous periods.
The Application of Delta Gamma Normal Value at Risk to Measure the Risk in the Call Option of Stock Astuti, Ayu; Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 8, No 2 (2024): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v8i2.19669

Abstract

Call options of stock have a nonlinear dependence on market risk factors, thus encouraging the development of a method capable of measuring the risk of call option of stock, namely the Delta Gamma Normal Value at Risk (DGN VaR) method. The DGN VaR method can provide a more accurate VaR estimate than Delta Normal VaR (DN VaR) because of the Delta and Gamma sensitivity measures in the formula. The DGN VaR method uses the second-order Taylor Polynomial approach to approximate the return of stock price underlying the call option. This research applies the DGN VaR method to analyze the risk of call options of Atlassian Corporation (TEAM) and MicroStrategy Incorporated (MSTR). Both companies operate in the technology sector and are among the top 100 largest software companies based on market capitalization for the analysis period September 21, 2022 to September 21, 2023. The analyzed options in this research consist of in-the-money and out-of-the-money options with several strike prices (K). For in-the-money options, the strike prices are $105, $110, and $115 for TEAM, and $150, $160, and $170 for MSTR, while for out-of-the-money options, the strike prices are $190, $195, and $200 for TEAM, and $330, $340, and $350 for MSTR with varying confidence levels of 80%, 90%, 95%, and 99%. Based on the results of the analysis, the DGN VaR for the analyzed in-the-money option has a greater value than the DGN VaR for the analyzed out-of-the-money option.
Co-Authors , Syahru Rahmayuda, Haris Febriyanto Ramadhan , Sampe Hotlan Sitorus Agustono, Hendri Al Azizi, Fudhail Azzam Thoriqi Amarrullah, Rido Andani, Wirda Anggria, Vini Apriliyanti, Rita Aprizkiyandari, Siti Asmara, Mira AYU ASTUTI, AYU Ayuni, Anisa Putri Bartolomius, Bartolomius Cahyani, Cristy Framedia Calissta, Leanna Belva Clarenda Siboro, Viren Marcellya Cornellia, Amanda Dadan Kusnandar Deanovela, Adelea Delvi Debataraja, Naomi Nessyana Dwi Marisa Midyanti Dyaherawati, Oktavia Ersawahyuni, Aisna Evy Sulistianingsih Fallah, Khalishah Ghina Fiqriah, Isnaini Gunawan, Risky Hamdari, Ela Hariadi, Wahyudio Shaney Fikri Hendra Perdana Hidayatullah Hidayatullah Ilhamsyah Imro'ah, Nurfitri IMRO’AH, NURFITRI Indriani, Maria Meilinda Istighfarani, Ridha Maharani, Cinta Priscillia Mori, Stepanus Armadi Muhtadi, Radhi Neva Satyahadewi Novi Safitri Nurhaliza, Sy. Farini Oktitannia, Dea Pebriyandi, Rifki Pramesti, Surya Adinda Fitria Prianti, Sabrina Eka Purwanty, Purwanty Puspita, Risma Radinasari, Nur Ismi Rahmadanti, Putri Rahmawati, Fenti Nurdiana Rezaldi, Muhammad Fachri Riovani, Vriska Dwi Rivaldi, Syahrul Rizki, Setyo Wira Robbiati, Dian Sadikin, Utin Azwa Sayhani Salsabila, Salsabila Salsabila, Yumna Hanum Sindia, Eri Tamtama, Ray Umiati, Wiji Vebriyanti, Lo Mei Ly Wuri, Hastri Sastia Yogi, Vinsensius Yudhi Yundari, Yundari Zaria, Della