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All Journal Jurnal Matematika BIMASTER Epsilon: Jurnal Matematika Murni dan Terapan CESS (Journal of Computer Engineering, System and Science) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) KABILAH : Journal of Social Community Jurnal Pendidikan Sejarah dan Riset Sosial Humaniora (KAGANGA) BULETIN AL-RIBAATH Jambura Journal of Mathematics GERVASI: Jurnal Pengabdian kepada Masyarakat Sarwahita : Jurnal Pengabdian Kepada Masyarakat M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Transformasi : Jurnal Pendidikan Matematika dan Matematika Variance : Journal of Statistics and Its Applications International Journal of Economics, Business and Accounting Research (IJEBAR) Insan Cita Jurnal Pengabdian Kepada Masyarakat Jurnal Pendidikan Dasar Perkhasa Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Mathematics Education and Science AR-RIAYAH : Jurnal Pendidikan Dasar Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi CENDEKIA : Jurnal Ilmu Pengetahuan Journal of Community Development Empiricism Journal Jurnal Eurekamatika Jurnal Forum Analisis Statistik Pattimura International Journal of Mathematics (PIJMath) STATISTIKA Bina Bahari : Jurnal Pengabdian Kepada Masyarakat Equiva
Neva Satyahadewi
Program Studi Statistika Fakultas MIPA Universitas Tanjungpura, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities 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 Economics, Econometrics & Finance Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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PREDIKSI HARGA SAHAM PT SEMEN INDONESIA (PERSERO) TBK PADA MASA PEMBANGUNAN AWAL IKN DENGAN GERAK BROWN GEOMETRIK Nurfadilah, Kori’ah; Yundari, Yundari; Satyahadewi, Neva
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 5 (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.v14i5.99546

Abstract

PT Semen Indonesia (SMGR) memiliki peranan penting dalam pemasok Green Cement pembangunan Ibu Kota Nusantara (IKN). Tingginya permintaan semen yang berkelanjutan untuk proyek besar seperti IKN, potensi pendapatan dan laba SMGR diharapkan meningkat. Harga saham SMGR tetap rentan terhadap risiko meskipun prospek pertumbuhan sangat menjanjikan, kondisi ini sulit untuk diprediksi dan mengakibatkan nilai return yang tidak pasti. Oleh karena itu diperlukan suatu model matematis yang bisa memodelkan harga saham yaitu Gerak Brown Geometrik (GBG). Tujuan dari penelitian ini adalah untuk menentukan tingkat volatilitas saham dan pola pergerakannya selama masa pembangunan awal IKN tahun 2022-2024 serta menghitung tingkat keakuratan model GBG dalam memprediksi saham SMGR. Data yang digunakan adalah data harga saham penutupan pada 15 Februari 2022 hingga 17 Agustus 2024. Tahapan dalam penelitian ini yaitu pengumpulan data, menghitung return saham, menguji data return (uji normalitas), menghitung estimasi parameter, memprediksi harga saham, dan menghitung nilai Mean Absolute Percentage Error (MAPE). Model GBG yang diperoleh nilai volatilitas 1,472 % yang menunjukkan fluktuasi relatif harga saham dalam model dianggap kecil, nilai drift -0,176 % yang artinya pola pergerakan harga saham selama masa pengamatan mengalami penurunan dan diperoleh nilai MAPE dengan melakukan iterasi sebanyak 1,100,500, dan 1000 berturut-turut bernilai 4,747 %, 3,717 %, 2,488 %, dan 2,453 %. Dari iterasi terkecil kemudian dilanjutkan untuk memperoleh proyeksi prediksi dengan jumlah periode waktu 68 dan menghasilkan nilai rata-rata MAPE 7,65%.Hal ini menunjukkan bahwa nilai MAPE prediksi
ANALISIS K-MEANS CLUSTERING DENGAN BOOTSTRAP PADA PENGELOMPOKAN DESA DI KABUPATEN MEMPAWAH BERDASARKAN INDEKS DESA MEMBANGUN Trifaiza, Fadhela; Perdana, Hendra; Satyahadewi, Neva
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 3 (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.v14i3.95737

Abstract

Pembangunan desa merupakan suatu upaya yang dilakukan dalam meningkatkan kesejahteraan desa yang diukur melalui status desa berdasarkan Indeks Desa Membangun atau IDM. Pembangunan desa melalui IDM dilakukan pemerintah sebagai upaya menciptakan kesejahteraan desa, mengurangi kesenjangan yang terjadi antar desa, dan dapat memberikan perhatian khusus bagi desa dengan status yang rendah. Sehingga diperlukan pengelompokan desa berdasarkan IDM menggunakan analisis cluster. Analisis cluster merupakan teknik statistik yang mengelompokkan objek berdasarkan kesamaan karakteristik tiap objek. Tujuan penelitian ini adalah menganalisis pengelompokan desa di Kabupaten Mempawah berdasarkan IDM menggunakan K-Means Clustering dengan Bootstrap. Data yang digunakan pada penelitian ini merupakan data indikator Indeks Ketahanan Ekonomi (IKE) berdasarkan IDM di Kabupaten Mempawah tahun 2022 yang terdiri dari 60 desa dan 12 variabel, yaitu Keragaman Produksi, Pertokoan, Pasar, Toko/Warung Kelontong, Kedai dan Penginapan, Pos dan Logistik, Bank dan BPR, Kredit, Lembaga Ekonomi, Moda Transportasi Umum, Keterbukaan Wilayah, dan Kualitas Jalan. Berdasarkan analisis yang dilakukan diperoleh nilai akurasi hasil perbandingan metode K-Means Bootstrap dengan status desa di IDM yaitu sebesar 68%. Cluster 1 dengan status desa tertinggal memiliki anggota sebanyak 4 desa. Cluster 2 dengan status paling tinggi yaitu mandiri terdiri dari 32 anggota cluster. Cluster 3 dengan status desa maju terdiri dari 8 anggota cluster. Cluster 4 dengan status desa berkembang memiliki anggota sebanyak 13 desa. Cluster 5 dengan status paling rendah yaitu sangat tertinggal memiliki anggota sebanyak 3 desa, yaitu desa Ansiap, Pentek, dan Suak Barangan.
ANALISIS KARAKTERISTIK DAFTAR PEMILIH TETAP MENGGUNAKAN METODE TWO STEP CLUSTER (Studi Kasus: Daftar Pemilih Tetap Kelurahan Sungai Jawi Kota Pontianak) Rizki, Muhammad; Satyahadewi, Neva
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 14, No 3 (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.v14i3.95795

Abstract

Pemilihan umum adalah sarana bagi rakyat untuk memilih pemimpin dan perwakilan yang akan mewakili kepentingan di pemerintahan. Pemilihan umum menjadi salah satu aspek paling penting yang memungkinkan warga negara untuk berpartisipasi dalam proses politik, maka diperlukan analisis tentang karakteristik pemilih yang terdaftar dalam daftar pemilih tetap. Penelitian ini bertujuan untuk mengetahui karakteristik pemilih yang terdaftar dalam daftar pemilih tetap di Kelurahan Sungai Jawi, Kota Pontianak untuk pemilihan umum tahun 2024 dengan menggunakan metode Two Step Cluster. Metode Two Step Cluster memiliki dua tahapan dalam proses pengelompokan. Tahap awal dimulai dengan membentuk Cluster Feature Tree dengan pengukuran jarak Log-likelihood. Tahap selanjutnya adalah pembentukan cluster optimal dengan menghitung nilai BIC dan membandingkan rasio ukuran jarak antar cluster. Berdasarkan hasil penelitian, dapat disimpulkan metode Two Step Cluster dengan menghasilkan lima cluster optimal. Cluster pertama terdiri dari pemilih muda perempuan dengan rata-rata usia 26 tahun, belum menikah, dan sebagian merupakan pemilih pemula. Cluster kedua adalah pemilih muda laki-laki, rata-rata berusia 27 tahun, belum menikah, dan terdapat pemilih belum memiliki e-KTP. Cluster ketiga merupakan pemilih dewasa perempuan dengan rata-rata usia 45 tahun, sudah memiliki e-KTP, dan sudah menikah. Cluster keempat berisi pemilih dewasa laki-laki dengan rata-rata usia 47 tahun, sudah menikah, dan sudah pernah memberikan hak pilihnya di pemilu sebelumnya. Cluster kelima adalah pemilih pra lanjut usia dengan rata-rata usia 57 tahun, terdapat pemilih penyandang disabilitas dan pemilih baru dari pensiunan TNI/Polri.
Pelatihan Infografis Untuk Pegawai PPN Pemangkat Martha, Shantika; Debataraja, Naomi Nessyana; Rizki, Setyo Wira; Imro'ah, Nurfitri; Perdana, Hendra; Kusnandar, Dadan; Satyahadewi, Neva; Tamtama, Ray
Insan Cita : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 1 (2025): Februari 2025-Insan Cita: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32662/insancita.v7i1.2658

Abstract

PPN Pemangkat sebagai sentra perikanan mempunyai beberapa keunggulan, yaitu lokasi strategis, dekat dengan fishing ground dan daerah pemasaran. Dengan berbagai keunggulan tersebut diharapkan dapat meningkatkan kualitas perekonomian masyarakat sekitar. Pentingnya ketersediaan informasi tentang PPN Pemangkat untuk masyarakat dapat menjadi faktor pendukung untuk meningkatkan kualitas perekonomian masyarakat yang terhubung dengan keberadaan PPN Pemangkat seperti nelayan. Infografis sangat diperlukan untuk penyajian data di PPN Pemangkat. Baik itu data tentang kapal, nelayan maupun hasil tangkapan. Infografis dapat menyederhanakan informasi yang rumit, sehingga informasi data lebih dapat dipahami untuk semua kalangan. Untuk itu pelatihan infografis bagi pegawai PPN Pemangkat sangat diperlukan. Hasil dari kegiatan ini yaitu bertambahnya pengetahuan serta kemampuan pegawai PPN Pemangkat dalam mengolah data melalui pembuatan infografis menggunakan excel.
Application of Classification Data Mining Technique for Pattern Analysis of Student Graduation Data with Emerging Pattern Method Handayani, Aditya; Satyahadewi, Neva; Perdana, Hendra
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp01-06

Abstract

Data mining has been applied in various fields of life because it is very helpful in extracting information from large data sets. Student graduation data is one example of data that can be extracted for information and become a recommendation. This study used a classification data mining technique to extract information from the student graduation data. The classification technique used was the Emerging Pattern method to search for patterns in the student graduation data. The data in this study were graduation data for students of the Statistics Study Program, Faculty of Mathematics and Natural Sciences, Tanjungpura University, from 2013-2018. The sample data used amounted to 186 records. Attributes used in this study include as many as four attributes, including gender, batch, GPA, and TUTEP scores. This research began by finding the class and frequency values obtained. It was continued by calculating each item set's support, growth rate, and confidence values. This study obtained the highest confidence value among all the attributes owned, namely 91% in the 2013 batch itemized list and the 2018 batch. Female students dominated the class attribute. TUTEP dominated the TUTEP value attribute with a score of 425, and the GPA attribute of 3.51-4.00 dominated the class with a confidence value of 60%.
Comparison of Adaboost Application to C4.5 and C5.0 Algorithms in Student Graduation Classification Crismayella, Yuveinsiana; Satyahadewi, Neva; Perdana, Hendra
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 1 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss1pp07-16

Abstract

Students become a benchmark used to assess quality and evaluate college learning plans. Therefore, students who graduate not on time can have an effect on accreditation assessment. The characteristics of students who graduate on time or not on time in determining student graduation can be analyzed using classification techniques in data mining, namely the C4.5 and C5.0 algorithms. The purpose of this study is to compare the application of the Adaboost Algorithm to the C4.5 and C5.0 Algorithms in the classification of student graduation. The data used is the graduation data of students of the Statistics Study Program at Tanjungpura University Period I of the 2017/2018 Academic Year to Period II of the 2022/2023 Academic Year. The analysis begins by calculating the entropy, gain and gain ratio values. After that, each data was given the same initial weight and iterated 100 times. Based on the classification results using the C5.0 Algorithm, the attribute that has the highest gain ratio value is school accreditation, meaning that the school accreditation attribute has the most influence in the classification of student graduation. The application of the Adaboost Algorithm to the C5.0 Algorithm is better than the C4.5 Algorithm in classifying the graduation of students of the Untan Statistics Study Program. The Adaboost algorithm was able to increase the accuracy of the C5.0 Algorithm by 12.14%. While in the C4.5 Algorithm, the Adaboost Algorithm increases accuracy by 10.71%.
Determination of the Annual Pension Fund Premium for Joint-Life Status Using the Aggregate Cost Method syuradi, Syuradi; Satyahadewi, Neva; Perdana, Hendra
Pattimura International Journal of Mathematics (PIJMath) Vol 2 No 2 (2023): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol2iss2pp71-78

Abstract

A pension fund is one of the responsibilities of an institution or company for all employees during their working life. In pension fund insurance, several agreements must be agreed upon by the insured and the insurer for the agreement, namely the premium. The premium to be paid by the insured (employee) of the pension fund insurance must adjust to the income earned, so that the premium to pay does not burden the insured. This study aims to determine the annual pension fund premium amount that must pay use the Aggregate Cost method in the joint-life case. The case study uses information from a husband and wife as civil servants with a husband class III B and wife III A participating in a pension program with a retirement age limit of 58 years (r = 58). The husband (insured x) was 28 years old, and the wife (insured y) was 24 when they started working and joined the pension program. The result of calculating the value of the annual pension fund insurance premium that must pay use the Aggregate Cost method is Rp.41,440,163. If the husband's age is lower than the wife's (x=24, y=28), then the value of the premium paid is more significant than when the husband's age is higher than the wife's (x=28, y=24), which is IDR 41,594,217. That is because the husband's working period is more extended than the wife's, while the chance of death for men is higher than for women. Meanwhile, premiums producing if the husband and wife are of the same age, which is cheaper than when the husband and wife are of different ages
Factor Analysis on Poverty in Kalimantan Island with Geographically Weighted Negative Binomial Regression Halim, Alvin Octavianus; Satyahadewi, Neva; Preatin, Preatin
Pattimura International Journal of Mathematics (PIJMath) Vol 4 No 1 (2025): Pattimura International Journal of Mathematics (PIJMath)
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pijmathvol4iss1pp41-52

Abstract

Poverty is one of the problems still faced by Indonesia. The problem of poverty is a development priority because poverty is a complex and multidimensional problem. Therefore, to reduce poverty, it is necessary to know the factors that influence the number of people living in poverty. The influencing factors in each region are different due to the effects of spatial heterogeneity between regions such as geographical, economic, and socio-cultural conditions. This research considers spatial factors by using the Geographically Weighted Negative Binomial Regression (GWNBR) method on poverty-based regions in Kalimantan Island. This research uses eleven independent variables. The weighting function used is the Adaptive gaussian kernel because the adaptive kernel can produce the number of weights that adjust to the distribution of observations. The stage starts with descriptive statistics and checking multicollinearity. Then proceed with the formation of Poisson Regression, because the data used is enumerated data. Then check for overdispersion. If overdispersion is detected where the variance is bigger than the mean, then Negative Binomial Regression is continued. After that, it is tested for the presence or absence of spatial heterogeneity. If there is, proceed to find the bandwidth and Euclidean distance. After that, the graphical weighting matrix is searched. Then proceed with GWNBR modeling. The results of the analysis show that there are seven significant variables, including the percentage of households with the main source of lighting is non-state electricity company (PLN), average monthly net income of informal workers, population density for every square kilometer, monthly per capita expense on food and non-food essentials, percentage of people who have a health complaint and do not treat it because there is no money and percentage of population 15 years and above who do not have a diploma. Based on the categories of significant variables, six groups were formed in 56 districts/cities in Kalimantan Island.
COMPARISON OF SENTIMENT ANALYSIS USING NAÏVE BAYES CLASSIFICATION METHOD AND LEXICON BASED ON JIWA+ BY JANJI JIWA APPLICATION REVIEWS Arti, Reyana Hilda; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page135-146

Abstract

The coffee beverage industry in Indonesia is experiencing significant growth, intensifying competition among businesses striving to maintain quality for customer loyalty. E-commerce applications play a vital role in preserving business standards as they directly engage with consumers. Janji Jiwa is among the coffee brands leveraging an application named Jiwa+ in their operations. Analyzing reviews on this e-commerce platform provides valuable insights for business owners and app developers. In this study, sentiment analysis was conducted by classifying reviews into positive, neutral, and negative sentiments using two methods: Lexicon Based and Naïve Bayes. The Lexicon Based method uses a predefined dictionary as the basis for labeling, while Naïve Bayes relies on training data to provide new insights into how both methods handle this type of data. A total of 597 Jiwa+ application reviews from the Google Play Store were utilized, split into 90% training and 10% testing data sets. The study results indicate that Naïve Bayes produces a better model than the Lexicon-Based method, as shown by its higher accuracy, sensitivity, and specificity. This is because Lexicon-Based relies on labeling words from a dictionary, which may not cover all words in the reviews, leading to labeling errors and misclassification.
STRUCTURAL EQUATION MODELING ANALYSIS ON POVERTY IN WEST KALIMANTAN WITH FINITE MIXTURE IN PARTIAL LEAST SQUARE APPROACH Fauzan, Muhammad; Perdana, Hendra; Satyahadewi, Neva
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss1pp0001-0016

Abstract

Poverty occurs when individuals or groups lack the necessary resources to fulfill their basic needs. In Indonesia, including West Kalimantan, poverty remains a significant issue influenced by various socio-economic factors. This study aims to identify valid and reliable indicators of poverty and classify regencies/cities in West Kalimantan using the 2023 data from the Central Statistics Agency of West Kalimantan and Indonesia. The analysis applies the Structural Equation Modeling approach with Finite Mixture in Partial Least Squares (FIMIX-PLS). From 19 observed indicators, only 12 were found valid and reliable based on measurement and structural model evaluation. The structural model reveals three significant relationships: the Economy significantly influences Poverty, Health influences Education, and Education influences the Economy. Based on the FIMIX-PLS results, the regencies/cities are segmented into four groups with distinct structural characteristics. Segment 1 reflects the influence of Health on Education, Segment 2 reflects the influence of Health on the Economy, Segment 3 highlights the influence of Economy on Poverty, and Segment 4 captures the influence of Education on the Economy. Detailed interpretations of each segment and their policy implications are presented in the conclusion. The results support the importance of tailored poverty alleviation strategies based on latent regional characteristics and validated model findings.
Co-Authors . Apriansyah Afghani Jayuska Afghany Jayuska Al-Ham, Hairil Amriani Amir Amriani Amir Amriani Amir Andani, Wirda Antoni, Frans Xavier Natalius Apriliyanti, Rita Aprizkiyandari, Siti Ardhitha, Tiffany Ari Hepi Yanti Arsyi, Fritzgerald Muhammad Arti, Reyana Hilda Ashari, Asri Mulya Asri Mulya Ashari Asty Fistia Ningrum Atikasari, Awang Aulia Puteri Amari Bambang Kurniadi Banu, Syarifah Syahr ciptadi, wahyudin Cornellia, Amanda Crismayella, Yuveinsiana Dadan Kusnandar Dadan Kusnandar Dadan Kusnandar David Jordy Dhandio Debataraja, Naomi Nessyana Della Zaria Desriani Lestari Desriani Lestari Desriani Lestari Dhandio, David Jordy Dinda Lestari Dwi Nining Indrasari Dwinanda, Maria Welita Eka Febrianti, Eka Esta Br Tarigan Evy Sulistianingsih Ewaldus Okta Ferdina Ferdina Feriliani Maria Nani Fitriawan, Della Fransisca Febrianti Sundari Fransiska Fransiska Grikus Romi Gusti Eva Tavita Gusti Eva Tavita Hairil Al-Ham Halim, Alvin Octavianus Hamzah, Erwin Rizal Handayani, Aditya Hanin, Noerul Harimurti, Puspito Harnanta, Nabila Izza Helena, Shifa Hendra Perdana Hendrianto, El Herina Marlisa Huda, Nur'ainul Miftahul Huriyah, Syifa Khansa Ibnur Rusi Ikha Safitri Imro'ah, Nurfitri IMRO’AH, NURFITRI Imtiyaz, Widad Isra’ Sagita Jawani Jawani Karlina, Sela Kusnandar, Dadan Tonny Lucky Hartanti Lucky Hartanti Lucky Hartanti M. Deny Hafizzul Muttaqin Maga, Fahmi Giovani Margareta, Tiara Margaretha, Ledy Claudia Marlisa, Herina Marola, Geby Martha, Shantika Mega Sari Juane Sofiana Mega Sari Juane Sofiana Mega Tri Junika Millennia Taraly Misrawi Misrawi Muhammad Ahyar Muhammad fauzan Muhammad Radhi Muhammad Rizki Muliadi Muliadi Muslimah (F54210032) Nabil, Ilhan Nail Nanda Shalsadilla Naomi Nessyana Debataraja Naomi Nessyana Debataraja Noerul Hanin Nona Lusia Nugrahaeni, Indah Nur Asih Kurniawati Nur Asiska Nurfadilah, Kori’ah Nurfitri Imro'ah Nurfitri Imro’ah Nurhalita Nurhalita Nurmaulia Ningsih Oktaviani, Indah Ovi Indah Afriani Paisal Paisal Pertiwi, Retno Pratama, Aditya Nugraha Preatin, Preatin Putri Putri Putri, Aulia Nabila Qalbi Aliklas R Puspito Harimurti Radhi, Muhammad Radinasari, Nur Ismi Rafdinal Rafdinal Rahadi Ramlan Rahmadanti, Putri Rahmanita Febrianti Rusmaningtyas Rahmawati, Fenti Nurdiana Ramadhan, Nanda Ramadhania, Wahida Reni Unaeni Retnani, Hani Dwi Ria Andini Ria Fuji Astuti Rina Rina Risky Oprasianti Rita Kurnia Apindiati Rivaldo, Rendi Riza Linda Rizki Nur Rahmalita Rizki, Setyo Wira Rosi Kismonika Roslina Rosi Tamara Rovi Christova Safira, Shafa Alya Salsabilla, Arla Santika Santika Sary, Rifkah Alfiyyah Seftiani, Seftiani Selvy Putri Agustianto Setyo Wir Rizki Setyo Wira Rizki Setyo Wira Rizki Setyo Wira Rizki Shantika Martha Shantika Martha Sinaga, Steven Jansen Sintia Margun Sista, Sekar Aulia Siti Aprizkiyandari Siti Aprizkiyandari, Nurul Qomariyah, Shantika Martha, Siti Hardianti Suci Angriani Sukal Minsas Sukal Minsas syuradi, Syuradi Tamtama, Ray Taraly, Inggriani Millennia Tiara, Dinda Trifaiza, Fadhela Wahyu Diyan Ramadana Wahyudin Ciptadi Warsidah Warsidah Warsidah, Warsidah Wilda Ariani Wirda Andani Yopi Saputra Yudhi Yuliono, Agus Yumna Siska Fitriyani Yundari, Yundari Yuveinsiana Crismayella Zakiah, Ainun