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All Journal Media Statistika BIMASTER BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Sarwahita : Jurnal Pengabdian Kepada Masyarakat Variance : Journal of Statistics and Its Applications Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi CENDEKIA : Jurnal Ilmu Pengetahuan Parameter: Journal of Statistics Jurnal Forum Analisis Statistik
Andani, Wirda
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Analisis Kelayakan Kredit Menggunakan Classification Tree dengan Teknik Random Oversampling Vebriyanti, Lo Mei Ly; Martha, Shantika; Andani, Wirda; Rizki, Setyo Wira
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EULER: Volume 12 Issue 1 June 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i1.24182

Abstract

Credit is providing money or bills based on the agreement between a bank and another party. Lending is inseparable from bad credit risk, so credit analysis must be conducted on prospective debtors before approving a proposed loan. This research aims to analyze creditworthiness using a Classification Tree as a classification method with Random Oversampling to overcome imbalanced data. This study uses secondary data on the status of debtors from a bank in West Kalimantan. Research data amounted to 800 data samples consisting of collectability variables as target variables and 10 independent variables, namely limit, rate, tenor, total installments, age, salary, premium and admin, agency, type credit, and type need. The Classification Tree method with Random Oversampling is used to overcome imbalanced data. Classification begins with data preprocessing, then the data is divided into training and test data with proportions of 70:30, 80:20 and 90:10 for each treatment without Random Oversampling and with Random Oversampling. Next, a classification model is formed using training data, and the classification model is validated using test data. After that, an overall evaluation of the model is carried out to determine the best model used in the classification process. Based on the research results, the best model is the model Classification Tree with Random Oversampling in proportion 70:30, with an accuracy value of 89.17%, specificity of 75.00%, and recall of 89.66%. The model can be used to classify current and non-current debtor data. The most influential variable in classifying debtor status is the total installment variable.
PERAMALAN CURAH HUJAN DI KOTA PONTIANAK MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE NEURAL NETWORK (VAR-NN) Istighfarani, Ridha; Martha, Shantika; Andani, Wirda
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 3 (2024): 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.v13i3.77470

Abstract

Fenomena cuaca ekstrim di Indonesia cenderung meningkat akibat dampak perubahan iklim. Perubahan iklim mengakibatkan perubahan cuaca, sehingga diperlukan cara untuk meramalkan agar mempermudah masyarakat untuk mengetahui informasi tentang terjadi atau tidaknya hujan. Penelitian ini menggunakan metode Vector Autoregressive Neural Network (VAR-NN) yang bertujuan meramalkan curah hujan di Kota Pontianak berdasarkan data bulanan dari Januari 2019 hingga Desember 2022 yang diperoleh dari Stasiun Meteorologi Maritim Pontianak. Vector Autoregressive (VAR) adalah metode deret waktu multivariat yang variabelnya tidak perlu dipisahkan menjadi variabel endogen atau eksogen. Dalam kasus curah hujan biasanya juga mengandung pola nonlinier, sehingga diperlukan pemodelan nonlinier untuk mengantisipasi masalah tersebut. Adapun metode peramalan yang bersifat nonlinier salah satunya adalah Neural Network (NN). NN memiliki kemampuan dalam menganalisis berbagai jenis data. Hasil analisis menunjukkan bahwa VAR-NN (5) dengan jumlah lapisan (4-2-1) menghasilkan peramalan curah hujan selama 12 bulan ke depan termasuk dalam kategori rendah. Berdasarkan perhitungan MAPE bahwa hasil peramalan termasuk dalam kategori cukup baik dengan nilai MAPE sebesar 45,080%. Hal ini disebabkan karena nilai varians dari curah hujan yang besar, sehingga nilai MAPE yang dihasilkan besar pula.  Kata Kunci: curah hujan, VAR, NN.
PERAMALAN INTENSITAS CURAH HUJAN DI KOTA PONTIANAK DENGAN METODE VECTOR AUTOREGRESSIVE Hutami, Bintang Ratna; Kusnandar, Dadan; Andani, Wirda
BIMASTER : Buletin Ilmiah Matematika, Statistika dan Terapannya Vol 13, No 5 (2024): 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.v13i5.84844

Abstract

Kota Pontianak mengalami curah hujan yang tidak menentu, hal tersebut sejalan dengan pendapat World Wildlife Fund For  Nature (WWF) Indonesia beberapa tahun terakhir ini perubahan iklim global terasa ditandai dengan tidak menentunya perputaran musim kemarau maupun musim penghujan. Untuk mengetahui perubahan pola hujan tersebut maka perlu peramalan curah hujan dan menganalisis pola hujan yang akan datang. Pada penelitian ini menggunakan data curah hujan, kecepatan angin, tekanan udara, temperatur udara, dan kelembaban udara yang masing-masing datanya berupa data time series, sehingga metode peramalan yang dapat digunakan adalah metode Vector Autoregressive. Penelitian ini bertujuan untuk menentukan model dan hasil peramalan curah hujan bulan November dan Desember 2022 di Kota Pontianak. Studi kasus yang digunakan pada penelitian ini adalah data kelembaban udara, temperatur udara, kecepatan angin, tekanan udara dan curah hujan bulan Januari 2018 sampai Oktober 2022. Hasil analisis yang diperoleh adalah hasil peramalan    data curah hujan bulan November 2022 sebesar 261,5918 mm sedangkan data curah hujan bulan Desember 2022 sebesar 223,7606 mm. Model VAR yang terbentuk adalah VAR (7), maka    dapat disimpulkan    variabel-variabel yang digunakan saling berpengaruh tidak hanya pada bulan Juni, Juli, Agustus, September, Oktober,    November, Desember 2018 namun juga pada bulan Juni, Juli, Agustus, September, Oktober, November, Desember sebelumnya di 2017. Selain itu diperoleh MAPE peramalan curah hujan pada bulan November dan Desember 2022 yaitu 45,563%.  Kata Kunci:  Curah Hujan, VAR, VAR (7)
IMPLEMENTATION GRID SEARCH OF RBF AND POLYNOMIAL ON SUPPORT VECTOR REGRESSON FOR CLOSING STOCK PRICES PREDICTION ON PT INDOFARMA (INAF) Salsabilla, Arla; Satyahadewi, Neva; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): 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/variancevol6iss2page133-142

Abstract

Stocks represent evidence of ownership of an asset. The highly volatile nature of stock prices makes it difficult for investors to predict stock prices, necessitating the analysis of stock investments. This research aims to forecast for the next 30 days the closing price of PT Indofarma (INAF) stocks using the best model, and the accuracy level of the employed model was analyzed based on the data from the last seven years. The research used the Support Vector Regression (SVR) method, which is known for its capability to handle nonlinear data through kernel functions. The Radial Basis Function (RBF) and polynomial kernels are used in this case. The challenge with SVR lies in determining the optimal hyperparameter, which can be addressed through hyperparameter tuning using grid search. The research results show that the best model is the SVR kernel RBF model with optimal hyperparameter C=1,γ=0.01, and ε=0.01. Based on the performance evaluation results of the best model, the MAPE, MSE, and MAE values are equal to 1.537%,1483.936, and 23.409.
GROSS PREMIUM VALUATION METHOD IN DETERMINING PREMIUM RESERVES IN LIFE INSURANCE Rivaldo, Rendi; Perdana, Hendra; Andani, Wirda
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): 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/variancevol6iss2page215-222

Abstract

Abstract: Life insurance companies maintain reserve funds to pay insurance policy claims, known as premium reserves. Premium reserves are calculated using two approaches: retrospective and prospective. The prospective approach involves calculating the present value of all future expenses minus the total future income for each policyholder, using the Gross Premium Valuation (GPV) method. The GPV method takes into account initial costs, maintenance costs, and administration costs. The case study results indicate that the premium reserve using the GPV method starts at zero in the first year, increases until the last payment year, and then decreases after the payment period until the end of the coverage period. For policyholders of different genders but the same age, the premium reserve for men is greater than for women. Additionally, for male policyholders of varying ages, the premium reserves required increase with age. Furthermore, for male policyholders of the same age but with different interest rates, a higher interest rate results in a smaller premium reserve requirement.
Penentuan Metode Cluster Hierarki Terbaik dengan Korelasi Cophenetic pada Pengelompokan Kabupaten/Kota di Indonesia Berdasarkan Variabel yang Memengaruhi Indeks Pembangunan Manusia: Determination of the Best Hierarchical Clustering Method with Cophenetic Correlation in the Clustering of Districts/Cities in Indonesia Based on Variables Affecting the Human Development Index Andini, Syarifah; Andani, Wirda; Kurniawati, Nur Asih
Jurnal Forum Analisis Statistik Vol. 4 No. 2 (2024): Jurnal Forum Analisis Statistik (FORMASI)
Publisher : Badan Pusat Statistik Provinsi Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57059/formasi.v4i2.83

Abstract

Capaian Indeks Pembangunan Manusia di Indonesia pada tahun 2023 mencapai 73,55 poin menunjukkan bahwa angka tersebut berada pada kategori tinggi. Akan tetapi, jika ditinjau berdasarkan wilayah kabupaten/kota, maka terjadi ketimpangan terhadap wilayah pembangunan. Ketimpangan ini bisa dilihat berdasarkan perbedaan capaian Indeks Pembangunan Manusia pada enam wilayah kabupaten/kota di Provinsi DKI Jakarta yang masuk dalam kategori tinggi dan sangat tinggi, sementara Provinsi Papua mendominasi pada kategori rendah. Hal tersebut menunjukkan bahwa terjadi ketimpangan pembangunan manusia pada wilayah kabupaten/kota di Indonesia. Penelitian yang dilakukan ini bertujuan untuk membantu merancang strategi yang lebih tepat sasaran untuk meningkatkan kualitas hidup masyarakat, baik di daerah dengan Indeks Pembangunan Manusia dengan kategori rendah maupun di daerah dengan Indeks Pembangunan Manusia dengan kategori tinggi melalui pendekatan yang berbasis pada kesamaan kondisi wilayah sosial ekonomi masing-masing wilayah. Analisis dalam penelitian ini memanfaatkan data yang mencakup Umur Harapan Hidup, Harapan Lama Sekolah, Rata-rata Lama Sekolah, Pengeluaran Riil per Kapita yang Disesuaikan, Tingkat Pengangguran Terbuka, dan Upah Minimum. Penelitian ini menentukan analisis cluster hierarki terbaik menggunakan korelasi Cophenetic lalu menentukan jumlah cluster optimum menggunakan package NbClust pada software RStudio. Hasil dari penelitian tersebut yaitu diperoleh metode terbaik yang digunakan ialah metode Average Linkage yang terbagi menjadi 5 cluster berdasarkan karakteristik wilayah pembangunan manusia.
STOCK PRICE FORECASTING USING THE HYBRID ARIMA-GARCH MODEL Oprasianti, Risky; Kusnandar, Dadan; Andani, Wirda
Parameter: Journal of Statistics Vol. 4 No. 2 (2024)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2024.v4.i2.17162

Abstract

In the current era, many people have made investments, namely capital investment activities within a certain period to seek and get profits. One of the most popular investment instruments in the capital market is stocks, which consist of conventional stocks and Islamic stocks. Conventional stocks are shares traded on the stock market without adhering to Sharia principles. In contrast, Sharia-compliant stocks meet Islamic principles and are traded in the sharia capital market. One form of development of the Islamic capital market in Indonesia is the existence of the Indonesian Sharia Stock Index (ISSI), which projects the movement of all Islamic stocks on the Indonesia Stock Exchange (IDX). Stock prices change every day so modeling is needed that can be used by investors to determine decisions. The Autoregressive Integrated Moving Average (ARIMA) model is one of the forecasting models that is applicable. Stock prices have volatility that tends to be high, this results in variance that is not constant or there is a heteroscedasticity problem, at the same time the ARIMA model must fulfill the assumption of homoscedasticity. Therefore, it is necessary to combine the ARIMA model with a model that can overcome the problem of heteroscedasticity, namely the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This research aims to get the best hybrid ARIMA-GARCH model that will be used to forecast the stock price of the ISSI. The daily closing data of the ISSI stock price from May 4, 2020, to January 13, 2023, is the data that was used. The study’s findings suggest that ARIMA (0,1,3)-GARCH (2,0) is the best model among all possible models for ISSI stock price forecasting. By evaluating the predictive accuracy of the model using Mean Absolute Percentage Error (MAPE), the forecasting result for ISSI stock prices using the best model, ARIMA(0,1,3)-GARCH(2,0) at 0,6092%, shows a forecasting that is close to the actual data, which means that the model used is highly effective at forecasting stock priced
PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLAR AS MENERAPKAN ARIMA, VAR DAN RANDOM FOREST ANDANI, WIRDA; SATYAHADEWI, NEVA
CENDEKIA: Jurnal Ilmu Pengetahuan Vol. 5 No. 1 (2025)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/cendekia.v5i1.4305

Abstract

The weakening of the rupiah affects imported goods, pushing up products that use imported raw materials so that production costs will increase and logistics costs soar. Consequently, UMKM players and t society are victimized. Another impact is that foreign debt becomes more expensive to pay. This certainly impacts the suppression of the State Budget (APBN). The assumption of the rupiah exchange rate against the United States dollar (US) plays a vital role in the structure of the APBN, so analysis is needed to determine the dynamics of changes in the rupiah exchange rate against the US dollar. Therefore, an accurate rupiah exchange rate forecasting model is required. Various methods can be used to produce accurate predictions. This research, will conduct forecasting of the Rupiah exchange rate against the US Dollar by comparing the ARIMA, VAR, and Random Forest methods. The best method will be selected based on the smallest MAPE. The data is secondary data from January 2021 to March 2024 obtained from the BI and BPS websites. Based on the MAPE, the best model was chosen in forecasting the rupiah exchange rate against the US dollar, namely ARIMA (0,2,1) with a MAPE of 1%. The output of forecasting the rupiah exchange rate against the US dollar for April - December 2024 using ARIMA (0,2,1) ranges from Rp. 15,841 - Rp. 16,202 with an average of Rp. 16,021. ABSTRAKMelemahnya rupiah berpengaruh terhadap barang impor yang mendorong kenaikan produk-produk yang menggunakan bahan baku tersebut. Akibatnya, biaya produksi akan meningkat dan ongkos logistik melonjak. Konsekuensinya, pelaku UMKM dan masyarakat menjadi korban. Dampak lainnya adalah meningkatnya biaya untuk melunasi utang luar negeri. Hal ini tentu berimbas pada penekanan Anggaran Pendapatan dan Belanja Negara (APBN). Asumsi nilai tukar rupiah terhadap dolar Amerika Serikat (AS) memainkan peran vital dalam struktur APBN, maka diperlukan analisis untuk mengetahui dinamika perubahan nilai tukar rupiah terhadap dolar AS. Oleh karena itu, diperlukan model peramlaan nilai tukar rupiah yang akurat. Terdapat berbagai metode yang dapat dioperasikan untuk menghasilkan prediksi yang akurat. Pada penelitian akan dilakukan peramalan nilai tukar Rupiah terhadap Dolar AS dengan membandingkan metode ARIMA, VAR dan Random Forest. Metode terbaik akan dipilih berdasarkan nilai MAPE terkecil. Data yang diaplikasikan merupakan data bulanan dari bulan Januari 2021 sampai dengan bulan Maret 2024 yang berasal dari website BI dan BPS. Berdasarkan nilai MAPE, terpilihlah model terbaik dalam meramalkan nilai tukar rupiah terhadap dolar AS yaitu ARIMA (0,2,1) dengan MAPE sebesar 1%. Output peramalan nilai tukar rupiah terhadap dolar AS untuk bulan April – Desember 2024 menggunakan ARIMA (0,2,1) berkisar antara Rp. 15.841 – Rp. 16.202 dengan rata-rata Rp. 16.021.
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.
Co-Authors Agustono, Hendri Amarrullah, Rido Andini, Syarifah Anggria, Vini Apriliyanti, Rita Arti, Reyana Hilda AYU ASTUTI, AYU Dadan Kusnandar Debataraja, Naomi Nessyana Dwinanda, Maria Welita Evy Sulistianingsih Fitriawan, Della Gunawan, Risky Handayany, Indry Hendra Perdana Hutami, Bintang Ratna Imro'ah, Nurfitri IMRO’AH, NURFITRI Istighfarani, Ridha Maharani, Cinta Priscillia Marola, Geby Martha, Shantika Neva Satyahadewi Nur Asih Kurniawati Oprasianti, Risky Pebriyandi, Rifki Rivaldo, Rendi Rizki, Setyo Wira Salsabila, Salsabila Salsabilla, Arla Sary, Rifkah Alfiyyah Selvia, Eva Tamtama, Ray Umiati, Wiji Vebriyanti, Lo Mei Ly Wulan, Elisabet Yusril, Alqaida Zaria, Della