Article Per Year (5 Year)
p-Index From 2021 - 2026
5.932
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) FORUM STATISTIKA DAN KOMPUTASI Media Statistika SAINSMAT CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Padjadjaran Journal of Dentistry Jurnal Matematika Sains dan Teknologi Journal of Regional and Rural Development Planning BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jambura Journal of Mathematics ComTech: Computer, Mathematics and Engineering Applications Inferensi International Journal of Advances in Data and Information Systems Jurnal Statistika dan Aplikasinya Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam Jurnal Natural Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Yenni Angraini
Department Of Statistics And Data Science, Faculty Of Mathematics And Natural Science, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
Implementation of Clustering Time Series with DTW to Clustering and Forecasting Rice Prices Each Provinces in Indonesia Tsabitah, Dhiya; Angraini, Yenni; Sumertajaya, I Made
Inferensi Vol 8, No 1 (2025)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v8i1.21952

Abstract

Indonesia faces a significant imbalance between domestic supply and demand, leading to escalating rice prices and pronounced regional disparities. To elucidate underlying price patterns and forecast future trends, this study employed Hierarchical Clustering Time-Series with DTW and ARIMA modelling at both individual and cluster levels. Comprehensive analysis, incorporating visualization and threshold comparisons, identified Central Kalimantan as an outlier. Individual ARIMA models demonstrated exceptional performance, with MAPE values below 10%. The clustering time-series correlation using Cophenetic coefficient, reached 0.68 for ward linkages. Two clustering approaches were explored: (1) ignoring the outlier province, (2) excluding Central Kalimantan and incorporating it into a separate cluster. Optimal cluster measurement, the Elbow, Silhouette, Calinski-Harabasz, and Davies-Bouldin, yielded 6-7 clusters for the former approach and 3-5 clusters for the latter. Comparative analysis of individual and cluster forecasts, coupled with paired t-tests, revealed that Ward linkage in the second approach produced the most favorable results, with 27/34 provinces exhibiting cluster MAPE values less than or equal totheir individual MAPE. This finding underscores the efficacy of cluster-based modeling in generating accurate and representative estimates for a substantial portion of provinces. A 12-period rice price forecast indicates a prevailing trend of rising prices in most regions of Indonesia.
Enhancing Weather Monitoring for Agriculture with Deep Learning: Anomaly Detection in East Java Using LSTM Autoencoder and OCSVM Fadillah, Maulana Ahsan; Angraini, Yenni; Anisa, Rahma
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1571

Abstract

Agricultural productivity in East Java is under threat from unpredictable and harsh weather patterns, particularly rapid variations in sunlight length and rainfall intensity.  These abnormalities can interrupt agricultural cycles, lower yields, and make farming communities more vulnerable to climatic calamities.  However, current weather monitoring systems frequently fall short of detecting small anomalies in time series weather data that could serve as early warning signs of such disasters.  This study seeks to close this gap by creating a robust anomaly detection methodology adapted to time-dependent weather variables important to agriculture. In this study, a hybrid model combining Long Short-Term Memory (LSTM) autoencoder and One-Class Support Vector Machine (OCSVM) is proposed. The LSTM autoencoder's structure reconstructs time series data and signifies anomalies through reconstruction errors (MSE), while OCSVM validates these anomalies to reduce false positives. The model was applied to daily weather data from East Java spanning 2015–2024. The results showed that the model effectively detected 11 anomalies in sunlight duration and 7 in rainfall, with F1-scores of 0.71 and 0.82, respectively. Several of these anomalies corresponded to actual disaster events such as floods, landslides, and droughts. This research contributed to the field by demonstrating the effectiveness of combining deep learning and machine learning for weather anomaly detection. The proposed framework offers valuable insights for early warning systems and can support local governments and farmers in improving disaster preparedness and enhancing agricultural resilience in East Java.
Evaluation of the SARIMA and Prophet Models in Forecasting Ship Passenger Numbers at Balikpapan Port Cintani, Meavi; Nizar, Yeky Abil; Angraini, Yenni; Notodiputro, Khairil Anwar; Mualifah, Laily Nissa Atul
Jambura Journal of Mathematics Vol 7, No 2: August 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v7i2.31606

Abstract

Balikpapan Port serves as a vital transportation hub in eastern Indonesia, particularly in supporting the development of the Nusantara Capital City (IKN). This study evaluates the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet models in predicting short-term ship passenger volumes using monthly data from January 2006 to December 2024 obtained from the East Kalimantan Provincial Transportation Office. Our analysis identifies SARIMA (MAPE = 24%) as the more accurate model compared to Prophet (MAPE = 34%). The optimal SARIMA model was then used to generate a focused forecast for December 2025, providing targeted insights for peak-season port management. These results assist port authorities in resource allocation, infrastructure planning, and policy formulation to accommodate anticipated passenger surges during critical periods.
Analysis of VAE-LSTM Performance in Detecting Anomalies in Average Daily Temperature Data in Jakarta 2000-2023 Angraini, Yenni; Ramdani, Indri; Indahwati, Indahwati
Jurnal Natural Volume 25 Number 2, June 2025
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jn.v25i2.41856

Abstract

Climate change is happening worldwide, so global climate conditions are a major concern. In densely populated urban areas such as Jakarta, it is impossible to avoid the impacts of climate change, particularly the daily changes in air temperature. Therefore, a sophisticated and efficient approach is needed to find inconsistencies in daily air temperature data to provide critical information for sustainable urban planning and efforts to reduce risks. This research will combine two innovative approaches for hybrid anomaly detection. The method combines generative methods and can extract complex features, such as variational autoencoder (VAE), along with the temporal coding capabilities of long-short-term memory (LSTM), a type of Recurrent Neural Network (RNN). The data used in this study is the average daily air temperature data in Jakarta, obtained from the Kemayoran Meteorological Station and provide by the Meteorology, Climatology, and Geophysics Agency (BMKG). The data used is daily from April 2000 to December 2023. The threshold used to detect anomalies was 229.5, which resulted in excellent performance, namely F1-Score 0.985, Recall 1.000, and Precision 0.971. The VAE-LSTM model identified all dates with significant temperature anomalies, including January 21, 2014, February 22, 2014, November 12, 2014, and February 9, 2015. These dates are significant as they represent extreme weather events that can have severe implications for urban planning and climate change adaptation. The anomalies fall into the categories of point and contextual anomalies. This study contributes to climate research by providing evidence of the effectiveness of deep learning-based hybrid models in detecting complex and context-sensitive temperature anomalies.
Perbandingan Performa Arimax-Garch Dan Lstm Pada Data Harga Penutupan Saham PT Aneka Tambang Tbk (ANTM.JK) Suwarso, Dhiya Khalishah Tsany; Rizki, Akbar; Rahmi, Salsabila Dwi; Mahesa, Hakim Zoelva; Gunawan, Windi; Fitri, Zafira Ilma; Angraini, Yenni; Putri, Adelia; Nurhambali, Muhammad Rizky
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 3: Juni 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128756

Abstract

Banyaknya data deret waktu dengan pola nonlinear dan memiliki volatilitas tinggi pada berbagai sektor membuat sulit untuk melakukan pemodelan klasik seperti Autoregressive Integrated Moving Average (ARIMA). Permasalahan ini dapat diatasi salah satunya dengan mengembangkan metode Autoregressive Integrated Moving Average with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) yang memanfaatkan kovariat eksternal, sehingga memberikan solusi lebih baik pada data yang tidak stasioner. Di sisi lain, metode deep learning seperti Long Short-Term Memory (LSTM) unggul dalam menangkap pola non-linear dan dependensi jangka panjang. Oleh karena itu, penelitian ini membandingkan performa ARIMAX-GARCH dan LSTM dalam memprediksi harga saham PT Aneka Tambang Tbk (ANTM.JK). Data mingguan penutupan harga saham ANTM.JK periode 1 Januari 2018 hingga 30 Oktober 2023 digunakan dalam penelitian ini. Pemodelan ARIMAX-GARCH dengan peubah kovariat berupa data harga nikel berjangka dunia digunakan karena terdapat pengaruh signifikan harga nikel terhadap harga penutupan saham ANTM.JK dan terdeteksi adanya heteroskedastisitas dalam model. Metode berbasis machine learning, LSTM digunakan karena metode ini dikenal memiliki akurasi prediksi yang baik. Pengolahan data dilakukan menggunakan bantuan software R-Studio dan Python. Hasil penelitian menunjukkan LSTM memiliki performa yang lebih baik dengan nilai MAPE sebesar 4,425%, nilai ini lebih kecil jika dibandingkan model terbaik ARIMAX(2,1,2)-GARCH(1,1) dengan MAPE 7,326%.   Abstract The large number of time series data with nonlinear patterns and high volatility in various sectors makes it difficult to perform classical modeling such as Autoregressive Integrated Moving Average (ARIMA). This problem can be overcome by developing the ARIMA with Exogenous- Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH) that utilizes external covariates, thus providing a better solution to non-stationary data. On the other hand, deep learning methods such as Long Short-Term Memory (LSTM) excel in capturing non-linear patterns and long-term dependencies. Therefore, this study compares the performance of ARIMAX-GARCH and LSTM in predicting the stock price of PT Aneka Tambang Tbk (ANTM.JK). Weekly closing data of ANTM.JK stock price from January 1, 2018 to October 30, 2023 are used in this study. ARIMAX-GARCH modeling with covariate variables in the form of world nickel futures price data is used because there is a significant effect of nickel prices on the closing price of ANTM.JK shares and heteroscedasticity is detected in the model. Machine learning-based method, LSTM is used because this method is known to have good prediction accuracy. Data processing is done using R-Studio and Python software. The results show that LSTM has better performance with a MAPE value of 4.425%, this value is smaller than the best model ARIMAX(2,1,2)-GARCH(1,1) with a MAPE of 7.326%.
Peramalan Harga Emas Berjangka Menggunakan Metode ARIMA-GARCH Hasanah, Mauizatun; Putri, Mega Ramatika; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 2 August 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i2.32723

Abstract

Gold futures price forecasting plays an important role in investment decision-making and risk management, especially in the midst of volatile commodity market dynamics. This research aims to build an accurate gold futures price forecasting model by combining Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The ARIMA model is used to capture linear patterns and historical trends in time series data, while the GARCH model is able to handle the high volatility characteristic of gold price movements, something that conventional forecasting models often fail to capture. The data used in this study is daily gold futures price data collected over the period January 3, 2023 to March 31, 2025, which covers both normal market conditions and periods of turmoil, making it relevant to describe the overall market dynamics. The forecasting results show that the ARIMA-GARCH model with components (3,1,3) (1,1) with a MAPE of 4.52% indicates a good level of accuracy in the context of forecasting gold futures prices that have high volatility. Thus, this model provides precise forecasting results with actual data so that it can be used by market participants and policy makers in managing risks and designing strategies.
PERFORMANCE COMPARISON OF SARIMA INTERVENTION AND PROPHET MODELS FOR FORECASTING THE NUMBER OF AIRLINE PASSENGER AT SOEKARNO-HATTA INTERNATIONAL AIRPORT Nur Aziza, Vivin; Moh'd, Fatma Hilali; Maghfiroh, Firda Aulia; Notodiputro, Khairil Anwar; Angraini, Yenni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2107-2120

Abstract

The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.
THE COMPARISON OF LONG SHORT-TERM MEMORY AND BIDIRECTIONAL LONG SHORT-TERM MEMORY FOR FORECASTING COAL PRICE Siregar, Indra Rivaldi; Nugraha, Adhiyatma; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp245-258

Abstract

Coal remains vital for global energy despite recent demand fluctuations due to the COVID-19 pandemic and geopolitical tensions. The International Energy Agency (IEA) projected a decline in global coal demand starting in early 2024, driven by increasing renewable energy adoption. As one of the top coal exporters, Indonesia must adjust to these changes. This study aims to forecast future coal prices using historical data from Indonesia's Ministry of Energy and Mineral Resources (KESDM), applying and comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models. While BiLSTM has shown advantages in other contexts and studies, its effectiveness for coal price forecasting remains underexplored. To ensure robust predictions, we employ walk-forward validation, which divides the data into six segments and evaluates 90 hyperparameter combinations across all segments. The BiLSTM model consistently outperforms the LSTM model, achieving lower average RMSE and MAPE values. Specifically, BiLSTM records an average MAPE of 7.847 and RMSE of 10.485, compared to LSTM's 10.442 and 11.993, respectively. The Diebold-Mariano (DM) test using squared error and absolute error loss functions further corroborates these findings, with most segments showing significant improvements in favor of BiLSTM, indicated by negative DM-test statistics and p-values below 0.01 or 0.10. This superior performance continues into the testing data, where BiLSTM maintains lower error metrics and a significant result of the DM test, underscoring its reliability for forecasting. In the final stage, the forecasts from both models indicate a nearly linear downward trend in coal prices over the next 18 months, aligning with the International Energy Agency's 2023 projection of a structural decline in coal demand driven by the sustained growth of clean energy technologies.
COMPARISON OF SARIMA AND SARIMAX METHODS FOR FORECASTING HARVESTED DRY GRAIN PRICES IN INDONESIA Yulianti, Riska; Amanda, Nabila Tri; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp319-330

Abstract

Harvested dry grain (HDG) is a vital commodity for rice availability and plays a strategic role in Indonesia’s agricultural economy. Farmers typically sell HDG to rice millers post-harvest, yet disparities between farm-level selling prices and consumer-level purchase prices. This price gap can lead to financial losses for farmers, highlighting the need for accurate forecasting can lead to potential losses for farmers. SARIMA models are effective in capturing seasonality and trends but often fail to incorporate external factors influencing the dependent variable, resulting in less accurate forecasts when such factors have significant impacts. SARIMAX models, however, can include exogenous variables like the government purchase price (GPP), which supports farmer income by establishing a price floor for HDG and directly influencing farm-level price dynamics. This study aims to compare the SARIMA and SARIMAX models in forecasting HDG prices at the farm level in Indonesia, using GPP as an exogenous variable. The dataset, obtained from Statistics Indonesia, covers January 2008 to March 2024, and the forecasting accuracy is measured using Mean Absolute Percentage Error (MAPE). The findings indicate that the best model is the SARIMAX model (1,1,1)(0,1,2)12, achieving a MAPE of 10.919%. The forecasted results show that HDG prices in 2024 are expected to remain stable, with only a gradual increase throughout the year.
COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS Kurnadipare, Aleytha Ilahnugrah; Amaliya, Sri; Notodiputro, Khairil Anwar; Angraini, Yenni; Mualifah, Laily Nissa Atul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss1pp385-396

Abstract

Indonesia's agricultural sector plays a crucial role in the national economy, with significant export potential and supporting the livelihoods of the majority of the population. As part of the government's vision to make Indonesia the world's food barn by 2045, increasing the volume and value of agricultural product exports is a primary focus, making export value forecasting essential for supporting strategic decision-making. Sequential data analysis is an important approach in analyzing data collected over a specific period. This study aims to compare two popular methods in forecasting the export value of the agricultural sector, namely the Seasonal AutoRegressive Integrated Moving Average (SARIMA) model and the Long Short-Term Memory (LSTM) model. Monthly agricultural export data from West Java Province from January 2013 to February 2024 were used as the dataset. The best SARIMA model generated was (1,1,1)(0,1,1)12, while the optimal parameters for the LSTM model were neuron: 50, dropout rate: 0.3, number of layers: 2, activation function: relu, epochs: 500, batch size: 64, optimizer: Adam, and learning rate: 0.01. Evaluation was performed using the Root Mean Squared Error (RMSE) method to measure the accuracy of both models in forecasting the export value of the agricultural sector. The results showed that the LSTM model outperformed the SARIMA model, with a lower RMSE value (SARIMA: 4182.133 and LSTM: 1939.02). This research provides valuable insights for decision-makers in planning agricultural sector export strategies in the future. With this comparison, it is expected to provide better guidance in selecting forecasting methods suitable for the characteristics of the data.
Co-Authors Aam Alamudi Achmad Noerkhaerin Putra Adelia Putri Pangestika Akbar Rizki Akbar Rizki Amaliya, Sri Amanda, Nabila Tri Amatullah, Fida Fariha Anang Kurnia Andi Illa Erviani Nensi Andika Putri Ratnasari Anisa, Rahma Anistia Iswari Antique Yusuf, Rakesha Putra Arbaynah, Siti Arianti, Reza Ariesanti, Yessy ASEP SAEFUDDIN Azahran, Muhammad Ryan Azkiya, Azka Al Bagus Sartono Berliana Apriyanti Billy, Billy Cintani, Meavi Dian Kusumaningrum Dzulhij Rizki, Muhammad Abshor Eka Dewi Pertiwi Else Virdiani Fachry Abda El Rahman Fadhilah, Nur Anggraini Fadillah, Maulana Ahsan Fira Nurahmah Al Aminy Fitri, Zafira Ilma Fitrianti, Dwi Fitrianto, Anwar Gerry Alfa Dito Ghiffary, Ghardapaty Ghaly Gunawan, Windi Hakim, Bashir Ammar Hari Wijayanto Hasanah, Mauizatun Hilali Moh’d, Fatma I Made Sumertajaya Ilma, Meisyatul Ilmani, Erdanisa Aghnia Indah Ratih Anggriyani Indahwati Isnaini, Mardatunnisa Itasia Dina Sulvianti Jamaluddin Rabbani Harahap Kenia Maulidia Kurnadipare, Aleytha Ilahnugrah Kusman Sadik Laily Nissa Atul Mualifah Lia Ratih Kusuma Dewi Magfirrah, Indah Maghfiroh, Firda Aulia Mahda Al Maida Mahesa Ahmad Rahmawan Mahesa, Hakim Zoelva Maulidiyah, Wildatul Moh'd, Fatma Hilali Mohammad Abror Gustiansyah Mohammad Masjkur Mualifah, Laily Nissa Atul Mualifah, Laily Nissa Atul  Muhammad Rizky Nurhambali MY, Hadyanti Utami Nabila Ghoni Trisno Hidayatulloh Nabila Ghoni Trisno Hidayatulloh Nabila Syukri Nickyta Shavira Maharani Nizar, Yeky Abil Nugraha, Adhiyatma Nur Aziza, Vivin Nurhambali, Muhammad Rizky Oksi Al Hadi Oktaviani Aisyah Putri Pratiwi, Windy Ayu Putri Zainal Putri, Adelia Putri, Mega Ramatika Putri, Rizki Alifah Raffael Julio Roger Roa Rahmasari, Hazelita Dwi Rahmi, Salsabila Dwi Ramadhani, Dini Ramdani, Indri Riana Riskinandini Riska Yulianti, Riska Rizki, Akbar Rizki, Anwar Fajar Setyowati, Silfiana Lis Siregar, Indra Rivaldi Siti Aisyah Steven Kurniawan Suci Pujiani Prahesti Suwarso, Dhiya Khalishah Tsany Syam, Ummul Auliyah Tendi Ferdian Diputra Tias Amalia Safitri Tsabitah, Dhiya Tsabitah, Dhiya Ulayya Ulfia, Ratu Risha Utami Dyah Syafitri Wahyudina, Salsa Putri Windi Pangesti Wiwiek Poedjiastoeti, Wiwiek Wiwik Andriyani Lestari Ningsih Wiwik Andriyani Lestari Ningsih Yanuari, Eka Dicky Darmawan Yully Sofyah Waode Zulhijrah, Zulhijrah