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All Journal EKSAKTA: Journal of Sciences and Data Analysis Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Fourier Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Jurnal Ilmiah Pendidikan dan Pembelajaran EIGEN MATHEMATICS JOURNAL Variance : Journal of Statistics and Its Applications Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam Square : Journal of Mathematics and Mathematics Education ESTIMASI: Journal of Statistics and Its Application Majalah Ilmiah Matematika dan Statistika (MIMS) Soeropati: Journal of Community Service Journal of Intelligent Computing and Health Informatics (JICHI) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS LOSARI: Jurnal Pengabdian Kepada Masyarakat JURNAL INOVASI DAN PENGABDIAN MASYARAKAT INDONESIA Tepis Wiring : Jurnal Pengabdian Masyarakat Jurnal Statistika dan Komputasi (STATKOM) Journal of Data Insights Prosiding Seminar Nasional Unimus Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal Statistika Industri dan Komputasi Journal of Mathematics, Computation and Statistics (JMATHCOS) Emerging Statistics and Data Science Journal Amalgamasi: Journal of Mathematics and Applications RAGAM: Journal of Statistics and Its Application
M. Al Haris
Departement Of Statistics, Universitas Muhammadiyah Semarang
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FORECASTING THE NUMBER OF FOREIGN TOURISM IN BALI USING THE HYBRID HOLT-WINTERS-ARTIFICIAL NEURAL NETWORK METHOD Haris, M. Al; Himmaturrohmah, Laily; Nur, Indah Manfaati; Ayomi, Nun Maulida Suci
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1027-1038

Abstract

Bali was one of the destinations frequently visited by tourists because it had natural beauty, especially in the tourism sector. The number of foreign tourists coming to Bali until 2019 had increased, but there had been a very significant decrease in 2020. Forecasting the number of tourists coming to Bali in the future was needed to provide input or recommendations to the government and business people in anticipating decisions taken in the process of developing the tourism sector in Bali. One of the forecasting methods that can be used was the Holt-Winters method. The Holt-Winters method was part of Exponential Smoothing which is based on smoothing stationary, trend and seasonal elements. However, the Holt-Winters method can only capture linear patterns, so a method was needed that can capture non-linear patterns. The Artificial Neural Network method was proposed to overcome the shortcomings of the Holt-Winters Method. This research was focused on the number of foreign tourists visiting Bali using the Hybrid Holt Winters-Artificial Neural Network method. The results showed that the data on the number of foreign tourists fluctuated every month. The best method for predicting the number of foreign tourists was the Hybrid Holt-Winters (α = 0.987, β = 0.000001, and γ = 1)-Artificial Neural Network (12-15-1) because it has the best accuracy as indicated by the MAD value of 0.036684, MSE 0.01098698 and MAPE 6.30417%.
FORECASTING THE CONSUMER PRICE INDEX WITH GENERALIZED SPACE-TIME AUTOREGRESSIVE SEEMINGLY UNRELATED REGRESSION (GSTAR-SUR): COMPROMISE REGION AND TIME Arum, Prizka Rismawati; Indriani, Anita Retno; Haris, M Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp1183-1192

Abstract

Economic success will provide benefits for improving people’s welfare. An important indicator to determine economic success can be seen through inflation by calculating the Consumer Price Index (CPI). CPI is a time series data that is influenced by elements between locations. The GeneralizedSpace-Time Autoregressive (GSTAR) method is a suitable method to be applied to CPI data because it involves elements of time and location (spatiotemporal). The problem is that the GSTAR model cannot detect any correlated residuals. The GSTAR model was developed into the GSTAR-SUR model to estimate parameters with correlated residuals so produce more efficient estimates. The purpose of this study was to determine the best GSTAR-SUR model to predict the CPI of six cities in Central Java, namely Cilacap, Purwokerto, Kudus, Surakarta, Semarang, and Tegal. The data that used is secondary data sourced from BPS Central Java Province. Based on the results of the analysis, the best model formed is the GSTAR-SUR (11)-I(1) model with an RMSE value of 6.213. Forecasting results show that the CPI value for the next 6 months will increase every month for each city
FORECASTING THE NUMBER OF AIRPLANE PASSENGERS USING HOLT WINTER'S EXPONENTIAL SMOOTHING METHOD AND EXTREME LEARNING MACHINE METHOD Wasono, Rochdi; Fitri, Yulia; Haris, M. Al
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 1 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss1pp0427-0436

Abstract

Airplanes provide comfort and speed for their users, especially for those who have limited time. The number of passengers has continued to increase in the last few months at Ahmad Yani International Airport, so a forecast is needed in making decisions to predict the number of passengers in order to maximize existing performance. The data used is secondary data on the number of airplane passengers at Ahmad Yani International Airport from 2012 to 2022 obtained from PT Angkasa Pura 1 (Persero). The Holt Winters Exponential Smoothing method is used because it aligns with the data pattern that includes trends and seasonality in the research, and it has a low level of accuracy. In this study also used the Extreme Learning Machine (ELM) method, apart from being a relatively new method, it has a fast learning speed and has low accuracy. This study aims to predict the number of airplane passengers at Ahmad Yani International Airport in Semarang using the Holt Winters Exponential Smoothing and ELM methods. The results of the analysis show that the MAPE value in the Holt Winters Exponential Smoothing method is 8,18% and in the ELM method using 12 input neurons and 43 neurons in the hidden layer, a MAPE of 6,04% is obtained. so that the ELM method is the right method for predicting the number of airplane passengers at Ahmad Yani International Airport in Semarang.
Projection of PT Aneka Tambang Tbk Share Risk Value Based on Backpropagation Artificial Neural Network Forecasting Result Haris, M. Al; Setyaningsih, Laras Indah; Fauzi, Fatkhurokhman; Amri, Saeful
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.v8i2.20267

Abstract

PT Aneka Tambang Tbk (ANTAM) received an award as the most sought-after stock issuer in Indonesia in 2016. That stock continued to attract investors in 2022 due to a 105% increase in net profit and a 19% increase in sales from the previous year. Despite the upward trend, investors still had doubts due to the fluctuating movement of ANTAM's stock prices. Therefore, forecasting was needed to determine the future movement of stock prices. The Backpropagation Neural Network method had good capabilities for fluctuating data types. However, this method has the disadvantage of a lengthy iteration process. To handle this limitation, The Nguyen-Widrow weighted setting was applied to address this constraint. The expected Shortfall (ES) method used the forecasting results to measure investment risk. This research uses ANTAM stock closing price data from May 2, 2018, to May 31, 2023. Based on the analysis results, the best architecture was obtained with a configuration of 5-11-1, using Nguyen-Widrow weight initialization and a combination of a learning rate of 0.5 and momentum of 0.9. This architecture yielded a prediction error based on the Mean Absolute Percentage Error (MAPE) of 1.9947%. Risk measurement with the ES method based on the prediction for the next 60 periods showed that at a 95% confidence level, the risk value was 0.002181; at a 90% confidence level, it was 0.002165; at an 85% confidence level, it was 0.002148, and at an 80% confidence level, it was 0.002132.
KLASIFIKASI STATUS KESEJAHTERAAN MASYARAKAT KABUPATEN KEPULAUAN MENTAWAI DENGAN METODE REGRESI LOGISTIK BINER DAN CLASSIFICATION AND REGRESSION TREE (CART) Sanur, Lulu Anata; Haris, M Al; Fauzi, Fatkhurokhman
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 3 No 1 (2024): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv3i01pp71-84

Abstract

Kabupaten Kepulauan Mentawai merupakan salah satu daerah yang saat ini masih berstatus sebagai daerah tertinggal, ditandai dengan minimnya ketersediaan fasilitas sarana prasarana. Pembangunan infrastruktur adalah kunci utama untuk memajukan daerah Kabupaten Kepulauan Mentawai seperti adanya trans daerah sebagai penghubung antar pulau sehingga ekonomi kemasyarakatan akan turut tumbuh. Tujuan penelitian ini untuk mengetahui karakteristik atau variabel yang memiliki pengaruh pada pengkategorian status kesejahteraan rumah tangga ke dalam klasifikasi miskin dan tidak miskin. Klasifikasi adalah suatu teknik statistik yang digunakan untuk mengelompokkan data yang telah tersusun secara sistematis. Ada dua pendekatan berbeda untuk mengklasifikasi objek, yaitu metode parametrik dan metode nonparametrik. Penelitian ini memakai metode regresi logistik biner dan Classification and Regression Tree (CART) karena memiliki performasi yang baik, sehingga dalam penelitian ini akan mencoba memperoleh perbandingan nilai akurasi terbaik diantara kedua metode tersebut. Lalu hasilnya akan di evaluasi dengan nilai APER dan nilai akurasi klasifikasi. Data yang digunakan adalah hasil Susenas tahun 2022 sebanyak 326 sampel dengan data testing dan data training adalah 20% dan 80%. Dari hasil penelitian kedua metode, variabel umur, tingkat pendidikan terakhir, dan kesehatan kepala rumah tangga memiliki pengaruh signifikan terhadap model klasifikasi. Akurasi klasifikasi model regresi logistik biner mencapai 93,94% yang lebih tinggi dibandingkan dengan model klasifikasi CART yang bernilai 89,40%. Oleh karena itu, bisa ditarik kesimpulan bahwa model regresi logistik biner ialah pemilihan terbaik untuk memprediksi faktor kesejahteraan rumah tangga di Kabupaten Kepulauan Mentawai.
Survival Analysis of Kidney Failure Patients Using the Kaplan Meier Method and Log-Rank Test: Survival Analysis of Kidney Failure Patients Using the Kaplan Meier Method and Log-Rank Test Febrianti, Fatika Lovina; Firdatul Fahria; Siti Hamidah Ardhy; Rahma Nurmalita; Suci Laeliyah; Muhammad Rifqy Ardiansyah; Ihsan Fathoni; M. Al Haris
Emerging Statistics and Data Science Journal Vol. 3 No. 3 (2025): Emerging Statistics and Data Science Journal
Publisher : Statistics Department, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/esds.vol3.iss.3.art21

Abstract

Kidney failure is a global health issue that continues to rise, impacting patients' quality of life and placing significant pressure on healthcare systems. This study aims to analyze the factors influencing the survival probability of kidney failure patients using the Kaplan-Meier method and the log-rank test. Medical records from 106 kidney failure patients treated at Hasanuddin Hospital between 2018 and 2020 were used to examine the effects of age, gender, and disease severity on survival outcomes. The Kaplan-Meier analysis revealed that patients aged ≤ 50 years, females, and those with chronic conditions had better survival probabilities. However, the log-rank test indicated that survival differences based on these three variables were not statistically significant (p-value > 0,05). These findings provide an initial understanding of the survival patterns of kidney failure patients and highlight the need for further research with larger sample sizes or more advanced methods to support the development of personalized care strategies and improve patients' quality of life.
The ARIMA-GARCH Method in Case Study Forecasting the Daily Stock Price Index of PT. Jasa Marga (Persero) Amri, Ihsan Fathoni; Wulan Sari; Widyasari, Velia Arni; Nurohmah, Nufita; Haris, M. Al
Eigen Mathematics Journal Vol 7 No 1 (2024): June
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v7i1.174

Abstract

PT Jasa Marga is a large company in Indonesia that develop and operation the toll roads and is known as one of the blue chip companies with LQ45 shares. However, share prices have high volatility or rise and fall quickly so their value is always changing. Therefore, forecasting is needed to predict the share price of PT Jasa Marga in the future in order to know the movement of its share price. The Autoregressive Integrated Moving Average (ARIMA) method is a method that can predict data with high volatility, but has the disadvantage of residuals containing heteroscedasticity. So, the addition of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model was carried out to overcome the heteroscedasticity problem that was initially caused by the ARIMA model so it could predict data with high volatility more optimally. Therefore, this research applies the ARIMA-GARCH method to find the best model for forecasting the daily share price index of PT Jasa Marga. The data used comes from the daily closing stock price index of PT Jasa Marga (Persero) for the period January 2015 to May 2023. The measurement of forecasting accuracy uses the Mean Absolute Percentage Error (MAPE). The forecasting results show that the best model uses ARIMA (2,1,1) - GARCH (1,3) with a MAPE value of 6.825728%, which indicates very good forecasting results because the MAPE value is <10%.
Survival Analysis Using Kaplan-Meier and Cox Regression in Hypertension Patients at Kefamenanu Regional Hospital Khikman, Muhammad Alvaro; Multiyaningrum, Riska; Kholifah , Revika Inta Nur; Sa'adah , Lydia Nur; Safira, Elfina Latifah; Sarah, Albertus Dion; Amri, Ihsan Fathoni; Haris, M. Al
Eigen Mathematics Journal Vol 8 No 2 (2025): December
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v8i2.270

Abstract

Hypertension is a chronic disease with a steadily increasing global prevalence and is one of the leading causes of serious complications. Indonesia is among the countries with a high prevalence of hypertension, necessitating an understanding of the factors influencing patient treatment duration to enhance the effectiveness of healthcare services. This study aims to analyze differences in the survival rates of hypertensive patients at Kefamenanu Hospital based on gender. The Kaplan-Meier method was used to estimate patient survival rates, while Cox Proportional Hazards regression was used to evaluate the influence of gender on survival time. The Kaplan-Meier analysis results showed that female patients had a higher probability of survival than male patients during hospitalization. However, the Cox Proportional Hazards regression analysis indicated that this difference was not statistically significant. These findings suggest that while there are differences in survival patterns, gender is not the primary determinant of the duration of care for hypertensive patients. The results of this study are expected to provide input for hospitals in designing more effective care strategies that focus on other factors that may influence patient survival time.
Peramalan Laju Inflasi Di Indonesia Menggunakan Metode Fuzzy Time Series Saxena-Easo indah fitriyani; M. Al Haris; Arum, Prizka Rismawati
Jurnal Fourier Vol. 13 No. 2 (2024)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2024.132.94-110

Abstract

Inflasi adalah fenomena dimana harga barang dan jasa umumnya naik. Inflasi yang stabil sangat penting untuk menjaga pertumbuhan ekonomi agar dapat meningkatkan kesejahteraan masyarakat. Inflasi tidak hanya merupakan fenomena jangka pendek, tetapi juga merupakan fenomena jangka Panjang. Untuk itu, perlu adanya antisipasi dan tindakan untuk mencegah inflasi agar tidak melambung tinggi dan terlalu rendah. Salah satu caranya dengan melakukan peramalan. Fuzzy time series (FTS) salah satu metode yang digunakan dalam peramalan. Fuzzy time series Saxena-Easo memperbaiki metode yang diperkenalkan oleh Stevenson dan Porter dengan melakukan modifikasi pada pembentukan subinterval kelas himpunan fuzzy, yang didasarkan pada jumlah anggota di setiap interval kelas. Data yang digunakan yaitu data laju inflasi di Indonesia bulan Januari 2013 hingga April 2024. Hasil penerapan metode fuzzy time series Saxena-Easo mampu meramalkan laju inflasi sangat baik. Karena menghasilkan kesalahan peramalan berdasarkan MAPE sebesar 1,029%, dan nilai RMSE yang diperoleh adalah 0,1016. Nilai peramalan satu periode kedepan pada bulan Juli 2024 sebesar 2,54%.
Principal Component Analysis on Convolutional Neural Network Using Transfer Learning Method for Image Classification of Cifar-10 Dataset Al Haris, M.; Dzeaulfath, Muhammad; Wasono, Rochdi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.3517

Abstract

The current era was defined by an overwhelming abundance of information, including multimedia data such as audio, images, and videos. However, with such an enormous amount of image data available, accurately and efficiently selecting the necessary images poses a significant challenge. To address this, image classification has emerged as a viable solution for organizing and managing large volumes of image data, thereby mitigating the issue of cluttered image datasets. One of the most popular algorithms for image classification is the Convolutional Neural Network (CNN), which reduces the complexity of network structure and parameters by leveraging local receptive fields, weight sharing, and pooling operations. CNN is a type of artificial neural network specifically designed to process grid-like data, such as images, using convolutional layers to automatically detect local features. Nonetheless, CNN faces several challenges, such as gradient diffusion, large dataset requirements, and slow training processes. To overcome these issues, Transfer Learning has been widely adopted in CNN-based image classification, and Principal Component Analysis (PCA) has been employed to accelerate the training process. PCA is a technique used to reduce data dimensionality by identifying the principal components that account for most of the variance in the data. This study tested the efficacy of PCA-based CNN architecture using the Transfer Learning method on the Cifar-10 dataset. The results demonstrated that the PCA-based CNN architecture achieved the highest accuracy, with a testing accuracy rate of 0.8982 (89%).
Co-Authors Abdul Ghufron Abidah, Khansa Ni'mal Adhwaningrum, Arullah Salsabila Agi Khoerunnisa AHMADI Ainurrofiah, Safira Alambara, Ach Ridoi Ali Imron Ali Imron Alwan Fadlurohman Alya Febriyani Amalia Jihan Syafiqoh Amin Samiasih Amri, Ihsan Fathoni Amri, Saeful Amrullah, Ahmad Amrullah, Setiawan Andy Purnomo, Eko Angelina, Lea Anggoro, Vernanda Kresna Anne Mutiara Wardani Ariska Fitriyana Ningrum Arsusma, Jesicha Arya Praditya Arya, Abimanyu Astuti, Sofi Anggi Asyfani, Yusrisma athoni Amri, Ihsan F Aulia Dewi Gustiarni Aulia Fadhli Boer Ayesha Nayla Salsadella Ayomi, Nun Maulida Suci Ayu Wulandari Azzahrani, Rahma Dewi Barlian, Seftia Amelia Rizki Bunga Ayuningrum Choirudin, Mochamad Fahmi Cika Awani Ayuwida Dannu Purwanto Devina Nadifa Nur Aulia Diani, Nandini Lova Dzeaulfath, Muhammad Eny Winaryati Eny Winaryati Ermawati, Asti Evida Oktaviana Fabiola, Gwenda Fadhilah Azzahra Fadillah, Muhammad Reza Fauzi, Fatkhurokhman Fauzi, Fatkhurrokhman Fazia Risnita Widiyana Fazza Baita, Miftakhiyah Febrianti, Fatika Lovina Firdatul Fahria Firdaus, Falah Tinton Fisabilillah, Muh. Irodat Fitri Anjani Gautama, Rahmad Putra Ginasputri, Heppy Nur Asavia Haris, M Al Haris, M. Al Hidayat, Muhamad Arif Hilma Hanna Mahanna Haqq Himmaturrohmah, Laily Husna, Rizqa El Iffah Norma Hidayati Ihsan Fathoni Ihsan Fathoni Amri Ikhwanudin, Muhamad Ilham Khairul Anam Imelya Susianti Indah Fitriyani Indah Manfaati Nur Indah Manfaati Nur Indriani, Anita Retno Inta Nur Kholifah, Revika Irawan, Alfian Chandra Izzah, Nasyiatul Kaia Raissa Akmalia Khikman, Muhammad Alvaro Khoirul Huda Kholifah , Revika Inta Nur Kinanta, Ailsha Syafa Latisa Alifa Maura Lein, Raymond Bolly Linda Puspitasari Mandala Adikara Sencoko Marsela Ayu Irdiana Masichah, Firochul Masudah, Nurhidayatul Miftakhul Haris Miftakhurizki Mochamad Hasyim Mualim Tahari Mufidatul Ulya Muhammad Hali Mukron Muhammad Rifqy Ardiansyah Muhammad Saifuddin Nur Multiyaningrum, Riska Musa, Fitri Diana Nadia Khoirunnafisa Salma Nikmah Handayani Ninu, Maria Febronia Nugroho, Muhammad Dimas Alfian Nur, Rachmat Kahfiwan Nurfuad, Khilmi Nurhalisa, Siti Nurhidajah Nurmalita, Rahma Nurohmah, Nufita Okiyanto, Rizal Pandiriyan, Muhammad Tegar Permata, Alia Prastiwi, Harvina Sindy Prastyo, Ikwan Pratama, Rifin Fadilla Pratama, Rizky Adi Priambodo, Danu Prissy Nusaiba Yulisa Prizka Rismawati Arum Purnama, Estyaningsi Purnomo Putro, Dwi Puspitasari, Linda Putra, Septian Malik Putri Wahyu Muharamah Putri, Agata Dwi Putri Putri, Melfia Verahma RA. Qonita Syalsabilla Handayani Rahma Nurmalita Ramadhan, Abimanyu Arya Ramadhan, Wulan Nur Rangga Sa'adillah SAP Ridwanulhaq, Alfina Fauziah Rochdi Wasono Rochdi Wasono Ryan Mahardika Sa'adah , Lydia Nur Safira, Elfina Latifah Safira, Rahma Salsabila Rahma Anisa Salsabilla, Havinka Angel Sam'an, Muhammad Sanmas, Safril Ahmadi Saputri, Atika Dwi Sarah, Albertus Dion Sari, Selvi Ana Windia Sawiah Adam, Asriyanti Septi Winda Utami Septia, Siti Fajar Sesotyaning Harum Prabuningrat Shinta Amaria Sidqi, Isnaeni Miftahul Sintya, Salsabila Dhea Siti Hamidah Ardhy Suci Laeliyah Suci Mega Puji Lestari Suherdi, Andri Sulistiya, Indah Sulistiyani, Dwi Supriadin Supriadin Syafina Amira Firdaus Syaharani, Nabbila Dyah Tiani Wahyu Utami Tresiani Yunitasari Tri zahrotun Wahyuningsih Ulinuha, Samikoh Utami, Rossy Prima Nada Utiningtyas, Almas Rizki Wahid, Siti Nurasriyanti Wahyuningsih, Andria Watur, Annisa Cahyaningrum Widiyanti, Karin Dita Widyasari, Velia Arni Wulan Sari Wulan Sari, Wulan Yolan Triky Yulia Fitri Yulia Nur Kumala Yulianita, Tanti