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All Journal DIKSI Media Statistika MATEMATIKA Prosiding Seminar Biologi Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Pendidikan Dasar Nusantara Majalah Kulit, Karet, dan Plastik Jurnal Spektra Bioeksperimen: Jurnal Penelitian Biologi Pedagogia: Jurnal Pendidikan E-Dimas: Jurnal Pengabdian kepada Masyarakat Catharsis JURNAL KEPEMIMPINAN DAN PENGURUSAN SEKOLAH Briliant: Jurnal Riset dan Konseptual Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) BIOEDUSCIENCE Jurnal Pendidikan Terbuka Dan Jarak Jauh Journal of Education and Instruction (JOEAI) Jurnal Basicedu Indonesian Journal on Learning and Advanced Education (IJOLAE) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JOURNAL OF ENGLISH FOR ACADEMIC Inovasi: Jurnal Ilmiah Ilmu Manajemen JIKAP PGSD: Jurnal Ilmiah Ilmu Kependidikan International Journal for Educational and Vocational Studies JURNAL PENDIDIKAN MIPA Dinasti International Journal of Education Management and Social Science Jurnal Paedagogy Jurnal Graha Pengabdian Journal of Early Childhood Education (JECE) Indonesian Journal of Instructional Media and Model Epistema EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi JURNAL LENTERA AKUNTANSI Abdi Psikonomi Journal of Education Research Jurnal Ilmiah Mahasiswa Manajemen, Bisnis dan Akuntansi Journal of Advanced Sciences and Mathematics Education Innovation Business Management and Accounting Journal Jurnal Basicedu Prosiding University Research Colloquium JURNAL ABDIMAS PLJ JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Edukasi Pengabdian Masyarakat: EDUABDIMAS SERIBU SUNGAI: Journal of Research and Community Service Jurnal Manajemen dan Administrasi Antartika Arty: Jurnal Seni Rupa Pinisi Journal Pendidikan Guru Sekolah Dasar Jurnal Seni Tari Jurnal Teknik Informatika dan Desain Komunikasi Visual

Suparti Suparti

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 393390

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Social Sciences Other

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COPULA FRANK UNTUK PERHITUNGAN VALUE AT RISK PORTOFOLIO BIVARIAT PADA MODEL EXPONENTIAL GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY Eka Anisha; Di Asih I Maruddani; Suparti Suparti
Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i4.29932

Stocks are one type of investment that promises return for investors but often carries a high risk. Value at Risk (VaR) is a measuring tool that can calculate the amount of the worst loss that occurs in a stock portfolio with a certain level of confidence and within a certain time period. In general, financial data have a high volatility value, which causes the residuals are not normally distributed. ARCH/GARCH modoel is used to solve the heteroscedasticity problem. If the data also have an asymmetric effect, it is modelled with Exponential GARCH model. Copula-Frank is part of the Archimedian copula which is used to solve empirical cases. The data on this study were BBCA and KLBF stock price return data in the observation period 30 December 2011 – 6 December 2019. Furthermore, to test the validity of the VaR model, a backtesting test will be carried out using the Kupiec Test. The results showed that the best model used for BBCA stocks was ARIMA (1,0,1) EGARCH (1,1) and for KLBF stocks was ARIMA (1,0,1) EGARCH (1,2). The amount of risk with a 95% confidence level used a combination of the EGARCH and Copula-Frank models was 2.233% of today's investment. Based on the backtesting test used the Kupiec Test, the VaR model of the portfolio obtained was declared valid.

PEMODELAN JUB DAN BI RATE TERHADAP INFLASI DAN KURS RUPIAH MENGGUNAKAN REGRESI SEMIPARAMETRIK BIRESPON BERDASARKAN ESTIMATOR PENALIZED SPLINE Siti Fadhilla Femadiyanti; Suparti Suparti; Budi Warsito
Jurnal Gaussian Vol 9, No 2 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Some indicators of the Indonesian economy are inflation and the exchange rate of rupiah against US dollar. Inflation and the rupiah exchange rate are thought to be influenced by the money supply (JUB) and the BI Rate. The money supply has a nonparametric relationship pattern to inflation and the rupiah exchange rate, while the BI Rate has a parametric relationship pattern to inflation and the rupiah exchange rate. The right method for detecting the relationship between inflation and the exchange rate with JUB and BI Rate is birespon semiparametric regression with a splined penalized estimator. The semiparametric regression coefficient of birespon spline penalized is estimated using the Weighted Least square (WLS) method which is determined based on the degree of polynomials, the number and location of the optimal knot points, and the optimal lambda determined based on the minimum of Generalized Cross Validation (GCV). This research uses the R Program. Based on the results of the analysis, the best spline penalized birespon semiparametric regression model is located in the number of knots is 5 at the knot points of 5257,783; 6649,469; 8976,871; 11099,19 and 13535,51 found in the first degree of response is 1 and the second degree of response is 2 with an optimal lambda of 99,99. The results of the performance evaluation of the model produce value of is 99,9007%, meaning that the model's performance is very good for out samples of the data and the MAPE value of 2.89169% is less than 10% which means the model's performance is very good.

KETAHANAN HIDUP PASIEN GAGAL GINJAL DENGAN METODE KAPLAN MEIER (Studi Kasus di Rumah Sakit Umum Daerah dr. R. Soedjati Soemodiarjo Purwodadi) Immawati Ainun Habibah; Tatik Widiharih; Suparti Suparti
Jurnal Gaussian Vol 7, No 3 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Chronic Kidney Disease (CKD) is a failure of kidney function that which get slowly and can not recover. Most of the patients CKD get death sudden becuse of cardiovascular complications (related to the heart and blood vessels) however only minor part can reach terminal phase (CKD stage 5) which need replacement therapy of Kidney. Replacement therapy of Kidney are hemodialysis, peritoneal dialysis, and Kidney transplant. Because of that, the importance to study how long the patient opportunity is life endurance analysis. Survival analysis methods to life depend from the life time and status of individual life time. Survival analysis uses Kaplan-Meier method. During the observation process, there is different observations so censor type III is choosen. Censor type III is censoring type which research is done to individual in and out for determine time, because of that estimation value of survival can be caunted using Kaplan Meier method with censor type III. This research uses medical records data from the patients with kidney failure period 1 January 2014 until 30 November 2017 in RSUD dr.R. Soedjati Soemodiarjo Purwodadi Grobogan Regency. The results of the analysis and discussion are known that if hemodialysis getting longer done, estimation value of survival. With an average estimate of survival is 776 days. Keywords: Chronic Kidney Disease, Survival Analysis, Kaplan Meier

PEMODELAN REGRESI 3-LEVEL DENGAN METODE ITERATIVE GENERALIZED LEAST SQUARE (IGLS) (Studi Kasus: Lamanya pendidikan Anak di Kabupaten Semarang) Amanda Devi Paramitha; Suparti Suparti; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. The purpose of this final three-level regression analyzes to establish regression models about the length of a child's education in the District of Semarang where the individual level-1 with a factor of gender, lodged at the family level-2 by a factor of the length of father's education and duration of maternal education and nesting on the environment level-3 with factor of residence, number of elementary school the large number of junior high school and the large number of high school. Parameter estimation in 3-level regression models can use several methods, one of which is a method of Iterative Generalized Least Square (IGLS). Of cases the length of education in the district of Semarang indicate that factors influencing factor is the length of father's education and the duration of the mother's education. Keywords : Hierarchical structure, multistage sampling, multilevel regression, Iterative Generalized Least Square.

OPTIMASI PARAMETER MODel AUTOREGRESSIVE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION Setyoko Prismanu Ramadhan; Hasbi Yasin; Suparti Suparti
Jurnal Gaussian Vol 8, No 2 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Box-Jenkins ARIMA method is a linear model in time series analysis which is widely used in various fields. One estimation method for Box-Jenkins ARIMA model is OLS method which aims to minimize the number of squared errors. This method is not effective when applied to time series data that is random, nonlinear and non-stationary. In this study discussed the alternative method of the PSO algorithm as an parameter optimization of the ARIMA model. PSO algorithm is an optimization method based on the behavior of a flock of birds or fish. The main advantage of the PSO algorithm is having a simple, easy to implement and efficient concept in calculations. This method is applied to data from PT Perusahaan Gas Negara shares. The results of both methods will be compared. In the AR model (1) the value of MSE is 0.532 and MAPE is 0.993. Meanwhile, the PSO algorithm obtained MSE 0.531 and MAPE 0.988. It was found that the PSO algorithm resulted in smaller MSE and MAPE values and could provide better results.Keywords : Time Series Analysis, Autoregressive, PSO

ANALISIS SENTIMEN ULASAN APLIKASI TIKTOK DI GOOGLE PLAY MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) DAN ASOSIASI Sola Fide; Suparti Suparti; Sudarno Sudarno
Jurnal Gaussian Vol 10, No 3 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i3.32786

Corona virus pandemic requires people to do activities from home so the number of internet usage in Indonesia has increased because information is carried out through social media. One of the popular social media in Indonesia is TikTok. However, the Tiktok’s popularity cannot be separated from the footsteps of TikTok in Indonesia which was blocked by government for committing many violations. Each application allows users to provide a review about the application. To find out the users TikTok’s sentiment, sentiment analysis was carried out to classify reviews into positive and negative sentiments. Classification is carried out using the Support Vector Machine (SVM) with kernel Radial Basis Function (RBF) method which is more effective classification algorithm and kernel function, seen from previous studies. The parameters used in the SVM gamma default 0.0004255 and the Cost (C) parameter experiment used is 0,01; 0,1; 1; 10; 100; 1000. The results can provide information that can be retrieved using the association method. The steps are scrapping data, data preprocessing, sentiment scoring, TF-IDF weighting, classifying using the SVM RBF kernel method and text association. Evaluation of the model using a confusion matrix with the value of accuracy and kappa. The greater the value of accuracy and kappa, the better the performance of the classification model. The review classification resulted in the best accuracy rate of 90.62% and the best kappa of 81.24% which means that it includes an almost perfect classification result. Based on the data association, positive reviews are given because users like and are comfortable with the current version of TikTok which contains funny videos on fyp. Meanwhile, negative reviews were given because the user failed to register and his account was blocked, so the user asked TikTok to continue to make improvements.

PEMODELAN FUNGSI TRANSFER DAN BACKPROPAGATION NEURAL NETWORK UNTUK PERAMALAN HARGA EMAS (Studi Kasus Harga Emas Bulan Juli 2007 sampai Februari 2019) Silvia Nur Rinjani; Abdul Hoyyi; Suparti Suparti
Jurnal Gaussian Vol 8, No 4 (2019): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The prestige of investment is increasingly rising as the people educates in managing finances. Gold is an alternative that most people tend to choose to invest. One of the important knowledge in gold investing is to predict the price in the future with factors that influence the price of gold. Therefore, in this research we made a model of gold prices based on crude oil prices. One method to forecast gold prices based on crude oil prices is the transfer function and backpropagation neural network. The results of transfer function model will be used as input for the backpropagation neural network method. The purpose of this research is to get the right forecasting method through the transfer function and backpropagation neural network model that can be used to predict gold prices. The results showed that the transfer function model with b = 0, r = [2], s = 0 and the ARMA noise model (0, [6]) is the best model to forecast the price of gold with the MAPE value of data out sample as 3,3507%. Keywords : Gold Price, Crude Oil Prices, Transfer Function,Backpropagation Neural Network, Forecasting

ANALISIS KURVA SURVIVAL KAPLAN MEIER MENGGUNAKAN UJI LOG RANK (Studi Kasus :Pasien Penyakit Jantung Koroner di RSUD Undata Palu) Arianti Suhartini; Rita Rahmawati; Suparti Suparti
Jurnal Gaussian Vol 7, No 1 (2018): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Coronary heart disease is one of the leading causes of death in the world, including Indonesia. Based on doctor-diagnosed interviews, coronary heart disease’s prevalence in Indonesia on 2013 is 0,5% and based on a doctor-diagnosed is 1,5%. Central Sulawesi is ranked first and second for prevalence based on doctor-diagnosed interviews and doctor-diagnosed. The high number of people with coronary heart disease caused by lack of self-awareness in lifestyle changes. One of the parameters used to assess the success of treatment is the probability of survival. Survival analysis is a data analysis where the outcome of the variables studied is the time until an event occurs. This study raised the problem of survival of coronary heart patients at Undata Palu Hospital which is the main referral hospital for Central Sulawesi region. This research uses nonparametric method that is Kaplan Meier and Log Rank Test based on six factors are age, gender, stadium, disease status, complication and status of anemia. Nonparametric methods do not follow a particular distribution for survival time. Kaplan Meier's survival curve will describe the patient's characteristics of survival probability and followed by a Log Rank test to see if there are differences between curves. The result of analysis and discussion based on Log Rank test result showed that the factors of age, sex and disease status differ significantly. Keywords: Coronary heart disease, RSUD Undata Palu, Kaplan Meier analysis, Log Rank test.

PEMODELAN INDEKS HARGA PROPERTI RESIDENSIAL DI INDONESIA MENGGUNAKAN METODE GENERALIZED SPACE TIME AUTOREGRESSIVE Syazwina Aufa; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 11, No 1 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v11i1.34001

Generalized Space Time Autoregressive (GSTAR) is a model used for space time data analysis. Space time data is data related to events at previous times and different locations. GSTAR is an expansion of the Space Time Autoregressive (STAR) method. The STAR method is only suitable for homogeneous locations while GSTAR can be used for heterogeneous locations. This research uses Residensial Property Price Index (IHPR) data. IHPR data is in the form of a multivariate time series consisting of 18 cities/regions with a certain time span. In this study, the analysis of IHPR data is carried out by looking at the relationship between the previous time and other cities/regions. Therefore, the method that can be used is GSTAR method. Analysis of IHPR data in each city/region can help increase the supply of housing, thereby reducing the number of backlogs. The backlog of houses in Indonesia is still relatively high. Backlog is an indicator that is often used by the government to measure the number of housing needs in Indonesia. Based on the fulfillment of the assumptions and the smallest MSE value, the best model obtained is GSTAR(4;1,1,1,1) using cross-correlation normalized weight. The largest IHPR data on forcasting results is in the cities of Makassar, Manado, and Surabaya while the smallest IHPR data is in the city of Balikpapan. The GSTAR method produces forcasted data that is close to the actual data so it is good to use.Keywords : GSTAR, OLS, IHPR

Penerapan Text Mining untuk Melakukan Clustering Data Tweet Akun Blibli Pada Media Sosial Twitter Menggunakan K-Means Clustering Syiva Multi Fani; Rukun Santoso; Suparti Suparti
Jurnal Gaussian Vol 10, No 4 (2021): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v10i4.30409

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

Co-Authors A. Sulaksono, A. A.A. Ketut Agung Cahyawan W Aan Sofyan Abdul Hoyyi Adhytia, Rizkyhimawan Afandi, Adam Pri Agus Cahyono Agus Prasetya Agus Rusgiyono Agus Triyono Akbari, Windusiwi Asih Alan Prahutama Alanindra Saputra Alvita Rachma Devi Amanda Devi Paramitha Ambarwati Aminah Asngad Ananda, Refisa Angelia, Yuni Anggun Ella Indriyani Anik Rahmawati, Anik Anjarwati, Ani Any Setyaningsih, Any Arianti Suhartini Arieanti, Dian Dinarafika Arief Rachman Hakim Arief Rachman Hakim Arnisa Melani Kahar Ash Shiddiq, Fanchas Asismarta Asismarta, Asismarta Ayu Annisa Gharini AYU LESTARI Azizah, Adilla Nur Badriyah, Ratu Bahtiar Ilham Triyunanto Brillianing Pratiwi Budi Warsito Budiarti, Arivia Ayu C Yuwono Sumasto, C Yuwono Deden Aditya Nanda, Deden Aditya Dewi, Anggra Lita Sandra Dewi, P A R Dhany Efita Sari Dhea Dewanti Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Dwi Sambada Dwi Wahyuningsih, Dwi Dwikoranto Eka Anisha Eka Destiyani Eka Fadilah Eka Wijayanti Eko Sugiyanto Ermanuri, Ermanuri Erna Sulistianingsih Ernawati, Devi Ernik Yuliana Esti Pratiwi Evelyna, Feby Evi Oktaviana, Desy Fadilah, Eka Fitri Juniaty Simatupang, Fitri Juniaty Gina Wangsih Hamid, Lukman Hanifa Adityarahma Hanifah Nur Aini Happy Suci Puspitasari Hasbi Yasin Haya, Lovina Rizki I Made Sulandra Ihdayani Banun Afa Immawati Ainun Habibah Intaniasari, Yossinta Iut Tri Utami Iut Triutami Izzudin Khalid, Izzudin Janaka, Janaka Jefferio Gusti Putratama Jody Hendrian Juwanda, Farikhin Karimawati, Nurul Kartika, Aninda Ayu Karwanto, Karwanto Khaerul Anam Khansa Amalia Fitroh Khansa, I H Khoirunnisa, Siti Intan Khulaifiyah, Khulaifiyah Lamik Nabil Mu'affa Lanjari , Restu Lina Agustina Lintangesukmanjaya, R T Lismiyati Marfuah, Lismiyati Lisnayati, Lisnayati Lulu Maulatus Saidah Lulus Darwati, Lulus M. Noris Maman Suryaman MASLIHATIN, LINA Meiliawati Aniska Milawati Milawati Moch. Abdul Mukid Mokhamad Nurjam'i MUHAMAD SHOLEH Muhammad Sulaiman Muhammad Taufan Muhtadi Muhtadi Muqorobin, Masculine Muhammad Mustafid Mustafid Mustaji Mustaji, Mustaji Mustofa, Achmad Nastiti, Tri Dyah Netriwati Nia Istiana Noer Rachma, Gustyas Zella Nunuk Hariyati Nurhayati, Rizky Nurina Salma Alfiyyah Nurlia, Titim Nurmanita, Tiara Sevi Nurul Fitria Fitria Rizani Ovie Auliya’atul Faizah Paula Meilina Dwi Hapsari Peter Rajagukguk Pranata, Sepbrie Mulia Bingah Prasetyo, Mario Aditya Prastowo, Srihandono Budi Prastya, Agus Puspita Kartikasari Putra, D A Putri Agustina Rahma Dewi Hartati Rahman Kosasih, Fauzy Rahman, Syair Dafiq Faizur Rahmawati Patta, Rahmawati Rahyu Setiani Rambat Rambat, Rambat Renti Oktaria, Renti Retnowati, Lina Riana Ayu Andam Pradewi Richy Priyambodo Rismawati Rismawati Rita Rahmawati RIZKYHIMAWAN, ADHYTIA Rohayati, Menik Rudi Saputro Setyo Purnomo Rukun Santoso Sa'adah, Alfi Faridatus Sadjati, Ida Malati Safitri, Wardani Ana Salma Farah Aliyah Salsa Bella, Shella Salsabila Rizkia Gusman Sania Anisa Farah Sanitoria Nadeak, Sanitoria Septian Hendra Wijaya Setiawan, Fuad Alfaridzi Setyoko Prismanu Ramadhan Setyowati, Titik Sholihah, Zaimatu Silvia Elsa Suryana Silvia Nur Rinjani Singgih Subiyantoro Sirojuddin, Muhammad Siska Andriyani Siti Fadhilla Femadiyanti Sofiana Sofiana Sola Fide Sri Budiasih, Sri Sri Sumiyati Sri Wahyuni Sri Wahyuningrum Sudargo Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Sunardi Sunardi Supeno Supratmi, Nunung Supriyanto, Rudy Suranto Suranto Surasmi, W A Surasmi, Wuwuh Asrining Susilo, Mas Bayu Sutrisno, Supadi Bambang Syafruddin Syafruddin Syafruddin*, Syafruddin syah, naziah Syazwina Aufa Syiva Multi Fani T. Mart, T. Tarno Tarno Tarno Tarno Tatik Widiharih Tiani Wahyu Utami Triastuti Rahayu Triastuti Wuryandari Tyas Estiningrum Ul Haq, Hasna Faridah Dhiya Vera Handayani Victoria Dwi Murti WAHYU SUKARTININGSIH Wahyu Tiara Rosaamalia Widari Widari, Widari Wiradharma, Gunawan Yasir Sidiq YATIM RIYANTO Yon Haryono Yunianika, Ika Tri Yuningsih Yuningsih Yupitasari, Yupitasari Yusak, Suharno Zein, Secondta Habib Syarifah Zia, Nabila Ghaida Zubaidah, Lailia Zuhri, Thoha Syaifudin

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