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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI The Journal of Experimental Life Sciences (JELS) CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Jurnal EECCIS Journal of Innovation and Applied Technology Jurnal Teknologi Informasi dan Ilmu Komputer Natural B Plantropica: Journal of Agricultural Science Jurnal Ekonomi & Studi Pembangunan Ikonomika : Jurnal Ekonomi dan Bisnis Islam Science and Technology Indonesia Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) Jurnal Penelitian Pendidikan IPA (JPPIPA) BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Journal of Applied Data Sciences Economic Development Analysis Journal Proceeding Of International Conference On Education, Society And Humanity Indonesian Journal of Statistics and Its Applications
Suci Astutik
Departement Of Statistics, University Of Brawijaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Physics Transportation Other

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UTILIZATION OF SOCIAL MEDIA AS A LEARNING MEDIA FOR ISLAMIC RELIGIOUS EDUCATION Astutik, Suci; Abdullah, Abdullah; Sugiono, Sugiono
PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY Vol 1, No 1 (2023): First International Conference on Education, Society and Humanity
Publisher : PROCEEDING OF INTERNATIONAL CONFERENCE ON EDUCATION, SOCIETY AND HUMANITY

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research is to determine the impact of the implementation of social media as a learning media in particular. Field facts where the use of social media in the form of the internet in Indonesia shows that the development of technology has progressed rapidly. Not a few schools and universities in Indonesia that utilize technological advances as one of the media in learning, one of which is Islamic religious education. The author then by applying qualitative method, with a literature study examines the impact of the use of social media in learning Islamic religious education, both about positive and negative effects. This is also motivated by the use of social media which is recently used by students. Social media has a positive influence on socialization among its users but can also have a bad effect in real life. This then causes changes in the ability of children in learning and understanding the teaching material of Islamic religious education, both in the form of academic abilities or personality.
Principal Component Regression Modelling with Variational Bayesian Approach to Overcome Multicollinearity at Various Levels of Missing Data Proportion Balqis, Nabila Azarin; Astutik, Suci; Solimun, Solimun
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10223

Abstract

This study aims to model Principal Component Regression (PCR) using Variational Bayesian Principal Component Analysis (VBPCA) with Ordinary Least Square (OLS) as a method of estimating regression parameters to overcome multicollinearity at various levels of the proportion of missing data. The data used in this study are secondary data and simulation data contaminated with collinearity in the predictor variables with various missing data proportions of 1%, 5%, and 10%. The secondary data used is the Human Depth Index in Java in 2021, complete data without missing values. The results indicate that the multicollinearity in secondary and original data can be optimally overcome as indicated by the smaller standard error value of the regression parameter for the PCR using VBPCA method which is smaller and has a relative efficiency value of less than 1. VBPCA can handle the proportion of missing data to less than 10%. The proportion of missing data causes information from the original variable to decrease, as evidenced by immense MAPE value and the parameter estimation bias that gets bigger. Then the cross validation (Q^2 ) value and the coefficient of determination (adjusted R^2 ) are get smaller as the proportion of missing data increases. 
COVID-19 Vaccination and PPKM Policy with the Implementation of the Fuzzy Sugeno Method to Income Classification Wahyuni, Djihan; Sumarminingsih, Eni; Astutik, Suci
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 6, No 4 (2022): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v6i4.10096

Abstract

This study aims to determine the implementation of Fuzzy Sugeno in classifying textual data obtained from Twitter so as to determine the polarity of public opinion regarding PPKM policies and Covid-19 vaccinations. This study uses primary data via Twitter related to COVID-19 vaccination and PPKM policies in Indonesia starting from February 9, 2021 to January 17, 2022. There are several stages carried out, namely data collection, data pre-processing, data labeling, data weighting. , identification of membership functions, determination of fuzzy sets, formation of classification systems, and evaluation of classification results. The results of this study explain that Fuzzy Sugeno's performance in classifying tweets is quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding PPKM policies and Covid-19 vaccinations tends to be balanced with 36.92% of tweets classified as positive sentiments, 22.85% negative sentiments, and another 40.23% classified as neutral sentiments. In addition, the fuzzy set that is formed based on the data observation method is very well done because it is able to adjust the frequency of the data in each category. This really helps improve the performance of the built classification system. 
Enhancing Spatio-Temporal PCA with FASTMCD for Climate Comfort Assessment Yarcana, Agus; Pramoedyo, Henny; Astutik, Suci
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37866

Abstract

This study presents a robust formulation of the Spatio-Temporal Principal Component Analysis (STPCA) by integrating the Fast Minimum Covariance Determinant (FASTMCD) estimator into the spatio-temporal decomposition framework. Unlike classical STPCA—which constructs the spatio-temporal matrix from sample-based means and is therefore highly sensitive to extreme observations—the proposed STPCA–FASTMCD replaces the classical mean and scatter structure with robust estimates derived from FASTMCD. The method incorporates functional Fourier-based temporal smoothing and an inverse power–distance spatial weight matrix to better capture the underlying spatio-temporal patterns. Monthly climate data (thermal comfort, cloud cover, rainfall, and wind speed) from 24 monitoring locations in Bali during 2010–2019 are analyzed. Performance is evaluated using mean-shift analysis, eigenvalue-stability assessment, and eigenvector perturbation diagnostics. The classical STPCA produces inflated and unstable leading components, with the first eigenvalue reaching 63.36, whereas STPCA–FASTMCD reduces this value to 37.79 and yields smoother, more coherent spatial loading patterns. The robust STPC1 reveals a clear thermal–wind variability mode, enhancing the interpretability of spatial gradients relevant to climate comfort. Overall, the proposed formulation substantially improves the stability and climatic relevance of dominant spatio-temporal modes, providing a more reliable foundation for climate comfort assessment in Bali.
Bayesian IGARCH Modeling of Jakarta Composite Index Volatility Using Hamiltonian Monte Carlo Algorithm Maulana, Eka Dani; Sumarminingsih, Eni; Nurjannah; Astuti, Ani Budi; Astutik, Suci
Science and Technology Indonesia Vol. 11 No. 1 (2026): January
Publisher : Research Center of Inorganic Materials and Coordination Complexes, FMIPA Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/sti.2026.11.1.261-279

Abstract

Time series models that model volatility in financial data, especially in stock market indices such as the Jakarta Composite Index (JCI), are Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. Following the ratification of the revised Armed Forces Law in March 2025, the JCI experienced increasing volatility, indicating persistent volatility. The problems in the JCI data require a time series model that can capture persistent volatility, namely the Integrated Generalized Autoregressive Conditional Heteroskedasticity (IGARCH) model. Parameter estimation for IGARCH models generally uses the Maximum Likelihood Estimation (MLE) method, which has limitations in handling parameter uncertainty. The Bayesian approach can address parameter uncertainty through the Markov Chain Monte Carlo (MCMC) methods. Among these, Hamiltonian Monte Carlo (HMC) is more efficient than Metropolis-Hastings and Gibbs Sampling, particularly in exploring complex posterior distributions. This study utilizes daily closing price data of the Jakarta Composite Index (JCI) as the main observation variable, observed from April 3, 2023, to April 9, 2025. This study aims to construct a volatility model for the Jakarta Composite Index (JCI) using a Bayesian IGARCH model with an HMC algorithm. This research only uses the IGARCH(1,1) model. The model has a strong ability to capture the JCI’s volatility structure, and its point forecasts are stable. However, credible intervals reveal the uncertainty level, so the volatility of JCI may decrease or increase.
Flood Prediction Using Modeling Extreme Rainfall in East Java, Indonesia Irsandy, Diego; Astutik, Suci; Astuti, Ani Budi
Plantropica: Journal of Agricultural Science Vol. 11 No. 1 (2026): Februari
Publisher : Department of Agronomy, Faculty of Agriculture, Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jpt.2026.011.1.2

Abstract

Extreme value theory (EVT) is a statistical method that is concerned with the analysis of the extreme values of a distribution. EVT is often used to model the behavior of rare and extreme events, such as floods caused by extreme rainfall phenomena. There are two methods for identifying the movement of extreme values, namely Block Maxima (BM) and Peaks over Threshold (POT). The Generalized Extreme Value (GEV) distribution has three parameters and is used to model the distribution of extreme values using the BM method. On the other hand, the classic method of EVT does not capture uncertainty in the data. The Bayesian method is one of the statistical methods that can use information from data and prior knowledge. This research aims to model EVT-BM using a Bayesian approach for rainfall data at eleven weather stations in Jawa Timur. The result shows that all rainfall distributions at different weather conditions have a value of the parameter shape equal to 0, which implies a Weibull distribution. This paper also provides return level of 6 months, 2, 5, and 10 years respectively.
Co-Authors Abdullah Abdullah Abu Bakar Sambah, Abu Bakar Achmad Efendi Ani Budi Astuti Ani Budi Astuti Ari Purwanto Sarwo Prasojo Atiek Iriany Aulia, Silvia Intan Azizah, Laila Nur Balqis, Nabila Azarin Bestari Archita Safitri Budiarti, Laelita Damayanti, Rismania Hartanti Putri Yulianing Darmanto Darmanto Darmanto Darmanto Dewi Kurnia Sari Dewi, Vita Rosiana Diego Irsandy Djihan Wahyuni effendi, Achmad Elok Pratiwi Eni Sumarminingsih Evellin Dewi Lusiana, Evellin Dewi Fachri Faisal Fahimah Fauwziyah Fairuz Zada Zayyana Fakhrunnisa, Atmadani Rahayu Fernandes, Adji Achmad Rinaldo Fitriani, Suci Handayani, Sri Heni Kusdarwati Henny Pramoedyo Henny Pramoedyo Henny Pramoedyo Husnul Khatimah Irsandy, Diego Ismi Chai Runnisa Isnani Darti Kusdarwati, Heni Lee, Muhammad Hisyam Lestari, Dwi Retno Loekito Adi Soehono Loekito Adi Soehono Lusia, Dwi Ayu Lusiana, Evelin Dewi Maharani, Adinda Gita Maisaroh, Ulfah Mashfia, Fidia Raaihatul Masrokhah, Dwi Maulana, Eka Dani Meilina Retno Hapsari Meilinda Trisilia Muhammad, Alifiandi Rafi Nanda Rizqia Pradana Ratnasari, Nanda Rizqia Pradana Negara, Nur Aminah Kusuma Ni Wayan Surya Wardhani Ni Wayan Surya Wardhani Nisa Dwirahma Widhiasih Novi Nur Aini Nur Iriawan Nurjannah Nurjannah Nurjannah Ola, Petrus Kanisius pramoedyo, henny Pratama, Muhamad Liswansyah Qurrotu A’yun Nafidah Rahma Fitriani Rahma Fitriani Rahmi, Nur Silviyah Ramifidisoa, Lucius Risda, Intan Fadhila Rohma, Usriatur Rozy, Agus Fachrur Salsabila, Imelda Saniyawati, Fang You Dwi Ayu Shalu Sera Yunarizal P Setiarini, An Nisa Dwi Shahuneeza Naseer, Mariyam Siti Nurmardia Abdussamad Solimun, Solimun Sugiono Sugiono Sumarminingsih, Eni Susanto, Mohammad Hilmi Susi Wuryantini Syalsabilla, Alya Fitri Theresia Mitakda, Maria Bernadetha Tiza Ayu Virania Viera Wardhani Wahyuni, Djihan Widiarni Ginta Sasmita Wulaida Rizky Fitrilia Wulaida Rizky Fitrilia Yarcana, Agus Zamelina, Armando Jacquis Federal Zerlita Fahdha Pusdiktasari