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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 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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Comparison of Nonparametric Path Analysis and Biresponse Regression using Truncated Spline Approach Azizah, Laila Nur; Rohma, Usriatur; Fernandes, Adji Achmad Rinaldo; Wardhani, Ni Wayan Surya; Astutik, Suci
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.26739

Abstract

Nonparametric path analysis and biresponse nonparametric regression are two flexible statistical approaches to analyze the relationship between variables without assuming a certain form of relationship. This study compares the performance of the two methods with the truncated spline approach, which has the advantage of determining the shape of the regression curve through optimal selection of knot points. This study aims to evaluate the best model based on linear and quadratic polynomial degree with 1, 2, and 3 knot points. The model is applied to data with 100 samples and simulated data of various sample levels. The results show that the best model in nonparametric path analysis is a quadratic model with three knots, while the best model in biresponse nonparametric regression is a quadratic model with two knots. Biresponse nonparametric regression has a coefficient of determination of 88.8% which is higher than the nonparametric path analysis of 70.9%. The best biresponse nonparametric regression model is the model with quadratic order and two knots.
STRENGTHENING SYARIAH FINANCIAL MARKETS WITH GARCH-BASED STOCK PRICE FORECASTING AND VAR-RISK ASSESSMENT Darmanto, Darmanto; Darti, Isnani; Astutik, Suci; Nurjannah, Nurjannah; Lee, Muhammad Hisyam; Damayanti, Rismania Hartanti Putri Yulianing; Irsandy, Diego
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1217-1236

Abstract

Indonesia, as the largest Muslim-majority country, has significant potential to enhance its Shariah financial sector, which has been growing rapidly, around 7.43% from 2023 to 2024, and contributing to the national economy. However, political and natural disasters have influenced the economy and Shariah-compliant stocks. This study focuses on forecasting Shariah-compliant stock prices using Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models and estimating investment risks via Value at Risk (VaR) for four Islamic banks listed in IDX: BRIS, BTPS, BANK, and PNBS. The findings indicate that GARCH models effectively capture stock price dynamics and provide accurate 10-day forecasts. Additionally, the models reliably predict VaR, validated through backtesting at various confidence levels. These insights are valuable for financial regulators and risk managers, aiding in policy design to ensure market stability by enabling the implementation of measures such as stricter capital reserve requirements for institutions with high-risk exposure and mandatory adoption of advanced risk management techniques like dynamic stress testing. Such policies not only mitigate systemic risks during periods of financial volatility but also enhance the overall resilience and robustness of the financial system. For investors, accurate risk predictions support informed decision-making, enhance portfolio protection, and optimize risk management.
The Clustering of Provinces in Indonesia by The Economic Impact of Covid-19 using Cluster Analysis: Pengelompokkan Provinsi di Indonesia dengan Ekonomi Terdampak Covid-19 Menggunakan Analisis Cluster Zerlita Fahdha Pusdiktasari; Widiarni Ginta Sasmita; Wulaida Rizky Fitrilia; Rahma Fitriani; Suci Astutik
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p117-129

Abstract

The Covid-19 pandemic has hit Indonesia since March 2020. Several policies have been issued by the Indonesian government to reduce the level of the spread of Covid-19. This policy has an impact on various fields of life, especially the economic sector in various sectors. This study was conducted to analyze the grouping of provinces whose economies are at risk of being affected by Covid-19 based on various economic sectors, namely the unemployment rate, the percentage of poor people, the provincial minimum wage, and the occupancy rate of hotels using cluster analysis. Cluster analysis was performed using several hierarchical methods, namely Simple, Complete, Average, and Centroid Linkage and Ward. The Cophenetic correlation coefficient (rCoph) was used to determine the best method, while the number of clusters was determined based on the Dunn, Connectivity, and Silhoutte indexes. The analysis result shows that Average Linkage is the best method with two clusters. The first cluster consists of all provinces in Indonesia except Papua, whose economy is highly at risk of being affected by Covid-19, characterized by a low percentage of the poor and a low provincial minimum wage, as well as high levels of open unemployment and hotel occupancy rates. Meanwhile, the second cluster consists of the Province of Papua, which is an economic group with a low risk of being affected by Covid-19. By looking at the impact of the Covid-19 disaster, the government can make recovery efforts and generalize economic recovery policies due to Covid-19 which have an impact on the economy of almost all provinces in Indonesia.
CFGWC-PSO in Analyzing Factors Affecting the Spread of Dengue Fever in East Java Province Abdussamad, Siti Nurmardia; Astutik, Suci; Effendi, Achmad
The Journal of Experimental Life Science Vol. 9 No. 3 (2019)
Publisher : Graduate School, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1060.997 KB) | DOI: 10.21776/ub.jels.2019.009.03.10

Abstract

Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization using Context Based Clustering (CFGWC-PSO) has been developed to clustering in factors influencing the spread of dengue fever in East Java Province. CFGWC-PSO method can overcome slow computing time problems in terms of iterations, and produce accurate data partition with stable. In this research, CFGWC-PSO applied to 11 variables from data on the causes of the spread of dengue fever in East Java Province in 2017. CFGWC-PSO using the FCM method to determine the context variable. Processing used the results of clustering with 2 clusters until 5 clusters. From the three validation index that used to find out the right number of clustering, two clusters gave better clustering results. CFGWC-PSO shows that all districts/cities in cluster 2 become dengue fever endemic areas that need to be considered by the East Java Provincial Government.Keywords: Context-Based Clustering, dengue hemorrhagic fever, Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization.
An Informative Prior of Bayesian Kriging Approach for Monthly Rainfall Interpolation in East Java Damayanti, Rismania Hartanti Putri Yulianing; Astutik, Suci; Astuti, Ani Budi
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 3 (2025): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i3.31027

Abstract

In spatial data analysis, interpolation is used to estimate values at unobserved locations, but often faces challenges in capturing complex spatial patterns and estimation uncertainty. One of the main obstacles is the small sample size, which makes the empirical variogram difficult to define well in conventional Kriging methods. The Bayesian Kriging approach overcomes this problem by integrating prior information, so it can still produce stable estimates despite limited data. This study is a quantitative, spatial-based research aimed at interpolating monthly rainfall in East Java Province using the Bayesian Kriging approach. The data consist of monthly rainfall measurements from 11 rain gauge stations distributed across East Java, obtained from the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG) for the period of January to April 2024. The entire analysis was conducted using R software. A spherical semivariogram model was selected due to its superior fit to the spatial characteristics of the rainfall data in the study area with the smallest RMSE 37.17. This study demonstrates the effectiveness of Bayesian Kriging for rainfall interpolation in tropical regions with sparse data, providing more stable and accurate estimates compared to conventional methods. The scientific contribution of this research lies in showcasing how the integration of informative priors and Bayesian inference enhances interpolation accuracy in data-limited tropical environments. The resulting interpolated maps can inform land-use planning and flood risk mitigation by identifying areas of high rainfall for improved water infrastructure and lower-rainfall regions for targeted irrigation planning. 
Spatial Panel Regression Modelling of Rainfall in Indonesia Saniyawati, Fang You Dwi Ayu Shalu; Astutik, Suci; Pramoedyo, Henny
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 2 (2025): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i2.29882

Abstract

Rainfall is amount of water that falls to the earth's surface in the form of rain during a certain period of time, usually measured in millimeters. Rainfall data in Indonesia usually includes temporal and spatial dimensions, so the appropriate method for its analysis is spatial panel regression analysis. This study aims to identify factors that influence the amount of rainfall in Indonesia. This type of research is quantitative using secondary data from the central statistics agency website. The predictor variables used include air temperature, sunshine radiation, humidity, wind speed, and air pressure, while the response variable is amount of rainfall in 34 provinces in Indonesia. Spatial panel regression analysis is carried out using maximum likelihood estimation, which is used to estimate the regression coefficient and intercept that maximizes the likelihood of the existing data. Based on the lagrange multiplier test, spatial autocorrelation was found in the lag, so the appropriate model is SAR-FE. This model can overcome spatial autocorrelation by taking into account spatial interactions between locations, as well as controlling unobserved heterogeneity through fixed effects. The results show that sunshine radiation, humidity, and wind speed have significant effect on the amount of rainfall in Indonesia. The AIC value of SAR-FE model (-4.352594×〖10〗^(-13)) is smaller than SEM-FE model (-1.642001×〖10〗^(-12)), indicating that SAR-FE model is better at explaining the data.
Evaluation Indonesian Financial Performance of Islamic Commercial Bank using Gaussian Mixture Model with Intervention Analysis Syalsabilla, Alya Fitri; Astutik, Suci; Shahuneeza Naseer, Mariyam
IKONOMIKA Vol 9, No 1 (2024)
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijebi.v9i1.21760

Abstract

Indonesia, with its rich diversity and sizable Muslim population, holds a prominent position in global Islamic finance. Financial ratios like ROA, CAR, NPF, FDR, BOPO, and NOM are crucial for assessing bank performance. Employing Gaussian Mixture Model with Intervention Analysis enhances evaluation by identifying outliers and understanding their impact.Utilizing Islamic Banking Statistics from January to December 2023, this research employs purposive sampling to select relevant variables like CAR, NPF, FDR, BOPO, NOM, and ROA. Gaussian Mixture Model identifies data patterns, while Intervention Analysis examines factors affecting Islamic banking performance.Financial performance analysis reveals shifts from "Good Performance" to "Bad Performance" starting June 2023, linked to deteriorating metrics like NPF, FDR, and BOPO. Evaluation via GMM yields an AIC of 435.3409, indicating effective classification. Intervention analysis identifies significant NPF and ROA outliers, suggesting potential issues in loan quality and profitability.Analysis via GMM highlights performance dynamics in Islamic commercial banks, transitioning from "Good" to "Bad" post-June, mirroring critical metric shifts. Stable CAR indicates a solid base, but NPF outliers suggest risk management enhancements. BOPO outliers indicate inefficiencies, while ROA emphasizes profitability. Policy interventions should focus on risk management, cost efficiency, and profitability to sustain stability and competitiveness.
Evaluation of Implementation Context Based Clustering In Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization Algorithm Abdussamad, Siti Nurmardia; Astutik, Suci; Effendi, Achmad
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 14 No. 1 (2020)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v14i1.609

Abstract

This paper contains an evaluation of the implementation Context Based Clustering method into Fuzzy Geographically Weighted Clustering-Particle Swarm Optimization (FGWC-PSO) algorithm on 11 variable from data factors causing the spread of dengue in East Java. Integration of Particle Swarm Optimization as a metaheuristic algorithm makes the computation run longer so, the solution in this paper is FGWC-PSO will be combined with context based clustering to produce a hybrid method (CFGWC-PSO) which can shorten the computational time of the clustering algorithm. Context based clustering in this paper will use 3 ways, namely by using random values, using Fuzzy C-Means (FCM), and using mean and standard deviations. CFGWC-PSO algorithm using number of clusters = 2 and CFGWC-PSO will be evaluated using IFV index, based on processing results found that the best clustering algorithm is CFGWC-PSO using FCM
GENERALIZED CONFIRMATORY FACTOR ANALYSIS FOR KNOWING IMPACT OF KNOWLEDGE, ATTITUDES, AND BEHAVIORAL FACTORS HIV/AIDS IN INDONESIA Rahmi, Nur Silviyah; Astutik, Suci; Astuti, Ani Budi; Muhammad, Alifiandi Rafi; Maisaroh, Ulfah; Handayani, Sri
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/barekengvol17iss2pp0695-0706

Abstract

The cumulative number of detected HIV/AIDS cases in the January – March 2021 period is 9,327, consisting of 7,650 HIV and 1,677 AIDS reported by 498 districts and cities from 514 districts and cities in Indonesia. Human Immunodeficiency Virus (HIV) is the virus that causes Acquired Immunodeficiency Syndrome (AIDS). Several factors that influence the spread of HIV/AIDS include knowledge, attitudes and behavior about HIV/AIDS. Someone who gains knowledge about HIV/AIDS will have high self-confidence and a positive outlook on life and be more optimistic in taking HIV/AIDS prevention actions. The main objective of this study is to determine the influence of external factors which include demographic, social and economic aspects, as well as internal factors which include knowledge, attitudes and behavior to the level of transmission of HIV/AIDS. By using the CFA approach, it can be seen which indicators have the greatest influence on the latent variables of knowledge, attitudes, and behavior or called loading factors. The data used is secondary data from a 5-year survey from the Central Statistics Agency, namely the 2017 Indonesian Demographic and Health Survey (IDHS) published at the end of 2018. The CFA results show that the P11 variable (about known infections) has the largest loading factor value, which is 0.613 in the variable. . hidden. knowledge. In the latent variable of attitude, the S1 variable (about identifying how the respondent knows someone is infected with HIV-AIDS) has the largest loading factor value of 0.514. While the behavioral latent variable, the variable R8 (whether men have been infected with sexually transmitted diseases (STI) with symptoms) has the largest loading factor value, which is 0.954.
BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Astutik, Suci; Rahmi, Nur Silviyah; Irsandy, Diego; Saniyawati, Fang You Dwi Ayu Shalu; Mashfia, Fidia Raaihatul; Lusiana, Evelin Dewi; Risda, Intan Fadhila; Susanto, Mohammad Hilmi
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp1105-1116

Abstract

Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.
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 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 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 Zamelina, Armando Jacquis Federal Zerlita Fahdha Pusdiktasari