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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) CAUCHY: Jurnal Matematika Murni dan Aplikasi REKAYASA Scientific Journal of Informatics Jurnal Fourier INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JBMP (Jurnal Bisnis, Manajemen dan Perbankan) BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Computer Science and Information Technologies Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science
Yuniar Farida, Yuniar
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Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking Nanda, Nafa Nur Adifia; Farida, Yuniar; Utami, Wika Dianita
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.23930

Abstract

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. Methods: This study uses the Random Forest technique to categorize credit risk evaluation. Random Forest is a decision tree technique recognized for its high accuracy in data classification, utilizing an ensemble method of many decision trees. Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. This study employs the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to address class imbalance. The SMOTE algorithm is a method that emphasizes oversampling and is designed to augment the data in the minority class by generating synthetic data that aligns with the minority class data. The findings indicated that the ideal ratio for partitioning training and testing data was 80:20, and implementing the SMOTE technique within Random Forest enhanced performance assessment. Results: This research contributes to improving the accuracy of credit risk classification using the Random Forest algorithm, which effectively handles complex data and is supported by the implementation of SMOTE to overcome the class imbalance in the data. The classification accuracy value rose from 91.54% to 94.41%. The precision value rose from 90.83% to 97.03%, while the recall value increased from 60.26% to 91.55%. Conclusion: This method helps banks identify high-risk debtors more objectively and efficiently and supports appropriate credit decision-making.
PERAMALAN JUMLAH PENUMPANG PESAWAT DI BANDAR UDARA INTERNASIONAL JUANDA MENGGUNAKAN METODE EXPONENTIAL SMOOTHING EVENT-BASED Farida, Yuniar; Yusi, Suyesti; Yuliati, Dian
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (580.856 KB) | DOI: 10.30598/barekengvol15iss4pp709-718

Abstract

The increase in the number of airplane passengers occurs at certain times, such as Eid al-Adha, Eid al-Fitr, and Christmas holidays. Of course, an excessive rise in the number of passengers can cause extreme flight traffic density so that which can cause flight delays, decreased airport service level performance, and other impacts. This study predicts the number of aircraft passengers at Juanda International Airport using the Exponential Smoothing Event-Based method. The Exponential Smoothing Event-Based method is a forecasting method that considers special events using the Exponential Smoothing method as the initial calculation. This study uses data on the number of passengers from January 2014 to December 2020. From the forecasting model, MAPE is 11.8905%, and MSE is 4202958561.0706, so that the resulting forecast can be categorized as good.
COMPARING GAUSSIAN AND EPANECHNIKOV KERNEL OF NONPARAMETRIC REGRESSION IN FORECASTING ISSI (INDONESIA SHARIA STOCK INDEX) Farida, Yuniar; Purwanti, Ida; Ulinnuha, Nurissaidah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (715.853 KB) | DOI: 10.30598/barekengvol16iss1pp321-330

Abstract

ISSI reflects the movement of sharia stock prices as a whole. It is necessary to forecast the share price to help investors determine whether the shares should be sold, bought, or retained. This study aims to predict the value of ISSI using nonparametric kernel regression. The kernel regression method is one of the nonparametric regression methods used to estimate conditional expectations using kernel functions. Kernel functions used in this study are gaussian and Epanechnikov kernel functions. The estimator used is the estimator Nadaraya-Watson. This study aims to compare the two kernel functions to predict the value of ISSI in the period from January 2016 to October 2019. The analysis results obtained the best method in predicting ISSI values, namely nonparametric kernel regression using Nadaraya-Watson estimator and Gaussian kernel function with the MAPE value of 15% and the coefficient of determination of 85%. Independent variables that significantly affect ISSI are interest rates, exchange rates, and inflation. Curve smoothing is done using bandwidth value (h) searched by the Silverman rule. The calculation result with the Silverman rule obtained a bandwidth value of 101832.7431.
MODELING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION Farida, Yuniar; Farmita, Mayandah; Intan, Putroue Keumala; Khaulasari, Hani; Wibowo, Achmad Teguh
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss3pp1497-1508

Abstract

The high crime rate will create unrest and losses for the community. One of the provinces with high crime rates is East Java. This study aims to analyze the factors that influence criminality in East Java to ensure appropriate crime prevention and control measures can be taken. The factors that potentially influence crime in East Java studied include population density, the number of poor people, unemployment, Human Development Index (HDI), Gross Regional Domestic Product (GRDP), and per Capita Expenditure, which are associated with geographical conditions in each region (regency/city) collected from BPS East Java in 2022. Meanwhile, the number of crimes is collected from the East Java Regional Police. This research uses a statistical method, namely the Spatial Durbin Model (SDM), which is a particular form of the Spatial Autoregressive Model (SAR) method with Queen Contiguity weighting by analyzing geographically (spatial processes). Based on the results of the analysis, it was found that the influential factors were unemployment, HDI, GRDP, and per Capita Expenditure, and the R-square result was obtained at 85.18%. This shows a relationship between spatial accessibility and crime, where unemployment, HDI, GRDP, and per Capita Expenditure in an area can affect regional vulnerability to crime
Service Quality to the Level of Customer Satisfaction and Loyalty at Banking in Surabaya Using the SEM Method: Kualitas Layanan hingga Tingkat Kepuasan dan Loyalitas Pelanggan di Perbankan di Surabaya Menggunakan Metode SEM Farida, Yuniar; Nadiyah, Fithrotun; Khaulasari, Hani
JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Vol. 11 No. 2 (2025): September: JBMP Vol.11 No. 2 2025
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jbmp.v11i2.2122

Abstract

Service quality in the banking sector has become a primary focus to ensure customer satisfaction and loyalty. This study aims to analyze the influence of service quality on customer satisfaction, customer satisfaction on customer loyalty, and service quality on customer loyalty at the Banking in Surabaya Branch. The sample obtained from the questionnaire consists of 160 customers. The data analysis technique used in this study is the SEM method, which analyzes the relationships between variables in a model. The SEM method also includes the role of a mediating variable, which is customer satisfaction, between service quality and customer loyalty. The results of this study indicate that service quality significantly impacts customer satisfaction, and customer satisfaction also significantly influences customer loyalty. Additionally, service quality has a direct impact on customer loyalty. Moreover, service quality indirectly affects customer loyalty through the mediating variable of customer satisfaction at the Banking in Surabaya Branch. The benefits of this research include developing business strategies for competitive advantage and strengthening the relationship between customers.
Analysis of Factors that Influence Maternal Mortality Rates Using Generalized Poisson Regression pratiwi, Yuniar Ines; Khaulasari, Hani; Farida, Yuniar; Ferdani, Ayu
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art2

Abstract

Maternal Mortality Rate (MMR) is the number of deaths of women within 42 days after childbirth or during pregnancy. Objective: This study aims to identify factors affecting MMR in East Java and compare the performance of the Generalized Poisson Regression (GPR) model with Poisson regression. The method used is Generalized Poisson Regression, a regression model for count data, which extends Poisson regression to overcome the problem of overdispersion or underdispersion with data derived from the East Java Health Office, including MMR as the dependent variable, as well as five variables that are thought to affect it in 38 districts/cities. The GPR model proved superior to Poisson regression with an Akaike Information Criterion (AIC) value of 239.515 to identify factors affecting maternal mortality. Factors such as delivery handled by health workers, K6 visits by pregnant women, provision of diphtheria-tetanus immunization, and obstetric complications affect MMR in East Java in 2022.
Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method Farida, Yuniar; Hamidah, Afanin; Sari, Silvia Kartika; Hakim, Lutfi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3407

Abstract

The agricultural sector plays a crucial role in the Indonesian economy. However, the farm sector still has serious problems, including agricultural product prices, which often fall when the harvest supply is abundant. So often, the income obtained is not proportional to the price spent by farmers, which has an impact on decreasing the welfare of farmers. An indicator to observe changes in the interest of Indonesian farmers is the Farmer Exchange Rate Index (NTP). This study aims to form a model and project the welfare level of farmers in Indonesia, focusing on NTP indicators, which are caused by the influence of variables such as inflation, Gross Domestic Product (GDP), interest rates, and the rupiah exchange rate. The method used is the Vector Error Correction Model (VECM), used when there are indications that the research variables do not show stability at the initial level and there is a cointegration relationship. The results of this study show that in the long run, significant factors affecting NTP are inflation, interest rates, and the rupiah exchange rate. Meanwhile, in the short term, the variables that have an impact are GDP and the rupiah exchange rate. The resulting VECM model shows a MAPE error rate of 1.79%, indicating excellent performance, as the MAPE error rate is below 10%. The implication of this research is provides information related to NTP projection that can be used to formulate strategies to strengthen Indonesia's agricultural sector.
Classification of Hypertension in Pregnant Women Using Multinomial Logistic Regression Farida, Yuniar; Tiasti, Roro Niken Enggar; Sari, Silvia Kartika
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16481

Abstract

Maternal Mortality Rate (MMR) is still a crucial problem in Indonesia and other developing countries; one of the causes is Hypertension in Pregnancy (HDK). This study aims to classify hypertension in pregnant women based on the factors that influence it, with a case study of patients from the Obstetrics and Gynecology Specialist Clinic at the Regional General Hospital (RSUD) Dr. R. Sosodoro Djatikoesoemo Bojonegoro. The variables used were age, gravidity, gestational age, obesity, history of abortion, hypertension, and diabetes mellitus. The research method used in this study is multinomial logistic regression because it uses four categories of dependent variables, namely pregnant women without hypertension, pregnant women with chronic hypertension, pregnant women with gestational hypertension, and pregnant women with preeclampsia. The results obtained in this study were from 3 categories of hypertension in pregnant women; the influencing factors were obesity, gestational age > 36 weeks, having a history of hypertension, and diabetes mellitus, with the resulting model classification accuracy value of 79.6%, which means the classification is classified as good. This research contributes to applying statistical methods in the health sector and as a mitigation effort to help minimize the number (prevalence) of maternal deaths, especially those caused by hypertension. 
Model Geographically Weighted Regression Menggunakan Adaptive Gaussian Kernel untuk Pemetaan Faktor Penyebab Stunting Vianti, Febi; Khaulasari, Hani; Farida, Yuniar; Swantika, Cicik; Efendi, Havid
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 12 Issue 2 December 2024
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v12i2.28072

Abstract

Stunting is a child growth disorder that is evident from a lack of height for age. Jember Regency has a stunting prevalence rate of 34.90% in 2022, making it the region with the highest stunting cases in East Java. The purpose of this research is to map the factors that influence stunting in Jember Regency with a spatial analysis approach. The method applied in this study is Geographically Weighted Regression (GWR) to analyze the spatial relationship between predictors and responses. GWR uses an optimal kernel to determine the spatial weights based on distance accurately, as well as the AIC and  goodness criteria to calculate the goodness of the model. The research variables include the number of stunting cases in Jember Regency as the response variable (Y), and the predictor variables (X) are chronic energy deficiency pregnant women (), anemic pregnant women (), exclusive breastfeeding (), proper sanitation (), pregnant women consuming TTD at least 90 days (), complete basic immunization (), and wasting (). The results of the study using the adaptive gaussian kernel with the minimum CV compared to other kernels can improve accuracy, so it can be applied to data analysis.  The GWR model obtained an accuracy of 80.59% and AIC 360.  indicates the ability to explain 80.59% of the variability of the response data, and the AIC value is 360, which reflects the efficiency and suitability of the model to spatial data. From the GWR parameters, 14 groups were formed where there are several different factors in each area in the sub-districts in Jember Regency.
Implementing lee's model to apply fuzzy time series in forecasting bitcoin price Farida, Yuniar; Ainiyah, Lailatul
Computer Science and Information Technologies Vol 5, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i1.p72-83

Abstract

Over time, cryptocurrencies like Bitcoin have attracted investor's and speculators' interest. Bitcoin's dramatic rise in value in recent years has caught the attention of many who see it as a promising investment asset. After all, Bitcoin investment is inseparable from Bitcoin price volatility that investors must mitigate. This research aims to use Lee's Fuzzy Time Series approach to forecast the price of Bitcoin. A time series analysis method called Lee's Fuzzy Time Series to get around ambiguity and uncertainty in time series data. Ching-Cheng Lee first introduced this approach in his research on time series prediction. This method is a development of several previous fuzzy time series (FTS) models, namely Song and Chissom and Cheng and Chen. According to most previous studies, Lee's model was stated to be able to convey more precise forecasting results than the classic model from the FTS. This study used first and second orders, where researchers obtained error values from the first order of 5.419% and the second order of 4.042%, which means that the forecasting results are excellent. But of both orders, only the first order can be used to predict the next period's Bitcoin price. In the second order, the resulting relations in the next period do not have groups in their fuzzy logical relationship group (FLRG), so they can not predict the price in the next period. This study contributes to considering investors and the general public as a factor in keeping, selling, or purchasing cryptocurrencies.
Co-Authors Achmad Teguh Wibowo Ainiyah, Lailatul Akbar, Fadilah Ambadar, Panreshma Rizkha Aris Fanani Auditiyah, Cellyn Desinaini, Latifatun Nadya Efendi, Havid FAJAR SETIAWAN Farmita, Mayandah Ferdani, Ayu Hamidah, Afanin Hani Khaulasari Khasanah, Zhara Shafira Uswatun LULUK WULANDARI Lutfi Hakim Mahfiroh, Luluk Maulida, Eka Agustina Moh. Hafiyusholeh Moh. Hartono Nadiyah, Fithrotun Nanda, Nafa Nur Adifia Novitasari, Dian C Rini Nurfadila, Monika Refiana Nurissaidah Ulinnuha Pramesti, Diah Devi pratiwi, Yuniar Ines Purwanti, Ida Putroue Keumala Intan Sari, Dian Candra Rini Novita Sari, Ghaluh Indah Permata Sari, Silvia Kartika Siti Nur Aisah Swantika, Cicik Tiasti, Roro Niken Enggar Utami, Tri Mar'ati Nur Vianti, Febi Wika Dianita Utami Yuliati, Dian Yusi, Suyesti Zaen, Nanida Jenahara