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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) REKAYASA Scientific Journal of Informatics Jurnal Ilmiah Teknik Industri Journal of Information Systems Engineering and Business Intelligence Jurnal Fourier JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Matematika: MANTIK JBMP (Jurnal Bisnis, Manajemen dan Perbankan) BAREKENG: Jurnal Ilmu Matematika dan Terapan Matrix : Jurnal Manajemen Teknologi dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Teorema: Teori dan Riset Matematika Mathvision : Jurnal Matematika Systemic: Information System and Informatics Journal Computer Science and Information Technologies Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Enthusiastic : International Journal of Applied Statistics and Data Science International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Matematika Integratif Engagement: Jurnal Pengabdian Kepada Masyarakat International Journal of Computing Science and Applied Mathematics-IJCSAM
Yuniar Farida
Universitas Islam Negeri Sunan Ampel Surabaya
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Analyzing Factors Contributing to Gender Inequality in Indonesia using the Spatial Geographically Weighted Logistic Ordinal Regression Model Hani Khaulasari; Yuniar Farida
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 10 No. 2 (2024)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v10i2.4529

Abstract

Abstract—Gender inequality is a condition of discrimination caused by social systems and structures. The main objective of this research is to identify factors that influence gender inequality in each province in Indonesia and obtain classification accuracy values using Geographically Weighted Ordinal Logistic Regres- sion (GWOLR). The dataset used in this research consists of a response variable, namely the gender inequality index where theindex value is divided into ordinal categories (low, medium, and high) and four predictor variables from the dimensions of health,education, human empowerment, social-culture, and work. Theresults of this study show that the classification accuracy of theGWOLR model is 85%. The mapping of provinces in Indonesiabased on influential variables forms three groups. The first group(brown) is influenced by the percentage of women who givebirth with the assistance of health workers (X 1 ) and the femaleHuman Development Index (HDI) (X3 ). The second group (blue)is influenced by the ratio of women’s Pure Participation Rate(APM) (X 2 ) and the percentage of rape crimes against women(X 4 ). The third group (red) is influenced by the percentage ofwomen who give birth with the assistance of health workers (X1),the ratio of women’s Pure Participation Rate (APM) (X2 ), thepercentage of women’s Human Development Index (HDI) ratio(X 3 ), and the percentage of women’s rape crimes (X4 ).
Implementing Lee's model to apply fuzzy time series in forecasting Bitcoin price Yuniar Farida; Lailatul Ainiyah
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.
Performance Evaluation of XGBoost and Random Forest Models in Visibility Prediction at Juanda Airport Ananda Amelia Pramaisita; Nurissaidah Ulinnuha; Yuniar Farida; Addien Haniefardy
IJCONSIST JOURNALS Vol 7 No 2 (2026): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v7i2.171

Abstract

Predicting meteorological visibility is critical in enabling transportation safety and weather watch systems. The current study compares the accuracy of the Random Forest and XGBoost algorithms in performing time series prediction for visibility based on BMKG Juanda, Sidoarjo hourly observation records for one year. Process analysis only uses visibility as a primary variable. Preprocessing of the data involved handling missing values, normalization, and dividing the data into training and test datasets. Model training and hyperparameter tuning were followed by model evaluation using a combination of MAE, RMSE, and MAPE indices. From the results, it is found that Random Forest had an MAE of 808.54, RMSE of 1,312.64, MAPE of 21.09%, and a computation time of 1.02 seconds, while XGBoost had an MAE of 808.81, RMSE of 1,323.12, MAPE of 21.47%, and a computation time of 1.36 seconds. As such, Random Forest is proposed as a more efficient model for predicting visibility at Surabaya's Juanda Airport; however, XGBoost remains a consideration for applicability when there is excessive variability in the data.
Sentiment Analysis Of The Clash Of Champions Event By Ruangguru With The Naïve Bayes Method Yuniar Farida; Rofina Muti’atun Khasanah Khasanah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.17241

Abstract

The rapid development of information technology has encouraged people to actively express their opinions through social media platforms, including X (formerly Twitter). One topic that has attracted considerable public attention is Clash of Champions, an academic competition organized by Ruangguru. This study aims to analyze X users’ sentiments toward the Clash of Champions event and evaluate the performance of the Naïve Bayes method in classifying these sentiments. The dataset consisted of 5,121 tweets collected through a crawling process using Google Colab from June to November 2024. The data were subjected to preprocessing and represented using Term Frequency–Inverse Document Frequency (TF-IDF) weighting before being classified into positive and negative sentiment categories using the Naïve Bayes algorithm. The results indicate that most tweets expressed positive sentiment, suggesting that Clash of Champions received favorable public responses. The classification model achieved an accuracy of 89.74%, a precision of 91.94%, a recall of 86.91%, and an F1-score of 89.34%. These findings demonstrate that the Naïve Bayes method is effective for analyzing public sentiment on social media. This study contributes empirical evidence regarding public perceptions of educational competition programs and supports the application of Naïve Bayes for sentiment classification in Indonesian social media data.
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 Yuniar Farida; Fithrotun Nadiyah; Hani Khaulasari
JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Vol. 11 No. 2 (2025): September
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.
Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking Nafa Nur Adifia Nanda; Yuniar Farida; Wika Dianita Utami
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.
Analyzing the Determinants of the Gender Development Index (GDI) with an Ordinal Logistic Regression Approach Izza, Ananda Nur; Farida, Yuniar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art10

Abstract

Regional disparities in the gender development index (GDI) across districts and cities in East Java reflect unequal gender development outcomes despite provincial progress. These disparities are associated with differences in access to health, education, and economic opportunities. This study identified the determinants of GDI using a gender-specific ordinal logistic regression model based on district/city-level data. The analysis included indicators representing health, education, and economic dimensions. Results showed that the determinants of GDI differed by gender. For males, life expectancy and expected years of schooling significantly increased the likelihood of a region being classified into a higher GDI category. For females, expected years of schooling and mean years of schooling were the most influential factors, emphasizing the importance of educational access and attainment. The model demonstrated moderate explanatory power for male data (Nagelkerke R-squared = 0.414) and strong explanatory power for female data (Nagelkerke R-squared = 0.654). However, the models exhibited relatively high classification error rates, with misclassification rates of 39.47% for males and 52.63% for females. These findings provide evidence that the drivers of gender development vary by gender and offer practical insights for designing targeted, gender-responsive policies to reduce regional disparities and promote inclusive development in East Java.
Co-Authors Abdul Muhid Abdulloh Hamid Achmad Teguh Wibowo Adam Fahmi Khariri Addien Haniefardy Afanin Hamidah Ahmad Hanif Asyhar Ahmad Teguh Wibowo Ahmad Zaenal Arifin Akbar, Fadilah Ambadar, Panreshma Rizkha Ananda Amelia Pramaisita Aris Fanani Auditiyah, Cellyn Desinaini, Latifatun Nadya Diah Ayu Sulistiani Dian C Rini Dian C. Rini Novitasari Dian Candra Rini Novitasari Dian Yuliati Dwi Puspitasari Efendi, Havid FAJAR SETIAWAN Farmita, Mayandah Ferdani, Ayu Fifi D. Rosalina Fithrotun Nadiyah Galuh Andriani Ghina Salsabila Firdaus Hani Khaulasari Hani Khaulasari Husna Nur Laili Ida Purwanti Izza, Ananda Nur Khasanah, Zhara Shafira Uswatun Lailatul Ainiyah Latifatun Nadya Desinaini Latifatun Nadya Desinaini Lubab, Ahmad Luluk Mahfiroh LULUK WULANDARI Lutfi Hakim M Mahaputra Hidayat Mahfiroh, Luluk Maulida, Eka Agustina Mayandah Farmita Moh Hartono Moh. Hafiyusholeh Monike Febriyani Faris Montolalu, Billy Nafa Nur Adifia Nanda Novitasari, Dian C Rini Nurfadila, Monika Refiana Nurissaidah Ulinnuha Pramesti, Diah Devi pratiwi, Yuniar Ines Purwanti, Ida Putra Prima Arhandi, Putra Prima Putri Krismadewi Putroue Keumala Intan Rofina Muti’atun Khasanah Khasanah Sari, Ghaluh Indah Permata Sari, Silvia Kartika Silvia Kartika Sari Silvia Kartika Sari Silvia Kartika Sari Siti Nur Aisah Swantika, Cicik Tiarra Dellaviyanie Muryanto Tiasti, Roro Niken Enggar Utami, Tri Mar'ati Nur Vianti, Febi Wika Dianita Utami Wika Dianita Utami Yuliati, Dian Yusi, Suyesti Zaen, Nanida Jenahara Zaidatun Ni'mah Zainullah Zuhri