Article Per Year (5 Year)
p-Index From 2021 - 2026
6.156
P-Index
This Author published in this journals
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 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) Jurnal Sisfokom (Sistem Informasi dan Komputer) 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 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

Published : 47 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 47 Documents
Search

 1   2   3   4   5 
MODELLING THE NUMBER OF CRIMES IN EAST JAVA USING A TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION APPROACH Saputra, Yahya Vigo Tri; Hafiyusholeh, Moh.; Khaulasari, Hani; Farida, Yuniar; Intan, Putroue Keumala
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1627-1642

Abstract

High crime rates can lead to unrest and financial losses for the community. East Java is one of the provinces with high crime rates, with a total of 21,046 reported crimes in 2023. This study aims to identify the factors that influence crime rates in East Java and evaluate the goodness of the model through truncated spline semiparametric regression. Truncated spline semiparametric regression is a combination of parametric and nonparametric methods that can adjust changes in data patterns through the presence of knot points. The data used in this study were sourced from the Central Statistics Agency, including variables such as the number of people living in poverty, average years of schooling, gross regional domestic product, population, Gini ratio, per capita expenditure, and open unemployment rate. The results of the analysis indicate that the predictor variables have a significant influence on the number of crimes simultaneously. Partially, the variables that influence the number of crimes in East Java Province are average years of schooling, population, Gini ratio, per capita expenditure, and open unemployment rate. The best regression model is obtained using the combination knot point (4,2,4,3) with a minimum GCV value of 49636.60. The coefficient of determination obtained is 93.60%, indicating that the predictor variables can explain 93.60% of the variation in the crime rate, while the remaining 6.40% is attributed to variables outside the scope of the study.
Analysis Comparison of BiLSTM and BiGRU Models for Aircraft Visibility Prediction Saidah, Nayla Fitriyatus; Ulinnuha, Nurissaidah; Farida, Yuniar
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 1 (2026): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i1.34698

Abstract

Severe weather conditions such as fog and heavy precipitation pose significant threats to aviation safety. Accurate prediction of aircraft visibility is therefore essential to support operational decision-making and reduce the likelihood of accidents. This study aims to compare and evaluate the performance of two bidirectional deep learning models, BiLSTM and BiGRU, in predicting aircraft visibility using historical meteorological data from BMKG Juanda Sidoarjo. The novelty of this research lies in applying and comparing bidirectional recurrent architectures for visibility prediction, an approach rarely explored in aviation meteorology, to assess their capability in capturing temporal dependencies within time-series visibility patterns. Both models were trained using hyperparameter tuning, with the best configuration obtained from a 24-hour input window, batch size of 32, 64 neurons, a dropout rate of 0.1, and 100–200 epochs. The dataset was divided into training and testing sets (80:20), and model performance was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) to assess both predictive accuracy and computational efficiency. The results indicate that while BiLSTM achieved slightly higher accuracy, BiGRU demonstrated superior overall efficiency, obtaining competitive error metrics (MSE = 1.50 × 10⁶, RMSE = 1,223.5, MAPE = 19.35%) compared to BiLSTM (MSE = 1.58 × 10⁶, RMSE = 1,258.1, MAPE = 19.50%). BiGRU’s advantage lies in its simpler structure and faster computation, which reduce training complexity without sacrificing forecast accuracy. Overall, this research contributes to the development of efficient bidirectional time-series models for aviation meteorology, offering a practical framework for real-time visibility forecasting in computationally limited environments. The balance between accuracy, speed, and model simplicity makes BiGRU a more scalable and applicable choice for enhancing flight safety operations.
Clustering Couples of Childbearing Age to Get Family Planning Counseling Using K-Means Method Yuniar Farida; Adam Fahmi Khariri; Dian Yuliati; Hani Khaulasari
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1888

Abstract

Couples of Childbearing Age (CCA) in the Madiun Regency have increased in the last three years. It caused the population in Madiun to overgrow with the newborn, which implies the economic, social, and environmental aspects. This study aims to cluster villages in Madiun with CCA case studies instead of birth control participants who will give birth and want children to determine the priority of getting Family Planning (in Indonesia, namely Keluarga Berencana/KB) counseling. K-Means clustering is used in this study because it has a linear space of complexity that can be executed quickly and easily. The result of this study is four (4) CCA clusters. CCA cluster 1 is a very high level of giving birth and wanting children, consisting of 7 villages. CCA cluster 2 is a high level of giving birth and wanting children with 119 villages. CCA cluster 3 is a medium level of giving birth and wanting children in 50 villages, and CCA cluster 4 is a low level of giving birth and wanting children, including 34 villages. So, cluster 1, which includes seven villages, is the most prioritized to get Family Planning counseling because it is the CCA cluster with the most birthing rate and wants children. This research obtained a silhouette coefficient of 0.42, which belongs to the medium level.
Modeling the Farmer Exchange Rate in Indonesia Using the Vector Error Correction Model Method Yuniar Farida; Afanin Hamidah; Silvia Kartika Sari; Lutfi Hakim
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.
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.
O Optimization of Support Vector Machine Method Using Recursive Feature Elimination Feature Selection for Monkeypox Symptom Classification Yuniar Farida; Iftitakhun Ni'mah; Putroue Keumala Intan
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2636

Abstract

Regional outbreaks of monkeypox highlight the need for accurate and efficient symptom-based classification to support early detection. This study aims to improve the classification performance of monkeypox symptoms using a Support Vector Machine (SVM) optimized with Recursive Feature Elimination (RFE). The dataset consists of 1,000 cases, which were preprocessed via encoding and normalization, followed by feature selection using RFE and classification with SVMs using various kernel functions. Model performance was evaluated using accuracy, precision, sensitivity, and specificity. The results show that RFE successfully identified eight key features—Rectal Pain, Sore Throat, Penile Swelling, Oral Lesions, Swollen Tonsils, Single Lesions, HIV Infection, and Sexually Transmitted Infections—as the most influential variables. The optimized SVM, validated using a confusion matrix, achieved 77% accuracy, 84% precision, 66% sensitivity, and 88% specificity, representing a modest improvement over the baseline SVM (75%). The polynomial kernel demonstrated the best performance, indicating the presence of nonlinear relationships among symptoms. Although the improvement is relatively small, integrating RFE enhances feature relevance and model stability. These findings suggest that feature selection is an effective strategy for refining classification performance, while further validation and comparison with alternative methods are recommended to ensure robustness and generalizability.
Co-Authors Abdul Muhid Abdulloh Hamid Achmad Teguh Wibowo Adam Fahmi Khariri Afanin Hamidah Ahmad Hanif Asyhar Ahmad Teguh Wibowo Ahmad Zaenal Arifin Akbar, Fadilah Ambadar, Panreshma Rizkha Aris Fanani Auditiyah, Cellyn Desinaini, Latifatun Nadya Diah Ayu Sulistiani Dian C Rini Dian C. Rini Novitasari Dian Yuliati Dwi Puspitasari Efendi, Havid FAJAR SETIAWAN Farmita, Mayandah Ferdani, Ayu Fifi D. Rosalina Galuh Andriani Ghina Salsabila Firdaus Hani Khaulasari Husna Nur Laili Ida Purwanti Iftitakhun Ni'mah 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. Hafiyusholeh Moh. Hartono Monike Febriyani Faris Montolalu, Billy Nadiyah, Fithrotun Nanda, Nafa Nur Adifia 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 Putroue Keumala Intan Saidah, Nayla Fitriyatus Saputra, Yahya Vigo Tri Sari, Dian Candra Rini Novita 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