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All Journal CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Mahasiswa Statistik Jurnal Penelitian Pendidikan IPA (JPPIPA) UNP Journal of Statistics and Data Science
Sumarminingsih, Eni
Prodi Statistika Jurusan Matematika FMIPA Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Materials Science & Nanotechnology Mathematics Physics Social Sciences

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PERMODELAN REGRESI LOGISTIK DAN REGRESI PROBIT PADA PEUBAH RESPON MULTINOMIAL Kurniasari, Lia; Sumarminingsih, Eni; Solimun, Solimun
Jurnal Mahasiswa Statistik Vol 1, No 4 (2013)
Publisher : Jurnal Mahasiswa Statistik

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PENGGUNAAN ERROR CORRECTION MODEL ENGLE-GRANGER DAN DOMOWITZ EL-BADAWI PADA DATA ANALISIS DERET WAKTU NON STATIONER(MIGAS, PDB, ORI, IHSG) Aprianti, Dita Fitria; Kusdarwati, Heni; Sumarminingsih, Eni
Jurnal Mahasiswa Statistik Vol 2, No 1 (2014)
Publisher : Jurnal Mahasiswa Statistik

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PERAMALAN HARGA SAHAM HARIAN JAKARTA COMPOSITE INDEX (JCI) MENGGUNAKAN MODEL MIXTURE AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (MAR-ARCH) Widda, Fadlilah Prapta; Kusdarwati, Heni; Sumarminingsih, Eni
Jurnal Mahasiswa Statistik Vol 2, No 1 (2014)
Publisher : Jurnal Mahasiswa Statistik

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METODE STANDARD ERROR NEWEY WEST UNTUK MENGATASI HETEROSKEDASTISITAS DAN AUTOKORELASI PADA ANALISIS REGRESI LINIER BERGANDA Rachmawati, Dian Suci; Sumarminingsih, Eni
Jurnal Mahasiswa Statistik Vol 2, No 1 (2014)
Publisher : Jurnal Mahasiswa Statistik

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PERBANDINGAN METODE GEOGRAPHICALLY WEIGHTED PRINCIPAL COMPONENT ANALYSIS REGRESSION (GWPCAR) DAN GEOGRAPHICALLY WEIGHTED RIDGE REGRESSION (GWRR) Santoso, Esti Herdina; Soehono, Loekito Adi; Sumarminingsih, Eni
Jurnal Mahasiswa Statistik Vol 2, No 1 (2014)
Publisher : Jurnal Mahasiswa Statistik

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METODE REGRESI PANEL SPASIAL PADA PEMODELAN TINGKAT KEMISKINAN DI KABUPATEN / KOTA PROVINSI JAWA TIMUR Metyopandi, Vierkury; Fitriani, Rahma; Sumarminingsih, Eni
Jurnal Mahasiswa Statistik Vol 2, No 5 (2014)
Publisher : Jurnal Mahasiswa Statistik

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Fuzzy Sugeno Method for Opinion Classification Regarding Policy of PPKM and Covid-19 Vaccination Djihan Wahyuni; Eni Sumarminingsih; Suci Astutik
Jurnal Penelitian Pendidikan IPA Vol 8 No 5 (2022): November
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v8i5.1958

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The Indonesian government has implemented various interventions to overcome the impact of the Covid-19 pandemic, including those written in Minister of Home Affairs Instructions on PPKM (Community Activities Restrictions Enforcement) and Covid-19 vaccination policies. This policy are not at least reaping the pros and cons, so it is necessary to monitor public opinion to be able to provide solutions or become an evaluation of future policies. The aim of this study is to determine the polarity of public opinion regarding PPKM and Covid-19 vaccinations policies on Twitter, as well as to determine the implementation of FIS Sugeno in classifying textual data. There are several stages carried out, i.e. data collection, data pre-processing, data labeling, data weighting, identification of membership functions, determination of fuzzy sets, formation of a classification system, and evaluation of classification results. In this study, the performance of FIS Sugeno in classifying tweets was quite good with an average accuracy of 89.13%. Meanwhile, public opinion regarding the PPKM and Covid-19 vaccination policies tends to be balanced with 36.92% of tweets classified as a positive sentiments, 22.85% being negative sentiments, and another 40.23% belonging to neutral sentiments.
Hybrid LBFA-Based Feature Selection for Improving Machine Learning Classification Performance in Heart Disease Prediction Hana Azizah; Eni Sumarminingsih; Adji Achmad Rinaldo Fernandes
UNP Journal of Statistics and Data Science Vol. 4 No. 2 (2026): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol4-iss2/478

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Feature selection and feature engineering are essential steps in developing accurate machine learning models, particularly when dealing with imbalanced datasets and redundant variables. However, many feature augmentation methods are often applied without a consistent preprocessing strategy, which can reduce model reliability and increase the risk of information leakage. To overcome this issue, this study proposes a hybrid classification framework that combines CatBoost-based feature selection with two feature augmentation techniques: LOGIT transformation and Log Density Ratio (LDR). A structured preprocessing pipeline was designed to ensure consistency throughout the modeling process. One-hot encoding was applied for the LOGIT transformation, while numerical standardization was used for LDR estimation. The generated features were then integrated with the selected original variables to produce richer feature representations for classification. The proposed framework was evaluated using the Heart Disease dataset with three gradient boosting algorithms, namely LightGBM, XGBoost, and CatBoost. Model performance was assessed using accuracy, precision, sensitivity, specificity, and F1-score. The results show that the proposed approach consistently improved classification performance across all models. Among the tested models, LightGBM combined with LOGIT and LDR achieved the best performance, obtaining an accuracy of 0.9618, precision of 0.9485, sensitivity of 0.9620, specificity of 0.9625, and F1-score of 0.9552. These findings suggest that combining feature selection with structured feature augmentation can significantly improve predictive performance in imbalanced classification tasks
Hyperparameter Optimization Approach in GRU Model: A Case Study of Rainfall Prediction in DKI Jakarta Fidia Raaihatul Mashfia; Suci Astutik; Eni Sumarminingsih
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.32277

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Rainfall is a crucial factor in water resource management and disaster mitigation. This study develops a rainfall prediction model for DKI Jakarta using a Gated Recurrent Unit (GRU) with hyperparameter optimization to enhance prediction accuracy. Daily rainfall data is processed using a sliding window technique, where 30 days of historical data serve as input to predict rainfall on the 31st day. The model is trained with various configurations of batch sizes and the number of neurons in hidden layers to determine the optimal performance. The results of hyperparameter tuning show that the batch size configuration of 64, hidden layer 1 with 32 neurons, and hidden layer 2 with 128 neurons produces an MAE of 6.66 and an RMSE of 13.94. The model is quite good at capturing daily rainfall patterns but still has difficulty in predicting extreme rainfall spikes
Development of Semiparametric Smoothing Spline Path Analysis on Cashless Society Muhammad Rafi Hasan Nurdin; Muhammad Ohid Ullah; Adji Achmad Rinaldo Fernandes; Eni Sumarminingsih; Solimun Solimun
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 1 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i1.29846

Abstract

Path analysis requires assumptions to be met, particularly the linearity assumption, which can be tested using the Ramsey Regression Specification Error Test (RESET). Parametric path analysis is appropriate when all variable relationships are linear. For entirely non-linear relationships, a nonparametric model can be used, while a semiparametric model applies if there is a mix of linear and non-linear relationships. One nonparametric method is spline smoothing, which requires determining the spline polynomial order in estimating the nonparametric path function. Determining the spline polynomial order is challenging because there is no standard test for it. This study thus develops a modified Ramsey RESET to identify the optimal spline smoothing order. The development involves modifying the second regression equation with a nonparametric spline smoothing regression of orders 2 to 5. The modified Ramsey RESET algorithm is applied to cashless data, and the results are used to estimate a multi-group semiparametric smoothing spline function with a dummy variable approach. This estimation yields a goodness of fit of 94.14%, indicating that Product Quality and the Moderating Effect of Cashless Usage Frequency can explain Cashless User Satisfaction and Cashless User Loyalty by 94.14%, with the remaining 5.86% explained by variables outside the research model
Co-Authors Adji Achmad Rinaldo Fernandes Alannuariputri, Yuhendra Ajeng Antara, I Putu Ria Aprianti, Dita Fitria Candrawati, Efrida Dwi Cindy Cahyaning Astuti Djihan Wahyuni Fidia Raaihatul Mashfia Hana Azizah Handoyo, Samingun Henny Pramoedyo Hikmatyar, Achmad Bara Kurniasari, Lia Kusdarwati, Heni Loekito Adi Soehono Mandani, Lidia Sukma Meilianawati, Putri Mitakda, Maria Bernadetha Muhammad Ohid Ullah Muhammad Rafi Hasan Nurdin Nafiana, Irma Ni Wayan Surya Wardhani Nisa M., Choirun Nugroho, Loekito Adi Nugroho, Waego Hadi Pakarti, Arifa Retno Permatasari, Kiky Ariesta Pratiwi, Dyah Anggun Puspita Rani, Silviana Anggun Rachmawati, Dian Suci Rahma Fitriani Rakhmasanti, Lintang Asdya Rojin, Ali Santoso, Esti Herdina Sari, Virgiana Nanda Sariyya, Hanah Rista Simon, Simon Solimun Solimun Solimun, Solimun Suci Astutik Theresia Mitakda, Maria Bernadetha Vierkury Metyopandi Wardani, Dita Kusuma Widda, Fadlilah Prapta Yanuari, Anindita