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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

Abstract

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

Abstract

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