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All Journal AKSIOMA: Jurnal Program Studi Pendidikan Matematika JIPM (Jurnal Ilmiah Pendidikan Matematika) Math Didactic: Jurnal Pendidikan Matematika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Unisda Journal of Mathematics and Computer Science (UJMC) Transformasi : Jurnal Pendidikan Matematika dan Matematika Variance : Journal of Statistics and Its Applications JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Differential: Journal on Mathematics Education
Feby Indriana Yusuf
Universitas PGRI Banyuwangi
Humanities Computer Science & IT Education Engineering Mathematics Public Health Other

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EVALUATING NEARMISS AND SMOTE FOR VEHICLE INSURANCE FRAUD CLAIM CLASSIFICATION WITH A RANDOM FOREST CLASSIFIER Yusuf, Feby Indriana; Handamari, Endang Wahyu
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 2 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss2page219-230

Abstract

This study evaluates the detection of fraudulent car insurance claims in unbalanced data by comparing two resampling techniques, namely NearMiss (undersampling) and SMOTE (oversampling), combined with Random Forest. The public dataset, consisting of 1,000 observations and 40 features, was preprocessed for missing value handling, label encoding, and min–max normalization, and split into 70% training data and 30% test data. Three scenarios were evaluated: original data (unbalanced), NearMiss, and SMOTE, using accuracy, precision, sensitivity (recall), specificity, and F1-score evaluations. The analysis results show that NearMiss provides the most balanced performance for antifraud purposes, with a sensitivity of 0.865, an F1-score of 0.667, and an accuracy of 0.787. For the original unbalanced data, the model achieved a sensitivity of 0.297 and an accuracy of 0.767. SMOTE achieved the highest precision (0.567) and accuracy (0.783), but its sensitivity was lower than that of NearMiss. These findings confirm that the selection of resampling techniques must be aligned with operational objectives: NearMiss is more appropriate when the priority is to capture as many fraud cases as possible, while SMOTE is more suitable when false positive control is prioritized.
Co-Authors Adi, Puti Zakiyah Raisa Anggraini, Dessy Anggraini, Dessy Anjasari, Dwi Hilda Anjasari, Dwi Hilda Arvianti, Aurellia A’la, Kevina Alal Barep Yohanes Bukhori, Hilmi Aziz Dwi Hilda Anjasari Dwi Mifta Mahanani, Dwi Mifta Dzurotul Mutimmah Eko Listiwikono Endang Wahyu Handamari Fitri Nurmasari Hady Rasikhun Heri Susanto Imam Nurhadi Purwanto Julianti, Nurma Haya Nada, Qothrun Nadya Adharani Novi Prayekti Nurma Haya Julianti Permata, Wahyu Finda Permata, Wahyu Finda Rachmaniah Mirza Hariastuti Rahmi, Miftachul Rahmi, Miftachul Saiful Bukhori Saragih, Trecy Elisabet Tioralina Sari, Meylita Situmorang, Boy Nathanael Susanti, Reny Eka Evi Syaiful Anam Syamsiar, Syamsiar Wiwik Handayani