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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Inspiration Indonesian Journal of Computing and Modeling JUSIM (Jurnal Sistem Informasi Musirawas) Journal of Information Systems and Informatics Jurnal Mnemonic Jurnal Algoritma, Logika dan Komputasi Jurnal Pengabdian dan Kewirausahaan Journal of Information Technology (JIfoTech) Jurnal Teknologi dan Manajemen Industri Terapan JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Merryana Lestari
Universitas Bunda Mulia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : Jurnal Algoritma, Logika dan Komputasi

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GRADIENT BOOSTING TREES UNTUK PEMODELAN DAN PREDIKSI BIAYA KERUGIAN ASURANSI MOBIL Fammaldo, Eric; Lestari, Merryana; Hermawan, Chandra
Jurnal Algoritma, Logika dan Komputasi Vol 7, No 1 (2024): Maret 2024
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v7i1.6030

Abstract

Gradient Boosting is a machine learning algorithm that combines several simple parameter functions that aim to predict a fairly accurate information from existing data. In contrast to statistical methods in general, this Gradient boosting provides interpretable information, while requiring little data preprocessing and tuning of parameters. Boosting Gradient can be applied to classify or regress data, complex interaction is modeled simply and minimizes loss of information while in predictor management, so this algorithm is good enough to be used for modeling the cost of insurance loss. This paper presents the GB theory and its application to the problem of predicting '' at-fault '' accidents on auto loss costs using data from Canadian insurance companies. The predictive accuracy of the model is compared to the conventional Generalized Linear Model (GLM) approach.Gradient Boosting is a machine learning algorithm that combines several simple parameter functions that aim to predict a fairly accurate information from existing data. In contrast to statistical methods in general, this Gradient boosting provides interpretable information, while requiring little data preprocessing and tuning of parameters. Boosting Gradient can be applied to classify or regress data, complex interaction is modeled simply and minimizes loss of information while in predictor management, so this algorithm is good enough to be used for modeling the cost of insurance loss. This paper presents the GB theory and its application to the problem of predicting '' at-fault '' accidents on auto loss costs using data from Canadian insurance companies. The predictive accuracy of the model is compared to the conventional Generalized Linear Model (GLM) approach.
Co-Authors Ade Iriani Adhe Ronny Julians Agustinus Fritz Wijaya A’ang Subiyakto Bhustomy Hakim Chandra, Michelle Mei Diana Chandra, Michelle Mei Diana Charitas Fibriani Eko Sediyono Endang Haryani Fammaldo, Eric Geasela, Yemima Monica Gloria Batseyba Warella Gudiato, Candra Hendry Hermawan, Chandra Hindriyanto Dwi Purnomo Indrastanti Ratna Widiasari Irwan Sembiring Kaunang, Fergie Joanda Mira Mira Puspita, Maria Entina Ranny, Ranny Sri Yulianto Joko Prasetyo Teguh Wahyono Thenata, Angelina Pramana Vicky Wendy, Albert Wijayanti, Theresia Puspa Yessica Nataliani