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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) CSRID (Computer Science Research and Its Development Journal) EDUMATIC: Jurnal Pendidikan Informatika Abdimasku : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Informasi Cyberku
Ahmad Zainul Fanani
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Penerapan Random Oversampling dan Algoritma Boosting untuk Memprediksi Kualitas Buah Jeruk Ananda, Imanuel Khrisna; Fanani, Ahmad Zainul; Setiawan, Dicky; Wicaksono , Duta Firdaus
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25836

Abstract

According to the 2019 data, global orange production has increased significantly, reaching 79 million tons. However, despite the availability of various types of oranges in Indonesian markets, many vendors still sell low-quality oranges. To address this issue, researchers have applied random oversampling and boosting algorithms to predict orange quality, using the public Orange Quality Analysis Dataset. This study uses random oversampling to address data imbalance and combines it with boosting algorithms like Adaboost, Gradient Boosting, Light GBM, and CatBoost. The data features considered include size, weight, sweetness level, acidity level, and others. The accuracy of the boosting algorithms used varied, with CatBoost showing the highest accuracy rate of 91.42%. The hope is that this research can help orange producers create high-quality products and reduce the occurrence of low-quality oranges, ultimately providing consumers with better oranges. Additionally, this can help producers market their oranges both domestically and internationally.
Co-Authors Affandy Affandy Affandy Affandy Alzami, Farrikh Ananda, Imanuel Khrisna Arif, Muhammad Husnul Arry Maulana Syarif Arry Maulana Syarif, Arry Maulana Arwahyu Sugito Aton Rustandi Mulyana Aziz, Mochammad Ilham Dedi Nurcipto, Dedi Etika Kartikadarma Evi Rokhayati Fikri Budiman Hanny Haryanto Heribertus Himawan Indra Gamayanto Indrawan Setiaji Khafiizh Hastuti Khairul Fahmi Laksana, Deddy Award Widya Linda Marlinda Moch Arief Soeleman Moch Hariadi Mochamad Hariadi Mochammad Ilham Aziz Muhammad Syaifur Rohman Nanang Lestio Wibowo Pujiono Pujiono Rohman, Muhammad Syaifur Rudi Nurcahyo Ruri Suko Basuki Saraswati, Galuh Wilujeng Sari Ayu Wulandari Sendi Novianto Setiawan, Dicky Stefanus Santosa Sutriawan Sutriawan Tri Daryatni Usman Sudibyo Wicaksono , Duta Firdaus Winarsih, Nurul Anisa Sri Yulianto, Satria Pradana Rizki Yuliman Purwanto