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All Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Algoritma
Ananda, Imanuel Khrisna
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Computer Science & IT Education

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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.
Analisis Sentimen Ulasan Pengguna iPhone dengan Pendekatan Hibrida RoBERTa dan XGBoost Zain, Affa Fahmi; Azies, Harun Al; Ananda, Imanuel Khrisna
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2277

Abstract

User reviews play an important role in shaping perceptions of products, including the iPhone. Sentiment analysis of these reviews can provide valuable insights for companies to improve product and service quality. This study explores sentiment analysis of iPhone user reviews using a hybrid approach that combines RoBERTa and XGBoost to improve classification accuracy. The model was built and tested on a public dataset containing 2,960 reviews obtained from the Kaggle platform, following data cleaning processes. Preprocessing steps included handling missing values, encoding, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). RoBERTa was used to extract text features and understand contextual meaning, while XGBoost served as the classification algorithm. The evaluation showed an accuracy of 99.74%, with an increase in the F1-score from 0.99 to 1.00 after applying SMOTE, particularly in the minority class. These findings demonstrate the superiority of the RoBERTa-XGBoost approach over traditional methods and contribute to the development of more balanced and adaptive classification models for imbalanced data.
Co-Authors Ahmad Zainul Fanani Harun Al Azies Setiawan, Dicky Wicaksono , Duta Firdaus Zain, Affa Fahmi