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All Journal Jurnal Teknologi Informasi MURA Jurnal Teknologi Informasi Mura
Alda Zevana Putri Widodo
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Other

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Prediksi Status Pesanan Marketplace Menggunakan Algoritma Random Forest Alda Zevana Putri Widodo
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3216

Abstract

This study aims to predict order status on a marketplace platform using the Random Forest algorithm as a classification method. The dataset consists of 32,187 transaction records containing various attributes, including product price, discount, shipping cost, item weight, payment method, and product category. During the modeling process, the data were divided into 80% training data and 20% testing data to develop a model capable of classifying whether an order is likely to be successfully completed or canceled. Based on the experimental results, the Random Forest algorithm demonstrated excellent performance, achieving an accuracy of 94.78%, precision of 95.57%, recall of 99.05%, and an F1-score of 97.28%. These results indicate that the model can effectively identify transaction patterns and provide highly accurate predictions of order status. Therefore, the proposed model has the potential to serve as a decision-support tool for marketplace platforms in reducing order cancellation rates and improving transaction management efficiency.
Prediksi Status Pesanan Marketplace Menggunakan Algoritma Random Forest Alda Zevana Putri Widodo
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3216

Abstract

This study aims to predict order status on a marketplace platform using the Random Forest algorithm as a classification method. The dataset consists of 32,187 transaction records containing various attributes, including product price, discount, shipping cost, item weight, payment method, and product category. During the modeling process, the data were divided into 80% training data and 20% testing data to develop a model capable of classifying whether an order is likely to be successfully completed or canceled. Based on the experimental results, the Random Forest algorithm demonstrated excellent performance, achieving an accuracy of 94.78%, precision of 95.57%, recall of 99.05%, and an F1-score of 97.28%. These results indicate that the model can effectively identify transaction patterns and provide highly accurate predictions of order status. Therefore, the proposed model has the potential to serve as a decision-support tool for marketplace platforms in reducing order cancellation rates and improving transaction management efficiency.
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