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All Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan)
Kevin Ahessa Julian
Telkom University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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PREDIKSI STATUS PENGIRIMAN BARANG MENGGUNAKAN METODE MACHINE LEARNING Hardian Kokoh Pambudi; Putu Giri Artha Kusuma; Femi Yulianti; Kevin Ahessa Julian
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 6 No. 2 (2020)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (429.143 KB) | DOI: 10.33197/jitter.vol6.iss2.2020.396

Abstract

One of the key performance indicators for the logistics industry, especially freight forwarder company (cargo), is the delivery time. This is still a challenge in this industry in terms of ensuring the customer service level and reducing transportation costs. On the other hand, the development of information technology now allows an organization or company to collect large amounts of data automatically. A decent method that can be used to analyze the data for prediction purposes is machine learning, which is a method of extracting data into a certain pattern of information. This research aims to apply three machine learning methods to estimate the status of shipping goods. The method used in this study follows the machine learning process published by the Cross Industry Standard Process for Data Mining (CRISP-DM), namely; business processes understanding, data understanding, data preparation, model development, evaluation, and implementation. Based on the results of the study, the random forest method produces better accuracy than the logistic regression and artificial neural network (ANN) methods, which is 76.6%, while the results of ANN and logistic regression methods are 73.81% and 72.84% respectively.
Co-Authors Hardian Kokoh Pambudi Putu Giri Artha Kusuma Yulianti, Femi