Klasifikasi Nama Paket Pengadaan Menggunakan Long Short-Term Memory (LSTM) Pada Data Pengadaan

Abstract

Every year the government always holds procurement of goods and services (tenders) which are informed through the Electronic Procurement Service (LPSE) or the General Procurement Plan Information System (SIRUP). The process of selecting the type of procurement is still manual, namely by selecting the package category so that it is possible for mistakes to occur such as the type of service procurement into the category of goods procurement type or vice versa. Therefore, this research proposes to use the Natural Language Processing (NLP) method that can classify these packages based on existing categories. The method used is Long Short-Term Memory (LSTM) by comparing existing classification methods such as naïve bayes, logistic regression, decision tree, XG Boost, Gradient Boost, Random Forest and Support Vector Machine. The results obtained by the LSTM method have a higher accuracy than other methods, with an accuracy of 90.25%. With a parameter configuration of 100 units in the LSTM layer, epoch 10, batch size 64 and validation step 5