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All Journal Informatika Mulawarman TEKNOLOGI PANGAN : Media Informasi dan Komunikasi Ilmiah Teknologi Pertanian Building of Informatics, Technology and Science GUYUB: Journal of Community Engagement JURNAL INFORMATIKA DAN INDUSTRI TRILOGI: Jurnal Ilmu Teknologi, Kesehatan, dan Humaniora COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi JAR'S (Journal of Advanced Research in Informatics) Gotong Royong
Syaiful Syaiful
Universitas Nurul Jadid
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Building of Informatics, Technology and Science

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Klasifikasi Nama Paket Pengadaan Menggunakan Long Short-Term Memory (LSTM) Pada Data Pengadaan Fathorazi Nur Fajri; Syaiful Syaiful
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2635

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
Co-Authors Abdul Mustaqim Ach Zaini Ahmad Ali Afandi Ahmad As’ad Ahmad Saifur Rosi Aldino Marga Pratama Alvin Arief Setiawan Anugrah Teguh Wahyudi Arie Wahyudi Arik Wagiyanto Asik Kurniawan Bambang Bambang Bradhiansyah Tri Suryanto Cahyuni Novia Dimas Richo Eko Saputra Fahim Kerta Sanjaya Faisal Faisal Fathorazi Nur Fajri Kholid Murtadlo Lia Umami M Fadhilurrahman Mochammmad Faid Moh Jasri Nur Hasanah Nuril Diar Anas Rekna Wahyuni Sri Waliyul Hasanah Sulistiyanto, Sulistiyanto Utomo, Deny Wahyu Galih Priambodo