PERANCANGAN SISTEM WAREHOUSE BERBASIS TEKNOLOGI OCR UNTUK MENINGKATKAN EFEKTIVITAS DAN EFISIENSI
Bilah Samping Artikel
Isi Artikel Utama
Abstrak
The rapid development of information technology is massive worldwide, racing to create solutions as a form of digital supply chain movement, massive use of information technology is also being carried out in the logistics business world which is part of supply chain activities, requiring accurate and fast information is a very important area to guarantee delivery of goods on time and on target. One important aspect of the logistics process is effective warehouse management. One of the main challenges in effective warehouse management is the process of receiving goods in the warehouse which still causes errors such as writing errors, inaccurate recording of the quantity, type and name of goods received and removed from the warehouse which takes quite a long time to process. There has been a lot of research carried out on data capturing activities with the application of OCR but not much has been found that focuses on managing printed and handwritten physical documents in the warehouse area, and preparation of data bases on websites connected to systematic OCR technology, and there is no analysis of technology investment. OCR regarding cost efficiency. Therefore, to help overcome this problem, this research carried out the design and implementation of a warehouse system based on OCR (Optical Character Recognition) technology with the name WareOCR and analyzed it from an investment perspective using the ROI method. From the research results obtained using the OCR creation model using the CNN method, the accuracy of the WareOCR system on test data is 93%, Return on Investment is calculated at 60.38%, in the NPV calculation in year 3 the figure is IDR 112,911,033 where NPV > 0 for the project implementing WareOCR it is acceptable that this investment provides added value of 493% of the total project cost. This research also has research limitations in the form of invoice formats which need to be standardized so they can be read with the WareOCR tool and the advantages of this research are that they can shorten the working time of the data entry warehouse in inputting information into the database.
Rincian Artikel
Artikel ini berlisensiCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All publications by Jurnal Ilmiah Teknik Indsutri (Jurnal Keilmuan Teknik dan Manajemen Industri ) [p-ISSN: 2337-5841, e-ISSN: 2355-6528] is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Referensi
H. Ben-Shabat, P. Nilforoushan, M. Moriarty, and C. Yuen, “The 2015 Global Retail E-Commerce Index: Global Retail E-Commerce Keeps on Clicking,” A.T.Kearney, Inc., 2015.
I.N. Pujawan, and E. Mahendrawathi, Supply Chain Management (Edisi 3), Yogyakarta: Andi, 2017.
H.W.W. Pitoy, A.B.H. Jan, and J.S.B. Sumarauw, “Analisis Mananjemen Pergudangan pada Gudang Paris Superstore Kotamobagu,” Jurnal EMBA, 8(3), 252-260, 2020.
S.A. Berg, S.-Y. Seo, and R.H.Y. So, “Application of optical character recognition with Tesseract in logistics management,” International Journal of Information and Management Sciences, 30(3), 285-304, 2019. https://doi.org/10.1504/IJIMS.2019.100986.
A. Kurniawan, and M. Rahardjo, “Analisa Pengaruh Kebutuhan Sistem Informasi RFID (Radio Frequency Identification) pada Perusahaan Retail untuk Meningkatkan Produktivitas dan Mengurangi Recurring Cost,” Jurnal Manajemen Bisnis Dan Kewirausahaan, 6(4), 415–420, 2022. https://doi.org/10.24912/jmbk.v6i4.19341.
D.C.G.M. Simanjuntak, and A. Bakhtiar, “Analisis Perancangan Sistem Barcode dalam Menangani Aliran Raw Material Kayu pada Departemen Lumberyard di PT Ebako Nusantara Semarang,” Industrial Engineering Online Journal, 4(4), 2015. Retrieved from https://ejournal3.undip.ac.id/index.php/ieoj/article/view/9866.
R. Zhang, Z. Bahrami, T. Wang, and Z. Liu, “ An Adaptive Deep Learning Framework for Shipping Container Code Localization and Recognition,” IEEE Transactions on Instrumentation and Measurement, 70, 1-13, 2021. https://doi.org/10.1109/TIM.2020.3016108.
J.W.G. Putra, Pengenalan Konsep Pembelajaran Mesin dan Deep Learning, Edisi 1.4 (17 Agustus 2020), 2020.
A. Firdaus, M.S. Kurnia, T. Shafera, and W.I. Firdaus, “Implementasi Optical Character Recognition (OCR) pada Masa Pandemi Covid-19,” Jurnal Jupiter, vol. 13, no. 2, 188–194, 2021.
S. Cakic, A. Ismailisufi, T. Popovic, S. Krco, N. Gligoric, S. Kupresanin, and V. Maras, “Digital Transformation and Transparency in Wine Supply Chain Using OCR and DLT,” International Conference on Information Technology, 2021.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, London, MIT Press. 2016.
D. Dablain, K.N. Jacobson, C. Bellinger, M. Roberts, and N.V. Chawla, “Understanding CNN Fragility when Learning with Imbalanced Data,” Machine Learning, 2023.
T. Li, B. Huang, C. Li, and M. Huang, “Application of Convolution Neural Network Object Detection Algorithm in Logistics Warehouse,” the Journal of Engineering, 23, 9053-9058, 2019. https://doi.org/10.1049/joe.2018.9180.
S. Barrios, D. Buldain, M.P. Comech, I. Gilbert, and I. Orue, “Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress,” Energies, 12(13), 2485, 2019. https://doi.org/10.3390/en12132485.