Application of Artificial Neural Networks to Predict Monthly Targets for Receiving Section at PT. Indako Trading Coy Using the Backpropagation Method
DOI:
https://doi.org/10.35335/jict.v14i1.13Keywords:
Artificial Neural Network, Backpropagation, PredictionAbstract
PT. Indako Trading Coy is a company engaged in the sale of motorcycles and spare parts with 1000 employees who are given work targets every month. Through the targets given, it can be predicted whether employees can be rewarded or not. Prediction is an important tool in determining everything to be effective and efficient. So far, a lot of forecasting is done intuitively using statistical methods. The choice of the method depends on various influencing aspects, namely aspects of time, data patterns, type of system model being observed, level of forecast accuracy or desired prediction and so on. The forecasting method used is using an artificial neural network (ANN) with the Backpropagation method.
References
Abdul Kadir, Terra .CH Triwahyuni 2019 "Introduction to Information Technology Revised Edition" Publisher CV. Andi. Yogyakarta
Dimas Bhirawa Maharisna, Mochammad Al Musadieq, Heru Susilo. 2016 "Analysis and Design of Transportation Information Systems Using the Vogel's Approximation Method (Case Study at UD.Sumber Jaya Wholesale Malang)" Faculty of Administrative Sciences Universitas Brawijaya Malang
Dwi Purnomo, 2017 "Prototyping Model in Information System Development" Widyagama University Malang, Jl. Borobudur No.35 Malang
Gunawan BS,Linda S, D., 2020 "design of an online-based cattle management information system" Information System BSI University Yogyakarta
Habibi, Mochammad Yusuf, and Edwin Riksakomara. 2017. “Forecasting Consumption Salt Prices Using Artificial Neural Network Feedforward-Backpropagation (Case Study: PT. Garam Mas, Rembang, Central Java).” ITS Engineering Journal.
Hania, Abu Ahmad. 2017. “Knowing Artificial Intelligence, Machine Learning, Neural Networks, and Deep Learning.” Indonesian Journal of Technology.
Hendra Harmawan, Apik Sutikno, Daniel Fonseca Simatupang., 2018 "Information System Applications for Handling Production Problems at Pt.Gajah Tunggal.Tbk" Information Systems, STMIK RAHARJA Jl. General Sudirman N0.40 Modern Cikokol, Tangerang City 15117
Ritonga, Alven Safik, and Suryo Atmojo. 2018. “Development of an Artificial Neural Network Model to Predict the Number of New Students at PTS Surabaya (Case Study of Wijaya Putra University).” Asian Scientific Journal of Information Technology.
Rollos. Jeky KR, Sambul. Sofia AP, Rumawas. Wehelmina. 2018. “The Influence of Workload on Employee Performance at PT. Asuransi Jiwasraya Manado City Branch.”6(2): 19-26.
Surono, Surono (Pandanaran University Semarang), and Moh. Mukeri (Pandanaran University Semarang) Paramitha, Patricia Dhiana (Pandanaran University Semarang) Warso. 2016. “The Influence of Transformational Leadership Style, Organizational Culture and Work Discipline on Work Productivity With Work Ethic As Intervening Variable At Pt. Muliapack Gravurindo Semarang.” Maharaja Agrasen Institute of Management and Technology Journal of IT & Management.
Seminar, Rizky Mulya Sampurno and Kudang Boro. 2017. “Application of Artificial Neural Networks in Early Detection Systems for Food Crisis Management.” Application of Artificial Neural Networks in Early Detection Systems for Food Crisis Management 11(1).
Sudarsono, Aji. 2016. “Neural Networks to Predict Population Growth Rates Using the Backpropagation Method (Case Study in Bengkulu City).” Media Infotama.Sudarto, Singgih, Dewi Mayasari, Lia Listyana. 2002. “Artificial Neural Networks.” Dinamik-Journal of Information Technology 7(2): 17–75.
Zola, Fatmi. 2018. “Neural Networks Use Backpropagation Algorithm to Predict Student Achievement.” Journal of Technology And Open Source 1(1): 58–72.
Jurnal ICT : Information and Communication Technologies is licensed under a 