Prospect Prediction Model Of Indonesian Telematics Medium Large Size Enterprises Using Deep Learning Approach
Analysis of business prospects is an important part of predicting a country's economic conditions. Currently, the prediction of prospects for medium-big sized enterprises (MLE) in the telematics sector has not been widely researched and represented as a factor of economic development in Indonesia. In fact, in accordance with the development of the Industrial Revolution 4.0, the telematics sector business is one of the pillars that is a priority to be developed in Indonesia. The main purpose of this study is to construct the prediction model for prospects in the Indonesian telematics LME sector using a deep learning approach. We used data from the 2016 National Economic Census as many as 2500 preprocessed data. The deep learning approach in this study used a multilayer perceptrón (MLP) architecture, 17 attributes, 3 hidden layers and 5 target classes. The attributes in question include province, business owner education, legal entity status, length of operation, business network, total assets, business lava, number of workers, difficulties, partnerships, marketing innovations, comparison of profit with the previous year, and development plans. The target class of prospects are excellent, good, neutral, bad and very bad. The optimal results were achieved in epoch 50 conditions with a learning reate of 0.2 and an accuracy rate of 98.80%. Based on the prediction model, this business prospect can be used as a reference for the development of MLE in the telematics sector in Indonesia. This prospect model still lacks visualization and attribute analysis that affects the classification of prospects for Indonesian telematics MLE. Research development opportunities can be carried out through the integration of the whitebox model in the deep learning model and complementing a web-based graphical user interface (GUI) to make it easier for stakeholders to develop strategies based on the strength of attributes that affect the prospects for MLE Telematics Indonesia. This is expected to boost the competitiveness of the prospects for Indonesian telematics MLE.
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