Systematic Literature Review pada Analisis Prediktif dengan IoT: Tren Riset, Metode, dan Arsitektur - Fahrur Rozi (Jurnal Sistem Cerdas, 2020)
Nowadays IoT researches on intelligent service systems is becoming a trend. IoT produces a variety of data from sensors or smart phones. Data generated from IoT can be more useful and can be followed up if data analysis is carried out. Predictive analytic with IoT is part of data analysis that aims to predict something solution. This analysis utilization produces innovative applications in various fields with diverse predictive analytic methods or techniques. This study uses Systematic Literature Review (SLR) to understand about research trends, methods and architecture used in predictive analytic with IoT. So the first step is to determine the research question (RQ) and then search is carried out on several literature published in popular journal databases namely IEEE Xplore, Scopus and ACM from 2015 - 2019. As a result of a review of thirty (30) selected articles, there are several research fields which are trends, namely Transportation, Agriculture, Health, Industry, Smart Home, and Environment. The most studied fields are agriculture. Predictive analytic with IoT use varied method according to the conditions of data used. There are five most widely used methods, namely Bayesian Network (BN), Artificial Neural Network (ANN), Recurrent Neural Networks (RNN), Neural Network (NN), and Support Vector Machines (SVM). Some studies also propose architectures that use predictive analytic with IoT.