Mohammad Mi'radj Isnaini
The role of Industrial Area (Kawasan Industri / KI) in the growth of Indonesian economy is very significant. KI contributes to 40% of the total non-oil-and-gas export value and is also able to attract 60% of the total investment in the industry sector. However, there are still obstacles to realize the full economic potential of the KIs themselves. Therefore, a huge effort is still needed to find the solutions for the many problems of Indonesian KI. This research will try to design a model that could accommodate multiple objective functions, assess the competence of individual companies within a KI, and combine the multiple collaboration paths of industries in an effort to achieve an optimal system within a given KI. As such, this study aims to provide insights regarding the current state of Indonesian industrial sectors and their extent of fulfillment in terms of Industry 4.0 framework. Both unsupervised and supervised learning methods are applied to the case Principal Component Analysis (PCA) and K-Means Clustering are used to cluster the industrial sectors based on the similarity of their principal components. Further, the cluster assignments are used as an additional feature for the data set as it is fitted into a supervised learning model, Random Forest. Based on the results of the models, key features are analyzed, and important interactions are identified. This exploratory study has given some preliminary insights regarding IR 4.0 readiness in Indonesian industrial sectors; further diagnostic study is required to gain conclusive insight on the matter.
Penerapan Karya Tulis
There are still obstacles to realize the full economic potential of the KIs themselves.