Design of Grouping Packaging Palm Cooking Oil Distribution at Traditional Market in Jakarta with Fuzzy Clustering

Referring to the Indonesian National Standard 7709 numbers in 2012 that palm cookingoil should be fortified with vitamin A, so that the distribution process required packaging to protect Vitamin A.packaging palm cooking oil is purposed to protect and to make it hygiene. Packaged cooking oil was distributed from the factory to the traditional market directly. In accordance with Regulation of the Minister of Industry of the Republic of Indonesia Number 87 of 2013 on the application of ISO palm olein is mandatory and Trade Minister Regulation number 80 of 2013 on compulsory packaging cooking oil, we need a mechanism to bulk cooking oil distribution. Simple packaging in the traditional market of producers to effective and efficient consumer. The purpose of this paper is to design a system of distribution of cooking oil from producers to consumers in traditional markets by creating a central cluster automatically determined its distribution central. Design models created using fuzzy clustering method. The results of this study is there are 15 clusters of traditional markets in Jakarta with each of the distribution centers. Keywords: Fuzzy clustering, packaging cooking palm oil, traditional market, distribution cente

Temporal - spatial recognizer for multi-label data

Pattern recognition is an important artificial intelligence task with practical applications in many fields such as medical and species distribution. Such application involves overlapping data points which are demonstrated in the multi- label dataset. Hence, there is a need for a recognition algorithm that can separate the overlapping data points in order to recognize the correct pattern. Existing recognition methods suffer from sensitivity to noise and overlapping points as they could not recognize a pattern when there is a shift in the position of the data points. Furthermore, the methods do not implicate temporal information in the process of recognition, which leads to low quality of data clustering. In this study, an improved pattern recognition method based on Hierarchical Temporal Memory (HTM) is proposed to solve the overlapping in data points of multi- label dataset. The imHTM (Improved HTM) method includes improvement in two of its components; feature extraction and data clustering. The first improvement is realized as TS-Layer Neocognitron algorithm which solves the shift in position problem in feature extraction phase. On the other hand, the data clustering step, has two improvements, TFCM and cFCM (TFCM with limit- Chebyshev distance metric) that allows the overlapped data points which occur in patterns to be separated correctly into the relevant clusters by temporal clustering. Experiments on five datasets were conducted to compare the proposed method (imHTM) against statistical, template and structural pattern recognition methods. The results showed that the percentage of success in recognition accuracy is 99% as compared with the template matching method (Featured-Based Approach, Area-Based Approach), statistical method (Principal Component Analysis, Linear Discriminant Analysis, Support Vector Machines and Neural Network) and structural method (original HTM). The findings indicate that the improved HTM can give an optimum pattern recognition accuracy, especially the ones in multi- label dataset