{"title":"Pemodelan Kriminal di Jawa Timur dengan Metode Geographically Weighted Regression (GWR)","authors":"I. Nurhuda, I. Jaya","doi":"10.15642/MANTIK.2018.4.2.150-158","DOIUrl":"https://doi.org/10.15642/MANTIK.2018.4.2.150-158","url":null,"abstract":"Criminality constitutes all kinds of actions that are economically and psychologically harmful in violation of the law applicable in the state of Indonesia as well as social and religious norms, while the criminal data is the number of cases reported to the police institution. The higher the number of complainants the higher the number of criminals in the region. The greater the risk the community represents the more insecure a region is. This study aims to obtain the best model affecting crime or crime in East Java. The number of crimes in this study is limited to the number of theft cases (whether ordinary theft, theft by force, theft with theft, and the theft of motor vehicles). In this study, we use the Geographically Weighted Regression (GWR) model because this method is quite effective in estimating data that has spatial heterogeneity (uniformity in location / spatial). In essence, the model parameters in GWR can be calculated at the observation location with the dependent variable and one or more independent variables that have been measured at the sites where the location is known, where criminal acts in the research conducted in East Java involves the effects of spatial heterogeneity, with fixed kernel weighting function. The results showed that the variables affecting criminality in East Java Province are population density, economic growth, Gini Ratio, and poverty.","PeriodicalId":32704,"journal":{"name":"Mantik Jurnal Matematika","volume":"29 6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77111564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identifikasi Citra Daging Ayam Berformalin Menggunakan Metode Fitur Tekstur dan K-Nearest Neighbor (K-NN)","authors":"F. Amin","doi":"10.15642/MANTIK.2018.4.1.68-74","DOIUrl":"https://doi.org/10.15642/MANTIK.2018.4.1.68-74","url":null,"abstract":"The research aimed to create a fresh chicken meat identification system to detect differences between formalin and non-formalin chicken meat based on the image of raw chicken meat. Feature extraction method used is the Feature Texture method which is included in the statistical method where the statistical calculation uses a gray degree distribution (histogram) by measuring the level of contrast, granularity, and roughness of an area from the neighboring relationships between pixels in the image then feature extraction, results feature extraction is then classified by K-NN. With the classification using K-NN results obtained high classification accuracy. The K-NN method is a very good method of dealing with the problem of recognizing complex patterns in the form of data training and processing calibration, based on very fast and high accurate literature methods more than other methods. Observation images will be carried out at various distances between the smartphone camera and chicken meat samples.","PeriodicalId":32704,"journal":{"name":"Mantik Jurnal Matematika","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73145555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}