{"title":"Prediction of Arrival of Archipelago Tourists and Abroad Based on Regions Using Neural Network Algorithm Based on Genetic Algorithm","authors":"Mohamad Ilyas Abas, Alter Lasarudin","doi":"10.24014/ijaidm.v1i2.5640","DOIUrl":"https://doi.org/10.24014/ijaidm.v1i2.5640","url":null,"abstract":"Tourists are an integral part of the world of tourism. Generally tourists visit to see the diversity of an area. In Gorontalo, several tourist attractions have been visited by domestic and foreign tourists. This is certainly a large amount so that it can help improve economic growth in Gorontalo from the tourism sector. Therefore the need for knowledge of the number of tourists for the coming year. So that, it can provide an analysis of the consideration of the decision to the government to be able to prepare steps in building the economy of the tourism sector. The number of tourists can be made a prediction using the method in data mining namely the Neural Network. Neural Network is a good method for predicting non-linear datasets such as number of tourists. with the Neural Network method it can be done. Not only that, Genetic Algorithm will be used to optimize the parameters of the Neural Network so that it can increase the accuracy value that can be measured with the Root Mean Square Error (RMSE) value. The results of this study indicate that the value of RMSE for domestic tourist data as follows: Gorontalo City: 0.116, Gorontalo Regency: 0.220, Boalemo: 0.073, Pohuwato: 0.142, Bone Bolango: 0.078, North Gorontalo: 0.093. For foreign tourists, Gorontalo City: 0.117, Gorontalo Regency: 0.178, Boalemo: 0.075, Pohuwato: 0.099, Bone Bolango: 0.124, North Gorontalo: 0.155.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129311400","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":"Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification","authors":"J. Jasril, Suwanto Sanjaya","doi":"10.24014/IJAIDM.V1I2.5024","DOIUrl":"https://doi.org/10.24014/IJAIDM.V1I2.5024","url":null,"abstract":"Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%.","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541728","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":"Desease Identification In Plant Leaf Image of Chili (Capsicum Annum (L)) Using Image Processing and Automated Colour Equalization (ACE) Algorithm","authors":"Basiroh Basiroh","doi":"10.24014/ijaidm.v1i2.5644","DOIUrl":"https://doi.org/10.24014/ijaidm.v1i2.5644","url":null,"abstract":"The world of agriculture becomes one of the vital objects and one of the promising business prospects. To obtain optimal agricultural yield, the process of plant care and the way of planting should be really - maximal, because the main key in seeking maximum results in terms of quality and quantity. Harvest failures are the least desirable to farmers and crop failures are the number one scariest specter for cultivating farmers. Today's informatics technology has been developed in an effort to support increased yields in the agricultural sector. This study measured the level of accuracy of results ekstraksi texture and colour feature. This research method using SVM classification ( Support Vector Machine ) seeks image processing through analyzing with Automated Color Equalization (ACE). With this method the accuracy of the extraction results a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690488","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":"Clustering Application for UKT Determination Using Pillar K-Means Clustering Algorithm and Flask Web Framework","authors":"A. Ramdani, H. Firmansyah","doi":"10.24014/ijaidm.v1i2.5126","DOIUrl":"https://doi.org/10.24014/ijaidm.v1i2.5126","url":null,"abstract":"Clustering is one of technique in data mining which has purpose to group data into a cluster. At the end, a cluster will have different data compared with others. This paper discussed about the implementation of clustering technique in determining UKT (Uang Kuliah Tinggal) / Tuition Fee in Indonesia. UKT is a tuition fee where its amount is determined by considering students purchasing power. Most of University in Indonesia often use manual technique in order to classify UKT’s group for each student. Using web-based application, this paper proposed a new approach to automatise UKT’s grouping which leads to give an reasonable recommendation in determining the UKT’s group. Pillar K-Means algorithm had been implemented to conduct data clustering. This algorithm used pillar algorithm to initiate centroid value in K-means algorithm. By deploying students data at Institut Teknologi Sumatera Lampung as case study, the result illustrated that Pillar K-Means and silhouette coefficient value might be adopted in determining UKT’s group","PeriodicalId":385582,"journal":{"name":"Indonesian Journal of Artificial Intelligence and Data Mining","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132837892","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}