Pub Date : 2023-06-09DOI: 10.61398/ijist-das.v1i1.11
Moh. Arie Hasan
Apple is a fruit that is quite popular in Indonesia and is widely consumed by people. This fruit has various types of shapes and colors. Types of apples can be distinguished by their color, size, and shape, but it is still difficult for ordinary people to type apples that are more similar in color and size, such as the examples of Braeburn and Crimson Snow apples. This gave rise to the idea of researching image processing to classify the types of apples. This is to help determine the differences between the two types of apples. The classification process of apples is done by testing the image of an apple based on existing training data. The research method consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is a K-Nearest Neighbor (KNN). Using adequate training data will further improve the classification of types of apples. The final results of this study amounted to 91,67%.
{"title":"Classification of Apple Types Using Principal Component Analysis and K-Nearest Neighbor","authors":"Moh. Arie Hasan","doi":"10.61398/ijist-das.v1i1.11","DOIUrl":"https://doi.org/10.61398/ijist-das.v1i1.11","url":null,"abstract":"Apple is a fruit that is quite popular in Indonesia and is widely consumed by people. This fruit has various types of shapes and colors. Types of apples can be distinguished by their color, size, and shape, but it is still difficult for ordinary people to type apples that are more similar in color and size, such as the examples of Braeburn and Crimson Snow apples. This gave rise to the idea of researching image processing to classify the types of apples. This is to help determine the differences between the two types of apples. The classification process of apples is done by testing the image of an apple based on existing training data. The research method consisted of preprocessing image segmentation with morphological operations and feature extraction into Principal Component Analysis (PCA). The classification algorithm used is a K-Nearest Neighbor (KNN). Using adequate training data will further improve the classification of types of apples. The final results of this study amounted to 91,67%.","PeriodicalId":476292,"journal":{"name":"International Journal of Information System Technology and Data Science","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215532","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}
Pub Date : 2023-06-09DOI: 10.61398/ijist-das.v1i1.7
Ruri Faujana Dinda Pratiwi, Sri Sumarlinda, Faulinda Ely Nastiti
Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru's Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.
{"title":"Comparative Analysis of Restock Needs Bottled Water Using K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and the Naïve Bayes Algorithm","authors":"Ruri Faujana Dinda Pratiwi, Sri Sumarlinda, Faulinda Ely Nastiti","doi":"10.61398/ijist-das.v1i1.7","DOIUrl":"https://doi.org/10.61398/ijist-das.v1i1.7","url":null,"abstract":"Restocking goods is essential for bottled drinking water to ensure smooth production and maintain a stable product supply. This research aims to compare the K-Nearest Neighbor, Support Vector Machine, and the Naïve Bayes algorithm to predict the need to restock bottled water. The data set for training and training data is taken from Adimaru's Agent. The comparative analysis with three algorithms gives the results of the prediction analysis for the accuracy value of K-NN is 88.20%, SVM is 84.51%, and Naïve Bayes is 66.20%. The AUC values of the three result algorithms include Good Classification. The comparison of the K-NN and SVM with T-Test algorithms get obtained the best performance with an alpha value is 0.102. From this accuracy value, the classification method of the K-Nearest Neighbor algorithm has the best predictive model results for restocking needs of bottled water goods.","PeriodicalId":476292,"journal":{"name":"International Journal of Information System Technology and Data Science","volume":"381 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215530","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}
A cataract is an eye disease that causes visual impairment in the eye, the most significant cause of blindness in Indonesia. The rate of blindness in Indonesia caused by cataracts has reached 35% among the elderly 50 years and over. With the development of technology and the shortage of ophthalmologists, an expert system is needed to assist eye health experts by incorporating expert intelligence into the system in the form of fact-based data from the interview results. So that with this expert system, it is hoped that it can help society find cataracts in the eye as a form of early prevention of the chance of suffering from cataracts. The certainty factor method is used in the system to determine the certainty value of the facts that have been entered into the system to obtain a percentage level with a value of 93% so that with the help of this method, system users can find out the type of disease from each symptom
{"title":"Expert System For Detection of Cataracts Disease Using The Certainty Factor Method","authors":"None Vania Jusenda Prasetya, Herliyani Hasanah, Nurmalitasari Nurmalitasari","doi":"10.61398/ijist-das.v1i1.8","DOIUrl":"https://doi.org/10.61398/ijist-das.v1i1.8","url":null,"abstract":"A cataract is an eye disease that causes visual impairment in the eye, the most significant cause of blindness in Indonesia. The rate of blindness in Indonesia caused by cataracts has reached 35% among the elderly 50 years and over. With the development of technology and the shortage of ophthalmologists, an expert system is needed to assist eye health experts by incorporating expert intelligence into the system in the form of fact-based data from the interview results. So that with this expert system, it is hoped that it can help society find cataracts in the eye as a form of early prevention of the chance of suffering from cataracts. The certainty factor method is used in the system to determine the certainty value of the facts that have been entered into the system to obtain a percentage level with a value of 93% so that with the help of this method, system users can find out the type of disease from each symptom","PeriodicalId":476292,"journal":{"name":"International Journal of Information System Technology and Data Science","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135215533","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}
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