Tempe is one of the traditional foods in Indonesia which has nutritional content and benefits that are very much favored by all Indonesian people. To determine the maturity of tempe, it is generally done by fermenting it into tempeh using a certain temperature and usually tempe entrepreneurs are done traditionally. But in this way, tempe producers do not know what temperature and humidity are right for tempeh maturity. In this study, researchers used the MATLAB R2018a application with a total data set of 137 raw data, 137 ripe data and 136 rotten data, totaling 410 tempe image data. The purpose of this research is to produce a system that can detect the ripeness of tempe using the KNN (K-Nearest Neighbor) method which is equipped with GLCM texture feature extraction, with extraction of 8 color features, using the PCA (Principal Component Analysis) selection feature. And compare the results with the same method, namely KNN (K-Nearest Neighbor) without using the PCA (Principal Component Analysis) selection feature with the required running time between the two. KNN with PCA selection feature gets an average accuracy value of 80.63% and takes 1.06 seconds. Compared with the same method, namely KNN without using the selection feature, it gets an average accuracy value of 81.67% with a time of 1.18 seconds.
Coffee is one of Indonesia's foreign exchange earners and plays an important role in the development of the plantation industry. In previous studies, coffee bean quality research has been carried out using the ANN method using color features. RGB and GLCM. However, the results carried out in the study only had an accuracy value of up to 47%. Therefore, this study aims to improve the performance of coffee bean quality classification using four machine learning methods and 7 color features. From the results obtained, it shows that MultilayerPerceptron is better starting with RGB color with an accuracy of 38% split ratio 90:10. HSV has an accuracy of 57% split ratio 90:10. CMYK has an accuracy of 63% split ratio 90:10. LAB has a 58% curation split ratio of 90:10. The YUV type has an accuracy of 58% split ratio 90:10. Furthermore, the HSI color type has an accuracy of 42% split ratio 90:10. The HCL color type has an accuracy of 65% split ratio 90:10 and LCH has an accuracy of 78% split ratio 90:10. In testing, it can be concluded that the MultilayerPerceptron method is better than other methods for the coffee bean classification process.
The award is given by the company to the best employees always to provide the best performance for companies that carry out duties and obligations in the company. How to give rewards to employees not only for how long they have been working at the company but about the results achieved. The gift can be in the form of salary or inventory from the company. So employees have great loyalty to the company. Decision making to determine the company can do the best employee by assessing the performance that has been done by employees at a specific time. Performance evaluation of Hon Chuan Company is influenced by several criteria, namely discipline, innovation, and responsibility. The support system for the best employee selection method uses the Analytical Hierarchy Process (AHP) method. The decision-making system is carried out to assess choices based on predetermined criteria. AHP method is applied to choose the best prospective employees. The calculation of the AHP method gives the results of the order of importance of criteria, namely discipline level (0.5584), responsibility (0.3196), and innovation (0.1220) and the best employee selection results obtained by Hendra (0.189), Vienna (0.189), Yin (0.145), Paul (0.109), Kandi (0.108), Kusworo (0.101), Bayu (0.088), and Ade Muhidin (0.072).
<span lang="IN">In the production process, quality checking is very important, one of which is on the wire. In the process of making brass-coated steel tire straps sometimes produce quality goods not in accordance with the desired standard values. Checks that are carried out manually have low efficiency and quite high errors occur. So it is necessary to check by measuring the wavelength on the brass plated steel cord automatically. In this study, used 3 automatic measurement methods using 2 evaluations, namely RMSE and Cosine Similarity. The results showed the best measurement using RMSE with method 2. Whereas the worst method uses RMSE with method 1. The smallest RMSE value is 0.0098 and the largest RMSE is 0.0966. The lowest Cosine Similarity value is 0.1253, while the highest Cosine Similarity value is 0.2079.</span>
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