{"title":"Penerapan Metode Vikor untuk Menentukan Pemberian Dana Mekaar Plus pada PNM Kota Binjai","authors":"Enno Loria","doi":"10.47467/stj.v1i1.16","DOIUrl":"https://doi.org/10.47467/stj.v1i1.16","url":null,"abstract":"PT Permodalan Nasional Madani or PNM, is here as a solution to improve welfare through access to capital, assistance and capacity building programs for business actors. Along with business development, in 2016, PNM launched a capital loan service for underprivileged women who are ultra micro business actors through the Fostering a Prosperous Family Economy (PNM Mekaar) program. For this reason, PNM must be more careful and considerate in determining the provision of Mekaar Plus loans. In order not to make mistakes and cause disappointment in the future. So it is necessary to build a system that can be used as a determining and alternative system in determining how to provide Mekaar Plus funds by using a decision support system. This system will be able to make decisions quickly and precisely according to predetermined criteria. So that in the process of giving the blooming funds it can be done more effectively and can reduce the occurrence of errors in the decision-making process. There are many methods used in the decision-making process. One of the methods used in this research is the Vise Kriterijumska Optimizajica I Kompromisno Resenje (VIKOR) method. \u0000 Keywords: Decision Support System, PNM, Mekaar, VIKOR","PeriodicalId":17671,"journal":{"name":"Journal of Zhejiang Sci-Tech University","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74541054","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":"Implementasi Learning Vector Quantization (LVQ) Dalam Mengidentifikasi Gula Aren Asli dengan Gula Aren Campuran","authors":"Melisa Melisa","doi":"10.47467/stj.v1i1.18","DOIUrl":"https://doi.org/10.47467/stj.v1i1.18","url":null,"abstract":"Palm sugar is one type of sugar that is often used by the community as a sweet taste for cooking, making food and drinks. Palm sugar is made from palm sap or juice from coconut trees, by boiling. To distinguish real and mixed palm sugar, by naked eye it is difficult to tell the difference. Moreover, many people do not understand or lack knowledge about the authenticity of palm sugar circulating in the market. So far, people who buy palm sugar only see the authenticity of palm sugar from its sweet taste or color. For this reason, it is necessary to identify using a digital image of the palm sugar, to determine the type of original palm sugar and mixed palm sugar. This is done so that the public gets information and knowledge so that they can be more observant and thorough in choosing and distinguishing palm sugar on the market by knowing the image characteristics of real palm sugar and mixed palm sugar. The Learning Vector Quantization (LVQ) method is a type of competitive-based network where from the output value given by the neurons in the output layer, only the winning neurons are considered. The winning neuron will undergo weight renewal. From the results of the analysis of calculations carried out with test data, the smallest distance data is obtained, namely at weight 1, so that the test image input on the palm sugar image is included in class 1 or original palm sugar. Thus, the palm sugar test image data is in accordance with the expected result data. \u0000 Keywords : Palm Sugar, Digital Image Processing, Learning Vector Quantization","PeriodicalId":17671,"journal":{"name":"Journal of Zhejiang Sci-Tech University","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81513361","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 Kualitas Kesegaran Susu Kambing Melalui Pengolahan Citra Digital Menggunakan Metode Learning Vector Quantization (LVQ)","authors":"Dea Parahana Parahana","doi":"10.47467/stj.v1i1.17","DOIUrl":"https://doi.org/10.47467/stj.v1i1.17","url":null,"abstract":"Goat milk is milk produced by female goats after giving birth. Goat's milk contains many vitamins, minerals, electrolytes, chemical elements, enzymes, proteins, and fatty acids that are good for body health. The number of people's interest in goat's milk, makes goat's milk farmers to produce goat's milk in various ways for the sake of profit. For example, by reducing the level of purity and freshness of goat's milk by mixing other ingredients other than the original pure goat's milk. The identification process using imagery requires a method that can identify fresh and not fresh goat's milk. There are several methods that can be applied in digital image processing, one of which is using the Learning Vector Quantization (LVQ) method. LVQ is a single layer net with each input layer connected directly to the output neurons. Both are associated with a weight consisting of xi is the input, wii is the weight and yi is the output. Analysis of this calculation is used which becomes the initial value. Learning Rate (α) = 0.05, with a reduction of 0.1 * , and maximum epoch (MaxEpoch) = 1. The results of the analysis of the smallest distance on the 1st weight, so that the input image of the goat's milk test belongs to class 2. Thus, the image data of the goat's milk test is identified as mixed goat's milk. \u0000Keywords: Goat's Milk, Digital Image, Learning Vector Quantization","PeriodicalId":17671,"journal":{"name":"Journal of Zhejiang Sci-Tech University","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82199753","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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