{"title":"Mendiagnosa Penyakit pada Ayam Petelur Menggunakan Metode Certainty Factor","authors":"S. Nasution","doi":"10.47467/stj.v1i1.19","DOIUrl":"https://doi.org/10.47467/stj.v1i1.19","url":null,"abstract":"The nutritional status of chickens has a major effect on productivity and it is closely related to the health of chickens. Several diseases in chickens have an economic impact because they can reduce the quality of good chicken eggs to the detriment of farmers. The main problem which is the toughest challenge in chicken farming is the emergence of disease, so its management needs to be done efficiently and professionally. However, farmers usually only know the symptoms that occur in sick chickens, without knowing what disease they are suffering from. As for veterinarians, it is difficult to find, and it takes a long time to handle chickens because the cage is far from residential areas. The Certainty Factor method can be applied to diagnose laying hens disease based on the symptoms of laying hens. Based on the results of the CF calculation, the diagnosis of Avian Encephalomyelitis (AE) in red laying hens with a confidence value of 0.9654 × 100% or 96.54% and calculated with the value of Avian Influenza / Bird Flu with a confidence value of 0.6 × 100% or 60%. Thus, red chicken A is said to be diagnosed with Avian Encephalomyelitis (AE) with a Certainty Factor confidence value of 96.54%. Handling for AE disease is AE vaccination using MEDIVAC AE-Pox at the age of 10-14 weeks. With the application of giving through a wing web. \u0000 Key words : Laying hens, Certainty Factor","PeriodicalId":17671,"journal":{"name":"Journal of Zhejiang Sci-Tech University","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88737121","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}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
