Egg quality is determined by the quality of the inside and outside of the egg. To find out the quality of eggs canbe by looking at the outside in the form of skin condition, shape, size, and egg weight. In this study a system isdesigned to function to determine the quality of egg quality based on color and texture features. Broadlyspeaking, the system built consists of 4 main processes. This process begins with pre-processing which aims toimprove image quality and also to change the image size. The next process is segmentation to get the egg object.The next process is feature extraction which aims to get the characteristics of each egg object. The last processis a classification that aims to determine the class of egg images entered by the user, the method used is knearest neighbor. The data used in this study were 147 egg images consisting of 85 eggs of data testing and 62eggs of training data. The highest accuracy obtained from this study is 82.3% with a value of 8
In today's rapidly growing digital era, the role of computing in artificial intelligence is needed to be able to help business people. Both in the fields of economy, health, and education. The use of machine learning will help related parties in viewing, analyzing, and making decisions. With machine learning, all problems related to data can be solved quickly and precisely. The problem is that the thesis document will increase every year, it will become a useless document if the data processing is not carried out. Past thesis data can be used for analysis and decision-making in the next thesis era. Python is one of the most popular programming languages used for machine learning. One reason is that there are many python-based libraries. Keras is a python-based machine learning library. TensorFlow can be used when dealing with large amounts of data processing, including thesis abstract data. Thus, this study classified 140 thesis abstract documents using hard-TensorFlow with the aim that based on the abstract content it would be classified into 6 classes, namely Android Applications, Data Mining, RPL, SPK, Digital Image Processing, and Expert Systems. The results of the classification with training data as many as 82 documents with model setting batch size = 12 and epoch = 2 with an Accuracy value of 89.04%. While the test loss test data has a higher value than the Accuracy value obtained by 66.66%. By utilizing maximizing TensorFlow performance by adding a parameter that Scikit Learn has, namely Optuna. The test data was optimized with a trial value of 500, the Accuracy increased to 76.19%
An expert system is a type of artificial intelligence application that is used to tackle complex problems that require specialized knowledge. Expert systems can be used in a variety of disciplines, including healthcare, finance, and manufacturing. The aim of this study was to apply the Nave Bayes approach in a website-based ear illness diagnostic system and to determine its accuracy in an expert system for diagnosing ear disease. The naive Bayes approach is implemented in this research because it may assume that each symptom is independent of one another and can thus be used to assess the probability of a condition based on the symptoms that emerge. The results of this study show that the expert system for diagnosing ear disease using the Nave Bayes method is built on a website using the PHP programming language and the database maintained by MySQL, and this application has been tested 10 times, with 9 test data appropriate and 1 test data not appropriate. As a result of testing this application, the accuracy value obtained is 90%.
Penelitian ini bertujuan untuk membuat sistem identifikasi kesegaran ikan yang didasarkan pada citra insang ikan. Dengan adanya sistem ini diharapkan masyaratak akan semakin mudah dalam menentukan ikan segar. Metode yang digunakan pada penelitian ini adalah CNN. Data yang digunakan pada penelitian ini berjumlah 150 data citra insang ikan yang dikategorikan kedalam tigas kelas yaitu kelas ikan segar, tidak segar, dan busuk. Penelitian iini memberikan hasil akurasi 100% untuk proses training dan 97,7% untuk proses testing.
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