Proses binerisasi bertujuan untuk memudahkan pengenalan citra dalam tahap computer vision. Binerisasi merupakan cara mengubah bentuk warna citra ke hitam putih atau biner. Metode otsu merupakan metode konversi citra ke bentuk hitam putih. Metode iterative dan adaptive thresholding merupakan gabungan metode dalam mengubah citra ke biner. Tujuan dari penelitian ini, yaitu: memudahkan dalam tahap ekstraksi citra atau pengambilan informasi terpenting dalam citra. Sehingga proses selanjutnya seperti pengenalan citra atau recognition. Hasil dari penelitian ini berupa citra biner gigi kaninus foto panoramik. Dari perbandingan metode, metode iterative dan adaptive thresholding menghasilkan gambar biner yang lebih baik.
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%
Research related to forecasting is growing, starting with simple forecasting based on time or forecasting with certain criteria. Forecasting methods continue to be developed because they produce good models and can predict with high accuracy. The simplest method of forecasting is from the statistical value of the data, namely the average value or what is often called a moving average. Moving average calculates the next time prediction based on the previous time data and moves. The Moving Average (MA) method has several types, including Weight Moving Average (WMA), Autoregressions Moving Average (ARMA), and others. Referring to existing forecasting methods, we try to propose research related to the analysis of the MA, WMA, and Exponential Smoothing (ES) methods in forecasting shoe prices. The purpose of this research is to analyze the three methods in predicting the price of Adidas shoes. The data were taken from the Kaggle dataset and the analysis of the three methods used the MSE (Mean Squared Error) value. The forecasting analysis process uses the statsmodels library in Jupyter Notebooks. The MSE values of the three methods are MA with 2 times 15484.68, MA with 3 times 24829.42, WMA 3 times 14239.74, WMA 4 times 18386.77, and ES 3 times 38349.34, ES 4 times 43102.42. The conclusion of this research is that the lowest MSE value is the best prediction method, namely WMA with 2 times MSE 7268.3.
Poultry has many benefits such as its eggs and meat that cannot be separated from the needs of daily consumption. However, there is a major problem that almost experienced every year for the breders. The disease in poultry is a serious problem that becomes obstacles for the breeders. Based on this problem, the application maker made an expert system application to diagnose the disease in poultry with Bayes theorem method. This application is expected to help the breeders in diagnosing the disease in poultry, therefore the death of poultry can be minimized. This application has been successfully designed and implemented using notepad ++ and php MySQL through several steps of main menu design system, those are; consultation, diseases, and aid. The coding making is done in notepad ++ application. Then making the database is done in php MySQL. The result reveals that this application is made to identify the disease based on the existing symptoms. In testing, 10 breeders stated that this application can help diagnose the disease in poultry.
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