is data that can support in academic activities. validity and realbility of the attendance data become obstacles in academic activities. So, Required attendance system that can prevent the occurrence of cheating attendance, facilitate lecturers to recap student attendance and facilitate administrative staff to recap lecturers's attendance. In this research, student attendance system is made with two platforms, there are web based platform and platform based on android. Restful web service with JSON format is used to build communication between both platforms. In the attendence system android based, QRCode's Reader is used to read student QRCode cards to save attendance data. student dan lecture attendance can be monitored through attendance system web based. The attendance system test is done by testing all of the features and complexity of the algorithm with cyclomatic complexity. The test results show that all features can be used without error and cyclomatic complexity value show that algorithm of the attendance system is easy to understand, easy to test and easy to maintain.
<p class="AbstrakIsi"><span lang="EN-GB">Penerimaan mahasiswa baru adalah agenda yang rutin dijalani oleh sebuah institusi perguruan tinggi,. Jumlah penerimaan mahasiswa baru dapat meningkat atau menurun setiap tahunnya di Universitas Semarang. Oleh karenanya, perlu adanya prediksi jumlah penerimaan mahasiswa baru dengan teknologi berbasis artificial intelligent. Penelitian ini mengusulkan metode untuk memprediski jumlah penerimaan mahasiswa baru di Universitas Semarang menggunakan algoritma Multi Layer Perceptron, dengan dataset time series pendaftaran mahasiswa baru dari tahun 2008 hingga 2017. Uji coba Multi Layer Perceptron dengan arsitektur 5-9-1 menghasilkan Mean Squered Error pada data training sebesar 0.00096 dan Mean Squered Error pada data testing sebesar 0.1. Sehingga, metode yang diusulkan sangat bagus digunakan untuk prediksi penerimaan mahasiswa baru di Universitas Semarang </span></p>
Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility test usually use egg candling. The purpose of observation is to choose eggs fertile (eggs contained embryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered into the incubator for hatching eggs and infertile can be egg consumption. However, there are obstacles in the process of sorting the eggs are less time efficient and inaccuracies of human vision to distinguish between fertile and infertile eggs. To overcome this problem, it can be used Computer Vision technology is having such a principle of human vision. It used to identify an object based on certain characteristics, so that the object can be classified. The aim of this study to comparasion classify image fertile and infertile eggs with SVM (Support Vector Machine) algorithm and K-Nearest Neighbor Algorithm based on input from bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-ocurance Matrix). Eggs image studied are 6 day old eggs. It is expected that the proposed method is an appropriate method for classification image fertile and infertile eggs.
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