Perkembangan jumlah siswa yang terus bertambah dari tahun ke tahun, dituntut ketepatan dan ketelitian dalam memberikan informasi yang tepat dan akurat kepada siswa tanpa adanya pengulangan data yang sama. Pengelolaan informasi akademik pada Sekolah Pertanian Karawang dilakukan dengan konteks manual dan penggunaan kertas sebagai media penyimpanan data dan pengarsipan diantaranya data nilai, data jadwal mata pelajaran dan informasi akademik lainnya sehingga menimbulkan beberapa masalah dalam hal ketepatan waktu rekapitulasi nilai dan pembuatan jadwal yang kurang efesien. Adanya permasalahan tersebut, penulis membuat rancangan skema aplikasi berbasis website dengan metode waterfall, sistem informasi akademik berbasis website dapat mengelola mengenai informasi akademik pada sekolah tersebut baik data nilai dan jadwal mata pelajaran yang dapat di update secara berkala dan disimpan dalam database sehingga dapat mengolah informasi yang efektif dan efesien. Sistem informasi akademik berbasis web sangat berguna dalam memberikan kemudahan baik kepada pengajar ataupun pelajar. Sistem Informasi Akademik Berbasis Web merupakan solusi yang tepat untuk mewujudkan sebuah sistem informasi yang efektif dan efisien dalam membantu perihal penyajian informasi yang akan di salurkan atau di infokan terhadap siswa.
Congestion major cities in Indonesi caused by the proliferation of the use of private vehicles. Some expressing he thinks about busway user through the social media and other web site, This opinion can be used as a sentiment analysis to see if the user busway proposes a review of positive or negative. The results of the analysis sentiment can help in the sight of and evaluate the use of busway, also expected to improve and transjakarta facility from so they tend to have an opinion positive. Based on the results of the analysis, sentiment it is hoped people will switch to using the will of course will reduce congestion. In the study also added the stages preprocesing by using the framework gataframework to complete the process that cannot be done on tools rapidminer. The methodology that was used in this research was it is anticipated that analysis the sentiment of the by the application of an genetic algorithm for an election features with an algorithm naive bayes. From the results of the testing to the case in research it is found that classification algorithm naive bayes based genetic algorithm having the kind of accuracy that good enough 88,55 % and value of auc reached 0,813 % with the level of the diagnosis classifications good. So that in this research classification algorithm naive bayes based genetic algorithm can be recommended as algorithms classifications good enough to analyze the busway user sentimen. Based on analysis is expected to private transport users will switch to using the busway will reduce congestion
The use of e-commerce throughout the world in recent years is very rapid. The continuous increase in sales shows that e-commerce has huge market potential. Store profits are derived from the process of assessing data to identify and classify online shopper intentions. The process of assessing the data uses conventional machine learning algorithms and deep neural networks. Comparison of algorithms in this study using the python programming language by knowing the value of Accuracy, F1-Score, Precision, Recall, and ROC AUC. The test results show that the accuracy of the deep neural network algorithm is 98.48%, the F1 score is 95.06%, precision is 97.36%, recall is 96.81% and AUC is 96.81%. So, based on this research, deep neural network data mining techniques can be an effective algorithm for online shopper intention data sets with cross-validation folds of 10, six hidden layer decoder-encoder variations, relu-sigmoid activation function, adagrad optimizer, and learning rate of 0.01 and no dropout. The value of this deep neural network algorithm is quite dominant compared to conventional machine learning algorithms and related research.
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