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.
Congestion major cities in Indonesia caused by the proliferation of the use of private vehicles. Some people express their opinions and its opinion regarding public transport users through social media sites and other websites. This opinion can be used as a sentiment analysis material to find out whether the public transport service is positive or negative. The results of the sentiment analysis can help in the assessment and evaluation of the use of public transportation, it is also expected to improve services and facilities from public transportation so that the public tends to have a positive opinion. Based on the results of the sentiment analysis, it is expected that the community will switch to using public transportation which will certainly reduce congestion. In this study also added preprocessing stages by using the GataFramework framework to complete processes that cannot be done on RapidMiner tools. The method used in this study is sentiment analysis with the method of applying genetic algorithms for feature selection with comparative classification algorithms. Performed by testing the composition of various data. From the results of testing for the case in this study, it was found that the Support Vector Machine classification algorithm based on Genetic Algorithms had a fairly good average accuracy of 76.11% and AUC value of 0.778% with the Fair Classification diagnosis level compared to the three methods such as Naive Bayes, Support Vector Machine and Naive Bayes based on Genetic Algorithms. So that in this study Support Vector Machine classification algorithm based on Genetic Algorithm can be recommended as an algorithm classification good enough to analyze land transportation public sentiment. Based on the analysis it is expected that the public sentiment will switch to using public transport which would reduce congestion.
Analisis sentimen adalah proses untuk menentukan konten dataset berbasis teks yang positif atau negatif. Saat ini, opini publik menjadi sumber penting dalam keputusan seseorang dalam menemukan solusi. Algoritma klasifikasi seperti Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) diusulkan oleh banyak peneliti untuk digunakan dalam analisis sentimen untuk pendapat ulasan. Namun, klasifikasi sentimen teks memiliki masalah pada banyak atribut yang digunakan dalam dataset. Fitur pemilihan dapat digunakan sebagai proses optimasi untuk mengurangi set fitur asli ke subset yang relatif kecil dari fitur yang secara signifikan meningkatkan akurasi klasifikasi untuk cepat dan efektif. Masalah dalam penelitian ini adalah pemilihan pemilihan fitur untuk meningkatkan nilai akurasi Support Vector Machine (SVM) dan K-Nearest Neighbor (k-NN) dan membandingkan akurasi tertinggi untuk analisis sentimen tweet / komentar yang menggunakan tagar # 2019GantiPresiden. Algoritma perbandingan, SVM menghasilkan akurasi 88,00% dan AUC 0,964, kemudian dibandingkan dengan SVM berdasarkan PSO dengan akurasi 92,75% dan AUC 0,973. Data hasil pengujian untuk akurasi algoritma k-NN adalah 88,50% dan AUC 0,948, kemudian dibandingkan untuk akurasi dengan PSO berbasis k-NN sebesar 75,25% dan AUC 0,768. Hasil pengujian algoritma PSO dapat meningkatkan akurasi SVM, tetapi tidak mampu meningkatkan akurasi algoritma k-NN. Algoritma SVM berbasis PSO terbukti memberikan solusi untuk masalah klasifikasi tweets/ komentar yang menggunakan tagar # 2019GantiPresiden di Twitter agar lebih akurat dan optimal.
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