Forecasting is the best way to find out the number of website visitors. However, many researchers cannot determine which method is best used to solve the problem of forecasting website visitors. Several methods have been used in forecasting research. One of the best today is using deep learning methods. This study discusses forecasting website visitors using deep learning in one family, namely the RNN, LSTM, and GRU methods. The comparison made by these three methods can be used to get the best results in the field of forecasting. This study used two types of data: First Time Visits and Unique Visits. The test was carried out with epoch parameters starting from 1 to 500 at layers 1, 3, and 5. The test used first-time visit data and unique visit data. Although tested with different data, the test results obtained that the smallest MSE value is the LSTM method. The value of each MSE is 0.0125 for first-time visit data and 0.0265 for unique visit data. The contribution of this research has succeeded in showing the best performance of the three recurrent network methods with different MSE values.
Social media mining is an emerging technique for analyzing data to extract valuable knowledge related to various domains. However, traditional text matching techniques, such as exact matching, are not always suitable for social media data, which can contain spelling mistakes, abbreviations, and variations in the use of words. Fuzzy matching is a text matching technique that can handle such variations and identify similarities between two texts, even if there are differences in spelling or phrasing. The gap in existing research is the limited use of fuzzy matching in social media mining for tourism recovery analysis. By applying fuzzy matching to social media data related to COVID-19 and tourism recovery, this research seeks to bridge this gap and extract valuable insights related to the impact of the pandemic on tourism recovery. We manually retrieved 19,462 Twitter records and differentiated the data sources using four diver parameters to indicate data related to the impact of COVID-19 on the tourism industry, such as the economy, restrictions, government policies, and vaccination. We conducted text mining analysis on the collected 7,352 words and identified 25 highly recommended words that indicated COVID-19 recovery from a tourism perspective. We separated the four words representing the tourism perspective to perform fuzzy matching as a dataset. We then used the inbound dataset on the fuzzy matching process, with the 7,352-word data collected from the text mining process. The matching process resulted in 18 words representing COVID-19 recovery from a tourism perspective.
Student success is an important component of higher education institutions because it is considered an important criterion for assessing the quality of educational institutions. Student success is assessed based on academic achievement, activeness, satisfaction, willingness to learn, skills, and competence, attendance, educational outcomes, and final performance results. In this study, the focus is on the data object of student arrivals to make forecasts. In supporting forecasting, there are several methods that can be used, starting from artificial intelligence, or artificial intelligence (AI). The method of artificial intelligence used in this study is the backpropagation method. Forecasting results with a small error rate indicate that the method is good for forecasting. It is expected that forecasting carried out with the backpropagation method can achieve a small error rate. The best forecasting results came in semester 3 with an MSE value of 0.0388. The best GPA value is also in semester 3. In conclusion, semester 3 is the best semester both in terms of forecasting and GPA value. Keberhasilan mahasiswa merupakan komponen penting lembaga pendidikan tinggi karena dianggap sebagai kriteria penting untuk menilai kualitas lembaga pendidikan. Keberhasilan siswa dinilai berdasarkan prestasi akademik, keaktifan, kepuasan, kemauan belajar, keterampilan, dan kompetensi, kehadiran, hasil pendidikan, dan hasil kinerja akhir. Pada penelitian ini fokusnya adalah pada objek data kedatangan siswa untuk membuat peramalan. Dalam mendukung peramalan, ada beberapa metode yang bisa digunakan, mulai dari kecerdasan buatan, atau artificial intelligence (AI). Metode kecerdasan buatan yang digunakan dalam penelitian ini adalah metode backpropagation. Hasil peramalan dengan tingkat kesalahan yang kecil menunjukkan bahwa metode tersebut baik untuk peramalan. Diharapkan peramalan yang dilakukan dengan metode backpropagation dapat mencapai tingkat kesalahan yang kecil. Hasil peramalan terbaik diperoleh pada semester 3 dengan nilai MSE sebesar 0,0388. Terlihat bahwa nilai IPK terbaik juga ada di semester 3. Kesimpulannya, semester 3 adalah semester terbaik baik dari segi peramalan maupun nilai IPK.
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