Semakin meningkatnya perkembangan teknologi semakin banyak ragam buku yang beredar di internet. Seperti adanya sistem rekomendasi pada situs buku online yang menyediakan buku secara relevan dan sesuai kebutuhan dengan preferensi seseorang. Salah satu alternatifnya GoodReads yaitu situs jaringan sosial yang khusus pada katalogisasi buku dan pengguna dapat saling berbagi rekomendasi buku bacaan dengan memberikan rating, review maupun komentar. Sebagai situs rekomendasi buku yang besar, maka memiliki banyak data yang dapat diolah dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Dengan menggunakan model yang tepat, kita dapat memberikan rekomendasi yang lebih akurat. Untuk itu pada penelitian ini akan menganalisis data yang didapatkan dari www.kaggle.com yaitu dataset goodreads-books. Dalam penelitian ini, mengusulkan model klasifikasi data mining untuk mendapatkan model terbaik dalam merekomendasikan buku pada GoodReads. Algoritma yang digunakan yaitu Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest dan Support Vector Classifier, kemudian untuk evaluasi model menggunakan pengujian nilai accuracy, precision, recall, f1-score, confusion matrix, AUC dan Mean Error Absolute. Hasil pengujian beberapa algoritma klasifikasi diketahui bahwa Decision Tree memiliki akurasi tertinggi diantara metode yang dikomparasikan sebesar 99,95%, precision sebesar 100%, recall sebesar 96%, f1-score sebesar 98% dengan MAE sebesar 0.05 dan AUC sebesar 99,96%. Hal ini menjadi bukti bahwa algoritma Decision Tree dapat digunakan sebagai rekomendasi buku berdasarkan kategori buku pada GoodReads.
Personality is defined as the mix of features and qualities that make up an individual's particular character, including thoughts, feelings, and behaviors. With the rapid development of technology, personality computing is becoming a popular research field by providing users with personalization. Many researchers have used social media data to automatically predict personality. This research uses a public dataset from Kaggle, namely the Myers-Briggs Personality Type Dataset. The purpose of this study is to predict the accuracy and F1-score values so that the performance for predicting and classifying Myers–Briggs Type Indicator (MBTI) personality can work optimally by using attributes from the MBTI dataset, namely posts and types. Predictive accuracy analysis was carried out using the Long Short-Term Memory (LSTM) algorithm with random oversampling technique with the Imblearn library for MBTI personality type prediction and comparing the performance of the method proposed in this study with other popular machine learning algorithms. Experiments show that the LSTM model using the RMSprop optimizer and learning speed of 10-3 provides higher performance in terms of accuracy while for the F1-score the LSTM model using the RMSprop Optimizer and learning speed of 10-2 gives a higher value than the proposed machine learning algorithm so that the model MBTI dataset using LSTM with random oversampling can help in identifying the MBTI personality type.
Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes
In our current study, we are doing a comparison of several algorithms that we have tested, namely in searching for the accuracy level of learning performance in students, the problem of this research is how to get the results of excellent generalization abilities so that a higher accuracy value is obtained. Our goal is to get the best-performing accuracy level results and then to identify features that can affect student learning performance. From the results of the algorithm that we have tested, four of them are Naïve Bayes, Support Vectore Machine, Neural Network and KNN contained in machine learning. The results of the four algorithms for the Naïve Bayes algorithm have an accuracy value of 96.30%, the Support Vectore Machine algorithm has an accuracy of 98.70%, and the Naural Network algorithm has an accuracy of 99.50% and the last one with the KNN algorithm produces an accuracy of 94.80%. it can be concluded that using the Neural Network algorithm is an algorithm with the best performance than using other algorithms in evaluating student learning performance, besides that the Neural Network can be used as an excellent alternative to be used as predictions, especially in the field of education.
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