Predicting Airline Passenger Satisfaction with Classification Algorithms

Airline businesses around the world have been destroyed by Covid-19 as most international air travel has been banned. Almost all airlines around the world suffer losses, due to being prohibited from carrying out aviation transportation activities which are their biggest source of income. In fact, several airlines such as Thai Airways have filed for bankruptcy. Nonetheless, after the storm ends, demand for air travel is expected to spike as people return for holidays abroad. The research is aimed at analyzing the competition in the aviation industry and what factors are the keys to its success. This study uses several classification models such as KNN, Logistic Regression, Gaussian NB, Decision Trees and Random Forest which will later be compared. The results of this study get the Random Forest Algorithm using a threshold of 0.7 to get an accuracy of 99% and an important factor in getting customer satisfaction is the Inflight Wi-Fi Service

Sinyal Elektroensefalografi Untuk Deteksi Emosi Saat Mendengar Stimulus Pembacaan Al-Quran Menggunakan Wavelet Transform

Mendengarkan suara membaca Al-Qur'an (Murottal) diketahui sering digunakan untuk membuat suasana terasa santai. Oleh karena itu, dalam penelitian ini, kami menyelidiki sejauh mana stimulasi suara murottal mempengaruhi penampilan gelombang alfa yang terlihat pada gelombang otak menggunakan detektor sinyal Electoencephalography (EEG). Menggunakan Transformasi Wavelet. Gelombang otak yang terdeteksi oleh sinyal EEG kemudian dianalisis untuk setiap fase gelombang pada frekuensi alfa (8-13 Hz) untuk melihat keadaan rileks. Kami merekam data gelombang EEG dalam 4 kondisi, yaitu kondisi tenang, kondisi tegang, dan keduanya dengan stimulus suara murottal. Setiap kondisi dilakukan masing-masing selama 2 menit. Suara murottal diambil secara acak untuk mendapatkan variasi data. Hasil klasifikasi menggunakan Recurrent Neural Network (RNN) menunjukkan bahwa t raining menggunakan n data ormal dengan tombak s mencapai akurasi 52% ~ 59%, Normal dengan m urottal n ormal menghasilkan nilai akurasi 55% ~ 56%, normal dengan tombak m urottal s mendapatkan nilai akurasi terkecil 35% ~ 46%, s Pike dengan m urrottal n ormal mencapai akurasi 57% ~ 67%, pike S dengan pike M urottal smenghasilkan akurasi 51% ~ 60%, M urottal normal dengan pike M urottal S mencapai nilai akurasi tertinggi 78%. Hal ini menunjukkan bahwa terdapat pengaruh yang signifikan dalam mendengarkan Murottal Al-Quran

Development of a chatbot for the online application telegram chat with an approach to the emotion classification text using the IndoBERT-lite method

The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, textbased communication usually results in misunderstandings resulting from the absence of feeling intonation and emotions in the text. This study aims to create a chatbot that can detect emotions text to be entered into online chat applications. This study used a pre-trained model specifically trained from a collection of Indonesian-language datasets, namely IndoBERTlite. The dataset used to train the model is a collection of Indonesian tweets totaling 4,403 which have been labeled with 5 classes of emotions, namely love, happy, anger, sadness, and fear. The hyperparameters used in this study to train the model were 5 epochs, batch size 16, learning rate 0.000003, and adam optimizer. Based on the test results with the parameters already mentioned, the accuracy, F1 score, recall, and precision values were obtained in the training set of 89%, 89%, 89%, and 90%, while the validation set obtained 70%, 71%, 70%, and 72%

Revolutionizing digit image recognition: pushing the limits with simple CNN and challenging image augmentation techniques on MNIST

This study aims to apply Convolutional Neural Networks (CNN) and image augmentation techniques in digit recognition using the MNIST dataset. We built a CNN model and experimented with various image augmentation techniques to improve digit recognition accuracy. The results showed that the use of CNN with image augmentation techniques was effective in improving digit recognition performance. In the data collection stage, we used the MNIST dataset consisting of images of handwritten digits as training and testing data. After building the CNN model, we apply image augmentation techniques such as rotation, shift, and flipping to the training data to enrich the data variety and prevent overfitting. The evaluation results show that the CNN model that has been trained with image augmentation techniques produces significant accuracy, with a maximum accuracy of 99.81%. We also performed an ensemble of several CNN models and found that this approach increased the digit recognition accuracy to 99.79%. This research has the potential for further development. Recommendations for further research include exploring more specific and complex image augmentation techniques, as well as using more challenging datasets. In addition, future research may consider improvements to the CNN architecture used or combining it with other methods such as recurrent neural networks (RNN)

A benchmark of modeling for sentiment analysis of the Indonesian Presidential Election in 2019

Researching with a machine learning method approach, the truth is to try to solve a case by using various algorithmic approaches to obtain the most suitable model for a case. In this research, we want to know which process of modelling that has the best accuracy value for classifying emotions in the text. The algorithm used is using the LSTM algorithm, while the benchmarking that we tested is the Random Forest and Naive Bayes algorithm. This research takes public opinion about the 2019 Indonesian Presidential Election by classifying it into four types of emotions: happy, sad, angry, and afraid. The data we use contains more than 1200 Indonesian tweets. In this experiment, we achieved an accuracy of 68.25% using the Random Forest model, whereas, with the Multinomial Naïve Bayes model, the accuracy was 66%

Q-Madaline: Madaline Based On Qubit

This research focuses on developing the MADALINE algorithm using quantum computing. Quantum computing uses binary numbers 0 or 1 or a combination of 0 and 1. The main problem in this research is how to find other alternatives to the MADALINE algorithm to solve pattern recognition problems with a quantum computing approach. The data used in this study is heart failure data to predict whether a patient is at risk of death. The data source comes from KAGGLE, consisting of 299 data with 12 symptoms and one target, alive or dead. The result of this study is an alternative to the MADALINE algorithm using quantum computing. The accuracy of the test results with MADALINE with a learning rate of 0.1 = 100% with 2 epochs. The accuracy of the test results using a quantum approach with a learning rate of 0.1 is 85.71%. The results of this study can be an alternative to the MADALINE algorithm with a quantum computing approach, although it has not shown better accuracy than the classical MADALINE algorithm. Further research is needed to produce better accuracy with larger dat

Analysis of emotion recognition model using electroencephalogram (EEG) signals based on stimuli text

Recognizing emotions through the brain wave approach with facial or sound expression is widely used, but few use text stimuli. Therefore, this study aims to analyze the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely Support Vector Machine and decision tree as benchmarks. The raw data used comes from the results of scrapping Twitter data. The dataset of emotional annotation was carried out manually based on four classifications, specifically: happiness, sadness, fear, and anger. The annotated dataset was tested using an Electroencephalogram (EEG) device attached to the participant's head to determine the brain waves appearing after reading the text. The results showed that the random forest model has the highest accuracy level with a rate of 98% which is slightly different from the decision tree with 88%. Meanwhile, in SVM the accuracy results are less good with a rate of 32%. Furthermore, the match level of angry emotions from the three models above during manual annotation and using the EEG device showed a high number with an average value above 90%, because reading with angry expressions is easier to perform. For this reason, this study aims to test the emotion recognition experiment by stimulating sentiment-tones using EEG. The process of classifying emotions uses a random forest model approach which is compared with two models, namely SVM and decision tree as benchmarks. The dataset used comes from the results of scrapping Twitter data

Emotion recognition and brain mapping for sentiment analysis: a review

The rapid growth of the Internet has caused the increase in the amount of textual information available, such as in blogs, discussion forums and review sites on the web, where the texts surely have the emotion content. Emotion is one appearence of people behaviour and it is an important performance in human computer interaction (HCI). Human express the emotion in the form of facial expression, speech and writing text. Recently, researchers in computational linguistic (CL) areas are interested in the attention of emotion for Sentiment Analysis (SA). SA naturally observes the emotion conveyed by a text, and at the same time, distinguishing positive and negative valence. The wide areas of CL research, actually considerable for investigating the emotion dimension detection and searching the approaches and techniques in the term of emotion recognition (ER). There are two significant trends of research in the area, the emotion recognition based on state affective computing and the real time using brain signal machines. The two areas have the same aim for getting the improvement result in sentiment analysis with the mapping of emotion recognition provided. The exclusive work on emotion detection is comparatively rare and lacks empirical evaluation research. This paper provides the overview of past and recent research on emotion detection as well as some approaches and techniques used and shows the linked between both SA and ER

Indonesian affective word resources construction in valence and arousal dimension for sentiment analysis

Research in the field of text analysis will always be related to words, either the selection of words to be used or the position of the words in a sentence. Furthermore, a hypothesis that each language difference can cause different meanings, makes some researchers interested in doing research classifying words based on emotion or affective words. Research focuses on affective states as a continuous numerical value to the dimensions of valence and arousal. Sentiment analysis that is usually done with positive and negative category approaches, nowadays, the dimensional approach can provide more analysis of grained sentiments. On the other hand, the affective words dataset with valence and arousal rating are still very rare, especially for the Indonesian language. Therefore, this research does an affective lexicon dataset called Indonesian Valence and Arousal Words (IVAW) containing 1024 words by Self-Assessment Manikin (SAM) surveys. Furthermore, for the next study, we will also crawls status in twitter based on selected words from IVAW to get Indonesian Valence and Arousal Text (IVAT). To predict VA rating for obtaining the advance of annotation quality, experiment will be compared by brain signal using EEG tool