Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms

The accelerated development of e-commerce has been a concern for business people. Business people should be able to gain customer interest in a variety of ways so that their companies can compete with others.  Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values

Wavelet Transformation and Spectral Subtraction Method in Performing Automated Rindik Song Transcription

Rindik is Balinese traditional music consisting of bamboo rods arranged horizontally and played by hitting the rods with a mallet-like tool called "panggul". In this study, the transcription of Rindik's music songs was carried out automatically using the Wavelet transformation method and spectral subtraction. Spectral subtraction method is used with iterative estimation and separation approaches. While the Wavelet transformation method is used by matching the segment Wavelet results with the Wavelet result references in the dataset. The results of the transcription were also synthesized again using the concatenative synthesis method. The data used is the hit of 1 Rindik rod and a combination of 2 Rindik rods that are hit simultaneously, and for testing the system, 4 Rindik songs are used. Each data was recorded 3 times. Several parameters are used for the Wavelet transformation method and spectral subtraction, which are the length of the frame for the Wavelet transformation method and the tolerance interval for frequency difference in spectral subtraction method. The test is done by measuring the accuracy of the transcription from the system within all Rindik song data. As a result, the Wavelet transformation method produces an average accuracy of 83.42% and the spectral subtraction method produces an average accuracy of 78.51% in transcription of Rindik songs

A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our result

Optimization of 2D-CNN Setting for the classification of covid disease using Lung CT Scan

RT-PCR is considered the best diagnostic tool. Previous studies have demonstrated the reliability of CNN in classifying classifications, but CNN requires a lot of training data. Meanwhile, at the CT Scan clinic, patients are limited. Therefore, exploration of 2D-CNN settings is proposed to optimize CNN performance on limited data. We compare: (1) activation models, (2) output shapes per layer, (3) dropout layers, and (4) early stopping values. The test results show that RELU activation is better than Sigmoid. Rescaling (128x128) is better for scala (64x64) and (256x256) which affects the output shape model of each layer. In this learning stage, the use of dropouts in the CNN architecture achieves robust accuracy than the architecture that ignores dropouts. The use of 15 early stoppings is better than other values compared. 20 images of pneumonia and 20 images of covid have been tested using the proposed method and achieved 87.50% accuracy, 80.00% precision, 100% recall, and 99.89% F1-Score. Our method is superior to the the comparison method in terms of accuracy, precision, recall, and f1-score, which achieves 85%, 70%, 100%, and 82.35%, respectively

Sentiment Analysis of COVID-19 Vaccines in Indonesia on Twitter Using Pre-Trained and Self-Training Word Embeddings

Sentiment analysis regarding the COVID-19 vaccine can be obtained from social media because users usually express their opinions through social media. One of the social media that is most often used by Indonesian people to express their opinion is Twitter. The method used in this research is Bidirectional LSTM which will be combined with word embedding. In this study, fastText and GloVe were tested as word embedding. We created 8 test scenarios to inspect performance of the word embeddings, using both pre-trained and self-trained word embedding vectors. Dataset gathered from Twitter was prepared as stemmed dataset and unstemmed dataset. The highest accuracy from GloVe scenario group was generated by model which used self-trained GloVe and trained on unstemmed dataset. The accuracy reached 92.5%. On the other hand, the highest accuracy from fastText scenario group generated by model which used self-trained fastText and trained on stemmed dataset. The accuracy reached 92.3%. In other scenarios that used pre-trained embedding vector, the accuracy was quite lower than scenarios that used self-trained embedding vector, because the pre-trained embedding data was trained using the Wikipedia corpus which contains standard and well-structured language while the dataset used in this study came from Twitter which contains non-standard sentences. Even though the dataset was processed using stemming and slang words dictionary, the pre-trained embedding still can not recognize several words from our dataset

Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features

The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%

SGCF: Inductive Movie Recommendation System with Strongly Connected Neighborhood Sampling

User and item embeddings are key resources for the development of recommender systems. Recent works has exploited connectivity between users and items in graphs to incorporate the preferences of local neighborhoods into embeddings. Information inferred from graph connections is very useful, especially when interaction between user and item is sparse. In this paper, we propose graphSAGE Collaborative Filtering (SGCF), an inductive graph-based recommendation system with local sampling weight. We conducted an experiment to investigate recommendation performance for SGCF by comparing its performance with baseline and several SGCF variants in Movielens dataset, which are commonly used as recommendation system benchmark data. Our experiment shows that weighted SGCF perform 0.5% higher than benchmark in NDCG@5 and NDCG@10, and 0.8% in NDCG@100. Weighted SGCF perform 0.79% higher than benchmark in recall@5, 0.4% increase for recall@10 and 1.85% increase for recall@100. All the improvements are statistically significant with p-value 0.05

Bimodal Keystroke Dynamics-Based Authentication for Mobile Application Using Anagram

Currently, most of the smartphones recognize uses based on static biometrics, such as face and fingerprint. However, those traits were vulnerable against spoofing attack. For overcoming this problem, dynamic biometrics like the keystroke and gaze are introduced since it is more resistant against spoofing attack. This research focuses on keystroke dynamics for strengthening the user recognition system against spoofing attacks. For recognizing a user, the user keystrokes feature used in the login process is compared with keystroke features stored in the keystroke features database. For evaluating the accuracy of the proposed system, words generated based on the Indonesian anagram are used. Furthermore, for conducting the experiment, 34 participants were asked to type a set of words using the smartphone keyboard. Then, each user’s keystroke is recorded. The keystroke dynamic feature consists of latency and digraph which are extracted from the record. According to the experiment result, the error of the proposed method is decreased by 23.075% of EER with FAR and FRR are decreased by 16.381% and 10.41% respectively, compared with Kim’s method. It means that the proposed method is successful increase the biometrics performance by reducing the error rate

Analysis of Livestock Meat Production in Indonesia Using Fuzzy C-Means Clustering

The production of livestock in Indonesia is one type of food that the public can consume. Indonesia is still importing meat for food for its people. This study aims to classify provinces in Indonesia with high livestock meat production and low livestock meat production so that the government can maximize areas with high livestock meat production and can seek to increase livestock meat production in areas with low production. Clustering is needed to identify groups of livestock meat-producing provinces with high and low production. The data is grouped into 2 clusters using FCM with a silhouette index value of 0.95664, the first cluster with the highest meat production total in three provinces (West Java, Central Java, and East Java) and the second cluster with the lowest meat production total 31 provinces. West Java, Central Java, and East Java mostly work as livestock breeders due to the availability of sufficient land

Improving Recognition of SIBI Gesture by Combining Skeleton and Hand Shape Features

SIBI (Sign System for Indonesian Language) is an official sign language system used in school for hearing impairment students in Indonesia. This work uses the skeleton and hand shape features to classify SIBI gestures. In order to improve the performance of the gesture classification system, we tried to fuse the features in several different ways. The accuracy results achieved by the feature fusion methods are, in descending order of accuracy: 88.016%, when using sequence-feature-vector concatenation, 85.448% when using Conneau feature vector concatenation, 83.723% when using feature-vector concatenation, and 49.618% when using simple feature concatenation. The sequence-feature-vector concatenation techniques yield noticeably better results than those achieved using single features (82.849% with skeleton feature only, 55.530% for the hand shape feature only). The experiment results show that the combined features of the whole gesture sequence can better distinguish one gesture from another in SIBI than the combined features of each gesture frame. In addition to finding the best feature combination technique, this study also found the most suitable Recurrent Neural Network (RNN) model for recognizing SIBI. The models tested are 1-layer, 2-layer LSTM, and GRU. The experimental results show that the 2-layer bidirectional LSTM has the best performance