Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
Boarding houses are an inseparable part of students, mainly migrant students. In addition to resting, boarding can also be a place for study and discussion. Therefore, it needs reasonable consideration before choosing a boarding house. Choosing the right boarding house can increase student productivity. Currently looking for boarding houses you don't need to go around the campus, but you can use other boarding search applications. The application only displays data from the database directly without processing it first. When we open the application, the first display is boarding A. Then someone else opens the application the first to appear is boarding A again. Why not Boarding B? Even though the willingness of each user is different. Therefore, we need a recommendation algorithm to overcome these problems. In this research, the author uses the Collaborative Filtering algorithm. The dataset used comes from the Koseeker dataset from March 2020 - June 2020, totaling 1,897. The result of the calculation of Mean Absolute Error (MAE) is 0.4780. Applications made have an accuracy rate of 88.05%.
This study will classify sentiment and analysis of general topics against President Joko Widodo taken through social media Twitter. The influence and benefits of sentiment are so significant that the study of analytical sentiment is increasing. This research has several stages, including data collection, data cleaning, data extraction, data classification using the Support Vector Machine method, and topic analysis using the Latent Dirichlet Allocation method. This study's results took data on Twitter taken from September 23, 2018, to December 15, 2020, as many as 1200 pieces of data in the form of 600 negative data and 600 positive data. In conclusion, the topic analysis system and sentiment classification performed the classification results using the Support Vector Machine method in this research, which obtained 86.39% accuracy by 85.75%, recall by 85.86%, and F1 score 85.79%—as for topic analysis using Latent Dirichlet Allocation got a perplexity value of 7.1049 on positive sentiment and 7.3165 on negative sentiment.
Twitter is social media that can be used to exchange ideas and give opinions. Twitter users can write their opinions on the issue of President Joko Widodo's government. Tweet data or public opinion can be done sentiment analysis method to analyze public opinion. The Naïve Bayes method is used to classify Twitter data to determine sentiment and grouping into positive class and negative class. Furthermore, topic modeling is carried out with the Latent Dirichlet Allocation (LDA) method to determine the topic of discussion in each sentiment group. In the classification process, the value of accuracy depends on the preprocessing stage and relies on the data amount. In train data 80% and test data 20% obtained accuracy 84.58%, recall 85%, precision 85% and F1-Score 85%. At the LDA stage, performance testing with perplexity resulted in a perplexity value of 7.1049 based on the number of iterations of 30 for the positive sentiment group. Furthermore, the perplexity value is 7.3165, with the number of iterations is 60 for the negative sentiment group.
Direction of arrival (DOA) estimation methods of electromagnetic wave sources are necessary for very critical and significant applications, which is considered as a significant branch in array signal processing. There is a need to monitor spectrum and broadcast sources specifically in the military applications, which is very important for monitoring the direction of any threat. DOA is a set of calculations that employ for estimating the direction and number of incoming waves on the antenna elements at a specific range of frequency, which allows target detection and tracking. This paper presents types of super-resolution DOA algorithms with using uniform linear array (ULA) in case of white noise. As well as it clarifies the DOA estimation concepts with its mathematical model for each method. Consequently, we use MATLAB simulations to simulate each DOA method with various cases to evaluate its performance to obtain the required accuracy with the resolution for each DOA algorithm. Therefore, the main goal of this paper to show, which DOA algorithm achieves the best performance and better resolution for the all possible angles with the same number of antenna, and gives very high accuracy in target location estimation and its tracking.
This paper deals with the rotated constellations technique's performance, which results in additional diversity to enhance Bit Interleaved Coding and Modulation BICM in various fading channel environments that improve the overall DVB-T2 gain. The performance gain of the rotated constellation has been analyzed and compared with the case of using a non-rotated constellation. In this comparison, three constellation types have been used: QPSK, 16-QAM, and 64-QAM, with all available six code rates supported by the DVB-T2 system. The simulation has been performed with four different types of fading channels scenarios, including Gaussian, Ricean, Rayleigh, and 0 dB Echo channels. Finally, achieved results show an outstanding improvement in DVB-T2 performance when employing rotated constellation in terrible fading environments by choosing a particular parameters configuration.
