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All Journal ILKOM Jurnal Ilmiah Bulletin of Social Informatics Theory and Application
Uriu, Wako
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Computer Science & IT Social Sciences

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Uncovering negative sentiments: a study of indonesian twitter users' health opinions on coffee consumption Budiarto, Laksono; Rokhman, Nissa Mawada; Uriu, Wako
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.606

Abstract

The increase in coffee consumption among the public is due to several reasons, including health and lifestyle reasons. Awareness of the positive and negative effects of coffee consumption has also increased in society. This research is a sentiment analysis that aims to investigate Twitter users' opinions about the impact of coffee consumption on their health. The method used involves data collection using the RapidMiner application, utilizing the Twitter Application Programming Interface (API) function connected to a prepared Twitter account. The obtained data underwent data cleaning, saved as an Excel file type, training and testing, and model evaluation. Then, the data was classified into three categories: Negative Opinion, Neutral Opinion, and Positive Opinion. The results showed that less than 10% of opinions were positive, 19% were neutral, and 73% were negative. The opinions obtained are useful information for stakeholders in the coffee industry. They can also be used to determine better steps in educating the public about coffee.
Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques Pranolo, Andri; Setyaputri, Faradini Usha; Paramarta, Andien Khansa’a Iffat; Triono, Alfiansyah Putra Pertama; Fadhilla, Akhmad Fanny; Akbari, Ade Kurnia Ganesh; Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Uriu, Wako
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.2333.210-220

Abstract

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models
Co-Authors Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Andri Pranolo Fadhilla, Akhmad Fanny Laksono Budiarto Paramarta, Andien Khansa’a Iffat Rokhman, Nissa Mawada Setyaputri, Faradini Usha Triono, Alfiansyah Putra Pertama Utama, Agung Bella Putra