Journal of Information Science Research and Practice

January - March

2024-09-13T21:32:23+07:00

April - June

2024-06-15T09:31:25+07:00

Using Multimedia to Communicate Local Identity and Wisdom in the Digital Era

2024-06-12T11:05:06+07:00

In Process

Social Media Literacy Level of the Elderly in Muang District, Chiang Mai Province

2024-06-14T14:31:07+07:00

Purpose: This study examines the behavior and social media literacy level of the elderly in Muang District, Chiang Mai Province.

Methodology: This research used a quantitative method, using a questionnaire to collect data from 381 elderly people aged 60 years and over in Mueang District, Chiang Mai Province.

Findings:

The study results revealed that elderly people most frequently use social media in the morning, primarily through smartphones. They mainly use social media for communication, with YouTube being the most popular platform. The primary issue faced by the elderly in using social media is blurred vision, often due to deteriorating eyesight. The assessment of social media literacy among the elderly in Chiang Mai Province found that, overall, it is at a moderate level. The components with the highest average scores, indicating a high level of literacy, were element 2: analytical skills, and element 4: creative skills. Following these, at a moderate level, were element 3: media evaluation skills, element 5: participation skills, and element 1: accessibility skills

Applications of this study: The findings from this study can serve as a guideline for relevant agencies, such as the Chiang Mai Provincial Social Development and Human Security Office, public libraries, the Division of Social Welfare of Chiang Mai Municipality, and other subdistrict municipalities located in Mueang District. The focus should be on improving the skills identified as having the lowest average levels of social media literacy among the elderly. Additionally, it is recommended to use media to highlight the challenges faced by the elderly when using social media. This approach will help enhance their resilience to social media and future technologies.

Comparative Analysis of ARIMA and LSTM Models for Forecasting Bitcoin Prices: A Machine Learning Approach

2024-06-12T11:13:09+07:00

Purpose: The purpose of this study is to evaluate the effectiveness of ARIMA and LSTM models in forecasting Bitcoin prices. This research aims to identify which model provides the highest accuracy and is best suited for application under varying market conditions. Additionally, it seeks to determine the model’s ability to adapt to and respond to market dynamics and the volatile nature of cryptocurrency prices.

Methodology: The researchers conducted a comparative analysis of the performance of two models for predicting Bitcoin prices: the ARIMA and LSTM models. The study utilized a dataset of Bitcoin prices from 2020 to 2024, sourced from Yahoo Finance. The data was divided into two sets: a training set and a testing set, with an 80:20 split (80% for training and 20% for testing). The prediction outcomes were evaluated using key metrics, including RMSE and MAE, to measure the accuracy and efficiency of each model. This analysis provided insights into the strengths and weaknesses of each model in handling the volatility and uncertainty of the cryptocurrency market.

Findings: The LSTM model outperforms the ARIMA model in terms of accuracy. It is better equipped to capture the price volatility and adapt to market changes, as reflected by lower RMSE and MAE, compared to the ARIMA model. Furthermore, the study indicates that the LSTM model is more suitable for handling complex and volatile datasets, such as cryptocurrency price data, demonstrating superior predictive reliability and performance.

Applications of this study: This comparative study of ARIMA and LSTM models has significant practical applications in the finance and technology sectors. The findings that the LSTM model is superior in capturing market volatility can be leveraged to develop tools that aid investment decisions in the cryptocurrency market. Additionally, this research is beneficial for developers working on automated trading platforms, as improved predictions can lead to better risk management and increased profit opportunities for investors in the cryptocurrency market.