Shuhaida Mohamed Shuhidan

Sentiment Analysis for Financial News Headlines using Machine Learning Algorithm

The study covers the implementation of machine learning algorithm approaches in sentiment analysis of Malaysia financial news headlines. This study can be used for stakeholders who want to know about the financial news and seek knowledge or data in the financial world. The data are gained from Malaysia online financial news, which are from Business section of New Straits Times. Our study applies Opinion Lexicon-based algorithm and Naive Bayes algorithm as the method to perform sentiment analysis. This study consists of several phases in pre-processing such as extract data, stop word removal, and stemming to clean the dataset and make it as data preparation before performing the sentiment analysis with the selected machine learning algorithms. In the stop word removal, tm package in R is used to clean the dataset while for stemming process, Snowball stemmer is used to set the data to its root word. Sample outcomes of analysis are explained for both algorithms. The conclusion describes the summation of the study and future works.

Monitoring mechanisms and financial distress of public listed companies in Malaysia

This study examines the relationships between financial distress and financial ratio (liquidity, leverage, profitability, firm’s performance, and dividend) among public listed companies, using the Altman Z-Score to determine the financial distress levels among public listed companies in Malaysia. Five-year data has been collected (2010 to 2014) from the annual financial statements and from Data Stream of public listed companies in Malaysia. The findings indicate significant relationships between liquidity, leverage, profitability, firm’s performance, and dividend with the financial distress levels among the companies in question. This study also examines the interaction effects of financial ratios and the year after implementation of the Malaysian Code on Received: November, 2016 1st Revision: February, 2017 Accepted: April, 2017 DOI: 10.14254/20718330.2017/10-1/6 Journal of International Studies S ci en ti fi c P a pe rs

Industry 4.0 urban mobility: goNpark smart parking tracking module

Emerging technologies in today worlds have made tremendously evolution in digital smart world. Nowadays, implementation of Internet of Thing (IoT), sensor, seamless data transmission and other advance technology become future insights to Industry 4.0. Moreover, the urban population is currently growing and there is an increasing demand for mobility especially on street parking due to the private motor vehicles usage. In order to create a compassionate city, gearing urban mobility with industry 4.0 shall make efficient transport infrastructure and services as well as meeting the peoples preferences for urban. The aim of this research is to study the different types of smart parking system and vehicle detection technologies that can be used in existing smart parking system. Relevant parking information means allowing drivers to make educated decisions about optimal vehicle used in terms of energy, optimize parking space usage, improve the efficiency of the parking operations and help smoother traffic flow. Hence, in this goNpark smart parking systems application were designed and implemented to guide users to find the available parking space easily and accurately in an open space. goNpark embed real-time visualization using video image processing with interest point detection Scale Invariant Feature Transform (SIFT) as occupancy detection module and Geographic Information System (GIS).

Perceived Risk towards Mobile Banking: A case study of Malaysia Young Adulthood

The advancement of technology and the raise of smart devices ownership in Malaysia has eventually increase the exploration of mobile banking services. Mobile banking has been first commercialized in Malaysia on 2005 and expected to growth. Despite the exponential growth, the mobile banking penetration rate is slow compared to online banking. This study aims to highlight the issues and challenges of mobile banking and to have insight on young adulthood perceived risk towards mobile banking, specifically in Malaysia. In order to support the exploratory study, these risks are surveyed in quantitative study conducted among young adulthood in Malaysia. The self-administered questionnaire distributed through email with 384 respondents indicated that the most impacted facets perceiveed risks are performance risk, following by security risk. The results of this study can be used by the practitioner to address the customer challenges, customer interest and concern for mobile banking service improvement.

Market orientation within technological companies: Risk based approach

Technology companies are exposed to high risk of failures due to technological advancements globalization of the economy. In order to be in line with the competitive market, technological companies need to adapt to evolving environment and developments. Market orientations can be perceived as a vital element in ensuring the survival of business as well as ensuring successfulness. This study assesses the level of Market Orientation within technology-based companies in Malaysia. Quantitative approach is applied in which data is collected by distributing questionnaires. Findings revealed that the technological companies in Malaysia had a moderately high level of customer orientation, a significant level of inter-function coordination and a moderate level in competitor orientation.