Ariel M. Sison

Work place: Technological Institute of the Philippines, Cubao, Quezon City, 1109, Philippines



Research Interests: Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Dr. Ariel M. Sison earned his Doctor of Information Technology (DIT) at the Technological Institute of the Philippines Quezon City in 2013 and graduated with Highest Honors. He took up his master’s degree in Computer Science at De La Salle University Manila in 2006 and obtained BS Computer Science at Emilio Aguinaldo College Manila in 1994.
He is currently the Dean, School of Computer Studies, Emilio Aguinaldo College Manila. His research interests include Data Mining and Data Security.
Dr Sison is a member of International Association of Engineers (IAENG), Philippine Society of IT Educators and Computing Society of the Philippines. Currently, he is a Technical Committee Member of International Academy, Research, and Industry Association (IARIA) for International Conference on Systems (ICONS)

Author Articles
Test Bank Management System Applying Rasch Model and Data Encryption Standard (DES) Algorithm

By Maria Ellen L. Estrellado Ariel M. Sison Bartolome T. Tanguilig III

DOI:, Pub. Date: 8 Oct. 2016

Online examinations are of great importance to education. It has become a powerful tool for evaluating students’ knowledge and learning. Adopting modern technology that saves time and ensures security. The researcher developed a Test Bank Management System that can store test items in any subjects. The system is capable of conducting item analysis using the Rasch model scale. Items that undergo analysis based on Rasch scale helped faculty by quantifying each item as “good”, “rejected”, or “revised”. For securing items in the test bank, Data Encryption Standard (DES) algorithm was successfully applied thus ensuring the safety and reliability of the questions in the test bank. Only items that are ready for deployment to the student’s computer during the examinations will be decrypted. In conclusion, the system passed the evaluation process and eliminates redundancy of manual work.

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Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis

By Anthony R. Calingo Ariel M. Sison Bartolome T. Tanguilig III

DOI:, Pub. Date: 8 Oct. 2016

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of predicting the stock market found its way in different social networking platforms such as Twitter. Studies have shown that public moods and sentiments can affect one's opinion. This study explored the tweets of the Filipino public and its possible effects on the movement of the closing Index of the Philippine Stock Exchange. Sentiment Analysis was used in processing individual tweets and determining its polarity - either positive or negative. Tweets were given a positive and negative probability scores depending on the features that matched the trained classifier. Granger causality testing identified whether or not the past values of the Twitter time series were useful in predicting the future price of the PSE Index. Two prediction models were created based on the p-values and regression algorithms. The results suggested that the tweets collected using geo location and local news sources proved to be causative of the future values of the Philippine Stock Exchange closing Index.

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