Bartolome T. Tanguilig III

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



Research Interests: Applied computer science, Computer systems and computational processes, Computer Architecture and Organization, Computer Networks, Theoretical Computer Science


Dr. Bartolome T. Tanguilig III took his Bachelor of Science in Computer Engineering in Pamantasan ng Lungsod ng Maynila, Philippines in 1991. He finished his Master’s Degree in Computer Science from De La Salle University, Manila, Philippines in 1999, and his Doctor of Philosophy in Technology Management from Technological University of the Philippines, Manila in 2003. He is currently the Assistant Vice President for Academic Affairs and concurrent Dean of the Graduate Programs of the Technological Institute of the Philippines, Quezon City.
Dr. Tanguilig III is a member of the Commission on Higher Education (CHED) Technical Panel for IT Education (TPITE), the chair of the CHED Technical Committee for IT (TCIT), the founder of Junior Philippine ITE Researchers (JUPITER), Vice President – Luzon of the Philippine Society of IT Educators (PSITE), board member of the PCS Information and Computing Accreditation Board (PICAB), member of the Computing Society of the Philippines (CSP) and a program evaluator / accreditor of the Philippine Association of Colleges and Universities Commission on Accreditation (PACUCOA).

Author Articles
Enhanced Initial Centroids for K-means Algorithm

By Aleta C. Fabregas Bobby D. Gerardo Bartolome T. Tanguilig III

DOI:, Pub. Date: 8 Jan. 2017

This paper focuses on the enhanced initial centroids for the K-means algorithm. The original k-means is using the random choice of initial seeds which is a major limitation of the original K-means algorithm because it produces less reliable result of clustering the data. The enhanced method of the k-means algorithm includes the computation of the weighted mean to improve the centroids initialization. This paper shows the comparison between K-Means and the enhanced K-Means algorithm, and it proves that the new method of selecting initial seeds is better in terms of mathematical computation and reliability.

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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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