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Jonardo R. Asor
Laguna State Polytechnic University

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Fire incidents visualization and pattern recognition using machine learning algorithms Jonardo R. Asor; Jefferson L. Lerios; Sherwin B. Sapin; Jocelyn O. Padallan; Chester Alexis C. Buama
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i3.pp1427-1435

Abstract

A fire incident is a devastating event that can be avoided with enough knowledge on how and when it may occur. For the past years, fire incidents have become a big problem for the Philippines, since it affects the socio-economic growth of the country. Machine learning algorithm is a well-known technique to predict and analyze data. It can also be used to recognize pattern and develop models for artificial intelligence. Pattern recognition through machine learning algorithm is already established and have proven itself accurate in different fields such as education, crime, health and many others including fire incidents. This paper aims to develop a model for recognizing patterns of fire incidents in the province of Laguna, Philippines implementing a machine learning algorithm. With the foregoing project, it is found out that a recurrent neural network shows an astonishing result in terms of pattern recognition. Further, it is also found that Calamba City is the most vulnerable area in case of fire occurrence in the Province of Laguna.
Machine learning approach on road accidents analysis in Calabarzon, Philippines: an input to road safety management Kristelle Anne R. Torres; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp993-1000

Abstract

This research was conducted to help the traffic policy makers and general public in preventing road incidents using the collected traffic accident dataset between the years 2016 and 2019. Data mining using classification algorithm was utilized to develop a predictive model for predicting occurrences of traffic accidents. Classification algorithms such as decision tree, k-nn, naïve bayes and neural network have been compared in identifying better classification capability in classifying stage of felony. Neural network shows a very promising result in classifying road accident with a total accuracy result of 87.63%. Nonetheless, k-nn and naïve bayes both acquired a higher than 80% accuracy which shows that this classification algorithms were also good in predicting road accidents. Moreover, public vehicle is more prone in accident rather than private vehicle in both stage of felony and accident may occur between or on 3:00pm and 6:00pm.
Implementation of tourism circuit concept in an android-based tourist navigation application through A* algorithm Jonardo R. Asor; Gene Marck B. Catedrilla; Cris Q. De Leon; Sharon L. Ramos
Indonesian Journal of Electrical Engineering and Computer Science Vol 24, No 2: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v24.i2.pp986-992

Abstract

Circuit tourism allows for inclusive growth of tourism since it allows for multi-site visits. Normally three or more tourist attractions are linked together in a tour of the tourist’s choosing. The main objective of this study is to develop an android-based application for searching tourist spots in Los Baños, Laguna, Philippines that is also capable of recommending succeeding places to visit using A* algorithm. Upon development of the application, this paper proves that A* algorithm can be used to recommend another tourist spot by analyzing the distance of the tourist spots near the user, and the most convenient routes through which they can be reached. Based on backward and forward compatibility testing, the application is operating system friendly, since it can be used in lower versions of android. Further, it is shown in the users’ evaluation that the application is acceptable to the locals of Los Baños, Laguna, Philippines. The application also contributes to advertising other tourist spots in the said municipality, specially those who were not so popular to the tourists.
Intelligent aquaculture system for pisciculture simulation using deep learning algorithm Sherwin B. Sapin; Bryan A. Alibudbud; Paulo B. Molleno; Maureen B. Veluz; Jonardo R. Asor
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp561-568

Abstract

The project aims to develop an intelligent system for simulating pisciculture in Taal Lake in the Philippines through geographical information system and deep learning algorithm. Records of 2018-2020 from the database of Bureau of fisheries and aquatic resources IV-A-protected area management board (BFAR IVA-PAMB) was collected for model development. Deep learning algorithm model was developed and integrated to the system for time series analysis and simulation. Different technologies including tensorflow.js were used to successfully developed the intelligent system. It is found on this paper that recurrent neural network (RNN) is a good deep learning algorithm for predicting pisciculture in Taal lake. Further, it is also shown in the initial visualization of the system that barangay Sampaloc in Taal has highest rate of fish production in Taal while Tilapia nilotica sp. is the major product of the latter.
Co-Authors Bryan A. Alibudbud Chester Alexis C. Buama Cris Q. De Leon Gene Marck B. Catedrilla Jefferson L. Lerios Jocelyn O. Padallan Kristelle Anne R. Torres Maureen B. Veluz Paulo B. Molleno Sharon L. Ramos Sherwin B. Sapin