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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Zahriah Othman
Universiti Teknikal Malaysia Melaka
Computer Science & IT Engineering

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Spatial analysis model for traffic accident-prone roads classification: a proposed framework Anik Vega Vitianingsih; Nanna Suryana; Zahriah Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp365-373

Abstract

The classification method in the spatial analysis modeling based on the multi-criteria parameter is currently widely used to manage geographic information systems (GIS) software engineering. The accuracy of the proposed model will play an essential role in the successful software development of GIS. This is related to the nature of GIS used for mapping through spatial analysis. This paper aims to propose a framework of spatial analysis using a hybrid estimation model-based on a combination of multi-criteria decision-making (MCDM) and artificial neural networks (ANNs) (MCDM-ANNs) classification. The proposed framework is based on the comparison of existing frameworks through the concept of a literature review. The model in the proposed framework will be used for future work on the traffic accident-prone road classification through testing with a private or public spatial dataset. Model validation testing on the proposed framework uses metaheuristic optimization techniques. Policymakers can use the results of the model on the proposed framework for initial planning developing GIS software engineering through spatial analysis models.
Improving the efficiency of clustering algorithm for duplicates detection Abdulrazzak Ali; Nurul Akmar Emran; Safiza Suhana Kamal Baharin; Zahriah Othman; Awsan Thabet Salem; Maslita Abd Aziz; Nor Mas Aina Md Bohari; Noraswaliza Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1586-1595

Abstract

Clustering method is a technique used for comparisons reduction between the candidates records in the duplicate detection process. The process of clustering records is affected by the quality of data. The more error-free the data, the more efficient the clustering algorithm, as data errors cause data to be placed in incorrect groups. Window algorithms suffer from the window size. The larger the window, the greater the number of unnecessary comparisons, and the smaller the window size may prevent the detection of duplicates that are supposed to be within the window. In this paper, we propose a data pre-processing method that increases the efficiency of window algorithms in grouping similar records together. In addition, the proposed method also deal s with the window size problem. In the proposed method, high-rank attributes are selected and then preparators are applied to the selected traits. A compensation algorithm is implemented to reduce the problem of missing and distorted sort keys. Two datasets (compact disc database (CDDB) and MusicBrainz) were used to test duplicates detection algorithms. The duplicates detection toolkit(DuDe) was used as a benchmark for the proposed method. Experiments showed that the proposed method achieved a high rate of accuracy in detecting duplicates. In addition, the proposed method.
Co-Authors Abdulrazzak Ali Anik Vega Vitianingsih Awsan Thabet Salem Maslita Abd Aziz Nanna Suryana Nor Mas Aina Md Bohari Noraswaliza Abdullah Nurul Akmar Emran Safiza Suhana Kamal Baharin