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All Journal Jurnal Ilmu Komputer dan Informasi Indonesian Journal of Artificial Intelligence and Data Mining
Erlina Halim
STMIK Mikroskil
Computer Science & IT

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Automatic Image Self-Enhancement for Multi-Scale Spectral on Low Resolution Video Arwin Halim; Sunaryo Winardi; Erlina Halim
Jurnal Ilmu Komputer dan Informasi Vol 14, No 1 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i1.900

Abstract

MSR technique is a process used to reduce the search area in an image. MSR relies heavily on image salience from image capture devices. The purpose of this study is to improve the detection of people's objects on video by increasing the quality of the image frames on video and MSR. This research uses artificial video taken in a room installed by CCTV. Object detection is evaluated using precision, recall, and fscore values. The results showed an increase in the quality of the performance of object detection that was properly detected as a person. The average detection performance is indicated by an fscore of 14.47%. Increasing the quantity of detection of objects of people reached 168.8% compared to the detection of only using MSR
Consumer Opinion Extraction Using Text Mining for Product Recommendations On E-Commerce Erlina Halim; Ronsen Purba; Andri Andri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 1 (2021): March 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i1.10834

Abstract

This study aims to evaluate consumer opinions in text form on e-commerce to determine the accuracy of ratings given by consumers with opinions using text mining with the lexicon approach. The research data was obtained online using a crawling technique using the API provided by Shopee. The conditions of diverse opinions and use of non-standard words are challenges in processing opinions. Opinion must be processed normalization and repairs using dictionary of words before going to extract using lexicon approach. Dictionary of words contain opinions with weights that are worth 1 to 5 for positive opinions and are worth -1 to -5 for negative opinions. For each opinion will be classified using the maximum ratio of the weight of positive opinion compared to the weight of negative opinion. The classification of opinion produced is positive, negative or neutral. Opinion classification is then compared with the rating classification to work out the extent of accuracy. The comparison produces an accuracy of 80.34% by completing an opinion dictionary.
Co-Authors Andri, Andri Arwin Halim Ronsen Purba Sunaryo Winardi