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Pixel : Jurnal Ilmiah Komputer Grafis
Published by Universitas Sains Dan Teknologi Komputer
ISSN : 19790414     EISSN : 26216256     DOI : https://doi.org/10.51903/pixel.v14i1
Core Subject : Education,
Education
Arjuna Subject : -
Articles 332 Documents
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PERANCANGAN DESYGN SYGN SYSTEM PASAR TRADISIONAL MARISA: perancangan Risti Puspita Sari Hunowu; Siska Udilawaty
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 16 No. 1 (2023): Vol 16 No 1 (2023): Jurnal Ilmiah Komputer Grafis
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v16i1.1215

Abstract

The need for a sign system at the Marisa traditional market will make it easier for visitors to get information. Designing a sign system in an attractive, effective, informative and communicative market area will be the right solution. The purpose of the design is to make it easier for visitors to recognize the shopping flow and layout of the Marisa Traditional Market. This study uses a design method with a qualitative approach as a technique in collecting data. The design method is intended to produce a product in the form of a Marisa Traditional Market sign system design. While the qualitative approach is used as a way to find and collect data from the field. The data were then identified and analyzed descriptively using 5 W + 1 H so that it gave birth to a sign system design concept for the Marisa traditional market. Next, the concept is visualized until it becomes the final design in the form of the Marisa Traditional Market sign system
Characterization Of Composition Error Summary Using Machine Learning Techniques And Natural Language Processing Mars Caroline Wibowo; Budi Raharjo
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 16 No. 1 (2023): Vol 16 No 1 (2023): Jurnal Ilmiah Komputer Grafis
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v16i1.1885

Abstract

As software technology becomes more complex, software maintenance costs become more expensive. In connection with this, the development of software engineering makes the software system has many Composition choices that can be adjusted to the needs of the user. Error fixing involves analyzing Error Summary and modifying code. If bug-fixing steps are made as efficiently and effectively as possible then maintenance costs can be minimal. The purpose of this research is to establish a tool of machine learning for identifying Composition Error Summary and to find out the types of special Composition choices that can be used to save costs, time, and effort. In this study, the T-test was applied to appraise the analytical implication of conduct metrics when the “F-test” was taken to the Variance’s test. Classifiers used in this study are “All words” or “AW”, “Highly Informative Words” or “H-IW”, and “Highly Informative Words plus Bigram” or “H-WB”. Identical validation and Vexed validation techniques were used to calculate the effectiveness of machine learning tools. The results of this research denote that the instrument is competent for definitive Composition Error Summary and other Composition choices for definite Error Summary. This research determines the practicality of machine learning techniques in corrective issues relevant to Error summary. The result of this study also explained that Composition/non-Composition Error Summaries have contrasting aspects that can be accomplished by machine learning devices. The advanced tool could be upgraded in some areas to create it more powerful. The array identification section of the current study has limitations, an array with different words and Composition recognition tools tend to prefer Compositions with more words, so improvements to this could implicate consideration of the semantics of Error Summary, equivalent, and use of n-grams. Also, in using the technology of machine learning and Natural Language processing some advancements to be made to the present characterization structure so for future research it is highly recommended to clear up the first’s Error Summary before operating several operations in the present study.Composition Error Summary

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