Identifikasi Visual Cacat Produk Menggunakan Neural Network Model Backpropagation (Studi Kasus: PT. Panasonic Gobel Eco Solution) Article Swipe
Muhammad Rauuf Oktavian Nur
,
Sjaeful Irwan
,
Danang Santosa
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.30591/jpit.v4i2-2.1865
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.30591/jpit.v4i2-2.1865
Product defects are common in the production process. Visual identification of product defects is first carried out when the product is produced. Identification of vague defects in very small shapes with different sizes and positions is difficult to do with ordinary eye sight, so that often results in decisions about the status of the product that is not right. Product defects in visual form can be identified by patterns such as shape, size and position on the product image. In this study, we will apply a neural network with the backpropagation model as a classification of the pattern. Product images will be processed using image processing by converting the RGB pixel value of the image into a numeric value. Data in numerical form will be input for training values in the backpropagation model. Training results are used to identify identified product defects and produce product status decisions. The results show that the backpropagation neural network model is able to recognize product patterns with an accuracy of 99.24% and based on simulation test data with the final weight and bias of training results, able to identify product defects with success up to 91%.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.30591/jpit.v4i2-2.1865
- https://ejournal.poltektegal.ac.id/index.php/informatika/article/download/1865/1116
- OA Status
- gold
- Cited By
- 1
- References
- 9
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386642562
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386642562Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.30591/jpit.v4i2-2.1865Digital Object Identifier
- Title
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Identifikasi Visual Cacat Produk Menggunakan Neural Network Model Backpropagation (Studi Kasus: PT. Panasonic Gobel Eco Solution)Work title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-19Full publication date if available
- Authors
-
Muhammad Rauuf Oktavian Nur, Sjaeful Irwan, Danang SantosaList of authors in order
- Landing page
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https://doi.org/10.30591/jpit.v4i2-2.1865Publisher landing page
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https://ejournal.poltektegal.ac.id/index.php/informatika/article/download/1865/1116Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://ejournal.poltektegal.ac.id/index.php/informatika/article/download/1865/1116Direct OA link when available
- Concepts
-
Backpropagation, Artificial neural network, Computer science, Identification (biology), Artificial intelligence, Product (mathematics), Pixel, Pattern recognition (psychology), Process (computing), RGB color model, Computer vision, Mathematics, Botany, Geometry, Biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2021: 1Per-year citation counts (last 5 years)
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9Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.small | 28 |
| abstract_inverted_index.using | 103 |
| abstract_inverted_index.vague | 24 |
| abstract_inverted_index.value | 111 |
| abstract_inverted_index.99.24% | 166 |
| abstract_inverted_index.Visual | 8 |
| abstract_inverted_index.common | 3 |
| abstract_inverted_index.image. | 78 |
| abstract_inverted_index.images | 99 |
| abstract_inverted_index.model. | 132 |
| abstract_inverted_index.neural | 86, 153 |
| abstract_inverted_index.right. | 58 |
| abstract_inverted_index.shape, | 71 |
| abstract_inverted_index.shapes | 29 |
| abstract_inverted_index.sight, | 42 |
| abstract_inverted_index.status | 51, 145 |
| abstract_inverted_index.study, | 81 |
| abstract_inverted_index.value. | 118 |
| abstract_inverted_index.values | 128 |
| abstract_inverted_index.visual | 62 |
| abstract_inverted_index.weight | 176 |
| abstract_inverted_index.Product | 0, 59, 98 |
| abstract_inverted_index.carried | 15 |
| abstract_inverted_index.defects | 1, 12, 25, 60, 141, 186 |
| abstract_inverted_index.network | 87, 154 |
| abstract_inverted_index.numeric | 117 |
| abstract_inverted_index.produce | 143 |
| abstract_inverted_index.product | 11, 19, 54, 77, 140, 144, 160, 185 |
| abstract_inverted_index.results | 46, 134, 148 |
| abstract_inverted_index.success | 188 |
| abstract_inverted_index.Training | 133 |
| abstract_inverted_index.accuracy | 164 |
| abstract_inverted_index.identify | 138, 184 |
| abstract_inverted_index.ordinary | 40 |
| abstract_inverted_index.pattern. | 97 |
| abstract_inverted_index.patterns | 68, 161 |
| abstract_inverted_index.position | 74 |
| abstract_inverted_index.process. | 7 |
| abstract_inverted_index.results, | 181 |
| abstract_inverted_index.training | 127, 180 |
| abstract_inverted_index.decisions | 48 |
| abstract_inverted_index.different | 31 |
| abstract_inverted_index.difficult | 36 |
| abstract_inverted_index.numerical | 121 |
| abstract_inverted_index.positions | 34 |
| abstract_inverted_index.processed | 102 |
| abstract_inverted_index.produced. | 21 |
| abstract_inverted_index.recognize | 159 |
| abstract_inverted_index.converting | 107 |
| abstract_inverted_index.decisions. | 146 |
| abstract_inverted_index.identified | 66, 139 |
| abstract_inverted_index.processing | 105 |
| abstract_inverted_index.production | 6 |
| abstract_inverted_index.simulation | 170 |
| abstract_inverted_index.Identification | 22 |
| abstract_inverted_index.classification | 94 |
| abstract_inverted_index.identification | 9 |
| abstract_inverted_index.backpropagation | 90, 131, 152 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.63284318 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |