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All Journal Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
Budiman, Saiful Nur
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science

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Color Grading Systems to Classify Ripeness of Apple Mango Fruit Prayogi, Yanuar Risah; Budiman, Saiful Nur
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 3, No 2 (2018)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.527 KB) | DOI: 10.25139/inform.v3i2.1010

Abstract

Harvesting of apple mangoes fruit that simultaneously causes not all apple mango fruits are harvested ripe. Farmers should sort the apple mango manually. So, harvesting in large quantities will take a long time. There are several obstacles to sort the apple mango fruit manually. It needs skills and experiences to sort the apple mangoes fruit and it needs many workers. In addition, when the apple mango is ripe and not immediately sorted, it will be too ripe and decayed, so cause losses. Previous research has already made the apple mango classification system mango level but required an expensive tool and take a long time to process one apple mango. This research proposes a new strategy to measure the ripeness level of apple mango fruit, especially apple mango at low cost, without the expensive device and takes a short time based on the apple mango skin color region using the feed-forward neural network. The test used 214 images consisting of 4 classes. Validation test using k-fold of 7, 9, and 11 with an average accuracy of 90.61%, 91.41%, and 90.82%. Highest accuracy with a value of 91.41% on k-fold 9 while at k-fold 11 accuracy is lower but has the least standard deviation value. A small standard deviation means that the accuracy is more stable than others
Color Grading Systems to Classify Ripeness of Apple Mango Fruit Prayogi, Yanuar Risah; Budiman, Saiful Nur
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 3 No. 2 (2018)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.527 KB) | DOI: 10.25139/inform.v3i2.1010

Abstract

Harvesting of apple mangoes fruit that simultaneously causes not all apple mango fruits are harvested ripe. Farmers should sort the apple mango manually. So, harvesting in large quantities will take a long time. There are several obstacles to sort the apple mango fruit manually. It needs skills and experiences to sort the apple mangoes fruit and it needs many workers. In addition, when the apple mango is ripe and not immediately sorted, it will be too ripe and decayed, so cause losses. Previous research has already made the apple mango classification system mango level but required an expensive tool and take a long time to process one apple mango. This research proposes a new strategy to measure the ripeness level of apple mango fruit, especially apple mango at low cost, without the expensive device and takes a short time based on the apple mango skin color region using the feed-forward neural network. The test used 214 images consisting of 4 classes. Validation test using k-fold of 7, 9, and 11 with an average accuracy of 90.61%, 91.41%, and 90.82%. Highest accuracy with a value of 91.41% on k-fold 9 while at k-fold 11 accuracy is lower but has the least standard deviation value. A small standard deviation means that the accuracy is more stable than others
Co-Authors sakur, stendy Yanuar Risah Prayogi