|
| |
|
M SALSABILA JAMIL. KLASIFIKASI GAMBAR JAMUR BERACUN DAN BUKAN BERACUN MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN) STUDI KASUS: AMANITA PHALLOIDES, AMANITA CAESAREA, CANTHARELLUS CIBARIUS, OMPHALOTUS OLEARIUS, VOLVARIELLA VOLVACEA. Banda Aceh : Fakultas MIPA Universitas Syiah Kuala, 2020 |
|
AbstrakMushroom poisoning is one of the phenomena that occur in society due to errors in identifying
mushrooms and eating them. the effects are varied, ranging from mild cases, such as nausea,
vomiting, diarrhea, dizziness, to the more severe case, namely death. as part of efforts to
minimize the occurrence of similar incident, a solution is created through the approach of
artificial intelligence, which is to generete a model that can distinguish toxic mushrooms and
non-toxic one. there are five species of mushroom that is carried out in this research, those are
amanita caesarea, cantharellus cibarius, and volvariella volvacea as non-toxic mushrooms and
amanita phalloides and omphalotus olearius as toxic mushrooms. there are 142 total images
used in this research. for training data, there are 100 images, with 20 images for each species.
for validation data, there are 37 images in total, with 7 images for each species, except for
species v. volvacea, which has 9 images. and, as for
Baca Juga : PENGARUH BEBERAPA KOMPOSISI MEDIA TUMBUH TERHADAP KANDUNGAN PROTEIN, LIPID DAN KARBOHIDRAT PADA TUBUH BUAH JAMUR MERANG (VOLVARIELLA VOLVACEA) (Amalia, 2017) ,
Baca Juga : PENGARUH KOMPOSISI SUBSTRAT TERHADAP PERTUMBUHAN DAN PRODUKSI TUBUH BUAH JAMUR MERANG (VOLVARIELLA VOLVACEA) (Suari Eko Trisna, 2016) , est data, one image for each species. the model was created using convolutional neural network (cnn). the final model has an accuracy rate of 78%. during the training process, data augmentation techniques are used, which is useful to reproduce the same image, but different from original image, by using some transformations, such as rotation, horizontal and vertical flip, adding noise, affine transform, blurring, and center crop. there are 7 images generated from the data augmentation process plus one original image. the batch size used during training phase is 32. most prediction error are caused by v. volvacea, where 9 images used as validation in total, only 3 images are predicted to be correct, the rest are predicted to be a. phalloides. overall, the model’s performance is quite good in classifying the species of a. caesarea, a. phalloides, c. cibarius, and o. olearius but biased against v. volvacea. therefore, the model produced in this research is not reliable enough to be applied to the wider community as a “tool” to distinguish these mushrooms. keywords: a. caesarea, a. phalloides, c. cibarius, o. olearius, v. volvacea, deep Tulisan yang relevan KEANEKARAGAMAN JENIS JAMUR MAKROSKOPIS YANG TERDAPAT DI KAWASAN HUTAN PAYA REBOL KECAMATAN BENER KELIPAH KABUPATEN BENER MERIAH (Dini Taurina, 2019) ,PENGARUH KOMBINASI SUBSTRAT MEDIA TUMBUH TERHADAP PRODUKSI DAN KUALITAS JAMUR MERANG (VOLVARIELLA VOLVACEA) (Mardiana, 2016) , ANALISIS KANDUNGAN GIZI JAMUR MERANG (VOLVARIELLA VOLVACEA) YANG DIBUDIDAYAKAN PADA MEDIA TANAM JANJANG KELAPA SAWIT DAN JERAMI PADI (Sabaruddin, 2019) , |
|
|
|
Kembali ke halaman sebelumnya
Terkini
PROSPEK EKSPOR KOPI ARABIKA ORGANIK BERSERTIFIKAT DI KABUPATEN ACEH TENGAH |
ANALISIS KOMPARATIF TINGKAT PENDAPATAN USAHATANI PADI SAWAH IRIGASI DAN PADI SAWAH TADAH HUJAN BERDASARKAN STATUS PENGUASAAN LAHAN DI KECAMATAN KUTA COT GLIE KABUPATEN ACEH BESAR |
KAJIAN PEMASARAN DAN KEUNTUNGAN PETANI KACANG TANAH DI KECAMATAN DARUSSALAM KABUPATEN ACEH BESAR |
STUDI PENDAPATAN RUMAH TANGGA PERTANIAN DI DATARAN TINGGI (KASUS DESA URING KECAMATAN BUKIT KABUPATEN BENER MERIAH) |
ANALISIS PENDAPATAN USAHATANI TEMBAKAU DI KECAMATAN BANDAR BARU KABUPATEN PIDIE JAYA |