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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal CoreIT JIEET (Journal of Information Engineering and Educational Technology) Sebatik Building of Informatics, Technology and Science Jurnal Informatika Ekonomi Bisnis JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika Ekonomi Bisnis
Suwanto Sanjaya
UIN Suska Riau
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering

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Journal : KLIK: Kajian Ilmiah Informatika dan Komputer

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Deep Learning Menggunakan Algoritma Xception dan Augmentasi Flip Pada Klasifikasi Kematangan Sawit Masaugi, Fathan Fanrita; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1938

Abstract

Palm oil is an important commodity in Indonesia, especially as Indonesia is the highest palm oil exporting country in the world. Ripe palm fruit is marked by a change in color of the fruit from black to reddish yellow. Apart from that, immature palm fruit has a negative and significant effect on CPO production. The data collection process was carried out by directly taking pictures of palm fruit on oil palm plantations and data obtained from Kaggle. The total amount of data is 1000 images and 1000 data resulting from flip augmentation. The Xception algorithm is an algorithm in deep learning which stands for Extreme version of Inception. This combination was then proven to provide better accuracy in classifying images from a dataset. The optimizer used is the optimizer in TensorFlow, namely Adam (Adaptive Moment Estimation) using learning rate and dropout values. Images of mature and immature palm oil were classified using the Xception algorithm with augmented and without augmented data. In addition, experiments were carried out by changing the parameter values ??of learning rate to 0.1, 0.01, 0.001 and dropout to 0.1, 0.01, 0.001. It was found that the data division was (90;10) with the best accuracy reaching 95%. Test parameters carried out by trialling were proven to increase accuracy when compared to without using parameters and flip augmentation. The best accuracy of the Xception model is 95% on augmented data with a learning rate of 0.001 and a dropout of 0.1.
Implementasi VGG 16 dan Augmentasi Zoom Untuk Klasifikasi Kematangan Sawit Mazdavilaya, T Kaisyarendika; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1940

Abstract

Indonesia is a country that has very abundant palm oil plantations and makes palm oil one of the largest export commodities in Indonesia. Fruit maturity on oil palms has a significant influence on palm oil and kernel production. The level of ripeness in palm oil fruit can affect several contents in it, such as tocopherol content, yield and FFA. The classification will be divided into 2 classes, namely between ripe and immature fruit with data on 500 images of ripe fruit and 500 images of immature fruit, data taken from the Kaggle site and private gardens taken using a cellphone camera. The data that has been obtained is augmented which is useful for enriching the data to make it more abundant. Data augmentation uses zoom augmentation and makes the original 1000 data increase to 2000 data. The model used is VGG 16 which is part of deep learning. The existing dataset is then preprocessed, resized and rescaled, then divides the data into 3, namely train, test and valid data. After dividing the data, then carry out the classification process with VGG 16 and set the hyperparameters after that the model will learn with 20 epochs. The model will learn with 57 schemes to compare and find highest accuracy. After the model has finished learning, it is evaluated using a confusion matrix. The results obtained were that the 90:10 data division using data augmentation with a learning rate of 0.01 and a dropout of 0.001 obtained the best accuracy, reaching 93.8%.
Co-Authors Agustian, Surya Ahmad Fauzan Ahmad, Rizmah Zakiah Nur Al Fiqri, M. Faiz Aqilah, M Alfandri Aulia Ramadhani Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Ersad Alfarsy Absar, Ersad Alfarsy Fadhilah Syafria Febi Yanto, Febi Fitri Insani Fitri, Dina Deswara Gusti, Siska Kurnia Harni, Yulia Iis Afrianty Israldi, Tino Iwan Iskandar Jasril Jasril Jasril Jasril Kurnia Rahman, Fikri Kurniawan, Saifur Yusuf Lestari Handayani Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fikry Muhammad Irsyad Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Pangestu, Yoga Pizaini Pizaini, Pizaini Radili, Adi Rahmad Abdillah Saputra, Nugroho Wahyu Shahira, Fayza Sugandi, Hatami Karsa Syaputra, Muhammad Dwiky Vitriani, Yelfi Yani, Susmi Syahfrida Yusra, Yusra