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All Journal Journal of Economic, Bussines and Accounting (COSTING) INTECOMS: Journal of Information Technology and Computer Science Indonesia Berdampak: Jurnal Pengabdian Kepada Masyarakat
Mellysa Pratama
Universitas Muhammadiyah Bengkulu
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Social Sciences Other

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IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN RESNET 50 UNTUK MENGKLASIFIKASI TINGKAT KEMANTANGAN  BUAH PEPAYA Mellysa Pratama; Erwin Dwika Putra
Journal of Economic, Bussines and Accounting (COSTING) Vol. 9 No. 3 (2026): Journal of Economic, Bussines and Accounting (COSTING)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/nvnckc75

Abstract

Buah pepaya adalah salah satu jenis buah tropis yang banyak disukai karena kaya akan nutrisi. Namun, cara menilai kematangan buah pepaya masih sering dilakukan dengan cara manual, yang dapat menyebabkan kesalahan dalam proses pemisahan dan penyebarannya. Dengan demikian, penelitian ini bertujuan untuk mengembangkan sistem otomatis untuk mengklasifikasikan tingkat kematangan buah pepaya dengan menggunakan metode Convolutional Neural Network (CNN) yang berbasis arsitektur ResNet50. Dataset yang digunakan terdiri dari gambar buah pepaya yang dibagi menjadi empat tingkat kematangan, yaitu masih mentah, setengah matang, sudah matang, dan sudah busuk. Gambar tersebut kemudian menjalani proses preprocessing yang mencakup pengubahan ukuran gambar menjadi 224×224 piksel, penyesuaian nilai piksel, serta augmentasi data melalui teknik seperti rotasi, zoom, dan pembalikan horizontal untuk memperbanyak variasi data pelatihan. Model dilatih dengan menggunakan metode transfer learning dengan memanfaatkan bobot yang sudah ada dari dataset ImageNet. Evaluasi kinerja model dilakukan melalui penggunaan matriks kebingungan dan matriks klasifikasi yang mencakup akurasi, precision , recall, dan skor F1. Hasil dari proses pelatihan menunjukkan bahwa model mencapai akurasi pelatihan sebesar 91,72% dan akurasi validasi sebesar 83,56%, dengan nilai  validasition loss sebesar 0,4958. Temuan ini mengindikasikan bahwa model dapat mengklasifikasikan citra buah pepaya dengan performa yang relatif baik dan konsisten. Penelitian ini diharapkan dapat mendukung proses otomatisasi dalam mengidentifikasi tingkat kematangan buah pepaya di sektor pertanian dan industri pangan. Kata Kunci: klasifikasi citra , buah pepaya ,CNN, Resnet-50,deep learning.
IMPLEMENTASI CONVOLUTIONAL NEURAL NETWORK MENGGUNAKAN RESNET 50 UNTUK MENGKLASIFIKASI TINGKAT KEMANTANGAN  BUAH PEPAYA Mellysa Pratama; Erwin Dwika Putra
INTECOMS: Journal of Information Technology and Computer Science Vol. 9 No. 3 (2026): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/tr9sbe10

Abstract

Papaya is a very popular tropical fruit variety because it is rich in nutrients. However, the method of assessing the ripeness of papaya fruit is still often done manually, which can cause errors in the separation and distribution process. Thus, this study aims to develop an automatic system to classify the ripeness level of papaya fruit using the Convolutional Neural Network (CNN) method based on the ResNet50 architecture. The dataset used consists of papaya fruit images divided into four stages of ripeness, namely unripe, half-ripe, and unfit. The images then undergo a preprocessing process that includes resizing the image to 224 × 224 pixels, adjusting pixel values, and data augmentation through techniques such as rotation, zoom, and horizontal flipping to increase the variety of training data. The model is trained using a transfer learning approach by utilizing existing weights from the imagenet dataset. Model performance evaluation is carried out through the use of a confusion matrix and a classification matrix that includes accuracy, precision, recall, and F1 score. The results of the training process show that the model achieved a training accuracy of 91.72% and a validation accuracy of 83.56%, with a validation loss value of 0.4958. These findings indicate that the model can classify papaya fruit images with relatively good and consistent performance. This research is expected to support the automation process in identifying the ripeness level of papaya fruit in the agricultural and food industry sectors.  Keywords: image classification, papaya, CNN, Resnet-50, deep learning.  
Literasi Pemanfaatan Teknologi Digital “Canva” bagi Siswa di SDN 57 Pasar Bengkulu dan SMPN 7 Pasar Bengkulu Ardeya Wulandari; Putri Wahyuni; Mellysa Pratama; Khairul Bahrun
Indonesia Berdampak: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 2 (2026): JULI-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/0wqnem07

Abstract

The development of digital technology provides opportunities for students to use technology as a means of learning and producing creative works. One technology that can be utilized is Canva, a design platform that allows students to create various digital works in a simple and attractive way. This activity aimed to provide students with an understanding of the use of Canva as a digital technology and to encourage them to use technology wisely, creatively, and productively. The activity was conducted with students at SDN 57 Pasar Bengkulu and SMPN 7 Pasar Bengkulu through material presentation, discussion and question-and-answer sessions, demonstrations of Canva use, digital work creation practices, and simple evaluations. The materials covered the use of technology in everyday life, digital information selection, personal data security, digital ethics, and the use of Canva as a medium for creative work. The results showed that students responded positively and actively participated throughout the activities, particularly during the Canva practice session. Students were able to explore basic Canva features, such as selecting templates, adding text and design elements, and creating digital works based on their own ideas and creativity. These activities demonstrate that the use of Canva can provide students with experience in utilizing technology not only for entertainment but also as a means of learning and producing creative and educational works. Therefore, the use of Canva as a digital technology can be one of the activities that supports the development of students' technology literacy in schools.
Co-Authors Ardeya Wulandari Khairul Bahrun Putra, Erwin Dwika Putri Wahyuni