Article Per Year (5 Year)
p-Index From 2021 - 2026
1.532
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Proceedings of KNASTIK Jurnal Pekommas JURNAL ELEKTRO Indonesian Journal of Information System Jurnal Ilmiah Realtech Jurnal Restikom : Riset Teknik Informatika dan Komputer Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ilmiah Sains Widya Teknik Journal of Information Technology and its Utilization
Debby Paseru
Universitas Katolik De La Salle Manado
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Physics

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Pekommas

 1 
Pengembangan Aplikasi berbasis Android untuk Mengenali Jenis Lesi Kulit Menggunakan Convolutional Neural Network Sengkey, Blessynta Christesa; Pandelaki, Steven; Paseru, Debby
Jurnal Pekommas Vol 9 No 1 (2024): Juni 2024
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v9i1.5557

Abstract

Skin lesions are skin abnormalities or disorders in the form of changes, damage, abnormal growth of the skin, such as changes in texture, color, appearance of lumps and spots on the skin. This disease certainly disrupts people's activities and behavior every day because of the reactions it causes, such as sensations of itching, pain, stinging and excessive heat. However, knowledge of the types of skin lesions by the lay public is still lacking and a system is needed that can provide information regarding primary skin lesions. Image processing as part of machine learning can recognize types of primary skin lesions through applications that use Convolutional Neural Network (CNN). This method can perform good feature extraction and classification, so it is very suitable for image detection. Research was carried out on 4 classes of lesions, namely macular, urticarial, popular and vesicular. Based on the test results with the CNN model, it was found that the average accuracy value was 95% with the calculation of values in the macular class with precision 91%, recall 100%, f-1 score 95%, urticaria class with precision 100%, recall 91%, f-1 score 95%, papule class with precision 98%, recall 93%, f-1 score 96% and vesicular class with precision 93%, recall 99%, f-1 score 96%.
Implementasi Metode Convolutional Neural Network untuk Mendeteksi Penyakit Pada Citra Daun Tomat Kotta, Chrisno R.; Sumampouw, Michael; Paseru, Debby
Jurnal Pekommas Vol 7 No 2 (2022): December 2022
Publisher : Sekolah Tinggi Multi Media “MMTC” Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jpkm.v7i2.4961

Abstract

Tomato (Lycopersicum esculentum Mill) is one of the leading commodities that has the potential to be a contributor to exports. One of the main causes of decreased production of tomato plants, namely the emergence of various diseases. Plants are said to be affected by disease if there are changes in all or part of the plant organs that cause disruption of daily physiological activities. This study will use deep learning methods and Convolutional Neural Network (CNN) algorithms to determine disease in tomato plants through leaves. The CNN training model will be carried out using the Python and carried out on the Google Colab platform, while the Android-based application development will use Android Studio. Tests have been carried out by implementing various test scenarios, namely testing with image sources from the gallery and image sources directly from the camera. The result is an application that is built quite reliably with an accuracy of testing on images from the gallery of 94% and 80% accuracy for testing using images taken directly from the camera.
Co-Authors Alfonso Mario Tumewu Audy Aldrin Kenap Aurelia Koagouw Benny Pinontoan Chrisno R. Kotta Erich Sumendap Frederica, Gabrielle Hendri Hermawan Adinugraha Immanuela Saputro Indah Kairupan Inggrid Alista Paendong Junaidy Budi Sanger Kairupan, Indah Kevin Kindangen Kindangen, Kevin Koagouw, Aurelia Kotta, Chrisno R. Kumenap, Vivie Deyby Lijoed, Theovani Meirio Tengor Michael George Sumampouw Olvina Rante Reynaldi Kuera Rila Mandala Sengkey, Blessynta Christesa Serwin Gonzaga Rumagit Sofyana Ibrahim Stevanus Salmon Steven Pandelaki Tapidingan, Yohanes Christopher Tengor, Meirio R. Thomas Ch. Suwanto Vianry Teguh Pangemanan Vivie Kumenap Wikarsa, Liza Yohanes Christopher Tapidingan