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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah Jurnal Ilmiah Behongang Operations Research: International Conference Series Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application
Luther Latumakulita
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Social Sciences

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Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network Luther Latumakulita; Franklin Mandagi; Frangky Paat; Dedie Tooy; Sandra Pakasi; Sofia Wantasen; Diane Pioh; Rinny Mamarimbing; Bobby Polii; Jantje Pongoh; Arthur Pinaria; Edwin Tenda; Noorul Islam
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.601

Abstract

Indonesia has a variety of medicinal plants that are efficacious for preventing or treating various diseases. Each region has unique medicinal plants, such as in North Sulawesi, there are many medicinal plants with local names of "Jarak" (Jatropha curcas), "Jarak Merah" (Jatropha multifida), "Miana" (Coleus Scutellarioide), and "Sesewanua" (Clerodendron Squmatum Vahl). This research applies the Convolutional Neural Network (CNN) method to identify the types of medicinal plants of North Sulawesi based on leaf images. Data was collected directly by taking photos of medicinal plant leaves and then using the augmentation process to increase the data. The first stage is conducting training and validation processes using 10-fold cross-validation, resulting in 10 classification models. Evaluation results show that the lowest validation accuracy of 98.4% was obtained from fold-4, and the highest was 100% from fold-2. The third stage was to run the testing process using new data. The results showed that the worst model produced a test accuracy of 80.91% while the best model produced an accuracy of 87.73% which means that the identification model is quite good and stable in classifying types of medicinal plants based on its leaf images. The final stage is to develop a web-based system to deploy the best model so people can use it in real-time
Co-Authors Aji Prasetya Wibawa Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Andar Alwein Pinilas Arista Mandagi Arthur Pinaria Benny Pinontoan Benny Pinontoan Bobby Polii Chriestie E. J. C. Montolalu Chriestie E. J. C. Montolalu Dedie Tooy Deiby Tineke Salaki Diane Pioh Djoni Hatidja Fajar Purnama Felliks Tampinongkol, Felliks Frangky Paat Franklin Mandagi Gybert Saselah I Nyoman Gede Arya Astawa Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung, Julana Jullia Titaley Lenshy Aprisilia Anastasia Lindsay Mokosuli Mans Mananohas, Mans Marni Sumarno, Marni Max R Kumaseh Miske Silangen NELSON NAINGGOLAN NELSON NAINGGOLAN Nida Jabari Noorul Islam Noorul Islam Noorul Islam Rindengan, Altien J. Rinny Mamarimbing Sandra Pakasi Sandy Laurentius Lumintang Sofia Wantasen Steven Ray Sentinuwo Sulu, Brian Sumual, Gery Josua Tenda, Edwin Vitrail Gloria Mairi Winsy Weku Yohanes Langi