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Sofia Wantasen
Universitas Sam Ratulangi
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Bulletin of Social Informatics Theory and Application

 1 
Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network Latumakulita, Luther; Mandagi, Franklin; Paat, Frangky; Tooy, Dedie; Pakasi, Sandra; Wantasen, Sofia; Pioh, Diane; Mamarimbing, Rinny; Polii, Bobby; Pongoh, Jantje; Pinaria, Arthur; Tenda, Edwin; Islam, Noorul
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 Adeleyda Lumingkewas Adeleyda M. W. Lumingkewas Anatasya Siahaan Annie E. arore Apri Silia M. Dampi Arthur G. Pinaria Billy Fandy. Sagai Bobby J. V. Polii Bobby Polii Bobby Polii Chintya Gita Sartika Modeong Christine F Mamuaja Dedie Tooy Deflly A. S. Turang Deflly Ansye Shilfana Turang Defly A. S. Turang Demmassabu, Langimanapa Sofia Diane D. Pioh Diane Pioh Eka P. Manalu Eko Sugiharto Elisabeth R. M. Meray Enderika Enus Fadlika Mawali Frangky J. Paat Frangky Paat Fransiska Mitra.B.K Zebua Gurning, Aksay K. Hendra Yusuf Islam, Noorul Jackson Watung Jacqueline Joseph Jantje Pongoh Jantje Pongoh Jeanne Martje Paulus Jeanne Martje Paulus, Jeanne Martje Jellie Porong Jemmy Najoan Jemmy Najoan, Jemmy Jimmy Rimbing Johannes E. X. Rogi John L. Rantung, John L. Jooudie Luntungan Jooudie N. Luntungan Jooudie N. Luntungan Joudie N. Luntungan Joudie. N. Luntungan Junivia Virginia Jermias Koneri, R. Langimanapa Sofia Demmassabu Lumingkewas, Adeleyda Lumingkewas, Adeleyda M. W. Lumingkewas, Adeleyda M.W. Lumingkewas, Adeleyda Margharetha Luntungan, J. N. Luntungan, Jooudie Luther Latumakulita Mamuaja, Christine F Mandagi, Franklin MARJAM M. TODING Melkison Sanggel Moniaga, Wilson M. Noeng, Masshynio Ch. A. Ogie, Tommy B. Paat, Frangky Jessy Pioh, Diane Popo Fauza Gultom Porong, Jellie Rinny Mamarimbing Roni Koneri Salsabella, Syafira Sandra Pakasi Sandra Pakasi Selvie Tumbelaka Selvie Tumbelaka Siahaan, Anatasya Siti Mutmaina Pondaag Slamet Suprayogi Sudarmadji Sudarmadji Sumakud, Maria Yolanda Syafira Salsabella Tenda, Edwin TODING, MARJAM M. Tommy B Ogie Tommy B. Ogie Turang, Deflly Ansye Shilfana Watung, Jackson F. Wiske Rotinsulu Wiske Rotinsulu Wiske Rotinsulu Yulianti Gandi