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Tenda, Edwin
Sam Ratulangi University
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Mathematics 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 Aditya Lapu Kalua Alfonsius, Eric Alita, Debby Andreas Robert Langi , Yohanes Andreuw Vandy Lengkong Andrew Lengkong Andrew Lengkong Andria K. Wahyudi Aprilyani, Lilis Dwi Sapta Armando, Junando Arthur G. Pinaria Arundaa, Rillya Asalui, Darlene Syaroon Baba Barus Benny Pinontoan Benny Pinontoan Benny Pinontoan Bobby Polii Chriestie Ellyanne Juliet Clara Montolalu Chriestie Montolalu, Chriestie Christian A. J. Soewoeh Christian Alderi Jeffta Soewoeh Christian Soewoeh cindy Lahama Dedie Tooy Delon Daniel Wolayan Djoni Hatidja Dodisutarma Lapihu Dyah Ayu Megawaty Eliasta Ketaren Eliasta Ketaren Eliasta Ketaren, Eliasta Frangky J. Paat Gabriell Cristiano Agung Taroreh Imas Sukaesih Sitanggang Iqbal Firdaus Islam, Noorul Jantje Pongoh Juan Sebastian Sijron Lahope Kinzie Feliciano Pinontoan Lapihu, Dodisutarma Legi, Fabio Kevin Williams Lonteng, Alfa Luther Latumakulita Mahardika I. Takaendengan Mahardika Inra Takaendengan Mandagi, Franklin Megastin M. Lumembang Mongi, Charles Efraim Montolalu, Chriestie Ellyanne Clara Juliet Muhammad Saefudin Ngangi, Stephano C. W. Ngangi, Stephano Caesar Wenston Parluhutan Siahaan Pioh, Diane Prisilia Lumentah, Sweety Reymon Rotikan Rillya Arundaa Rinny Mamarimbing Samola, Jonathan Sandra Pakasi Sanriomi Sintaro Saroyo Saroyo Siska Ayu Widiana Soewoeh, Christian A. J. Soewoeh, Christian Alderi Jeffta Sofia Wantasen Sofiana Paendong, Marline Stenly Ibrahim Adam Stephano C. W. Ngangi Stephano Caesar Wenston Ngangi Stephano Caesar Wenston Ngangi Ngangi Takaendengan, Mahardika I. Takaendengan, Mahardika Inra Tambalean, Feergio Given Majesty Tohap Manurung Trianita, Maria Tutu, Elrica Meriana Isabel Wisard Kalengkongan Wisard Widsli Kalengkongan Wuaten, Frank Emmanuel