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All Journal Bulletin of Social Informatics Theory and Application EKOTON
Frangky Paat
Sam Ratulangi University
Agriculture, Biological Sciences & Forestry Computer Science & IT Environmental Science 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
Analysis Of Irrigation Water Quality For Fields In Treman Village, North Minahasa Kauditan Regency Syafira Salsabella; Bobby Polii; Wiske Rotinsulu; Frangky Paat; Jellie Porong; Adeleyda Lumingkewas; Jooudie Luntungan; Sofia Wantasen
EKOTON Vol. 4 No. 1 (2022): ISSUE JANUARI - JUNI 2022
Publisher : PPLH-SDA, Lembaga Penelitian Unsrat Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35801/ekoton.v5i1.49535

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Abstract. Syafira Salsabella1), Bobby J. V. Polii1*), Wiske Ch. Rotinsulu1) , Frangky J. Paat1), Jellie V. Porong1) , Adeleyda M. W. Lumingkewas1), Jooudie N. Luntungan 2) , Sofia Wantasen. Analysis Of Irrigation Water Quality For Fields In Treman Village, North Minahasa Kauditan Regency. Ekoton   5. 28-35. This study aims to determine the quality of irrigation water in rice fields in Treman Village, Kauditan District, North Minahasa. This research was conducted for two (2) months, namely from March to April 2023. This research was conducted using purposive sampling method, the sampling technique is Composite. Research samples were taken at three points, namely the Primary Canal, Secondary Canal, and Tertiary Canal. Samples were taken as much as 4500 mL in each Canal, then analyzed at the Manado Industrial Research and Standardization Center Laboratory. The data obtained was analyzed using Bar Chart Statistics. The research shows that the Irrigation Water Quality of Rice Field Areas in Treman Village, Kauditan District, North Minahasa is still classified as very good because it meets the requirements as irrigation water according to Water Quality Of Irrigation based on Ayers & Westcott (1995), and there is no pollution based on Government Regulation No. 22 of 2021 concerning the Implementation of Protection and Management of Irrigation Water. Keywords: Quality of irrigation water, Sodium Absorption Ratio
Co-Authors Adeleyda Lumingkewas Arthur Pinaria Bobby Polii Dedie Tooy Diane Pioh Franklin Mandagi Jantje Pongoh Jellie Porong Jooudie Luntungan Luther Latumakulita Noorul Islam Rinny Mamarimbing Sandra Pakasi Sofia Wantasen Syafira Salsabella Tenda, Edwin Wiske Rotinsulu