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All Journal Jurnal Bios Logos Jurnal Agroekoteknologi Terapan Biofaal Journal AGRI-SOSIOEKONOMI Bulletin of Social Informatics Theory and Application
Arthur G. Pinaria
Universitas Sam Ratulangi
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Economics, Econometrics & Finance Environmental Science Immunology & microbiology Materials Science & Nanotechnology Medicine & Pharmacology Social Sciences

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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
ANALISIS KANDUNGAN SULFORAFAN PADA FASE KECAMBAH BEBERAPA JENIS BROKOLI DALAM MEDIA MS YANG DIBERIKAN NAA DAN BAP SECARA IN VITRO Lengkong, Edy F.; Tilaar, Wenny; Pinaria, Arthur
AGRI-SOSIOEKONOMI Vol. 18 No. 1 (2022)
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35791/agrsosek.v18i1.55203

Abstract

This study aims to: (1) determine the content of sulforaphane in several types of broccoli sprouts. (2) Knowing the difference in sulforaphane content in the combination of NAA, BAP and types of broccoli grown on MS media; (3) knowing the difference in sulforaphane content in the combination of NAA and types of broccoli sprouts grown on MS media; (4) knowing the difference in sulforaphane content in the combination of BAP and the type of broccoli sprouts grown on MS media. This study used a completely randomized design (CRD) arranged in a factorial manner, namely factor A: NAA 0, 0,5, and 1 ppm. B: BAP 0, 1, 2 ppm and C : 2 Types of Broccoli. The variables observed were: germination time, germination weight, and sulforaphane content. The data were analyzed by analysis of variance and continued with the 5% BNT test. The results showed that germination time began to appear on day 3 and all seeds germinated. Sprout weight varied greatly, and the combination of treatments N1, B0 and BR2 gave the highest average weight and calculated concentration (ENT) of sulforaphane, followed by treatments N0B2BR2 and N0,5B0BR1.
PENGARUH WAKTU PEMANGKASAN DAUN DIBAWAH TONGKOL TERHADAP PRODUKSI TANAMAN JAGUNG (Zea mays L) DI DESA PADANG KECAMATAN BINTAUNA BOLAANG MONGONDOW UTARA Noeng, Masshynio Ch. A.; Lumingkewas, Adeleyda M. W.; Najoan, Jemmy; Pinaria, Arthur; Tumbelaka, Selvie; Wantasen, Sofia; Pakasi, Sandra
Biofaal Journal Vol 5 No 1 (2024): Biofaal Journal
Publisher : Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/biofaal.v5i1pp019-025

Abstract

Jagung (Zea mays L) adalah salah satu tanaman pangan yang merupakan sumber karbohidrat utama kedua setelah beras. Metode modifikasi lingkungan mikro dapat meningkatkan produksi jagung. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh waktu pemangkasan daun di bawah tongkol terhadap tingkat produksi jagung. Studi ini dilakukan di Desa Padang, Kecamatan Bintauna, Kabupaten Bolaang Mongondow Utara, Sulawesi Utara, dari Desember 2023 hingga April 2024. Metode penelitian adalah rancangan acak kelompok dengan lima perlakuan dan tiga kali ulangan. Penelitian menemukan bahwa perlakuan pemangkasan daun di bawah tongkol adalah tanpa pemangkasan (P0), pemangkasan 60 hari setelah tanam (P1), pemangkasan 70 hari setelah tanam (P2), pemangkasan 80 hari setelah tanam (P3) dan pemangkasan 90 hari setelah tanam. Hasil penelitian menunjukkan bahwa komponen produksi pada panjang tongkol, yaitu ukuran tongkol 19,26 cm dengan diameter 5,00 cm, berat biji sekitar 241.92 g, dan jumlah biji sekitar pertongkol 617.42, meskipun berdasarkan hasil analisis sidik ragam, pengaruh tanaman jagung hanya terbatas pada panjang dan diameter tongkol.
Co-Authors BEIVY JONATHAN KOLONDAM Bernadeth Vivi Montong Bobby Polii Christian Christhopher Sambur Claudia Juniar Monggesang Dedie Tooy Deivi V. Saburu, Deivi V. Diane D. Pioh Edy F Lengkong, Edy F Edy F. Lengkong Edy F. Lengkong Edy F. Lengkong Edy Lengkong Eka Julianti Frangky J. Paat hizkia mantiri Islam, Noorul J Polii-Mandang J. Polii, Mandang Jackson F. Watung Jailani ., Husain James B. Kaligis Jantje Pongoh Jemmy Najoan, Jemmy Lumingkewas, Adeleyda M. W. Luther Latumakulita Lydia Elisabeth Agatha Tulung Mandagi, Franklin Meilani Elseday Ma'tan Noeng, Masshynio Ch. A. Patricia Mandagi Pioh, Diane Raesita Dorhayne Timburas Rinny Mamarimbing Runtunuwu Semuel Runtunuwu, Semuel Sandra E. Pakasi Sandra Pakasi Selvie Tumbelaka Sofia Wantasen Tenda, Edwin Wenny ., Tilaar Wenny ., Tilaar Wenny Tilaar