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All Journal Journal of Applied Data Sciences Jurnal Agroekoteknologi Terapan
Frangky J. Paat
Sam Ratulangi University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science

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TEST THE POTENTIAL GROWTH AND YIELD OF PLANTS PAK CHOI (Brassica rapa Subsp. chinensis (L.) Hanelt Var. Green) THROUGH HYDROPONICS SYSTEM-NUTRIENT FILM TECHNIQUE AT THE LEVEL OF THE CONCENTRATION OF NUTRIENTS ®AB MIX Frisky Fernando Rembet; Frangky J. Paat; Jemmy Najoan
Jurnal Agroekoteknologi Terapan Vol. 2 No. 2 (2021): EDISI JULI-DESEMBER 2021
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35791/jat.v2i2.35335

Abstract

This study aims to obtain the results of the growth and production of pak choi plants. The level of nutrient concentration of ®AB Mix in this treatment is the independent variable, while the dependent variable is the pak choi plant. This study was arranged in a Randomized Block Design with 4 treatments and 3 replications, the factors studied were the concentration of AB mix nutrients with the following treatments: (P0) (Control), (P1) 900ppm, (P2) 1100 ppm and (P3) 1300 ppm. The results showed that the best plant height was produced at the nutrient concentration level of ®AB Mix in the treatment of 1100 ppm (P2) with an average of 15.35 ppm and 1300 ppm (P3) 16.23 cm. The growth parameters on the longest plant roots were produced at the level of nutrient concentration ®AB Mix 1100 ppm (P2) with an average of 25.536 cm and 1300 ppm (P3) with an average of 26,097 (P3) cm. The best fresh weight of plants was produced at the level of nutrient concentration ®AB Mix in the treatment of 1100 ppm (P2) 196.67 gr and 1300 ppm (P3) with an average of 236.67 gr (P3). Environmental variables with an average morning temperature of 28°C, 32°C during the day, 26°C at night, 60% humidity in the morning, 30% humidity during the day and 89% humidity at night for 42 days after planting.Keywoards:    Hydroponic; Agroecotechnology; Pak Choi. AbstrakPenelitian ini bertujuan untuk mendapatkan hasil pertumbuhan dan produksi tanaman pak choi. Taraf konsentrasi nutrisi ®AB Mix pada perlakuan ini sebagai variabel bebas (independent variable) sedangkan variabel terikat (dependent variable) adalah tanaman pak choi. Penelitian ini disusun dalam Rancangan Acak Kelompok dengan 4 perlakuan dan 3 ulangan, faktor yang diteliti adalah konsentrasi nutrisi AB mix dengan perlakuan : (P0) (Kontrol),  (P1) 900ppm, (P2) 1100 ppm dan (P3) 1300 ppm. Hasil penelitian didapatkan bahwa tinggi tanaman terbaik dihasilkan pada taraf konsentrasi nutrisi ®AB Mix pada perlakuan 1100 ppm (P2) dengan rataan 15,35 ppm dan 1300 ppm (P3) 16,23 cm. Parameter pertumbuhan pada akar tanaman terpanjang dihasilkan pada taraf konsentrasi nutrisi ®AB Mix 1100 ppm (P2) dengan rataan 25,536 cm dan 1300 ppm (P3) dengan rataan 26,097 (P3) cm. Bobot segar tanaman terbaik dihasilkan pada taraf konsentrasi nutrisi®AB Mix pada perlakuan 1100 ppm (P2) 196,67 gr dan 1300 ppm (P3) dengan rataan 236,67 gr (P3). Variabel lingkungan dengan rataan suhu pagi hari 28°C, Siang hari 32°C, malam hari 26°C, rataan kelembaban 60% pada pagi hari, kelembaban 30% pada siang hari dan kelembaban 89% pada malam hari selama 42 hari setelah tanam.Kata kunci:    Hidroponik; Agroekoteknologi; Pak Choi.
Comparison of MobileNet and VGG16 CNN Architectures for Web-based Starfish Species Identification System Luther Alexander Latumakulita; Frangky J. Paat; Saroyo Saroyo; Irwan Karim; I Nyoman Gede Arya Astawa; Hasanuddin Sirait
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.456

Abstract

Bunaken Marine Park (BMP) is famous for its rich marine biodiversity. BMP is an asset for the marine tourism industry of the Manado city government, and the North Sulawesi Province of Indonesia needs to be strengthened. This research aims to build a web-based intelligent system using a convolutional neural network (CNN) to identify starfish species to initiate developing a media center marine biota identification system of BMP. Two CNN architectures, namely MobileNet and VGG16, were conducted to produce identification models. The first stage carried out a training process on 1800 starfish image data and then evaluated using the 5-fold cross-validation technique. Validation results show that MobileNet is superior to the VGG16 architecture by achieving validation accuracy of 100% for each fold while VGG16 produces validation accuracy in the range of 94% to 100%. On the other hand, in the second stage of model testing, it was found that VGG16 worked better than MobileNet in identifying 200 new data. The Best Model produced by VGG16 achieved testing accuracy of 100% while MobileNet produced 99.5%. However, stability analysis of the identification models produced by both architectures shows that MobileNet has relatively small loss values ranging from 0.00069325 to 0.00214802 as well as smaller standard deviation values of 0.27 compared to 0.61 produced by VGG16. These findings indicate MobileNet is more stable in carrying out identification work compared to VGG16 of, thus the best model provided by MobileNet is taken to deploy in the web platform which is created using the Python flask framework. The proposed system can be used to strengthen the marine tourism industry as a media center of educational marine biota using deep learning approaches.
Co-Authors Edy F. Lengkong Friska Malintoi Frisky Fernando Rembet Hasanuddin Sirait I Nyoman Gede Arya Astawa Irwan Karim Jemmy Najoan Luther Latumakulita Ronny Nangoi Saroyo Saroyo Tommy B. Ogie