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All Journal AGRIVITA, Journal of Agricultural Science JPSriwijaya Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Candra Dewi
Fakultas Ilmu Komputer Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Neuroscience Public Health

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PENGUKURAN PENERIMAAN PETANI TERHADAP TEKNOLOGI WEB MENGGUNAKAN METODE TECHNOLOGY ACCEPTANCE MODEL Candra Dewi; Rekyan Regasari Mardi Putri
Jurnal Pengabdian Sriwijaya Vol 6, No 1 (2018)
Publisher : Lembaga Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37061/jps.v6i1.1955

Abstract

Website merapakan media yang saat ini banyak digunakan untuk mempromosikan usaha. Akan tetapi penggunaan website sebagai media promosi belum banyak dilakukan oleh petani dan kelompok usaha kecil karena sebagian besar dari mereka berpendidikan rendah dan tidak mengenal penggunaan website. Akan tetapi memperkenalkan teknologi ini kepada petani dan kelompok usaha kecil perlu untuk dilakukan. Dalam kegiatan ini dikembangkan media promosi tanaman sayur berbasis web bagi Kelompok Rumah Pangan Lestari (KRPL) Dewi Sri, Sumbergempol, Tulungagung. Website dikembangkan dengan tampilan yang sederhana dan dapat diakses menggunakan telepon genggam. Selanjutnya dilakukan pelatihan penggunaan media ini kepada anggota kelompok. Berdasarkan kuisioner yang dilakukan, kemudian dilakukan analisa  menggunakan Technology Acceptance Model (TAM). Dari hasil analisa dapat diketahui bahwa kelompok dapat menerima penggunaan teknologi ini dengan cukup mudah sehingga diharapkan dapat secara kontinyu memanfaatkan media ini untuk kegiatan promosi dan pemasaran produk.
Automatic Differentiating of Postharvest Banana Fruits with High Traits Using Imagery Data Candra Dewi; Wayan Firdaus Mahmudy; Solimun Solimun; Endang Arisoesilaningsih
AGRIVITA, Journal of Agricultural Science Vol 44, No 2 (2022)
Publisher : Faculty of Agriculture University of Brawijaya in collaboration with PERAGI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17503/agrivita.v44i2.3648

Abstract

Visually differentiating banana cultivar with high similarity in shape, color and peel texture requires skill and experience during harvesting to reduce mistake on identifying cultivar. This study aims to identify automatically some similar banana cultivars using banana finger imagery and computer vision. The identification process was carried out to distinguish two groups of bananas with high similarities, namely group 1 (Ambon, Hijau, Goroho) and group 2 (Barlin, Mas). The test was conducted on the pair of datasets of unripe Ambon-Hijau-Goroho, ripe Hijau-Goroho, ripe and unripe Barlin-Mas. Testing was done to determine the performance of identification and to find out the most effective characteristics that could be used as cultivar identification. Results of classification using extreme learning machine (ELM) showed that texture features extracted from local binary pattern (LBP) could accurately distinguish unripe Ambon-Goroho, unripe Goroho-Hijau, ripe Goroho-Hijau with 100% accuracy. While unripe Ambon-Hijau, unripe Barlin-Mas and ripe Barlin-Mas could be optimally distinguished using a combination of shape and peel texture features with accuracy of 93.39%, 89.68%, 99.31% respectively. This result indicated that the proposed method could be used as an alternative of automatic banana sortation during post-harvest. The use of shape and peel texture features had shown effectively differentiating these high similarity banana cultivars.
Optimasi Formulasi Pakan pada Proses Budidaya Ikan Bandeng Menggunakan Particle Swarm Optimization (PSO) Denny Irfan Darmawan; Imam Cholissodin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 2 (2018): Februari 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1265.536 KB)

Abstract

Milkfish is a cultivation commodity that contributes quite large on a national scale. But in practice, milkfish cultivation requires high production costs, especially in the procurement of fish feeds. This study will explore how to optimize the composition of milkfish feeds, minimize costs without lowering the production, as well as keeping the nutritional needs of milkfish in check. An optimization method, Particle Swarm Optimization (PSO) is used in the process of formulation and composition of milkfish feeds to keep fulfilling the nutritional needs of fish with minimal cost. The PSO algorithm process begins with initialization process for position, speed, and Pbest values ​​as much as the number of specified particles, and Gbest. Then the process progresses to the update stage of speed, position, Pbest, and Gbest as much as a predetermined iteration. Based on the results of the tests conducted in this study, optimal parameters have been obtained such as the number of particles as much as 100, the number of iterations of 70, lower boundary value and upper limit particles of 1,0 - 9,0, and coefficient value of 0,4. Using these parameters, the best fish feeds composition obtained for 10-week-old milkfish with a weight per unit of 0,25 kilograms and total fish population of 500 is 0,237 kilograms fish meal, 1,384 kilograms cassava flour, and 2,129 kilograms white leadtree leaf flour with total cost of Rp. 15.017,625.
Identifikasi Jenis Attention Deficit Hyperactivity Disorder pada Anak menggunakan Learning Vector Quantization dengan Seleksi Fitur menggunakan Algoritme Genetika Chalid Ahmad Aulia; Dewi Candra; Sutrisno Sutrisno
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (330.504 KB)

Abstract

Attention Deficit Hyperactivity Disorder is one of the common disorders that may occur on children which is indicated by certain kinds of behaviors such as the inability to calm down, not being able to pay much attention, and sudden desire to do excessive things. There are three types of ADHD in general: inattention, impulsiveness, and hyperactivity. Unfortunately, a lot of people are unaware of the dangers of this disorder if not treated from an early stage. Therefore, a system to identify the type of ADHD in a child is needed. This study implements Learning Vector Quantization as the algorithm to classify the types of ADHD and genetic algorithm as the selector of relevant features. In this study, there are 45 features which are the symptoms of ADHD that will be selected in advance by the genetic algorithm to determine which features are going to be used in the LVQ process to determine its accuracy value. The testing includes finding the numbers of variables that may have impacts to the results and can result the highest accuracy numbers. The best parameters with the highest accuracy results are the population size of 15, crossover rate of 0.9, mutation rate of 0.1, number of generations of 7, and the learning rate of 0.5 where the average accuracy is 96%.
Co-Authors Chalid Ahmad Aulia Denny Irfan Darmawan Endang Arisoesilaningsih Imam Cholissodin Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Solimun, Solimun Sutrisno Sutrisno Wayan Firdaus Mahmudy