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All Journal Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Jurnal Pengabdian Kepada Masyarakat Radisi
Untung Joko Basuki
Universitas Akprind Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Pelatihan Manajemen Produksi dan Pemasaran Digital bagi Peternak Ayam Petelur dan Kalkun di Dusun Karang, Desa Tuksono, Kecamatan Sentolo, Kabupaten Kulon Progo, Yogyakarta Amir Hamzah; Renna Yanwastika Ariyana; Untung Joko Basuki
Jurnal Pengabdian kepada Masyarakat Radisi Vol 6 No 1 (2026)
Publisher : Yayasan Kajian Riset dan Pengembangan RADISI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55266/pkmradisi.v6i1.665

Abstract

The development of digital technology and the internet requires businesses, including livestock businesses, to adapt to improve their performance and sustainability. Laying hen and turkey farmers in Karang Hamlet, Tuksongo Village, Sentolo District, as community service partners, face production management challenges such as feed control, livestock health, financial management, and traditional marketing management. One of these challenges stems from the low digital literacy of owners and employees. With 440 laying hens and 50 turkeys, all operational and marketing management has not yet utilised digital technology. This community service activity aims to improve the understanding and application of digital literacy in production and marketing management. The methods used include training in the application of digital applications in production management and marketing design using the internet and social media. The results of the activity indicate an increase in the understanding of farm owners and managers in production management (85%) and digital marketing management (90%). This increased understanding of digital literacy has proven to improve production aspects and better preparedness for development in marketing aspects.
KLASIFIKASI TINGKAT KEMATANGAN BUAH PISANG DENGAN ALGORITMA CNN  Amir Hamzah; Renna Yanwastika Ariyana; Untung Joko Basuki; Muhammad Sholeh; Bagas Tri Basgoro
Jurnal DutaCom Vol 19 No 1
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/xmqa2d79

Abstract

Bananas are the most widely produced fruit in Indonesia, which is around 9.69 million tons in 2024. With this amount of data, bananas have quite promising economic value. The production of large quantities of bananas according to the previous data needs to be carried out a rapid distribution process, because the time the bananas after harvesting may only last about 5 to 7 days in normal temperatures before the fruit rotting process will finally occur. For this reason, it is necessary to carry out a quick banana classification process, the process is carried out automatically using a machine. This study elaborates on the capabilities of the CNN algorithm  in the classification of bananas. Dataset was taken from Keagle's open source as many as 3000 image data. In their classification, researchers divided bananas into three levels of fruit ripeness, namely raw, ripe, and overripe. The study used a CNN model  consisting of several layers consisting of a 2D convolutional layer (Conv2D), a 2D pooling layer (MaxPooling2D), a flatten layer, a fully connected layer (Dense), and a Dropout layer. After the model creation process is complete, the model will be tested for accuracy with  the Confusion matrix method. In the 3rd experiment the model produced the highest level of accuracy in its trials, with 300 test images resulting in 290 correctly predicted images so that the accuracy reached 97%. From the results  of deploying using the website interface using flask, it was found that the classification had an accuracy of above 95% so it was good enough to be used as a prototype for classification engine applications.
Co-Authors Amir Hamzah Bagas Tri Basgoro Muhammad Sholeh Renna Yanwastika Ariyana