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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmu Komputer dan Agri-Informatika Jurnal CoreIT Jurnal Teknoinfo Petir Kilat TEKNOLOGIA Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Journal Social Science And Technology For Community Service BEES: Bulletin of Electrical and Electronics Engineering Jurnal Sisfotek Global Journal of Computing and Informatics Research Bulletin of Informatics and Data Science Jurnal Teknoinfo
Palupiningsih, Pritasari
Institut Teknologi Perusahaan Listrik Negara
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Journal : Jurnal Teknoinfo

 1 
IMPLEMENTASI DEEP LEARNING UNTUK IDENTIFIKASI UMUR TANAMAN BERDASARKAN CITRA DAUN PADA SMART FARMING Budi Prayitno; Pritasari Palupiningsih; Farhan Muhamad Ikhsan
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Technology plays an important role in optimizing agricultural production, one of which is through the application of smart farming. Smart Farming is a paradigm in agriculture that utilizes information and communication technology (ICT). The case study raised in this study is the use of smart farming in determining plant age. Plant age is an important factor in determining the harvest. Plants that are harvested at the right time can produce quality products in optimal quantities. Traditional farmers determine plant age manually. This has challenges, namely the process takes a long time and a lot of energy, especially for large agricultural areas. Plant age must be identified quickly and easily, the results of plant age identification are accurate and consistent and can be applied to large agricultural areas. The urgency of this research is the creation of a deep learning model that is used to detect the optimum plant age with a high accuracy value. The importance of this research lies not only in the development of technology but also in its contribution to the farmer's economy and the progress of the agricultural sector. This study aims to implement deep learning to form a classification model for identifying plant age based on leaf images and to evaluate the classification model to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preparation, modeling, and evaluation. The deep learning method used is classification with the application of the Convolutional Neural Network (CNN) VGG architecture algorithm, which has been proven effective in image analysis. The results of this study are Research on age classification models on plant leaf images using the classification method with the CNN algorithm is carried out with the stages of data collection and class division, image resizing, data augmentation, adding keras models, convolution, max pooling, flatten, relu, and with the training of 20 epochs. The results of model formation with the CNN algorithm using VGG16 get higher accuracy than VGG19. The best accuracy value is 78% from the confusion matrix results using VGG19 with a data ratio of 60% training data, 20% validation data, and 20% testing data.
Development of a Plant Weed Detection Model Using the Mask R-CNN Algorithm for Smart Farming Budi Prayitno; Pritasari Palupiningsih; Atam Rifai Sujiwanto
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.873

Abstract

A more efficient and sustainable agricultural system is urgently needed during world population growth and global climate change. One of the main challenges is that ineffective weed management can significantly reduce crop yields. Conventional farming methods, such as large-scale herbicide application, also negatively impact the environment. Therefore, the development of smart farming technology based on artificial intelligence (AI) is a crucial innovative solution. This research is urgent in the context of developing AI-based systems that significantly contribute to agricultural technology. The urgency of this study is the creation of a plant weed detection model using deep learning to determine the readiness of planting land with high accuracy values. The importance of this research lies not only in the development of technology, but also in its contribution to the farmer economy and the progress of the agricultural sector in Indonesia. This research aims to build and develop a plant weed detection model using deep learning to determine the readiness of planting land, as well as evaluate the detection model built to produce high accuracy. The research method used follows a flow consisting of problem understanding, data understanding, data preprocessing, modelling, and evaluation. The deep learning method used is object detection by applying the Mask R-CNN algorithm with the ResNet-50 architecture as the backbone. The evaluation of model performance was carried out using Mean Average Precision (MAP). The results of this study demonstrated the development of a deep learning-based weed detection model using the Mask R-CNN algorithm, which achieved a MAP of 37.32 and was able to overcome the challenges of varying weed types, lighting conditions, and complex field conditions.
Co-Authors Aditia Yudhistira Ahmad Ari Aldino Alwi Muhammad Andi Dahroni Andi Muhammad Fadel Omar Pasha Andi Nikma Sri Handayani Andika, Rio Ari Sulistiyawati Arshad, Muhammad Waqas Atam Rifai Sujiwanto Budi Prayitno Dahroni, Andi Dody Dody Eka Putra Farhan Muhamad Ikhsan Ferico Octaviansyah Pasaribu, Ahmad Firmansyah Apryadhi Gibtha Fitri Laxmi Hamdan Sobirin, Muhammad Haryono Haryono Heni Sulistiani Kusuma, Anndya Dyah Marcelindo, Fitto Megawaty, Dyah Ayu Mehta, Abhishek Meiwasandi, Putu Niar Muhammad Fadli Pratama Muhammad Fadli Prathama Muhammad fadli Prathama Nasril Sany Pratomo Prawirodirjo, Raden Ronggo Bintang Prawirodirjo, Raden Ronggo Bintang Pratomo Putri Ayu Mardhiyah Rakhmadi Irfansyah Putra Rd.M.Dimas Burhanudin Akbar Riki Ruli A. Siregar, Riki Ruli A. Setiawansyah Setiawansyah Sitna Hajar Hadad Sufiatul Maryana Sujiwanto, Atam Rifa'i Wang, Junhai Yessy Asri Yuri Rahmanto