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All Journal JOIV : International Journal on Informatics Visualization Studia Religia: Jurnal Pemikiran dan Pendidikan Islam
Rahma, Silvia
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Religion Humanities Computer Science & IT Education Other

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Comparison of Convolutional Neural Networks Transfer Learning Models for Disease Classification of Food Crop Faurina, Ruvita; Rahma, Silvia; Vatresia, Arie; Susanto, Agus
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.1936

Abstract

Indonesia is an agricultural country with 29% of the workforce working in the agricultural sector, however, farmers' knowledge and practices depend on informal local wisdom based on inherited past practices. Moreover, identifying diseases in plants is difficult to do with human vision so that intelligent technology is needed.  In this paper, an architecture of CNN models such as MobileNetV2, ResNetV50, InceptionV3 and DenseNet121 will be built to detect diseases based on leaf images of several crops obtained from the agroai dataset containing multiple crops namely bean, chili, corn, potato, tomato and tea. The model is used through transfer learning for feature extraction of the trained model with imagenet weights, with 4 fully connected layers. Each model for each crop will be compared to get the best model based on the accuracy of training, evaluation and testing. ResNet50 has the best performance for four type of plants, including bean plants with training accuracy of 99.49%, validation of 99.52%, testing of 98.96%, chili plants with training accuracy of 98.03%, evaluation of 98.75%, testing of 100%, tea plants with training accuracy of 99.62%, evaluation of 99.6%, testing of 99.74% and tomato plants with training accuracy of 99.62%, validation of 99.7%, testing of 99.37%. Moreover, MobileNetV3 has the best performance for 2 types of crops that is corn with training accuracy of 99.22%, validation of 99.69%, testing of 99.55%, and potato with training accuracy of 99.62%, evaluation of 99.60%, testing of 99.74%.
PROFESIONALISME GURU DALAM PENERAPAN KURIKULUM MERDEKA BELAJAR PADA MATA PELAJARAN PAI Rahma, Silvia; Munawwir; Rabiyatul Adawiyah
Studia Religia : Jurnal Pemikiran dan Pendidikan Islam Vol 9 No 02 (2025): Volume 09 Nomor 02 Tahun 2025
Publisher : UNIVERSITAS MUHAMMADIYAH SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/sr.v9i02.25765

Abstract

This study discusses teacher professionalism in implementing the Merdeka Curriculum in Islamic Religious Education (PAI) subjects. The Merdeka Curriculum is designed to provide flexibility to educators in developing learner-centered learning, thus requiring teachers to be more adaptive, innovative, and highly competent. However, in its implementation, teachers face various challenges, such as limited resources, lack of teacher readiness in adjusting learning methods, and paradigm shifts in learning evaluation. Therefore, an appropriate strategy is needed to improve teacher professionalism in order to optimize their role in the Merdeka Curriculum-based education system. This research uses the literature study method by analyzing various relevant reference sources to identify challenges and solutions in improving the professionalism of PAI teachers. The results show that strengthening continuous training, improving pedagogical and professional competencies, and supporting comprehensive education policies are the main factors in supporting the effective implementation of the Merdeka Curriculum. With the right strategy, PAI teachers are expected to be better prepared to develop innovative learning that meets the needs of students.
Co-Authors Arie Vatresia Faurina, Ruvita Ida Ayu Putu Sri Widnyani Munawwir Rabiyatul Adawiyah