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Journal : JOIV : International Journal on Informatics Visualization

Image Prediction of Exact Science and Social Science Learning Content with Convolutional Neural Network - Mambang; Finki Dona Marleny
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.923

Abstract

Learning content can be identified through text, images, and videos. This study aims to predict the learning content contained on YouTube. The images used are images contained in the learning content of the exact sciences, such as mathematics, and social science fields, such as culture. Prediction of images on learning content is done by creating a model on CNN. The collection of datasets carried out on learning content is found on YouTube. The first assessment was performed with an RMSProp optimizer with a learning rate of 0.001, which is used for all optimizers. Several other optimizers were used in this experiment, such as Adam, Nadam, SGD, Adamax, Adadelta, Adagrad, and Ftrl. The CNN model used in the dataset training process tested the image with multiple optimizers and obtained high accuracy results on RMSprop, Adam, and Adamax. There are still many shortcomings in the experiments we conducted in this study, such as not using the momentum component. The momentum component is carried out to improve the speed and quality of neural networks. We can develop a CNN model using the momentum component to obtain good training results and accuracy in later studies. All optimizers contained in Keras and TensorFlow can be used as a comparison. This study concluded that images of learning content on YouTube could be modeled and classified. Further research can add image variables and a momentum component in the testing of CNN models.
Intelligent Monitoring System Framework for Peatland Management in IoT-Integrated Precision Agriculture Marleny, Finki Dona; Novriansyah, Irvan; Maulida, Ihdalhubbi; Ansari, Rudy; Mambang, Mambang; Saubari, Nahdi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2955

Abstract

Peatlands have excellent air retention capabilities and are crucial for environmental health. They act as natural sponges, absorbing and releasing air, which helps maintain soil moisture levels vital for crops. However, peatlands are highly sensitive ecosystems often threatened by unsustainable agricultural practices. When managed sustainably, peatlands scattered across the globe can be utilized for various farming activities. Managing peatlands for food crops presents an alternative to agriculture in peatland areas, enhancing economic growth in rural regions. This research aims to introduce a framework that integrates IoT into the intelligent monitoring of peatland management for precision agriculture. The primary challenge is implementing effective monitoring and management strategies for sensitive peatlands within precision agriculture. The main principle of precision agriculture is data-driven decision-making, supported by modern agricultural management that employs technology and data analysis to optimize farming practices. The proposed system framework can be utilized to identify the best types of food crops for making new decisions while ensuring high yields at the agricultural level. Precision agriculture principles are then applied to enhance the accuracy of monitoring peatland management, focusing on suitable land potential and food crops planted in areas with the highest potential. The results indicate that prioritizing peatlands for food crops reduces inappropriate decisions in selecting food crops. Furthermore, the efficiency of agricultural management can be improved with lower management costs. This framework provides a practical and user-friendly basis for informing all stakeholders on automating Peatland agriculture for food crops using precision agriculture systems integrated with IoT. Management practices that apply information technology aim to optimize crop inputs based on temporal and spatial variability. The cost-effectiveness from this perspective creates transition opportunities for communities, positioning our framework as a solution for designing Peatland management with intelligent monitoring.
Co-Authors Ade Putri Maharani Adha, Muhammad Iqbal Ahadi Ningrum, Ayu Ahmad Faisal Hamidi Ahmad Hidayat Ahmad Hidayat Ahmad Nawawi Ahmad Riki Renaldy Akhmad Baddrudin Antonia Yenitia Aqli, Ahmad Aulia Fitri Aulia Fitri Aulia Fitri, Aulia Ayu Ahadi Ningrum Bambang Lareno, Bambang Bayu Nugraha Bima Wicaksono Damayanti, Alfisah Dixky Dixky Elisa Fitriana Fatahulrahman, Maman Fitriansyah, Muhammad Gazali, Mukhaimy Hamdani Hamdani Haniffah Sri Rinjani Hudatul Aulia Ihdalhubbi Maulida Ihsanudin Indah Wulandari Jaya Hari Santoso Johan Wahyudi Johan Wahyudi, Johan Kamaruddin Kamarudin Kamarudin Kamarudin Kartika Kartika Liliana Swastina Lufila, Lufila M Samsul Hasbi M Samsul Hasmi Maman Fatahulrahman Mambang Mambang Fitriansyah Maria Ulfah Maulida, Ihdalhubbi Meila Izzana, Meila Melda Melda Miranda Miranda Muhammad Khairul Akbar Muhammad Noval Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Tantowi Jauhari Muhammad Zaini Bakri Muhammad Ziki Elfirman Muhammad Ziki Elfirman Muhammad Ziki Elfirman, Muhammad Ziki Muhammad Zulfadhilah Mukhaimy Gazali Mutmainah Mutmainah Nahdi Saubari Nalo Valentino Ningrum, Ayu Ahadi Nor Azizah Novita Sari Novriansyah, Irvan Nur Hafiz Ansari Nur Meilianti Maulida Nurhaeni Nurhaeni Prastya, Septyan Eka Putri Putri Putri Putri, Putri Rahmini Rahmini Reni Emiliya Ricardus A P, Ricardus A Risma Maulida Risma Risma Rismawati Rismawati Rizkian Muhammad Fikri Ropikah Ropikah Rudy Ansari Rudy Ansari Rudy Ansari, Rudy Samita, Mambang Sandro Nesta Pembriano Sa’adah Sa’adah Septian Eka Prastya Septyan Eka Prastya Septyan Eka Prastya Subhan Panji Cipta Susanti, NurAina Tasya Salsabila Theresia Kurniati Seran Tiara, Astia Rahma Tumanggor, Agustina Hotma Uli Winda Astria Nuansa Saputri Winda Astria Nuansa Saputri Windarsyah Windarsyah Wulandari Febriani Yulisa Suryana Yuslena Sari, Yuslena