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All Journal International Journal of Advances in Intelligent Informatics Jurnal At-Tarbiyat (Jurnal Pendidikan Islam)
Khaleel, Amal Hameed
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Computer Science & IT Education

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Contextualizing The Value Of Islamic Education In The Digital Era: Challenges And Adaptations In The Khitbah Hadith Taufik, Akhmat; Kasman, Kasman; Wibowo, Safrudin Edi; Hosaini, Hosaini; Khaleel, Amal Hameed
At-Tarbiyat Vol 7 No 3 (2024): Islamic Education In Indonesia
Publisher : Institut Agama Islam An-Nawawi Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37758/jat.v7i3.932

Abstract

This study aims to examine the values of Islamic education contained in the khitbah hadith and its relevance in the digital era. The transformation of technology and social media in the digital era has changed the way individuals interact, including in the marriage proposal process. This study uses a qualitative method based on library research to explore the adaptation of the educational values of the khitbah hadith in facing digital challenges. The results of the study identified the main educational values in the khitbah hadith, namely maintaining intentions and goals, honor and modesty, openness, avoiding immorality, not interfering with other people's khitbah, careful consideration, and joint family decisions. In the digital context, these values are contextualized into maintaining intentions and integrity on digital platforms, using technology wisely, maintaining sharia restrictions in virtual spaces, and increasing openness and transparency through technology. This research has important implications in providing practical guidance for young Muslims to manage pre-marital interactions and relationships while adhering to the values of Islamic education in the digital era. In addition, this research also provides insights for Islamic educators, scholars and practitioners to develop learning materials that are relevant to the current digital situation, as well as integrating technology in Islamic education that is able to maintain and strengthen the values of Sharia. Thus, the values in the khitbah hadith can become an ethical foundation in responding to modern challenges without abandoning Islamic principles.
A novel convolutional feature-based method for predicting limited mobility eye gaze direction Khaleel, Amal Hameed; Abbas, Thekra H; Ibrahim, Abdul-Wahab Sami
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1370

Abstract

Eye gaze direction is a critical issue since several applications in computer vision technology rely on determining gaze direction, where individuals move their eyes to limited mobility locations for sensory information. Deep neural networks are considered one of the most essential and accurate image classification methods. Several methods of classification to determine the direction of the gaze employ convolutional neural network models, which are VGG, ResNet, Alex Net, etc. This research presents a new method of identifying human eye images and classifying eye gaze directions (left, right, up, down, straight) in addition to eye-closing discrimination. The proposed method (Di-eyeNET) stands out from the developed method (Split-HSV) for enhancing image lighting. It also reduces implementation time by utilizing only two blocks and employing dropout layers after each block to achieve fast response times and high accuracy. It focused on the characteristics of the human eye images, as it is small, so it cannot be greatly enlarged, and the eye's iris is in the middle of the image, so the edges are not important. The proposed method achieves excellent results compared to previous methods, classifying the five directions of eye gaze instead of the four directions. Both the global dataset and the built local dataset were utilized. Compared to previous methods, the suggested method's results demonstrate high accuracy (99%), minimal loss, and the lowest training time. The research benefits include an efficient method for classifying eye gaze directions, with faster implementation and improved image lighting.
Co-Authors Abbas, Thekra H Hosaini, Hosaini Ibrahim, Abdul-Wahab Sami Kasman Kasman Safrudin Edi Wibowo Taufik, Akhmat