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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Mantik ENDLESS : International Journal of Future Studies
Hadyan Mardhi Fadlillah
Universitas Gunadarma, Indonesia
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice

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Classification of good and damaged rice using convolutional neural network Dolly Indra; Hadyan Mardhi Fadlillah; Kasman Kasman; Lutfi Budi Ilmawan; Harlinda Lahuddin
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3385

Abstract

Rice production is massive in Indonesia, therefore maintaining the quality of the product is necessary. Detection and classification of objects have become a very important part in image processing. We performed object detection namely rice. After the object is found, it can be classified into two categories, namely good and damaged rice. We conducted a new study on rice which was carried out per group not per grain to obtain or classify good and damaged rice where we had carried out several steps, namely segmentation process using HSV (hue, saturation, value) color space. HSV is used because of its excellence in representing brightness of the image. We considered evaluating brightness because the tendency of damaged rice is darker or paler compared to good rice. To accomodate environment lighting ambiguity we perform the image acquisition in a controlled environment, so that all the images have the same light intensity. Here we use only channel V of HSV to be used in feature extraction using the gray-level co-occurrence matrix (GLCM) and finally convolutional neural network (CNN) is used for classification. From the test experiments that we have done, we have produced 83% prediction accuracy. Considering how similar the good rice is to the spoiled rice, the results are quite impressive.
Color image enhancement using filtering and contrast enhancement Budi Utami Fahnun; Lukman Safri Andani; Hadyan Mardhi Fadlillah; Hendri Dwi Putra
Jurnal Mantik Vol. 7 No. 1 (2023): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v7i1.3678

Abstract

Image is an efficient media for information and communication. Filtering on image enhancement is one of the processes in image processing to reduce noise in the image. This study uses the type of noise Salt & Pepper to be applied to flower images and scenic images that the author uses in this study as a comparison test of filtering methods. Filters used in testing and comparison are Median Filter, Mean Filter, and Gaussian Filter. This research also applies the Contrast Enhancement process to highlight certain aspects contained in the image. From the results of testing and comparison of filtering methods it can be concluded that the best method for handling Salt & Pepper noise is Median Filter, which has the best MSE value is 0.7329 to the worst MSE value is 26,766, the best RMSE value is 0.8561 to the worst RMSE value is 5.1736, and the best PSNR value is 49,513 to the worst PSNR value is 33,889 . This research concludes that it has succeeded in making an application of Image Enhancement and comparison Using Median Filter, Mean Filter, Gaussian Filter, And Contrast Enhancement using MATLAB 2016b which can reduce Salt & Pepper noise.
Bridging the Gap: Exploring the Role of Computer Science in Enhancing Interactive and Inclusive Learning Environments Budi Utami Fahnun; Eel Susilowati; Darmastuti; Hadyan Mardhi Fadlillah; Irawaty
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 6 No. 3 (2023): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/endlessjournal.v6i3.211

Abstract

This article explores the transformative role of computer science in shaping interactive and inclusive learning environments in the educational sector. With the rapid advancement of technology, computer science has become a pivotal element in redefining educational methodologies, facilitating personalized and accessible learning experiences for a diverse student population. This study employs a mixed-methods approach, integrating quantitative data from educational technology usage surveys with qualitative insights from interviews with educators and students. We examine the implementation of computer science tools such as adaptive learning platforms, virtual and augmented reality, and AI-driven educational software, assessing their impact on student engagement, learning outcomes, and inclusivity. Our findings reveal that these technologies not only enhance interactive learning experiences but also significantly contribute to the inclusivity of education by providing tailored learning paths and overcoming traditional barriers. The study highlights the potential of computer science to democratize education, making it more equitable and accessible to learners with varying needs and backgrounds. Furthermore, we discuss the challenges and opportunities in integrating these technologies into existing educational frameworks, offering recommendations for educators, policymakers, and technology developers. This article contributes to the growing body of research on the intersection of computer science and education, providing insights into the future of learning in an increasingly digital world.
Utilizing Machine Learning for Anomaly Detection in Cybersecurity Systems Budi Utami Fahnun; Eel Susilowati; Hadyan Mardhi Fadlillah; Irawaty
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 7 No. 2 (2024): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

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

Abstract

Anomalies in cybersecurity systems are increasingly complex and sophisticated, making detection difficult using traditional rule-based and signature-based approaches. In facing these challenges, machine learning is crucial to improve real-time anomaly detection capabilities. This study aims to explore the role of machine learning in detecting anomalies in cybersecurity systems. The research method is carried out using a qualitative approach, collecting data from relevant literature and interviews with experts in the fields of cybersecurity and machine learning. The results of this study indicate that machine learning can effectively improve the ability of cybersecurity systems to detect and respond to threats more quickly and accurately. Implementing machine learning allows for deeper analysis of complex cybersecurity data, recognizing unexpected anomalous patterns, and adapting to new attacks. Despite challenges such as data variability and dynamic operational environments, the evaluation of model performance shows significant progress in protecting information systems from increasingly complex threats. The future of anomaly detection in cybersecurity promises the possibility of developing more sophisticated technologies, strengthening defenses against evolving threats, and improving overall security.
Co-Authors Budi Utami Fahnun Budi Utami Fahnun Darmastuti Dolly Indra Eel Susilowati Harlinda Lahuddin Hendri Dwi Putra Irawaty Kasman Kasman Lukman Safri Andani Lutfi Budi Ilmawan