Sayed Muchallil

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer that predominantly affects children and requires early and accurate diagnosis to improve patient survival rates. Traditional diagnostic methods rely heavily on manual examination of blood smear images by pathologists, which is not only time-consuming but also susceptible to human error and variability. To address this limitation, this study proposed an automated detection model based on deep learning, specifically employing the EfficientNetB3 convolutional neural network architecture. A publicly available dataset containing microscopic images of ALL and normal blood cells was used for training and evaluation. The images were preprocessed using normalization and augmentation techniques and resized to 300×300 pixels to align with the EfficientNetB3 input requirements. The model was trained using the Adam optimizer and monitored with EarlyStopping to prevent overfitting. Experimental results showed that the proposed model achieved an accuracy of 92.23%, precision of 92.75%, and recall of 95.57%, significantly outperforming conventional approaches such as Canberra distance, K-Nearest Neighbor, and ensemble CNN methods. In addition to the classification model, a web-based ALL detection system was developed to make the solution more accessible and user-friendly. The frontend was built using ReactJS, while the backend API, built with Flask, handles image input, model inference, and output delivery. The interface allows users to upload cell images, input patient names, and receive instant classification results along with confidence scores. This integrated system demonstrates a practical application of AI in medical diagnostics and holds potential for use in real-world, resource-limited clinical settings.

VANETs is a wireless technology where vehicles act as mobile nodes to perform a communication network. All mobile nodes in the VANETs can communicate directly each other. As a wireless network, VANETs is vulnerable to some attacks include black hole attack. This kind of attack causes the VANETs service to be out of order. This attack absorbs and drops all of the data packets in the network. The data packets will not reach their destination because interfered by malicious nodes that perform black hole attack. This research will investigate and analyze the impact of the black hole attack on the performance of AODV and AOMDV routing protocols in VANETs. This study uses simulation software to mimic VANETs with multiple nodes. In the simulation scenario, one node acts as a malicious node that performs black hole attack. The result shows that the performance of both routing protocols decreases during the attack. The throughput of both routing protocols reduced while the packet loss increased. Although both routing protocols are vulnerable to black hole attacks, AOMDV has a better performance compared to AODV.

In the last decades, some extensive research explored new algorithms and protocols how to secure data or user information using password and username. There are new methods such as biometrics. The drawback of this method is that it needs other hardware or device. On the other hand, the username and password combination to authenticate user identity has been widely used because cheap and user-friendly. The latter method has some weakness such as dictionary attack, brute force or other method. The purpose of this research to find out the weakness of freshmen chosen password to protect their identity in the Academic Information System. The experiment is done in a public university in Indonesia. This research carried on around 12 faculties and 102 departments. The freshmen at this university is around 6499 students. The sampling method is population, so that all the freshmen student account investigated. The first scenario is to find out how many students use the same password as username. The second scenario is a dictionary attack on some freshmen account that cannot be found on the first scenario. The result of this experiment about 60% to 75% of freshmen’s password in each faculty can be guessed because the students used the same username and password. Other method used in this research has been revealed more password from freshmen.

Image denoising plays an important role in image processing. It is also part of the pre-processing technique in a binarization complete procedure that consists of pre-processing, thresholding, and post-processing. Our previous research has confirmed that the Discrete Cosine Transform (DCT)-based filtering as the new pre-processing process improved the performance of binarization output in terms of recall and precision. This research compares three classical denoising methods; Gaussian, mean, and median filtering with the DCT-based filtering. The noisy ancient document images are filtered using those classical filtering methods. The outputs of this process are used as the input for Otsu, Niblack, Sauvola and NICK binarization methods. Then the resulted binary images of the three classical methods are compared with those of DCT-based filtering. The performance of all denoising algorithms is evaluated by calculating recall and precision of the resulted binary images. The result of this research is that the DCT based filtering resulted in the highest recall and precision as compared to the other methods.

The Aceh Museum stores many digitized ancient manuscripts from hundreds of years ago. The condition of those manuscripts has degraded into several degradation types such as uneven contrast, show through effects, background spots, and text fading, which cause decreasing readability. A binarisation method is used to decrease the degradation effect on ancient manuscripts. Our research team is currently working on developing application software that consists of five binarisation methods, namely Otsu, Niblack, Sauvola, Lu, and Su for ancient manuscript restoration for the Aceh Museum staff to improve documents’ readability. In practice, a user still finds it difficult to choose the best method because there is no method that works best on every ancient manuscript for different types of degradation. This paper intends to determine a binarisation method that suits most manuscript conditions. The method used in this research includes the identification and classification of degradation types from 200 ancient Aceh digital manuscripts, followed by cropping the manuscripts to the size of 256 x 256 pixels. As many as five cropped areas from each degradation type are selected as research samples. These samples are binarisated using the methods. The last step is finding the most suitable binarisation method for each degradation type and classifying which methods are considered to have good readability, and that achieves at least 80% recall and precision values. From our experiments, we found that the Su binarisation methods demonstrate the best performance overall for every degradation type. Otsu, Lu, and Su are suited for uneven background; Sauvola, Lu, and Su are suited for showthrough effects; Otsu, Sauvola, and Su are suited for background spots; and Otsu and Su are suited for both text and background blurring and ‘fox’.

Ancient document images date back to several hundred years are commonly suffered from noises and degradations, such as ink-seeping from the back page, `fox'; that is local-brown discolorations of paper, text fading, background spots, uneven background and so on. Noise reduction (or denoising) is an important step in document image processing, because the step can enhance the optical character recognition (OCR) performance. Prior to employing a noise reduction algorithm, it is important to characterize noise types exist in the document. This paper proposes a method to characterize noise types exist in ancient document based on the DCT coefficient distribution of the image. The characterization are accomplished by analyzing the standard deviation of distribution of DCT coefficient higher frequency-band of cropped (localized) noise image. In simulations, three noise types exist in Acehnese ancient documents namely `fox', spots, and uneven background are characterized using the proposed method. The results suggest that the DCT coefficient distributions can be used to characterize the noises in ancient document. In addition, it has been shown that the proposed method can be used for document image classification.

This paper proposes a new pre-processing approach to a sequence of binarization process, by applying the discrete cosine transform (DCT) to document image prior to binarization. This procedure can be used as a complement or alternative to denoising process used in many binarization methods. Firstly, original document images are transformed by the DCT. Then, the transformed document images are passed to standard image binarization methods, namely Otsu, Niblack, Sauvola and NICK. Secondly, original document images are directly binarized by the same methods. Then performance of the latter are compared with that of the former in terms of recall and precision. Performance evaluation are conducted by using noiseless and noisy ancient Indonesian documents with Arabic characters. It turns out that this pre-processing procedure improves binarization performance, especially for noisy documents.

Meuruno is a simple mobile learning system that enables a teacher to nudge their students to review previously taught materials. Meuruno relies on the very basic SMS technology for its service that makes it especially suited for mobile-connected developing countries like Indonesia. A pilot study of Meuruno, in a real world learning situation, not only produced an increase in students' understanding of taught materials but also provided an alternative mobile learning experience that is purely based on basic SMS technology.