Allan Desi Alexander
@ubharajaya.ac.id
Informatics, Computer Science Fakulty
Universitas Bhayangkara Jakarta Raya
RESEARCH INTERESTS
Networking, Software Development, Machine Learning, Data Mining, Artificial Intelligence, Computer Vision
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Akhas Rahmadeyan, Mustakim, Imam Ahmad, Allan Desi Alexander, and Alkautsar Rahman
IEEE
Phishing is one of the most serious security threats. Most phishing attacks occur on online transaction websites such as banking, commercial businesses, e-commerce, and more. This type of attack is increasing every day. More than 90% of data breaches are done by phishing. In addition, ransomware viruses can also be delivered through phishing. Phishing websites have a similar appearance and URL to official and popular websites, making them very difficult to identify. Almost 75% of phishing websites use Secure Sockets Layer certificates (SSL) so the SSL protocol does not guarantee the website is legitimate. This research performs phishing detection with a URL-based approach, where URL data that has been extracted features will be analyzed and learned with machine learning techniques. Artificial Neural Network and AdaBoost are implemented to learn patterns in the data. To maximize the modeling results, some tests are also conducted on the ANN parameters. Based on the results of the ANN and AdaBoost implementation in detecting phishing websites, the hybrid ANN-AdaBoost model is the best with an accuracy of 98.60%, precision of 98.95%, and recall of 99.31%. This shows that the use of a combination technique of ANN and AdaBoost is the right choice to detect and increase accuracy and effectiveness in avoiding phishing threats.
Imam Ahmad, Yuri Rahmanto, Rohmat Indra Borman, Farli Rossi, Yessi Jusman, and Allan Desi Alexander
IEEE
Pineapple is one of the potential commodities in Indonesia. This is due to high market demand, potential suitable land in Indonesia and public awareness of fruit supply. The main factor of crop failure in pineapple plants is the delay in handling pineapple plant diseases. To identify in image processing requires class grouping. Self-Organizing Map (SOM) which divides the input pattern into several groups so that the network output is in the form of the group that is most similar to the given input. However, the SOM algorithm requires data input that characterizes an object to facilitate the identification process. So, in this study the SOM algorithm was improved through color feature extraction with parameters Red, Green, Blue, Hue, Saturation and Value, as well as texture feature extraction with parameters contrast, correlation, energy, and homogeneity in the Gray Level Co-occurrence Matrix (GLCM). Based on the results of tests carried out by the SOM algorithm with color and texture feature extraction parameters, it is able to assist in increasing the accuracy value. The results of testing the SOM model with color and texture feature extraction obtained a precision value of 93.33%, recall of 92.31% and accuracy of 92.78%.
Allan D Alexander, Ratna Salkiawati, Hendarman Lubis, Fathur Rahman, Herlawati Herlawati, and Rahmadya Trias Handayanto
IEEE
Student attendance record has an important role in the educational process. Universitas Bhayangkara Jakarta Raya, as a case study, uses attendance record as the factor for final grade calculation. Many attendance recording systems were developed using biometrics, e.g. face recognition, iris recognition, and fingerprint recognition. In this study, face recognition was proposed since the face cannot be duplicated and can eliminate fraud committed by students. In addition, this contactless method could minimize the risk of COVID-19 spread with some additional treatments. The local binary pattern (LBP) was proposed in this study. This method has the ability to describe the texture and shape of an image by dividing the image into small portions of feature extraction. The result showed that the proposed system can identify students with 86% accuracy.