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All Journal Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Software Engineering and Computational Intelligence
Irfani, Muhammad Hafiz
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Computer Science & IT Social Sciences

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Similarity Identification Model of Thesis Titles with Mahalanobis Distance Approach Fajri Munawar, Muhammad; Heriansyah, Rudi; Irfani, Muhammad Hafiz; Jambak, Muhammad Ikhwan; Ferano, Dwi Asa
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5413

Abstract

This study aims to identify the similarity of thesis titles by applying the Mahalanobis Distance method which is known to be effective in measuring the distance between vectors by considering data distribution and correlation between variables. In its implementation, each thesis title is represented in vector form using the TF-IDF scheme before calculating the level of similarity using Mahalanobis Distance. The test results show that this method is able to produce similarity values between titles, but its performance has not shown optimal effectiveness in the context of similarity classification. The highest precision value obtained of 1.0 indicates that this method is quite reliable in identifying pairs of titles that are truly similar. However, the low recall value of only 0.5 indicates that there are many pairs of similar titles that fail to be detected, resulting in an F1-score value of only 0.638. This shows an imbalance between the system's ability to detect similarity and its classification accuracy. Although the accuracy value is relatively high, ranging from 0.958 to 0.988, these results do not necessarily reflect the overall effectiveness of the method in handling minor classification errors. Testing of the threshold parameters also shows that a value of 0.1 provides the best performance compared to other threshold values because it is able to maintain a balance between precision, recall, F1-score, and accuracy.
Deteksi Pemalsuan Tanda Tangan Menggunakan Algoritma Convolutional Neural Network (CNN) Lulus, Lulus; Sartika, Dewi; Irfani, Muhammad Hafiz
Jurnal Pengembangan Sistem Informasi dan Informatika Vol. 4 No. 2 (2023): Jurnal Pengembangan Sistem Informasi dan Informatika (JPSII)
Publisher : Training & Research Institute - Jeramba Ilmu Sukses

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47747/jpsii.v4i2.1745

Abstract

A signature is a person's identity. This makes the existence of a signature necessary, usually taken from the name of a person or other style. Making signatures should not be changed because it will greatly affect the inauthenticity of identity, which is considered to be able to forge an important document and make transactions. Signature verification is mainly done manually, namely by comparing using the sense of sight, which still allows fraud to prevent signature forgery problems. It takes software that can perform original signature recognition to minimize fraud. This research built a signature recognition software that was done offline. The data used in this study were 144 signature images of lecturers at the computer science faculty, Universitas Indo Global Mandiri. The software implements a Deep Learning system by using the Convolutional Neural Network (CNN) algorithm. The CNN model is built with Python programming language and consists of a convolution, pooling, and fully connected layer. The research began with the preprocessing stage, followed by the construction of the CNN model and software testing. The input in this application is a signature image taken using a scanner. The output is in the form of the name of the signature owner and the accuracy of the match between the real and fake signatures. From the research that has been done, it got an accuracy value of 89.65% and a loss of 0.6018
Co-Authors DEWI SARTIKA Fajri Munawar, Muhammad Ferano, Dwi Asa Jambak, Muhammad Ikhwan Lulus, Lulus Rudi Heriansyah, Rudi