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All Journal Polygon: Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Prosiding Seminar Nasional Ilmu Teknik
Salsabila Putri Hati Siregar
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Pemanfaatan Pengolahan Citra untuk Identifikasi Wajah pada Sistem Keamanan Indonesia Salsabila Putri Hati Siregar; Zulia Lestari Nasution; Aninda Evioni; Khoiratul Azmi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 2 (2025): Mei: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i2.783

Abstract

Image processing is a branch of computer science that is growing rapidly and is widely used in various fields, including in security systems. Face identification is one of the main applications of image processing that aims to recognize and distinguish individual faces in a system. The methods used in face identification involve various techniques, such as facial feature detection, characteristic extraction, and classification using machine learning algorithms. This article discusses the application of image processing in a security system based on face identification and the technology used to improve the accuracy and reliability of the system. The results of the study show that the combination of deep learning algorithms with image pre-processing techniques can increase the success rate of face identification in security systems.
Keamanan Data melalui Enkripsi Studi Kasus dengan Algoritma RSA Salsabila Putri Hati Siregar; Nur Aisyah Pandia; Putri Ramadani; Ibnu Rusydi
Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam Vol. 4 No. 1 (2026): Januari : Polygon : Jurnal Ilmu Komputer dan Ilmu Pengetahuan Alam
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/polygon.v4i1.929

Abstract

Data security is a critical aspect in the digital era due to the increasing exchange of sensitive information through electronic media. One widely used approach to protect data confidentiality is cryptography, particularly asymmetric encryption algorithms. This study aims to analyze the implementation of the Rivest–Shamir–Adleman (RSA) algorithm as a data security mechanism through an encryption and decryption process. The research method used is an experimental approach by implementing the RSA algorithm in a text-based data security simulation. The stages include key generation, encryption, and decryption processes, followed by analysis of the correctness and effectiveness of the algorithm in maintaining data confidentiality. The results show that the RSA algorithm is capable of converting plaintext into unreadable ciphertext and successfully restoring it to its original form through the decryption process using the correct private key. This confirms that RSA provides a high level of security based on the difficulty of factoring large prime numbers. The implication of this study is that the RSA algorithm can be effectively applied to secure sensitive data transmission in information systems, especially in environments requiring strong authentication and confidentiality.
Klasifikasi Berita Hoaks Menggunakan Algoritma Support Vector Machine Putri Ramadani; Nur Aisyah Pandia; Salsabila Putri Hati Siregar
Prosiding Seminar Nasional Ilmu Teknik Vol. 2 No. 2 (2025): Desember: Prosiding Seminar Nasional Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/prosemnasproit.v2i2.201

Abstract

The spread of hoax news in digital media is a serious problem because it can affect public opinion and social stability. This study aims to classify hoax news using the Support Vector Machine (SVM) algorithm. The dataset used is a hoax clarification dataset from the Ministry of Communication and Digital (Komdigi) of the Republic of Indonesia, totaling 1,872 data. The research process includes data collection, text pre-processing, feature extraction using TF-IDF, and classification using the SVM algorithm. Implementation was carried out using Google Colaboratory (Google Colab). Test results show that the SVM algorithm is able to provide good performance in classifying hoax news based on its topic with satisfactory accuracy, precision, recall, and F1-score values.
Co-Authors Aninda Evioni Ibnu Rusydi Khoiratul Azmi Nur Aisyah Pandia Putri Ramadani Zulia Lestari Nasution