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All Journal JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Scientific Journal of Informatics
Isinkaye, Folasade Olubusola
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Imperceptible and Robust Encryption: Salsa20 Stream Cipher for Colour Image Data Arfian, Aldi Azmi; Sari, Christy Atika; Rachmawanto, Eko Hari; Isinkaye, Folasade Olubusola
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13049

Abstract

Data security has become crucial, especially in today's era, therefore we need to protect our personal data to avoid unwanted incidents. The primary objective of this research is to empirically demonstrate the viability of our proposed methodology for encrypting color images using the Salsa20 algorithm, renowned for its stream cipher characteristics, which inherently afford it a swift processing speed. The encryption method we use is to take each pixel from the original image and convert it into bytes based on the RGB value in it, then encrypt it using a keyword that has been converted using a hash function. In this study, we carried out several evaluations to evaluate the performance of the encrypted and decrypted images to test the method we propose, including histogram analysis and compare patterns, visual image testing, and key space analysis. Through this experiment, it has been proven that Salsa20 is effective in maintaining confidentiality and image integrity. Histogram analysis reveals differences in pixel distribution patterns between the original and encrypted images. Visual testing shows that the encrypted image maintains good optical quality. Keyspace analysis ensures the security of encryption keys. The performance evaluation resulted in an NPCR above 99%, UACI had been reached 69.28%, MSE was closes to 0, and the highest PSNR was around 61.89dB, this shows that encrypted images recovered with high accuracy.
Harnessing Item Features to Enhance Recommendation Quality of Collaborative Filtering Isinkaye, Folasade Olubusola
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.7915

Abstract

Recommendation systems provide ways of directing users to items that may be relevant to them by guiding them to relevant items that will be suitable to the users according to their profiles. Collaborative filtering is one of the most successful and mature techniques of recommender system because of its domain independent ability. Bayesian Personalized Ranking Smart Linear Model (BPRSLIM) is model-based collaborative filtering (CF) recommendation algorithm that usually reconstructs a scanty user-item matrix directly; also, using only user-rating matrix usually prevents the algorithm from accessing relevant information that could enhance its recommendation accuracy. Therefore, this work reconstructs BPRSLIM user-item rating matrix via item feature information in order to improve its performance accuracy. Comprehensive experiments were carried out on a real-world dataset using different evaluation metrics.  The performance of the model showed significant improvement in recommendation accuracy when compared with other top-N collaborative filtering-based recommendation algorithms, especially in precision and nDCG with 30.6% and 22.1% respectively.
Naive Bayes Sentiment Analysis Study On Street Boba And Gildak Kediri Consumer Reviews Prasentya, Cindy Aprilia Wijaya; Hermanto, Didik; Negar, Wana Pramudyawardana Kusuma; Isinkaye, Folasade Olubusola
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10309

Abstract

Streetboba & Gildak Kediri outlet is a restaurant that serves a variety of Korean food menus and various kinds of drinks with boba and jelly toppings that are sold at low prices that suit the student's budget. This restaurant is located in East Java province which is precisely on Jalan Yos Sudarso No.43, Kediri City. With technological advances that continue to grow to affect various aspects, especially in the business and industrial world. Sentiment analysis is a technology that extracts or manages text to be expressed using text that can also be classified into positive and negative polarity. Consumer reviews are a form of communication that occurs in the sales process, the stage where potential buyers receive an explanation of the product posted and buyers receive reviews that explain the advantages or disadvantages of purchasing the product. In this study, sentiment analysis was conducted based on consumer opinions regarding social media accounts. The study aimed to use social media data to assess the service, cleanliness and quality of products offered by categorizing companies as having positive and negative reviews. To classify sentiment, the Naive Bayes method is used, which combines survey data collection methods, questionnaires, and observation data.
Discrete Cosine Transform and Singular Value Decomposition Based on Canny Edge Detection for Image Watermarking Astuti, Erna Zuni; Sari, Christy Atika; Rachmawanto, Eko Hari; Astuti, Yani Parti; Oktaridha, Harwinanda; Isinkaye, Folasade Olubusola
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1768

Abstract

The development of an increasingly sophisticated internet allows for the distribution of digital images that can be done easily. However, with the development of increasingly sophisticated internet networks, it becomes an opportunity for some irresponsible people to misuse digital images, such as taking copyrights, modification and duplicating digital images. Watermarking is an information embedding technique to show ownership descriptions that can be conveyed into text, video, audio, and digital images. There are 2 groups of watermarking based on their working domain, namely the spatial domain and the transformation domain. In this study, three domain transformation techniques were used, namely Singular Value Descomposition (SVD), Discrete Cosine Transform (DCT) and Canny Edge Detection Techniques. The proposed attacks are rotation, gaussian blurness, salt and pepper, histogram equalization, and cropping. The results of the experiment after inserting the watermark image were measured by the Peak Signal to Noise Ratio (PSNR). The results of the image robustness test were measured by the Correlation Coefficient (Corr) and Normalized Correlation (NC). The analysis and experimental results show that the results of image extraction are good with PSNR values from watermarked images above 50dB and Corr values reaching 0.95. The NC value obtained is also high, reaching 0.98. Some of the extracted images are of fairly good quality and are similar with the original image.
A text security evaluation based on advanced encryption standard algorithm Bima, Aristides; Irawan, Candra; Laksana, Deddy Award Widya; Krismawan, Andi Danang; Isinkaye, Folasade Olubusola
Journal of Soft Computing Exploration Vol. 4 No. 4 (2023): December 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i4.274

Abstract

This research approach analysis and examines a number of advanced encryption standard (AES) performance factors, including as encryption and decryption speed, processing resource, consumption, and resilience, to cryptanalysis attacks. The study’s findings demonstrate that AES is successful in providing high-level data security, particularly when used in the CBC (Cipher Block Chaining) operating mode. Performance is dependent on the length of the key that is utilized. Increasing the level of security through the use of longer keys may result in an increase in the amount of time needed for encryption. The experimental results show that the highest results from the data are as follows the length of the encryption time is 0.00005317 seconds, the length of the decryption time is 0.00000882 seconds, the results of BER and CER are 0, the results of entropy are 7.44237, and the results of avalanche influence are 54.86%.
Quality Improvement for Invisible Watermarking using Singular Value Decomposition and Discrete Cosine Transform Utomo, Danang Wahyu; Sari, Christy Atika; Isinkaye, Folasade Olubusola
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3744

Abstract

Image watermarking is a sophisticated method often used to assert ownership and ensure the integrity of digital images. This research aimed to propose and evaluate an advanced watermarking technique that utilizes a combination of singular value decomposition methodology and discrete cosine transformation to embed the Dian Nuswantoro University symbol as proof of ownership into digital images. Specific goals included optimizing the embedding process to ensure high fidelity of the embedded watermark and evaluating the fuzziness of the watermark to maintain the visual quality of the watermarked image. The methods used in this research were singular value decomposition and discrete cosine transformation, which are implemented because of their complementary strengths. Singular value decomposition offers robustness and stability, while discrete cosine transformation provides efficient frequency domain transformation, thereby increasing the overall effectiveness of the watermarking process. The results of this study showed the efficacy of the Lena image technique in gray scale having a mean square error of 0.0001, a high peak signal-to-noise ratio of 89.13 decibels (dB), a universal quality index of 0.9945, and a similarity index structural of 0.999. These findings confirmed that the proposed approach maintains image quality while providing watermarking resistance. In conclusion, this research contributed a new watermarking technique designed to verify institutional ownership in digital images, specifically benefiting Dian Nuswantoro University. It showed significant potential for wider application in digital rights management.
SECURE TEXT ENCRYPTION FOR IOT COMMUNICATION USING AFFINE CIPHER AND DIFFIE-HELLMAN KEY DISTRIBUTION ON ARDUINO ATMEGA2560 IOT DEVICES Permana langgeng wicaksono ellwid putra; Sari, Christy Atika; Isinkaye, Folasade Olubusola
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.1129

Abstract

In an Internet of Things (IoT) system, devices connected to the system exchange data. The data contains sensitive information about the connected devices in the system so it needs to be protected. Without security, the data in the IoT system can be easily retrieved. One way to prevent this is by implementing cryptography. Cryptography is a technique for protecting information by using encryption so that only the sender and receiver can see the contents of the information contained therein. The implementation of cryptography on IoT devices must consider the capabilities of IoT devices because in general IoT devices have limited processing capabilities compared to computer devices. Therefore, the selection of encryption algorithms needs to be adjusted to the computational capabilities of IoT devices. In this research, the affine cipher cryptography algorithm and Diffie-hellman key distribution algorithm are applied to the arduino atmega2560 IoT device. The purpose of this research is to increase the security of the IoT system by implementing cryptography. The method used in this research involves setting up a sequence of encryption and decryption steps using an affine cipher and diffie-hellman algorithms. Furthermore, these algorithms were implemented on an Arduino IoT device. Finally, the decryption time based on the number of characters and the avalanche test were tested. The results showed that on average, Arduino can perform decryption using affine cipher and diffie-hellman algorithms in 0.07 milliseconds per character. The avalanche test produced an average percentage of 45.51% from five trials.
Performance Analysis of Support Vector Classification and Random Forest in Phishing Email Classification Umam, Chaerul; Handoko, Lekso Budi; Isinkaye, Folasade Olubusola
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.3301

Abstract

Purpose: This study aims to conduct a performance analysis of phishing email classification system using machine learning algorithms, specifically Random Forest and Support Vector Classification (SVC). Methods/Study design/approach: The study employed a systematic approach to develop a phishing email classification system utilizing machine learning algorithms. Implementation of the system was conducted within the Jupyter Notebook IDE using the Python programming language. The dataset, sourced from kaggle.com, comprised 18,650 email samples categorized into secure and phishing emails. Prior to model training, the dataset was divided into training and testing sets using three distinct split percentages: 60:40, 70:30, and 80:20. Subsequently, parameters for both the Random Forest and Support Vector Classification models were carefully selected to optimize performance. The TF-IDF Vectorizer method was employed to convert text data into vector form, facilitating structured data processing. Result/Findings: The study's findings reveal notable performance accuracies for both the Random Forest model and Support Vector Classification across varying data split percentages. Specifically, the Support Vector Classification consistently outperforms the Random Forest model, achieving higher accuracy rates. At a 70:30 split percentage, the Support Vector Classification attains the highest accuracy of 97.52%, followed closely by 97.37% at a 60:40 split percentage. Novelty/Originality/Value: Comparisons with previous studies underscored the superiority of the Support Vector Classification model. Therefore, this research contributes novel insights into the effectiveness of this machine learning algorithms in phishing email classification, emphasizing its potential in enhancing cybersecurity measures.
Co-Authors Arfian, Aldi Azmi Astuti, Yani Parti Bima, Aristides Candra Irawan Chaerul Umam Christy Atika Sari Didik Hermanto Eko Hari Rachmawanto Erna Zuni Astuti Krismawan, Andi Danang L. Budi Handoko Laksana, Deddy Award Widya Oktaridha, Harwinanda Permana langgeng wicaksono ellwid putra Prasentya, Cindy Aprilia Wijaya Raden Mohamad Herdian Bhakti Utomo, Danang Wahyu