JICDA: Journal of Informatics, Computer Science, Data Science And Artificial Intelligence

RSA ENCRYPTION FOR DATA SECURITY IN QR CODE BASED DIGITAL PAYMENT SYSTEM

2025-06-22T18:16:31+07:00

This research, titled "RSA Encryption for Securing Data in QR Code-Based Digital Payment Systems," aims to enhance transaction data security in the increasingly popular digital payment environment. In today’s digital era, the use of cashless payment methods through applications such as OVO, DANA, and GoPay simplifies transactions but also introduces risks related to data security. Therefore, a solution is needed to ensure the confidentiality and integrity of data. In this study, the RSA (Rivest-Shamir-Adleman) algorithm is employed to encrypt transaction data before it is embedded in a QR Code format. This process involves the generation of public and private keys, data encryption, and the creation of a unique QR Code for each transaction. The results indicate that using RSA with a 2048-bit key length provides a high level of security, effectively protecting data from unauthorized access and ensuring data integrity throughout the transmission process.Based on the analysis, the integration of RSA encryption and QR Code technology proves to be effective in mitigating data theft risks. This research is expected to serve as a reference for the further development of more secure digital payment applications, while also offering insights into the real-world application of cryptography.

Keywords : Digital Payment, QR Code, RSA, Key Generator

IMPELEMENTATION OF DSA ALGORITHM IN DIGITAL DOCUMENT SECURITY

2025-06-07T10:05:26+07:00

The Digital Signature Algorithm (DSA) is a cryptographic method used to ensure the integrity and authenticity of data by generating a unique digital signature for each document. In this study, the author examines the implementation steps of DSA, including the generation of public and private keys, as well as how digital signatures can be used to verify the sender's identity and prevent forgery. The implementation results indicate that the use of DSA significantly enhances the security of digital documents, providing strong protection against data tampering. These findings are expected to contribute to the development of more effective and reliable information security systems in the digital era.

Keywords : Cryptography, Hash Function, Asymmetric Key, Digital Signature Algorithm, Secure Hash Algorithm-256

Classification Of Oil Palm Fruit Ripeness Level Using Artificial Neural Network

2025-07-07T19:27:51+07:00

The manual sorting process for determining the ripeness of oil palm fruit is subjective and inefficient, leading to a decline in Crude Palm Oil (CPO) quality and economic losses. This study aims to develop an automatic classification system for oil palm fruit ripeness to address these issues. It employs a digital image processing approach using a Neural Network model. The methodology involves using a pre-trained InceptionV3 model for feature extraction from a dataset of 3,000 fruit images, which are then fed into a custom-designed neural network with three hidden layers, using ReLU as the activation function and Adam as the optimizer. The model successfully classifies the fruits into 'unripe', 'ripe', and 'overripe' categories. The results show a high overall accuracy of 96.56 percent, with an F1-Score of 96.55 percent. The study concludes that the proposed Neural Network model is highly effective and reliable for automating oil palm fruit sorting, offering a feasible solution to improve efficiency and standardization in the palm oil industry

Keywords: Oil Palm Fruit, Ripeness Classification, Neural Network, Image Processing, InceptionV3

IMPLEMENTATION OF DEEP LEARNING ALGORITHM IN HANDWRITING TO TEXT DOCUMENT CONVERSION APPLICATION

2025-06-22T09:34:30+07:00

The development of information and communication technology has driven the need for systems capable of efficiently converting handwritten text into digital text. This study aims to develop a web-based application capable of real-time handwriting recognition using Tesseract.js, a JavaScript library for optical character recognition (OCR). The application is designed to assist users in converting handwritten documents into editable text formats, thereby enhancing productivity and information accessibility.

The methods used in this study include uploading handwritten images, preprocessing the images to improve input quality, and applying OCR algorithms using Tesseract.js to recognize characters and words. The recognized results are then displayed on the user interface, with an option for manual correction if needed. The study also evaluates the accuracy of the text recognition produced by the application by comparing the recognition results with the original text.

The results show that the developed application is capable of recognizing handwriting with a satisfactory level of accuracy, despite variations in handwriting styles. This application is expected to make a significant contribution in the field of document digitization and data processing, and serve as a reference for the development of similar systems in the future.