Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Informatika
Ramadhan Renaldy
Universitas PGRI Semarang
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementation of Image Data Security Using the AES-256 Algorithm in the Work Accident Recording System Dzulfiqar Alang Setiawan; Bambang Agus Herlambang; Ramadhan Renaldy
Jurnal Informatika Vol. 13 No. 1 (2026): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i1.12372

Abstract

This study aims to implement an image data security mechanism in a web-based work accident recording system in an industrial environment by applying the Advanced Encryption Standard (AES) 256-bit cryptographic algorithm in Cipher Block Chaining (CBC) mode. The problem faced is the lack of an adequate protection mechanism for sensitive work accident photo files so that they have the potential to be accessed, copied, or modified by unauthorized parties. This study uses the Software-Oriented Prototyping method which allows system development to be carried out iteratively based on user needs and evaluation results at each development stage. The encryption process is carried out when the image file is uploaded into the system by generating a random Initialization Vector (IV) of 16 bytes, then the image data is encrypted using the AES-256-CBC algorithm and stored in ciphertext form with the file extension .enc. The decryption process is carried out when the file will be displayed again using the appropriate secret key and IV without storing the file in plaintext form on the server. The test results show that the encryption process has an average execution time of around 0.002–0.008 seconds, while the decryption process takes around 0.00004–0.001 seconds. In addition, the size of the encrypted file relatively follows the size of the original image file with an additional size of around 16–32 bytes due to the padding process and the use of initialization vectors. The results of the study indicate that the application of the AES-256-CBC algorithm is able to maintain the confidentiality and integrity of image data without having a significant impact on system performance. Thus, the developed system can improve the security of digital file storage and support a more structured, secure, and efficient management of work accident data.
IMPLEMENTATION OF THE MOVING AVERAGE ALGORITHM IN A WEB-BASED FOOD AND BEVERAGE RAW MATERIAL STOCK REQUIREMENT PREDICTION INFORMATION SYSTEM Ainia Hasna Salsabila; Bambang Agus Herlambang; Ramadhan Renaldy
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 6 (2026): MAY
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Inventory is a critical component in business operations, particularly in the Food and Beverages (F&B) sector, which demands accurate management to mitigate the risks of stockouts and overstocking. However, many MSMEs still rely on conventional methods based on subjective assumptions, which are prone to errors and unable to accommodate demand fluctuations. This research aims to develop a web-based raw material stock forecasting information system integrated with the Single Moving Average (SMA) method to enhance inventory management efficiency. The system development follows a prototyping approach, encompassing data collection, quick design using UML, prototype construction using PHP Laravel and Python, evaluation, and system refinement. The dataset utilized consists of 12 periods of monthly financial reports from Benjiro Sushi, Lamper branch. Accuracy evaluation was conducted using MAPE, MSE, and RMSE metrics. The results indicate that the application of SMA without data pre-processing resulted in a high error rate (MAPE 77.25%). However, after implementing pre-processing techniques—including backward interpolation, outlier capping, and iterative smoothing—the accuracy improved significantly, with MAPE values ranging between 20.4% and 21.0%. The developed system provides automated and real-time stock predictions, facilitating more precise, efficient, and structured procurement decision-making. Thus, this system is considered effective in addressing inventory challenges within the F&B sector.
Co-Authors Ainia Hasna Salsabila Bambang Agus Herlambang, Bambang Dzulfiqar Alang Setiawan