Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
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Ingredients Identification Through Label Scanning Using PaddleOCR and ChatGPT for Information Retrieval
No full textHuman health depends on choosing food ingredients that align with dietary needs and avoid allergens. However, consumers often encounter unfamiliar ingredients that require additional information. Traditionally, they search online by typing in the ingredient's name which can be time-consuming and may not yield relevant results. Therefore, a system to identify and display ingredient information is necessary. This study proposes a new system that identifies ingredients by scanning the composition label on packaging using PaddleOCR and retrieving information through ChatGPT on a smartphone. The process begins with capturing an image of the composition label. Then PaddleOCR is employed to extract text from the scanned label, enabling identification of the listed ingredients. Subsequently, ChatGPT retrieves detailed information about the desired ingredients and displays it, allowing users to easily understand the ingredients. The system's effectiveness in text recognition is assessed using the character error rate (CER). The results show robust performance by achieving an average CER of 0.14, with flat packaging reaching an impressive CER of 0.05. Additionally, the system's usability was assessed through pilot testing which received significant positive user feedback achieving 4.37 satisfaction level on Likert scale, particularly regarding the clarity and relevance of the ingredient information provided
Prototype of Swiftlet Nest Moisture Content Measurement Using Resistance Sensor and Machine Learning
No full textSwiftlet nests are highly valued for their health and cosmetic benefits, with moisture content crucial in determining their quality. Traditional moisture measurement methods are often slow and can potentially damage the samples. This study introduces PRORESKA, an innovative system utilizing resistance sensors and Machine Learning (ML) for non-destructive, and real-time moisture measurement. The system incorporates a voltage divider circuit to establish a correlation between resistance data and moisture content. Three mathematical models (linear, exponential, and modulated exponential) and a neural network were employed to predict moisture content. Validation tests conducted on paper and swiftlet nests indicated that the neural network model, enhanced through transfer learning, achieved superior accuracy. The results demonstrated a strong correlation between predicted and actual moisture content (R² = 0.9759), with the neural network model attaining a mean squared error (MSE) of 0.01. This method holds significant potential to improve the efficiency and cost-effectiveness of moisture measurement for swiftlet nests and similar applications
Rice Price Prediction with Long Short-Term Memory (LSTM) Neural Network
Get PDFRice is a crucial commodity, especially in countries that rely on rice as a staple food. Fluctuations in rice prices can impact inflation, purchasing power, and economic stability. Therefore, an effective method for forecasting rice prices is essential for timely decision-making. This study aims to develop a rice price forecasting model by incorporating weather variability. Using Long Short-Term Memory (LSTM) neural networks, the model is expected to provide accurate predictions and guide decision-making in rice trading. LSTM is effective in analyzing time-series data. In this study, LSTM was used to examine the relationship between weather variability, crop yields, and land area with rice prices. Daily data from 2015 to 2023 were collected to build a model capable of predicting future rice prices. The results showed that the LSTM model achieved a Root Mean Squared Error (RMSE) of 0.054, indicating high prediction accuracy. This model allows stakeholders, including farmers, traders, and government officials, to better understand future rice price movements. This, in turn, helps them implement more effective strategies in managing rice supply and stabilizing prices
Implementing Continuous Integration and Deployment Strategy: Cloversy.id RESTful API Development
No full textThe software development cycle involves testing and deployment stages that can be laborious and time consuming, especially in collaborative projects that involve several developers. Implementing Continuous Integration (CI) and Continuous Delivery (CD) offers a solution to streamline this process. This study presents a case study of the Cloversy.id RESTful API project, highlighting challenges encountered during development and the implementation of a new system using GitHub Actions as the DevOps tool. The research resulted in the adoption of a new system, which replaces the conventional practices previously employed by the Cloversy.id development team. Using flow charts, the study meticulously mapped out the development flow, pinpointing bottlenecks and areas for optimization within the cycle. In particular, the implementation of a CI/CD pipeline resulted in a notable improvement, with a 35% increase in speed for CI and a remarkable 39% enhancement for CD. GitHub Actions played a pivotal role in automating critical tasks, reducing the reliance on manual intervention, and minimizing the dependency on team leaders. The platform's features, including detailed logs and email notifications, empowered team leaders and developers alike to take informed actions swiftly. Furthermore, the study highlights the novelty of integrating CI / CD considering factors such as branching strategy, code review practices, testing methodologies, deployment methods, and infrastructure
Comparative Analysis of Recurrent Neural Network Models Performance in Predicting Bitcoin Prices
Get PDFThe recurring neural network is a deep learning algorithm that is commonly used to develop prediction systems. There are many variants of RNN such as RNN itself, long-short-term memory (LSTM), and gated recurring unit, so it is frequently debatable which algorithm from the RNN family has the most optimal efficiency and computation time. When developing a prediction system, sequential or time series data is required so that an accurate prediction can be made. Sequential or time series data involve data arranged in a time sequence, such as weather data, financial data, carbon emission data, and traffic data recorded over time. This research will be carried out by predicting the three RNN models against historical Bitcoin value data. The research method used is Experimental Design by comparing the performance between the three models on bitcoin value time series data, testing is done by involving hyperparameters such as Tanh, Sigmoid, and ReLU activation functions, batch size, and epochs. The aim of this research is to find out which RNN model can produce the most optimal performance and find out what performance measures can be used to evaluate and compare the performance between the three models. The results of the study show that LSTM is the most effective model with RMSE 0.012441 and MSE 0.000155 but inefficient because it takes 3 minutes 24 seconds to run the computation; in the meantime, the Tanh activation function gives the most optimal prediction than Sigmoid and RelU and therefore should be the main candidate to be used with RNN models when predicting Bitcoin prices
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN)
No full textWood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures
A Novel Framework for Information Security During the SDLC Implementation Stage: A Systematic Literature Review
Get PDFThis research delves into the critical aspects of information security during the implementation stage of the Software Development Life Cycle (SDLC). Using a systematic review of the literature, the study synthesizes the findings of various digital repositories, including IEEE Xplore, ACM Digital Library, Scopus, and ScienceDirect, to outline a comprehensive framework that addresses the unique security challenges of the implementation stage. This research contributes to the field by proposing a novel assurance model for software development vendors, focusing on improving information security measures during the implementation stage. The study's findings reveal 12 key steps organizations can adopt to mitigate security risks and improve information security measures during this critical phase. These steps provide actionable insights and strategies designed to support security protocols effectively. The paper concludes that by incorporating these steps, organizations can significantly improve their security posture, ensuring the integrity and reliability of the software development process, particularly during the implementation stage. This approach not only addresses immediate security concerns but also sets a precedent for future research and practice in secure software development, particularly in the critical implementation stage of the SDLC
Indonesian Crude Oil Price (ICP) Prediction Using Support Vector Regression Algorithm
Get PDFIndonesian crude oil prices (ICP) experience fluctuating movements, influenced by several factors and other conditions that make ICP prices difficult to predict. ICP price prediction can be done with the Support Vector Regression (SVR) method. The information utilized originates from the Ministry of Energy and Mineral Resources' official website, specifically focusing on crude oil pricing data for six primary types of crude oil: SLC, Attaka, Duri, Belida, Banyu and SC. The data applied covers the time frame from January 2018 to August 2023. The forecast of the ICP relies on the date Brent variable and the Alpha factor through the use of support vector regression (SVR. In the case of a linear kernel, the parameters (epsilon) and C (cost) are determined using the Grid Search algorithm. In the Dated-Brent variable, the best parameter value is obtained with the value of C = 100 and = 1 while for the Alpha variable, the best parameter value for the SLC crude oil type is C= 0.01 and = 0.01, SC value C = 10 and = 1, Banyu value C = 100 and = 0.1, Banyu value C = 100 and = 0.1, Belida value C = 0.01 and = 0.1, Attaka value C = 0.1 and = 0.01 and Duri value C = 1 and = 1. The Alpha value of the main crude oil type is the Duri crude oil type with the lowest RMSE value of 0.9651. The MAPE value for SC crude oil type = 19.55% and Duri = 19.46% is in the good category. The R2 value for Banyu crude oil = 0.60610, SC = 0.42717 and Duri = 0.50421 is in the good category and the MAPE value for Dated-Brent of 49.73% is included in the fair category
YOLO-based Small-scaled Model for On-Shelf Availability in Retail
Get PDFThe availability of the shelf (OSA) in the retail industry plays a very crucial role in continuous sales. Unavailability of products can make a bad impression on customers and reduce sales. The retail industry may continue to develop through the rapidly advancing technology era to thrive in a market where competition is increasingly tough. Along with technological advances in recent decades, artificial intelligence has begun to be applied to support OSA, particularly by using object detection technology. In this research, we develop a small-scale object detection model based on four versions of the You Only Look Once (YOLO) algorithm, namely YOLOv5-nano, YOLOv6-nano, YOLOv7-tiny, and YOLOv8-nano. The developed model can be used to support automatic detection of OSA. A small-scale model has developed in the sense of postpractical implementation through low-cost mobile applications. We also use the quantization method to reduce the model size, INT8 and FP16. This small-scale model implementation also offers flexibility in implementation. With a total of 7697 milk-based retail product images and 125 different product classes, the experiment results show that the developed YOLOv8-nano model, with a mAP50 score of 0.933 and an inference time of 13.4 ms, achieved the best performance
LR-GLASSO Method for Solving Multiple Explanatory Variables of the Village Development Index
Get PDFSustainable Development Goals (SDGs) are developments that maintain sustainable improvement in society’s economic, social, and environmental welfare. Kemendes PDTT RI has issued the Village Development Index (VDI) to provide information and the status of village progress to support village development to improve the National SDGS. Modeling with multiple explanatory variables causes a high correlation between explanatory variables, multicollinearity, and coefficient estimation results, which have a large variance and overfitting in the prediction results. The modeling solution uses LASSO and GLASSO. The binary categorical response data use binary logistic regression (LR), so LR-LASSO and LR-GLASSO are used. North Maluku Province has a VDI ranking that tends to fall in 2018-2022. On the basis of the mean and variance of the coefficient estimation results and misclassification errors, LR-GLASSO is better than LR-LASSO and LR. LR-GLASSO is recommended for analyzing VDI data because it has many explanatory variables and the correlation between them is relatively high. The Indonesian government recommendation, if it is to increase the status of VDI in Indonesia, especially in the north Maluku province, is to increase the number of electricity users, food and beverage stores, and other cooperatives. The Indonesian government also needs to pay attention to villages relatively far from the regent's office, between food and beverage stalls, and supporting health centers, because they still need to be developed compared to other villages, and more than 50% of the villages are underdeveloped. If the Village SDGs are formulated by increasing the VDI status, it will support the achievement of the SDGs goals nationally