Journal of Information Systems and Informatics (Journal-ISI)
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Analyzing the Relationship Between Meteorological Parameters and Electric Energy Consumption Using Support Vector Machine and Cooling Degree Days Algorithm
Get PDFNowadays, electricity is increasing rapidly. This increase is caused by several factors, one of which is meteorological factors. Meteorological parameters have various types, but this research uses three types in the form of temperature, humidity, and wind speed. The selection of these three types is due to the fact that they have a very close relationship with human life. In line with that, this research uses datasets obtained from the official websites of BMKG (Meteorology, Climatology and Geophysics Agency) and PLN (State Electricity Company). On this occasion, researchers used several methods, namely Cross-Industry Standard Process for Data Mining (CRISP-DM), Cooling Degree Days (CDD), and Support Vector Machine (SVM). The CRISP-DM method is useful for describing the data mining cycle so that the process can be more organized. The SVM algorithm is useful for predicting electricity consumption based on meteorological parameters in January to April 2024, while the CDD method is useful for knowing the correlation of meteorological parameters to electricity consumption in winter. In line with this, this research produces predictions of electricity consumption based on meteorological parameters in January 2024 to April 2024 with an average range of 20.9 Watts per day. In addition, trends and predictions during model evaluation obtained a precision value of 0.796, recall of 0.793, F1 score of 0.793, MAPE of 17.2%, RMSE of 0.41, MAE of 0.167 and accurate of 0.98. These values indicate that the performance of the accuracy model is very high
Measuring the User Experience of LMS CLASS-IPB Using the User Experience Questionnaire Method
Get PDFThis study conducts the measurement of user experience (UX) of the CLASS-IPB Learning Management System (LMS) using the User Experience Questionnaire (UEQ) method. The research was carried out through several stages, including a literature review, method determination, data collection, and data analysis using UEQ Data Analysis Tools. It involved 45 students who provided feedback on six UX aspects: attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty. Results indicated that CLASS-IPB excels in pragmatic qualities, particularly in efficiency, perspicuity, and dependability, with scores above the benchmark average. However, the system scored lower in hedonic qualities, specifically in stimulation and novelty, suggesting areas for improvement. The study concludes that while CLASS-IPB is effective in aiding task completion and user control, it needs enhancements to become more motivating and innovative
Evaluating User Experience of the IPB Help Center Website Using the Usability Testing Method
Get PDFThe IPB Help Center Website is an essential source for IPB University students to get help in solving technical problems. Research was conducted to evaluate the user experience on the IPB Help Center Website through usability testing methods. This research involved 50 IPB University student respondents who used the IPB Help Center website and filled out a survey through Google Forms. The results showed that the overall level of usability was satisfactory, with high ratings for five usability indicators, including learnability, efficiency, memorability, errors, and satisfaction. The average scores of learnability, efficiency, memorability, errors, and satisfaction indicators are 3.88, 3.95, 3.88, 4.40, and 3.85, respectively. This shows that the IPB Help Center Website has been effective in helping users solve technical problems. This research provides an evaluation to improve the user experience and effectiveness of the IPB Help Center website as a valuable resource for IPB University students. Future research will explore the correlation between various usability indicators to gain further insights
PyLe: An Interactive Tool for Improving Python Syntax Mastery in Non-Computing Students
Get PDFThe learning and mastering of programming language syntax pose a significant challenge for non-computing students. Most teaching approaches and existing educational tools often fail to address this issue. Therefore, this paper introduces an interactive learning environment called PyLe, specifically designed for introductory programming in Python programming courses. We evaluated the effectiveness of PyLe on first-year students at North-West University in South Africa and the University of Yaoundé 1, Cameroon. Firstly, the study conducts an experiment to assess the effect of PyLe on the time taken to solve a problem and the response quality. Secondly, PyLe’s usability and its instructional value were evaluated by the students and the instructors, respectively. The results from post-test method and a quantitative survey indicate that PyLe improves students’ ability to learn and master program syntax and has a high usability rate. Moreover, feedback from students and teachers affirms PyLe’s potential to address programming syntax challenges for non-computing students. However, the analyses revealed no real relationship between the time taken to complete a task in PyLe and the quality of the solution. This study contributes to improving the teaching and learning of computer programming, which has been considered difficult for both computing and non-computing students
Sentiment Analysis of Indonesian Citizens on Electric Vehicle Using FastText and BERT Method
Get PDFElectric vehicles have become one of the most important innovations in the automotive industry in recent years. This is not only related to technological developments, but also to its significant impact on the environment and lifestyle of global society. Lot of people do not know about the benefit of using electric vehicles for our environment. The transition from conventional vehicles to electric vehicles can really make our environment healthier and also reducing the pollution. At the same time, debates and feelings about electric vehicles continue to grow around the world. This study aims to understand the dynamics of people's feelings and opinions about electric vehicles through sentiment analysis using the FastText and IndoBERT methods. FastText is an efficient text classification and representation learning method developed by Facebook's AI Research (FAIR) lab. IndoBERT is a pre-trained language model specifically designed for the Indonesian language, leveraging the Bidirectional Encoder Representations from Transformers (BERT) architecture. By analyzing a total of 119,310 data from January 2020 to June 2023, the tweets data were categorized into negative, neutral, and positive classes. Model yielded the highest accuracy of 82.5% using IndoBERT method. The results outcomes positive perceptions of electric vehicles among Indonesian citizen with a percentage of 58%. By carrying out this research, it is hoped that it can produce quality information for producers, the community and the government in developing and advancing public interest in purchasing electric vehicles considering the very positive impact they have on the surrounding environment
Predicting Forest Areas Susceptible to Fire Risk Using Convolutional Neural Networks
Get PDFWildfires pose a grave danger and threat to both human health and the environment, which is why early detection of wildfires is crucial. In this study, a convolutional neural network, which is a deep learning technique for computer vision, that is capable of classifying satellite imaging of forest cover in Canada as either being prone to wildfires or not being prone to wildfires is created. This model achieved an accuracy of 95.06% and is not only accurate but also reliable and unbiased in terms of the training set and the test set. We also review an existing model for the same dataset. Furthermore, this study discusses the application of this model in the real world, its feasibility, its future scope, and strategies to improve it
Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining
Get PDFA recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%
Coral Database and Monitoring System Design for Ecological Sustainability
Get PDFSeveral factors contribute to the importance of designing a coral monitoring system. Firstly, coral reefs are ecologically crucial ecosystems, providing habitat for numerous marine species and supporting biodiversity. Therefore, monitoring coral reefs is essential for understanding population dynamics and ecosystem health. Secondly, coral reefs are vulnerable to climate change, pollution, overfishing, and human activities. With a monitoring system, we can identify factors damaging coral reefs and take necessary prevention or restoration actions. Thirdly, coral reef monitoring aids in informing policies and sustainable resource management. By comprehensively understanding coral reef conditions, we can develop more effective management strategies to protect and preserve these ecosystems for future generations. This research aims to design a coral monitoring system to identify factors contributing to coral reef degradation. The method employed is Rapid Application Development (RAD), with stages including requirement planning, user design, construction, and cutover. The findings of this study indicate that the application can meet user needs. The findings of this research emphasize the urgent need for the development and implementation of coral monitoring applications as a strategic step toward reducing environmental degradation for ecological sustainability. The research underscores the critical role of monitoring tools in assessing and mitigating the impacts of human activities and environmental stressors on coral reef ecosystems through comprehensive data analysis and evaluation. This highlights the importance of proactive measures to address the increasing threats facing coral reefs and emphasizes the significance of technological innovations in facilitating practical conservation efforts
Utilizing ORB Algorithm in Web-Based Sales Application
Get PDFE-commerce has become common and important for businesses, but Jaya Sentosa Store has not implemented it. E-commerce commonly has only a search by keyword feature, but that cannot replicate Jaya Sentosa Store order process. An image-based search is needed to replicate the order process. Our research purpose is to develop a web-based sales application and an image search feature for Jaya Sentosa Store. We apply Scrum when developing this application. We use Javascript (JS) programming language. Back-end and front-end development employ Express JS and React JS framework, respectively. To get the right feature-matching algorithm, we conduct a test between the SIFT, KAZE, and ORB algorithms. We write Python scripts to implement ORB algorithm in image-based search feature. Our test shows that the ORB algorithm has the fastest average running time, i.e., 3.415 s, compared to SIFT and KAZE. Black box testing of the sales application shows that all cases are valid. It means that our application can replicate Jaya Sentosa Store order process and gain a competitive advantage
Shielding Social Media: BERT and SVM Unite for Cyberbullying Detection and Classification
Get PDFThis paper presents a novel approach for cyberbullying detection and classification in social media text using an ensemble model that combines BERT (Bidirectional Encoder Representations from Transformers) and Support Vector Machine (SVM) with grid search for multiclass classification. We have also compared the performance of our proposed with various machine and deep learning models and the results show that our proposed model outperforms other models achieving an accuracy of 90% on testing data. Further, we have used to used SHapley Additive exPlanations (SHAP) an Explainable (XAI) technique to interpret the predictions of the BERT-SVM ensemble model