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Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

The occurrence of scorch during the production of flexible polyurethane is a significant issue that negatively impacts foam products' resilience and generally jeopardizes their integrity. The likelihood of foam product failure can be decreased by optimizing production variables based on machine learning algorithms used to predict the occurrence of scorch. Investigating technology is required because prevention is the best approach to dealing with this problem. Hence, machine learning algorithms were trained to predict the occurrence of scorch using the thermodynamic profile of polyurethane foam, which is made up of recorded production variables. A variety of heuristics algorithms were trained and assessed for how well they performed, namely XGBoost, Decision trees, Random Forest, K-nearest neighbors, Naive Bayes, Support Vector Machines, and Logistic Regression. The XGboost ensemble was found to perform best. It outperformed others with an accuracy of 98.3% (i.e., 0.983), followed by logistic regression, decision tree, random forest, K-nearest neighbors, and naïve Bayes, yielding a training accuracy of 88.1%, 66.7%, 84.2%, 87.5%, and 67.5% respectively. The XGBoost was finally used, yielding 2-distinct cases of non(occurrence) of scorch. Ensemble demonstrates that it is quite capable and is an effective way to predict the occurrence of scorch.

This research uses the Support Vector Machine (SVM) algorithm to predict Persebaya Surabaya's ranking in BRI Liga 1. The data used includes goals scored, goals given away, total end-of-season points, and status as champions. The results of the analysis using Orange software show that Persebaya Surabaya does not necessarily become a champion if it has a point value of 42 and an SVM value of 41. To become a champion, Persebaya Surabaya must score 69 points or more in a season and achieve an average of more than 54 goals per season. The suggestion of this research is to have more data so that the results of data processing using Orange software are more optimal and accuracy is more precise.

Chronic Kidney Disease (CKD) is a serious health problem, with significant impact on patients' quality of life and healthcare costs. In an effort to improve early diagnosis, a comparison was made between several Machine Learning algorithms used for analysis of patient clinical data. This clinical data contains the medical history or health records of patients. The Machine Learning algorithms used in this study include K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression. By searching for the best algorithm through the calculation of Accuracy, Precision, and Recall with comparasion when using SMOTE (Synthetic Minority Oversampling) to balancing the class attribute.   

This study aims to analyze the communication of marine mammals, especially whales and dolphins, through a bioacoustic approach combined with computational science as an effort to support conservation in the Tropical Ocean region. The focus of the location is on the Banda Sea, the Seram Sea, and the tropical Pacific region which are important migration routes for marine mammals. Data were obtained from underwater sound recordings using hydrophones, accompanied by visual observations to validate the behavior and existence of species. The analysis is carried out through several stages, including signal pre-processing with noise filtering and sound segmentation, spectral analysis using Fast Fourier Transform (FFT), as well as the creation of a spectrogram to visualize vocalization patterns. Machine learning algorithms such as Support Vector Machine (SVM) are used to classify interspecies voices, while deep learning approaches are applied to identify more complex communication patterns, including dialect variations. The results showed that whales produced low-frequency vocalizations (20–200 Hz) for long-distance communication, while dolphins used high-frequency clicks and whistles (5–20 kHz) for echolocation and social interaction. The integration of bioacoustics and artificial intelligence improves the accuracy of sound classification by more than 90%. These findings confirm the effectiveness of computational-based non-invasive methods in monitoring the presence and behavior of marine mammals and provide a scientific basis for sustainable conservation.

PeduliLindungi is an application from the Government of Indonesia that was made in response to the COVID-19 pandemic. Since its initial release in 2020, this application has received many updates with the goal of improving its overall performance. One of the basics of updating applications is to process the reviews given by users at the Google Play Store using sentiment analysis. The methods used this time are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). The sample data used were 300 reviews with positive feedback and 300 reviews with negative feedback, for a total of 600 user reviews. The results of the NBC algorithm calculations produce an accuracy of 76%, a precision of 76%, a recall of 82%, and an f1-score of 79%. As for the SVM algorithm, it produces an accuracy rate of 80%, a precision of 83%, a recall of 80%, and an f1-score of 81%.

Plants are constantly exposure to pathogens such as virus, bacteria and fungi. Plant diseases caused by pathogens lead significant crop yield loss globally. Numerous researchers have been studying how to reduce the damage of plant diseases. Plant disease has long been one of the major threats to agriculture security in India because it dramatically reduces the crop yield and compromises its quality. Pests and Diseases results in the destruction of crops or part of the plant resulting in decreased food production leading to food insecurity. Accurate and precise diagnosis of diseases has been a significant challenge. Traditionally, identification of plant diseases has relied on human annotation by visual inspection. Plant diseases affect the growth of their respective species; therefore their early identification is very important. Modern technological approaches such as machine learning and deep learning algorithm have been employed to increase the recognition rate and the accuracy of the results. Various researches have taken place under the field of machine learning for plant disease detection and diagnosis, such traditional machine learning approach being random forest, artificial neural network, support vector machine(SVM), fuzzy logic, K-means method, Convolutional neural networks etc. In this paper a comparative study on machine learning techniques for plant disease detection is performed. In this survey it observed that Convolutional Neural Network gives high accuracy and detects more number of diseases of multiple crops.

Plants are constantly exposure to pathogens such as virus, bacteria and fungi. Plant diseases caused by pathogens lead significant crop yield loss globally. Numerous researchers have been studying how to reduce the damage of plant diseases. Plant disease has long been one of the major threats to agriculture security in India because it dramatically reduces the crop yield and compromises its quality. Pests and Diseases results in the destruction of crops or part of the plant resulting in decreased food production leading to food insecurity. Accurate and precise diagnosis of diseases has been a significant challenge. Traditionally, identification of plant diseases has relied on human annotation by visual inspection. Plant diseases affect the growth of their respective species; therefore their early identification is very important. Modern technological approaches such as machine learning and deep learning algorithm have been employed to increase the recognition rate and the accuracy of the results. Various researches have taken place under the field of machine learning for plant disease detection and diagnosis, such traditional machine learning approach being random forest, artificial neural network, support vector machine(SVM), fuzzy logic, K-means method, Convolutional neural networks etc. In this paper a comparative study on machine learning techniques for plant disease detection is performed. In this survey it observed that Convolutional Neural Network gives high accuracy and detects more number of diseases of multiple crops.