The Journal of Student Research Exploration (JOSRE) has been accredited Sinta 5 and migrated to a new platform starting in 2026 to enhance security and protect against potential threats, including journal hacking and other malicious activities. To submit your manuscript, please visit our new journal website at: https://josre.shmpublisher.com We are pleased to inform you that the Journal of Students Research Exploration (JOSRE) has officially achieved SINTA 5 accreditation. The official accreditation announcement can be accessed here. Journal of Student Research Exploration (JOSRE) (e-ISSN: 2964-8246, p-ISSN: 2964-1691) is a peer-reviewed scientific journal that publishes high-quality theoretical and empirical research conducted by undergraduate and postgraduate students. The journal serves as a platform for disseminating innovative ideas, experimental studies, and applied research in various fields of engineering and computing. Each publication contains original research articles closely related to engineering disciplines, including Mechanical Engineering, Electrical Engineering, Industrial Engineering, Civil Engineering, Chemical Engineering, Architecture, and Computer Science/Computer Engineering. The journal emphasizes research contributions that address real-world challenges, industrial applications, and technological advancements. JOSRE focuses on research themes that promote innovation in industrial applications and energy development, particularly those aligned with global trends in green technology, sustainability, and environmentally friendly solutions. The journal encourages interdisciplinary approaches and integration of emerging technologies to support sustainable development goals. The journal is registered in the CrossRef system with a Digital Object Identifier (DOI) to ensure global accessibility and citation tracking. JOSRE is indexed in reputable databases, including Sinta 5, Copernicus, Garuda, Crossref, BASE, Dimensions, and Google Scholar.
Enhanced Out-of-Fold Stacking with Feature Grouping and Model-Specific Transformations for Diabetes Prediction Improvement
Diabetes mellitus is a chronic disease with serious implications for global health. Early detection is essential to reduce these risks, and machine learning methods are widely used in diabetes prediction. However, improving accuracy remains a major challenge in the development of predictive models. This study proposes a stacking-based ensemble learning approach with an out-of-fold (OOF) scheme to improve classification performance. The proposed method consists of several systematic steps, namely (1) data preprocessing via median imputation of invalid values and feature transformation according to model characteristics, (2) the creation of base learners comprising Logistic Regression, Gaussian Naïve Bayes, Support Vector Machine, Random Forest, and XGBoost, (3) model training using Stratified Cross Validation 5 Fold to generate OOF predictions, (4) combining all OOF predictions into a meta-feature matrix, and (5) training an XGBoost-based meta-model to generate the final prediction. This approach enables the meta-model to optimally learn the relationships among the outputs of the baseline models. Experimental results show that the proposed method achieves an accuracy of 91.15%, precision of 90.65%, recall of 83.21%, and an F1-score of 86.77%. These results indicate that stacking is effective in improving the accuracy of diabetes predictions.
Merci de créditer les auteurs lors de toute citation : Journal of Student Research Exploration (2026) https://creativecommons.org/licenses/by-sa/4.0
The Comparison of Statement Analysis on Disney Case Using Naive Bayes, SNM, and Logistic Algorithm Methods
The Walt Disney Company or also known as Disney, is one of the most famous companies in the world that focuses on the production of animation and film. Disney has been serve in this entertainment industry for over 90 years. Since Disney’s first film was released, Disney was become very famous until this day, especially when Disney has collaborates with many companies, it’s not only focusing on animation production but also making films and many live-action versions of the animations. Recently, Disney has been a hot topic among Disney’s movie fans due to the selection of actors for live action movie characters. Therefore, on this time, the author will conduct a sentiment analysis of Disney using analysis methods called Naïve Bayes, Support Vector Machine, and Logistic Algorithm. After passing through all the testing stages, the highest accuracy result is analysis using the Support Vector Machine (SVM) method. The test results show a high accuracy of 0.60 or 60% with the highest F1-Score in the Positive sentiment class by 67%.
Implementation of Lexicon-Based and SVM Methods in Sentiment Analysis of Sayurbox App Users
The ever-growing technology certainly produces a large amount of data, which can provide useful information if analyzed and used properly. The purpose of this research is to analyze user sentiment towards the Sayurbox application on the Google Play Store with a Lexicon-Based approach and the Support Vector Machine (SVM) algorithm. User review data is obtained through web scraping with a total of 16,468 reviews. After preprocessing and sentiment labeling, training and test data were divided. The results showed that SVM achieved accuracy, recall, and precision of 94%, 96%, and 96% respectively, with 9 prediction errors. The model tends to predict reviews as positive sentiment, indicating user satisfaction with Sayurbox's product service, delivery, quality, and price. The findings make a contribution to the understanding of user sentiment in e-commerce services and can assist Sayurbox in improving their user experience.
Integrating Convolutional Neural Network Features Extraction with Extreme Learning Machines for Image Classification of Pandava Characters in Wayang Kulit
This research focuses on the utilization of image processing techniques—the Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELMs)—to classify the characters of Wayang Kulit automatically. The Pandava characters or casts are classified in accordance with the characters from traditional Indonesian puppets, commonly known as shadow puppets. The focus is to introduce such rich cultural heritage to younger generations by using technology. Prior research has utilized classification of characters using Convolutional Neural Networks(CNNs), Extreme Learnings Machines(ELMs), and Support Vector Machines(SVMs), which led to varied accuracy levels. In our subsequent experiments, three proposed models, with varying underlying model assumptions, were evaluated. The proposed models generated moderate accuracies ranging from 39 to 52%. The results suggest that our models have room for further development to enhance their performance. Strategies from parameter tuning to the in-depth analysis of the confusion matrix are discussed. Above all, the research is geared towards ensuring the appreciation and preservation of traditional cultural heritage in this digital era.
Sentiment analysis of comments on youtube on react native vs flutter using the support vector machine and naïve bayes classifier algorithm
The study analyzed the sentiment of YouTube comments about React Native and Flutter, two popular platforms in cross-platform mobile app development, using the algorithms of Support Vector Machine and Naïve Bayes Classifier. The data was collected through YouTube comment scraping and analyzed using Google Colab with preprocessing techniques such as case folding, text cleaning, stopword removal, and stemming. Frequency-Inverse Term Document Frequency (TF-IDF) is used to extract features. The results showed that SVM was more effective in classifying positive and neutral sentiments, while NBC was superior in positive sentiment. Both algorithms have difficulty identifying negative sentiment, especially with the dominance of positive sentiment data. The study suggests the use of advanced strategies such as better feature selection or ensemble learning to improve the accuracy of sentiment classification on social media.
Merci de créditer les auteurs lors de toute citation : Journal of Student Research Exploration (2025) https://creativecommons.org/licenses/by-sa/4.0
Support vector machine on two-class classification problem to determine an otaku
Machine Learning has become a popular topic among academics and practitioners in recent years. This paper describes the use of SVM for otaku classification problem. The dataset used is a dummy dataset created with a python programme. In this research, SVM will be used as a model. The model aims to predict whether someone is an otaku or not, based on several attributes. The optimal parameters are obtained after several experiments. The parameters consist of kernel=‘poly’, C=0.1, gamma=‘auto’, degree=2, and attribute class_weight=None. The performance obtained by applying the above parameters is 100% accuracy.
Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews
Dana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app.
Clustering Analysis of Customers Based on Purchasing Patterns with K-Means Clustering
There are various techniques to classify data, one of which is clustering. What distinguishes clustering techniques from classification techniques is that they do not rely on the labels in the dataset. The main purpose of clustering is to divide data into several clusters based on similar characteristics, while Classification Technique is a technique of grouping data based on the similarity of the labels of the data under study. In this study, the dataset was created using secondary data from kaggle. The analysis process begins with data pre-processing to normalize the variables used, followed by the application of the K-Means Clustering method to group customers into several clusters based on the similarity of their purchasing patterns. This research demonstrates the potential of using clustering analysis to improve understanding of customer behavior and develop more effective business strategies.
Sentiment analysis spotify applications on google play store with naïve bayes and neural network methods
Digital advancements have significantly changed the way music is accessed and enjoyed, with streaming platforms such as Spotify emerging as one of the most widely used applications worldwide. Along with this growth, user reviews on platforms like the Google Play Store have become an important source of information, offering insights into user satisfaction and areas for improvement. In this study, sentiment analysis was conducted on Spotify reviews using two classification methods, Naïve Bayes and Neural Networks. The reviews were collected, processed, and then analyzed with both approaches to evaluate their performance. The results show that Neural Networks outperformed in terms of accuracy, F1-score, and recall, while Naïve Bayes performed better in AUC, precision, and MCC. Analysis of the dataset also revealed that negative reviews dominated at 52.8%, followed by positive at 28.3%, and neutral at 19%. These findings highlight the value of sentiment analysis in understanding user perspectives and can support developers in improving application quality and user experience.
Analyzing reading preferences based on gender and education with decision tree method
This study aims to analyze the suitability of book genre selection with gender and education level. A classification method using a decision tree algorithm with four different criterion parameters is used to examine reading preferences based on various demographic factors, namely Gain Index, Information Gain, Gini Index, and accuracy. Data was obtained from a dummy dataset involving 120 records with three main attributes. The results show variations in accuracy depending on the criteria selected, with the highest accuracy rate achieved being 78.57%.
Comparative sentiment analysis on starbucks boycott related to Israel's genocide using svm, naïve bayes, and knn methods
The #BoycottStarbucks movement emerged in response to allegations of Starbucks' involvement in supporting the genocide carried out by Israel in Palestine. This research aims to analyze public sentiment towards Starbucks regarding its alleged involvement in the Israeli genocide. Data was collected through crawling English-language comments on a YouTube channel named Firstpost, which discussed the boycott of Starbucks. Three classification methods used were Naive Bayes, KNN, and SVM. The research findings indicate that the SVM model had the highest accuracy (82%) compared to Naive Bayes (75%) and KNN (56%). The distribution of sentiment in the comment data was dominated by neutral sentiment (44.5%), followed by positive sentiment (37.3%) and negative sentiment (18.2%). However, positive comments were also significant, indicating support or interest in the boycott movement. The hypotheses proposed supported the finding that public sentiment towards Starbucks tends to be negative and supportive of the boycott movement regarding its alleged involvement in the Israeli genocide. The research also concludes that various public opinions are reflected in neutral, positive, and negative comments, indicating that the boycott of Starbucks still attracts significant attention and discussion.
Sentiment analysis of user comments on the shopeepay feature in the shopee application: Evaluation of accuracy with k-nearest neighbors (KNN) algorithm
This research analyzes Shopeepay user reviews on the Shopee app using the K-Nearest Neighbor (KNN) algorithm with TF-IDF weighting and a Cosine Similarity matrix. Data was collected through web scraping 500 reviews from the Google PlayStore and labelled into positive, neutral, and negative sentiments. The process includes literature study, data collection, labelling, text preprocessing, word weighting, and sentiment classification using KNN. Results show an accuracy range of 86%-91%, with Precision, Recall, and F1-Score as evaluation metrics. The findings indicate that convenience, trust, and risk significantly affect users' interest in Shopeepay, especially during the Covid-19 pandemic. A Word Cloud was also used to visualize common terms in the reviews, providing insights for Shopee to enhance Shopeepay based on user feedback.
Sentiment analysis of youtube comments on the palestine-israel conflict: Performance comparison of SVM, KNN, and RFC
The Palestine-Israel conflict, rooted in territorial and religious identity disputes in the Middle East, notably over the sanctity of Jerusalem, is impacted by various political, economic, and social factors. This study employs text-mining techniques to analyze the sentiment of YouTube comments concerning the conflict. Utilizing data collected via the YouTube API, the study preprocesses, analyzes sentiment, and classifies comments using three machine learning algorithms: K-Nearest Neighbors (K-NN), Random Forest Classifier (RFC), and Support Vector Machine (SVM). The categorization report measures are utilized to compare how well the models performed in classifying estimation as positive or negative. Outflanking all other classifiers, the Irregular Woodland Classifier (RFC) accomplishes 78curacy with accuracy rates of 0.76 for positive and 0.79 for negative assumptions. With a precision rate of 77%, SVM illustrates an inclination in favor of negative sentiments, though K-NN, with an exactness rate of 60%, shows an imbalance favoring negative over positive estimations.
Correlation analysis of climate conditions on rice prices in semarang 2017-2023
Rice is a vital commodity in Indonesia whose price stability is highly sensitive to climate variability. Semarang City, as a coastal urban center prone to floods and extreme rainfall, plays a strategic role in rice production and distribution in Central Java. This research analyzes the correlation between climate variables and rice prices during the 2017–2023 period using Pearson, Spearman, cross-correlation, and autocorrelation methods. The results show that rice prices have weak to moderate correlations with climate factors, with temperature showing a stronger influence compared to rainfall, humidity, or wind speed. Although Pearson and Spearman tests mostly indicate weak correlations, several results are statistically significant, while cross-correlation analysis demonstrates that climate impacts on rice prices often appear indirectly with time lags. Furthermore, autocorrelation reveals that rice prices are characterized by short-term dependencies without clear seasonality, whereas climate parameters exhibit stronger cyclical patterns. These findings provide valuable insights for developing adaptive strategies and policies to strengthen food security in the face of climate change.
Comparison of naïve bayes and support vector machine methods for jkt48 music video comment classification
The research was conducted to discuss the classification of comments on music video JKT48 "Magic Hour" in YouTube using method Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). YouTube monitors viewer emotion by adjective comments Adjectives are the descriptive powers of human communication we use to help personify how different types, i.e. different "personalities" flavors and depths reflect artistic expressions The place where interactivity meets with digital marketing signifying a shared contribution to music lore In this work, we study the comparison of The Support Vector Machine (SVM) and Naive Bayes Classifier in terms of Accuracy, Precision & Recall. This Project includes data pre-processing, collecting the data by YouTube API and build classification models which involves Support Vector Machine and Naive Bayes Classifier. SVM displayed more stable performance than NBC, showing consistent results across different data split ratios. SVM achieved its highest accuracy of 93.42% at an 80:20 ratio, with precision and recall rates reaching 92.57% and 93.42%, respectively.
Analysis of k-means clustering algorithm in advanced country clustering using rapid miner
In the era of globalization, the understanding of developed countries is no longer limited to the level of per capita income alone. As part of the analysis of developed countries based on aspects of government revenue, income balance, national savings, and domestic output based on sales. This research aims to cluster and to find out how these economic indicators are interrelated and affect the status of a country as a developed country. The K-means algorithm is used to identify patterns of countries with similar economic characteristics. From the research conducted, there are 4 clusters generated based on the characteristics of developed countries.
Merci de créditer les auteurs lors de toute citation : Journal of Student Research Exploration (2024) https://creativecommons.org/licenses/by-sa/4.0
Classification of travel class with k-nearest neighbors algorithm using rapidminer
he tourism industry in Indonesia plays an important role in the national economy. The selection of travel class according to the needs and budget of tourists is an important aspect in the tourism industry. This research aims to develop a travel class classification model using dummy datasets and the K-Nearest Neighbors (KNN) algorithm with RapidMiner software. The travel class dummy data set was obtained from the internet and modified according to research needs. The KNN algorithm was used to classify new travel classes based on previously classified dummy data. These dummy data were preprocessed and analyzed using RapidMiner software. The performance of the KNN model was evaluated using accuracy, precision, recall and F1-score. The results showed that the KNN algorithm with the values k = 1-2, k = 3-6, k = 8-10, k = 11-14 and k = 15 resulted in accuracy of 35.71%, 39.29%, 48.26%, 46.43% and 50.00%, respectively. This shows that the KNN algorithm with a value of k=15 produces the highest accuracy that can be effectively used to classify new travel classes based on dummy data.
Classification of risk of death from heart disease or cigarette influence using the k-nearest neighbors (KNN) method
Heart disease is one of the leading causes of death in Indonesia. In addition to coronary heart disease, smoking is the leading contributor to the death rate in Indonesia. This study aims to analyze the risk of death with the main variables of heart disease history and smoking history. This study classifies the risk of death of heart disease sufferers and smokers using the KNearest Neighbors (KNN) algorithm. The results showed that the KNN model had an accuracy of 52.38% in predicting the risk of death of smokers and heart disease patients. Confusion matrix analysis revealed that the model performed well in predicting classes 0 and 2, but had difficulty in predicting class 1. This study shows that KNN can be used to predict the risk of death of smokers and patients with heart disease with a satisfactory success rate.
Application of k-nearest neighbor algorithm in classification of engine performance in car companies using Rapidminer
Implementation of the k-Nearest Neighbor (k-NN) algorithm in the classification of CAR Car company engine performance using RapidMiner software. The company's engine performance is a very important aspect in the automotive industry that greatly affects operational efficiency and customer satisfaction. As an effort to monitor and improve engine performance, classification is an important key to identify machines that are feasible and require repair. The dataset used is a generated dataset from the AI Chat GPT bot whose prompts have been adapted to the research needs. The k-NN algorithm was chosen due to its ability to produce accurate predictions. The k-NN classification method utilizes training and testing data and calculates the distance between the data to determine the appropriate class. The results of this study show excellent performance in terms of accuracy, precision, and recall. The highest accuracy is 90.62% at the value of k = 2. The highest precision and recall are 100% and 93.75% at the values of k = 2, k = 4, and k = 7.
Online payment fraud prediction with machine learning approach using naive bayes algorithm
The increase in e-commerce has provided easy access for the public, but it also opens up opportunities for fraud in online transactions. Payment fraud is also a problem that often arises in transactions through electronic media. This research aims to analyze payment fraud in e-commerce transactions. This research uses a machine learning approach using the Naive Bayes algorithm. This research uses online transaction datasets involving various attributes such as payment and shipping methods. The developed Naive Bayes model achieved an accuracy of 61.03% with K = 7. The evaluation shows a balance between precision (59.46%) and recall (62.05%), although this study is limited by data quality and basic assumptions of Naive Bayes. In future research, it is worth considering the use of additional features or more complex data processing to improve the performance of fraud detection in online transactions. This research provides important insights in the fight against financial crime in the context of electronic commerce.
Email spam detection: a comparison of svm and naive bayes using bayesian optimization and grid search parameters
Spam emails are still a big problem, crowding out inboxes and annoying email users everywhere. SVM and Naive Bayes are frequently used algorithms that have demonstrated excellent performance in performing text classification, including spam detection. The purpose of this study is to evaluate the overall performance of SVM and Naive Bayes in the context of detecting spam emails using default parameters. This research utilizes Bayesian Optimization and Grid Search Parameters for both SVM and Naive Bayes models to help maximize the performance of the constructed models. This study uses a spam email dataset that has 2 sample groups, namely spam and ham. Of the three parameter selection methods that have been tested on the SVM Algorithm, Bayesian Optimization is a parameter tuning method that has the most satisfying results in accuracy, precision, recall, and f1 scores respectively with values of 98.5642%, 99.4048%, 89.
Merci de créditer les auteurs lors de toute citation : Journal of Student Research Exploration (2023) https://creativecommons.org/licenses/by-sa/4.0
Detection and prediction of rice plant diseases using convolutional neural network (CNN) method
Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.
Customer churn prediction in the case of telecommunication company using support vector machine (SVM) method and oversampling
hurn is the act by which a customer withdraws from service, including service provider-initiated churn and customer-initiated churn. Churn is a big challenge for companies, especially churn-prone enterprise sectors such as telecommunications. Churn can affect both revenue and reputation if occurs for negative reasons. This study aims to predict customer churn in a telecommunication company dataset, investigating the impact of various variables and classes on churn occurrences to inform strategic decision-making for businesses. The Support Vector Machine (SVM) model is employed, and dataset imbalance is addressed through oversampling techniques, specifically Synthetic Minority Over-sampling Technique (SMOTE) and random oversampling (ROS). Three SVM models are created with different training datasets (normal, SMOTE, ROS), yielding varying results. The normal dataset achieves the highest accuracy at 92%, outperforming SVM with ROS (89%) and SVM with SMOTE (87%). However, the normal dataset exhibits lower sensitivity compared to both oversampling techniques. The study identifies the cause of decreased accuracy in oversampling and low sensitivity in the normal dataset. The novelty of this research lies in testing the SVM model's ability to surpass the accuracy of previous models on the same dataset and in exploring the unique impact of oversampling in churn prediction.
Impact of sales promotion and product quality on zoya customer purchase interest
This research analyses the influence of Sales Promotion and product Quality on Purchase Interest through Brand Awareness. This is a significant concern because product quality is an important thing that every company must strive for if it wants to compete in the market. The object of this research is Zoya Kudus. The sampling technique used purposive sampling with the rule of thumb formula to produce a sample of 120. The analysis tool in this research used SEM-AMOS. This research shows that Sales Promotion and Product Quality have a positive and significant effect on Brand Awareness. Sales Promotion and product quality have influenced purchase Intention. Sales promotion on purchasing interest through brand awareness influences partial mediation. Product quality on purchase intention through brand awareness has a mediating influence, but the influence is weak.
Impact of product design and sales promotion on eiger customer loyalty
The needs of society and developing lifestyles will result in hobbies also developing, such as the hobby of adventuring in the wild. One brand of outdoor equipment is Eiger. This research analyzes the influence of Product Design and sales Promotion on Customer Loyalty through Consumer Satisfaction. The object of this research is the Eiger product in Kudus. The data used in this research was by distributing questionnaires via online form. The instrument test in this study used a reliability test and validity test. The analytical tool in this research uses SEM-AMOS. This research shows that product design has no effect on consumer satisfaction. Sales promotions have a positive and significant effect on consumer satisfaction. Product design has a positive and significant effect on customer loyalty. Sales promotions do not affect customer loyalty. Consumer satisfaction does not affect customer loyalty. Product design and sales promotions on customer loyalty through consumer satisfaction have a weak mediating influence. Product design, sales promotions, and consumer satisfaction are important in shaping consumer perceptions of loyalty. This perception will influence customer attitudes and behavior. Therefore, companies must design good strategies so that consumers can behave and behave as expected.
Application design for web-based car services to increase work time estimates
The aim of this research is to increase the estimated service process time by creating an online-based car service ordering application at the Sinar Jaya repair shop and introducing information about Sinar Jaya car service services to the wider public. In this information systems research, the author of this research software development method uses the waterfall model development method. By implementing a waterfall, the research stages carried out by researchers start from data analysis, system analysis, system design, implementation, and testing. Creating a website-based car service ordering application at the Sinar Jaya Workshop can help customers find out the information available at the Sinar Jaya Workshop and the car service ordering process. Before there was an application, customers had to come to the location to place an order, so it took a long time to arrive at the location. So, with the online booking application, you can save time in the service process and get a queue number online. The data processing process for ordering car services becomes more practical so that it can be processed properly by the admin.
The Optimization house price prediction model using gradient boosted regression trees (GBRT) and xgboost algorithm
In this rapidly advancing technological era, the demand for the real estate industry has also increased, including in the field of house price prediction. House prices fluctuate every year due to several factors such as changes in land prices, location, year of construction, infrastructure developments, and other factors. Numerous studies have been conducted on this issue. However, the challenge lies in building a proven accurate and effective model for predicting house prices with the abundance of features present in the dataset. The objective of this research is to develop a predictive model that can accurately estimate house prices based on relevant features or variables. The researcher utilizes ensemble learning techniques, combining the Gradient Boosted Regression Trees (GBRT) and XGBoost algorithms. The dataset used in this article is titled "Ames Housing dataset" obtained from Kaggle. The predictive model is then evaluated using the Root Mean Squared Error (RMSE) method. The RMSE result from a previous study that used the combination of Lasso and XGBoost was 0.11260, while the RMSE result from this research is 0.00480. This indicates a decrease in the RMSE value, indicating a lower level of error in the model. It also means that the combination of GBRT and XGBoost algorithms successfully improves the prediction accuracy of the previous research model.
Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset
Health is crucial for humans to carry out daily activities, and cancer is the second leading cause of death worldwide. Maintaining health is essential in minimizing factors associated with cancer. Immunotherapy is a new cancer treatment technique that has s shown a bigger success rate compared with conventional techniques. However, the effectiveness of this method depends on accurate diagnosis, which requires deeper analysis and research on classification methods. This study compares the accuracy of KNN, Naive Bayes, and Decision Tree classification methods in predicting the accuracy of immunotherapy treatment. The goal is to find the most effective classification techniques that can provide more accurate predictive results in treating diseases using immunotherapy. Based on the test results of Naive Bayes, Decision Tree, and K-Nearest Neighbor, the result obtained of accuracy rates are 81.11%, 80.00%, and 74.44%. From the accuracy comparison, it is known that the Naive Bayes algorithm is the most effective algorithm with the highest accuracy value of 81.11%.
Increased accuracy in predicting student academic performance using random forest classifier
This research aims to classify the academic performance of students who are successful and who have dropped out of school with high accuracy so that these matters can be addressed quickly. Things like this need fast handling to find out what factors influence it. In addition, this research was conducted to test how good the random forest algorithm is in classifying a problem. Random forest, which includes an algorithm that is commonly used for classifying a problem. By using the random forest algorithm, the accuracy results will be better than a single decision tree. This algorithm is quite good at handling and managing large datasets. From this study it can be concluded that this method can provide good prediction accuracy with a fairly high level of accuracy, namely 89%. Utilization of this random forest can be an alternative in classifying student academic achievement. This algorithm can work well in handling large datasets. This study discusses how the use of Random Forest can work to classify students' academic performance.
Application go-sport as a solution to search information on facilities, places, partners, and sports events for students
Sport is a physical and mental activity that is beneficial for people to maintain the body and develop the quality of health. This makes exercise an activity that needs to be done for everyone to maintain their stamina. However, the lack of information about places, facilities, partners, and sports events is a strong reason in terms of reducing student motivation in carrying out sports activities themselves. The purpose of this research is none other than to design an application that can help students get all sports information. These things are none other than to foster a strong desire to do sports activities. Through technology smartphone which has been owned by the wider community, this research creates a solution by designing an application called "Go-Sport". This study uses the "Design Thinking" method, which focuses on finding and understanding user needs to obtain an optimal solution in the form of the results of the features to be made. From this research, a design or prototype of the "Go-Sport" application was produced which is ready to be implemented and tested on users.