1 10 28 https://repository.horizon.ac.id/files/original/8e4009fb01dd43a21641f52df15fc78e.pdf d030a7011af66f7c894f49b99a4449d9 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of Red Foxes: Logistic Regression and SVM with VGG-16, VGG-19, and Inception V3 Subject The topic of the resource red fox images; image classification; deep learning models Description An account of the resource Deep learning models demonstrate a high degree of accuracy in image classification. The task of distinguishing between various sources of red fox images—such as authentic photographs, game-captured images, hand-drawn illustrations, and AI-generated images—raises important considerations regarding realism, texture, and style.This study conducts an evaluation of three deep learning architectures: Inception V3, VGG-16, and VGG-19, utilizing images of red foxes. The research employs Silhouette Graphs, Multidimensional Scaling (MDS), and t-Distributed Stochastic Neighbor Embedding (t-SNE) to assess clustering and classification efficiency. Support Vector Machines (SVM) and Logistic Regression are utilized to compute the Area Under the Curve (AUC), Classification Accuracy (CA), and Mean Squared Error (MSE). The MDS plots and t-SNE data clearly demonstrate the capability of the three deep learning models to distinguish between the image categories.For game-captured images, VGG-16 and VGG-19 demonstrate quite outstanding performance with silhouette scores of 0.398 and 0.315, respectively. This study explores the enhancement of classification accuracy in logistic regression and support vector machines (SVM) through the refinement of decision boundaries for overlapping categories. Utilizing Inception V3, an artificial intelligence-generated image silhouette score of 0.244 was achieved, demonstrating proficiency in image classification. The research highlights the challenges posed by diverse datasets and the efficacy of deep learning models in the classification of red fox images. The findings suggest that integrating deep learning with machine learning classifiers, such as logistic regression and SVM, may improve classification accuracy. Creator An entity primarily responsible for making the resource Brian Sabayu1*, Imam Yuadi2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6356/1054 Publisher An entity responsible for making the resource available Master’s Program Human Resource Development-Data Analytics, Graduate School, Universitas Airlangga,Surabaya,Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/1429755af966552d26d35c637c538123.pdf 5cd10e98fe9ded1c1318e52dbf291e59 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Comparative Evaluation of Preprocessing Methods for MobileNetV1 and V2 in Waste Classification Subject The topic of the resource waste;MobileNetV1; MobileNetV2; preprocessing; waste classification Description An account of the resource Waste management remains a critical challenge for many countries, including Indonesia, which ranks as the world's second-largest contributor of waste. As tens of millions of tons are produced each year and the management system remains ineffective, environmental conditions and public health continue to deteriorate. To address this issue, it is imperative to develop more accurate and efficient solutions to enhance waste classification and management. This study investigates the influence of various image preprocessing techniques on the performance of MobileNetV1 and MobileNetV2 models in the classification of waste images. Preprocessing is crucial for enhancing data quality, particularly when dealing with real-world images that are affected by inconsistent lighting, texture, and clarity. Five preprocessing scenarios were evaluated: Baseline, CLAHE with Bilateral Filtering, CLAHE with Sharpening, Grayscale with CLAHE, and Gaussian Blur with Bilateral Filtering. Among these, the combination of CLAHE and Bilateral Filtering applied to MobileNetV1 achieved the best results, with 85% training accuracy, 96% validation accuracy, a training loss of 0.3178, and the lowest validation loss of 0.1630. Overall, MobileNetV1 benefited more significantly from preprocessing variations than MobileNetV2, particularly in terms of accuracy improvement and reduction in prediction error. These findings underscore the importance of effective preprocessing in enhancing model performance for waste image classification Creator An entity primarily responsible for making the resource Aulia Afifah1, Endah Ratna Arumi2*, Maimunah3, Setiya Nugroho Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6211/1055 Publisher An entity responsible for making the resource available Informatics Engineering, Engineering, Universitas Muhammadiyah Magelang, Magelang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/4286b2dc674ada4d7b6c4612e56ea588.pdf 59f37a5e46977cf1be701ddce553afdc Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource University Students Stress Detection During Final Report Subject by Using NASA TLX Method and Logistic Regression Subject The topic of the resource stress; wearable system; NASA-TLX; heart rate; body temperature Description An account of the resource Stress is a psychological response that occurs when someone faces pressure or demands that exceed their ability to adapt. In the context of a final-year student, stress is often a significant problem due to academic pressure, such as completing final assignments, as well as demands to immediately prepare to enter the workforce and demands to immediately prepare to enter the workforce. Research shows that stress that is not managed properly can cause various negative effects, such as sleep disorders and decreased cognitive function. This study aimed to identify and analyze stress levels among final-year students who completed a final report by integrating physiological and psychological data. In this study, 30 students were assessed using a wearable system to obtain physiological data, such as heart rate and body temperature, while subjective assessments were carried out using the NASA-TLX method to measure mental workload. The results showed that 19 out of 30 respondents experienced significant levels of stress and 11 respondents were in normal conditions, with the main causal factors including high academic pressure and distance regarding the future. In addition, the logistic regression analysis applied in this studysucceeded in developing a predictive model with an accuracy of 94% in identifying students' stress conditions. This shows that this method is sufficiently accurate for detecting stress symptoms in final-year students. Creator An entity primarily responsible for making the resource Alfita Khairah1, Melinda Melinda2*, Iskandar Hasanuddin3, Didi Asmadi4, Riski Arifin5, Rizka Miftahujjannah6 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6401/1057 Publisher An entity responsible for making the resource available ndustrial Engineering Department, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/6443ec4eccc4c9bb43b882d526a9414a.pdf 62b191ce67583a6d7abdc573a8a72499 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms Subject The topic of the resource adjusted TF-IDF; ACM classification; BERT; expertise; FastText; BERT multilingual; SBERT; XLM-RoBERTA Description An account of the resource In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach usedwas keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1 Creator An entity primarily responsible for making the resource Lyla Ruslana Aini1*, Evi Yulianti1 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6397/1060 Publisher An entity responsible for making the resource available Department of Computer Science, Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 25, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/baea07f27d41bd371d8e1f5022ec5390.pdf eb9020a4d5b5534a77d9e0fda98978ac Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Developmentof aDocument-Based Gait System With Interactive Visualisation forClinical Analysis Subject The topic of the resource biomechanics; dashboard; database; gait analysis; mongoDB Description An account of the resource Gait analysis is a crucial aspect of biomechanics and medical rehabilitation, used to detect movement disorders, assess therapy effectiveness, and understand human walking patterns. In Indonesia, gait research remains limited, with most data sourced from abroad, which may not reflect the characteristicsof the local population. This study uses data from Vicon camera recordings that track marker movements on the subject's body and convert them into kinematic data in spatial coordinates, stored in Excel files. To support clinical applications, an efficientsystem is needed to manage gait data and present analysis results interactively. Therefore, a MongoDB-based gait data management system was developed due to its flexibility in handling unstructured data and scalability. The system was designed to preprocess gait data and display the results through an interactive Streamlit dashboard. The analysis involved calculating gait angle parameters, visualized in a gait cycle anglegraph and analyzed statistically using mean and standard error to improve interpretation accuracy. Testing shows that the system can store data in an average of 1.52 seconds, retrieve it in 3.598 seconds, and render visualizations in 0.192 seconds, with high accuracy and only a 0.1-degree error between the input and output. This system effectively addresses the challenge of managing local gait data and supports comprehensive biomechanical analysis, enabling clinicians to make informed decisions regarding rehabilitation needs based on deviations from normal gait angle ranges. Creator An entity primarily responsible for making the resource Rizal Rahman Rizkika1, Helisyah Nur Fadhilah2, Tanzilal Mustaqim3, Rifdatun Ni’mah Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6451/1074 Publisher An entity responsible for making the resource available Departmentof Data Science, Surabaya Directorate, Telkom University, Surabaya, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 16, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/45435dd5b5f5febdd04fa31775558f7b.pdf 5cd10e98fe9ded1c1318e52dbf291e59 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis Subject The topic of the resource cervical cancer screening; computational oncology; machine learning; risk stratification; XGBoost Description An account of the resource Cervical cancer continues to pose a significant global health challenge, with early detection remaining the cornerstone for effective intervention. This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. An XGBoost model was developed to predict cervical cancer risk. This model incorporates demographic, behavioral, and clinical parameters. The model was developed using data from 858 patients at the Hospital Universitario de Caracas. The preprocessing pipeline was designed to address the complexities inherent in medical data, including strategic management of missing values and standardizing heterogeneous features. The model demonstrated an overall accuracy of 96.3%, with a sensitivity of 66.7% and a specificityof 97.6%. This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. The confusion matrix analysis revealed the clinical implications of the model predictions, demonstrating a promising positive predictive value of 55.0% despite the pronounced class imbalance. These findings suggest that ensemble learning approaches can effectively synthesize diverse patient data into meaningful risk assessments, potentially enhancing screening efficiency through personalized stratification. Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation. Creator An entity primarily responsible for making the resource Sudi Suryadi1*, Masrizal2 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6587/1085 Publisher An entity responsible for making the resource available Information System, Facultyof Science and Technology, Universitas Labuhanbatu,Rantauprapat, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 21, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/d93d2f356af9be425e756f45ad3e1055.pdf 637aaab4fb0536e016621e0c671dd3c3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Improving Frame-based Engagement Classification in E-Learning Using EfficientNet and Normalized Loss Weighting Subject The topic of the resource classification; deep learning; engagement; EfficientNet; normalized loss Description An account of the resource Engagement can be defined as how individuals are involved in and interact with a task that requires attention and emotional conditions. Engagement is an affective state positively correlated with learning processes. Engagement along with other affective states, such as boredom, confusion, and frustration must be analyzed to identify students’ learning behavior. Implementing proper prevention by measuring student engagement levels could increase students’ learning intake. Such implementation involves building an effective feedback system or rearranging the learning design. Several researchers have proposed deep-learning approaches using the DAiSEE dataset to classify student engagement levels. In addition, previous studies utilized various loss functions equipped with class weighting to assign higher importance to the minor classes, which are low and very low engagement classes. Most of the state-of-the-art models achieved high accuracy, but the f1-score was still low because of the minor class struggle. This research tries to solve engagement level classification on imbalance conditions by proposing a normalized loss function weighting based on the Inverse Class Frequency formula based on each class’ instances to give more importance and focus to the classes and trained on Vanilla EfficientNet model rather than experimenting on more advanced model to keep the efficient and suit the memory constraint on the e-learning implementation. Based on the conducted experiments, the normalized ICF obtained the highest accuracy of 51.64% and weighted f1-score of 50.86%, which is superior to the standard ICF performance, which received 50.32% accuracy and weighted f1-score of 50.49% using the same settings Creator An entity primarily responsible for making the resource Joseph A. Sugihdharma1, Fitra AbdurrachmanBachtiar2*, Novanto Yudistira3 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6161/1088 Publisher An entity responsible for making the resource available ntelligent System Laboratory, Facultyof Computer Science, Brawijaya University, Malang, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 21, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/75b93a49ebb1460c7fb3a1c18537b7ae.pdf c9618f86e595e6ae9b5e21db9d68a12c Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements Subject The topic of the resource forecasting; inflation; long short-term memory; stock forecasting; stock return Description An account of the resource The stock market is crucial for economic growth and development, offering profit opportunities that attract investors worldwide. However, its inherent volatility necessitates the inclusion of macroeconomic indicators like inflation, which can affect stock valuation and investor behavior.This study explores predicting stock returns using a Long Short-Term Memory (LSTM) model by incorporating inflation data, historical stock price movements, and calculated returns as input features. Thedataset was split into 80% for training and 20% for testing, with hyperparameter tuning conducted using the RMSprop optimizer under varying batch sizes and epoch settings. Experimental results show that the configuration using RMSprop with a batch size of 8 and 200 epochs delivered the best performance, achieving a Root Mean Squared Error (RMSE) of 0.0167 and a Mean Absolute Percentage Error (MAPE) of 25.89%. These results represent a significant improvement over alternative configurations and previous benchmarks. This study underscores the importance of including inflation as a predictive variable, enhancing the model's accuracy. The findings highlight the relevance of incorporating macroeconomic factors into stock returnforecasting, providing valuable insights for investors and financial analysts seeking data-driven strategies in decision-making processes Creator An entity primarily responsible for making the resource Nur Faid Prasetyo1, Wina Witanti2*, Asep Id Hadiana Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6422/1056 Publisher An entity responsible for making the resource available Departmentof Informatics, Facultyof Science and Informatics, Universitas Jenderal Achmad Yani, Cimahi, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/7d56ec55c275fcb2aded1e41f653c209.pdf 54877d49de2f23442359b73d6703ec35 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression Subject The topic of the resource big data; cancer; health policy; OLS regression; poverty Description An account of the resource Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyzethe relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance,as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model Creator An entity primarily responsible for making the resource Heny Pratiwi1,Muhammad Ibnu Sa’ad2*, Wahyuni3, Syamsuddin Mallala4 Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6112/1059 Publisher An entity responsible for making the resource available Information Systems, STMIK Widya Cipta Dharma, Samarinda, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource May 24, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT https://repository.horizon.ac.id/files/original/2894b1510fc05822b57852772848be9a.pdf f601f1510b965867be8aa756c0cab0ad Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Vol 9 No 3 (2025) Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Obesity Status Prediction Through Artificial Intelligence and Balanced Label Distribution Using SMOTE Subject The topic of the resource obesity prediction; SMOTE; random forest; artificial neural network; AI in healthcare Description An account of the resource Obesity, a global health challenge influenced by genetic and environmental factors, is characterized by excessive body fat that increases the risk of various diseases. With over two billion individuals affected worldwide, addressing this issue is crucial. This study investigated the application of Artificial Intelligence (AI) to predict obesity status using a dataset of 1,610 individuals, including demographic and anthropometric data. Four AI algorithms were analyzed: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to address dataset imbalance. The results demonstrate that SMOTE significantly enhanced the models' performance, especially in recall andF1-score for minority classes, such as obesity. Random Forest achieved the highest accuracy (92%) and recall (92%) post-SMOTE. The ANN showed substantial improvement in recall, increasing from 77% to 89%, whereas the SVM achieved the highest precision (89%), minimizing false positives. Despite these improvements, KNN remained the least effective. The findings underscore the critical role of SMOTE in improving AI model accuracy for obesity prediction and highlight Random Forest as the most reliable algorithm for clinical decision-making. Limitations, such as dataset representativeness, suggest future research directions, including expanding data diversity and advanced feature selection techniques. This study provides valuable insights into leveraging AI and preprocessing methods for obesity management Creator An entity primarily responsible for making the resource Arif Riyandi1*, Mahazam Afrad2, M Yoka Fathoni3, YogoDwiPrasetyo Source A related resource from which the described resource is derived https://jurnal.iaii.or.id/index.php/RESTI/article/view/6204/1063 Publisher An entity responsible for making the resource available Department of Information System, Information System, Telkom University, Purwokerto, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource June 12, 2025 Contributor An entity responsible for making contributions to the resource FAJAR BAGUS W Format The file format, physical medium, or dimensions of the resource PDF Language A language of the resource ENGLISH Type The nature or genre of the resource TEXT