1 10 5 https://repository.horizon.ac.id/files/original/2b30b87e851a5cf0215f705501edab17.pdf c001f3fd8715550fb214326204f4a8da 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 8, NO 4 DECEMBER 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 A Text Classification Approach for Detecting Cyberbullying Risk on Twitter Using Support Vector Machine with Naive Bayes and Random Forest Comparison Subject The topic of the resource Cyberbullying Detection, Twitter, Support Vector Machine, TF-IDF, Text Classification, Machine Learning Description An account of the resource The rapid development of social media as a means of digital interaction also presents serious challenges in the form of the spread of negative content, including cyberbullying. Cyberbullying is a form of verbal violence committed online and has a significant impact on mental health, especially in adolescents. This research aims to develop a text classification model to detect the risk of cyberbullying using the Support Vector Machine (SVM) algorithm. The data used comes from a collection of cyberbullying-themed tweets. The research stages include text preprocessing (normalization, cleaning, tokenization, stopword removal, and stemming), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), data division into training and testing sets, and model training using linear kernel of SVM. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that this approach is able to identify risky comments quite accurately, with optimal performance on the linear kernel. This research contributes to the development of automated detection systems to create a safer and healthier digital ecosystem, and supports preventive efforts in mitigating cyberbullying online. Creator An entity primarily responsible for making the resource Sri Yarsasi1,* , Angga Iskoko2 Source A related resource from which the described resource is derived https://ijiis.org/index.php/IJIIS/article/view/290/173 Publisher An entity responsible for making the resource available Amikom Purwokerto University Date A point or period of time associated with an event in the lifecycle of the resource desember 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/87ef4ce72d91202e85de593c98cd15ba.pdf 4aec4173233c3e8992173231a03018ba 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 8, NO 4 DECEMBER 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 A Comparative Analysis of Machine Learning Classifier of Anemia Diagnosis Based on Complete Blood Count (CBC) Data Subject The topic of the resource Anemia classification, Complete Blood Count (CBC), Machine Learning, XGBoost, SVM, Naive Bayes, Medical Diagnosis Description An account of the resource Anemia is a prevalent hematological condition that requires accurate and timely diagnosis to ensure effective treatment. This study aims to compare the performance of several machine learning algorithms Random Forest, Support Vector Machine (SVM), Naive Bayes, and XGBoost in classifying different types of anemia based on Complete Blood Count (CBC) data. The dataset includes three diagnostic categories: Healthy, Normocytic hypochromic anemia, and Normocytic normochromic anemia. After preprocessing and normalization, each model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that XGBoost achieved the highest overall performance with 99% accuracy and a perfect AUC of 1.00, followed closely by SVM and Naive Bayes. Naive Bayes showed lower performance, particularly in identifying normocytic normochromic anemia. These findings suggest that machine learning, especially ensemble-based models, holds strong potential in supporting clinical diagnosis of anemia using CBC data. Creator An entity primarily responsible for making the resource Nadya Awalia Putri1,* , Bayu Priya Mukti2 Source A related resource from which the described resource is derived https://ijiis.org/index.php/IJIIS/article/view/286/169 Publisher An entity responsible for making the resource available Amikom Purwokerto University Date A point or period of time associated with an event in the lifecycle of the resource desember 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/7b328b4194cbdb4ef4d48ab417611ce2.pdf 25d1c079d4bad48de22c11ea2d01d766 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 8, NO 4 DECEMBER 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 An Analysis of the Relationship Between Social Media Usage Intensity and Anxiety Levels Among University Students Using a Quantitative Approach Subject The topic of the resource Social Media, Anxiety, University Students, Correlation Analysis, Secondary Data Description An account of the resource The rapid development of social media has significantly influenced students' communication patterns and daily habits. While it offers ease in accessing information and interacting with others, excessive use of social media can negatively affect mental health, particularly anxiety. This study aims to analyze the relationship between social media usage intensity and anxiety levels among university students. A descriptivecorrelational quantitative approach was applied using secondary data. The analysis was conducted using the Python programming language through several stages, including data cleaning, descriptive statistics, data visualization, and Pearson correlation testing. The results show a significant positive relationship between the duration of social media usage and students' anxiety levels, with a correlation coefficient of 0.52 and a p-value of 0.003. These findings indicate that the more time students spend on social media, the higher their reported anxiety levels. This study is expected to serve as a basis for promoting digital literacy and raising awareness of the importance of mental health among university students. Creator An entity primarily responsible for making the resource Jayvie Ochona Guballo1,* Source A related resource from which the described resource is derived https://ijiis.org/index.php/IJIIS/article/view/287/170 Publisher An entity responsible for making the resource available Rizal Technological University, Mandaluyong City, Metro Manila, Philippines Date A point or period of time associated with an event in the lifecycle of the resource desember 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/edbfdceffd1fb66b84d5f05278a6c13f.pdf 4f3e15911b1b548693474615a0d41540 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 8, NO 4 DECEMBER 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 Predicting Demand for MSME Products Using Artificial Neural Networks (ANN) Based on Historical Sales Data Subject The topic of the resource Artificial Neural Network, Demand Forecasting, MSME, Sales Data Prediction, Supply Chain Optimization, Small Business Analytics Description An account of the resource Accurate demand forecasting plays a crucial role in supporting inventory and sales strategies, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face resource constraints. This study aims to develop a predictive model using Artificial Neural Networks (ANN) to forecast product demand based on historical sales data. The ANN model is trained and evaluated using a structured experimental approach, adjusting parameters such as the number of hidden layers, learning rate, and epochs to identify the best-performing architecture. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) are used to measure model performance. The results demonstrate that the ANN model is capable of capturing complex nonlinear relationships in multidimensional data and producing accurate demand forecasts. The model particularly performs well in predicting demand trends for products in the Electronics and Household categories. These findings provide valuable insights for MSME stakeholders in optimizing inventory planning and making data-driven business decisions. Creator An entity primarily responsible for making the resource Les Endahti1,* , Muhammad Shihab Faturahman2 Source A related resource from which the described resource is derived https://ijiis.org/index.php/IJIIS/article/view/288/171 Publisher An entity responsible for making the resource available AMIK-YPAT Purwakarta, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource desember 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/9b1f1773b85eae787ae2831190c08eed.pdf a6f3f71be736635a6129c401e677663b 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 8, NO 4 DECEMBER 2025 Still Image A static visual representation. Examples include paintings, drawings, graphic designs, plans and maps. Recommended best practice is to assign the type Text to images of textual materials. 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 Customer Segmentation Using an Enhanced RFM–K-Means Framework on The Online Retail Dataset Subject The topic of the resource Customer Segmentation, Online Retail Transactions, RFM Analysis, K-Means Clustering, Cluster Optimization, Targeted Marketing, Retail Analytics Description An account of the resource Effective customer segmentation is crucial for online retailers to enhance marketing strategies and boost profitability. However, analyzing transactional data often reveals challenges, such as noisy records and incomplete temporal patterns, which hinder accurate customer profiling. This paper proposes a robust methodology combining RFM (Recency, Frequency, Monetary) analysis with enhanced K-means clustering to segment customers of a UK-based online retailer, using data from December 2010 to December 2011. We preprocess the data to handle anomalies, engineer RFM features, and optimize cluster selection using the Elbow Method and Davies-Bouldin score, identifying four distinct segments: Best Customers, Loyal Customers, Almost Lost, and Lost Cheap Customers. Results show a 5% improvement in segmentation accuracy compared to baseline methods, with actionable insights for targeted marketing. This approach not only advances customer segmentation techniques but also offers practical value for retail businesses aiming to improve customer retention and sales. Creator An entity primarily responsible for making the resource Isnandar Agus1,*, MS Hasibuan2 Source A related resource from which the described resource is derived https://ijiis.org/index.php/IJIIS/article/view/289/172 Publisher An entity responsible for making the resource available Institute Informatics and Business Darmajaya, Indonesia, Bandar Lampung, Indonesia Date A point or period of time associated with an event in the lifecycle of the resource desember 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