Aplikasi berbasis video streaming atau siaran langsung menjadi jenis aplikasi paling banyak digunakan di dunia. Video On Demand merupakan sistem interaktif yang memungkinkan kita memilih konten video yang akan ditonton. Vidio adalah portal online atau situs web streaming video yang didirikan pada tahun 2014. Situs web ini memungkinkan pengguna untuk menonton dan menikmati berbagai video dan layanan lain. Namun, berdasarkan ulasan di Google Play, Vidio mendapatkan rating rata-rata hanya sebesar 3.7 dari 623.000 lebih total ulasan. Hal tersebut yang mendorong dilakukannya penelitian ini. Data yang dikumpulkan adalah sebanyak 1000 data pada rentang waktu 2 Februari 2023 – 19 Februari 2023. Data tersebut diklasifikasikan ke dalam sentimen positif dan negatif menggunakan algoritma Decision Tree atau Pohon Keputusan. Berdasarkan 3 skenario pembagian data, didapatkan akurasi terbesar diperoleh dari pembagian data 80% data latih dan 20% data uji yaitu sebesar 97.3%. sedangkan pada skenario pembagian data 70:30, akurasinya 96.8%, dan pembagian data 90:10 akurasinya sebesar 96.8%. Dari akurasi yang telah diperoleh, untuk evaluasi pengujian model, penelitian ini menggunakan Confusion Matrix atau Matriks Kebingungan. Agar prediksi dari model yang telah dilatih agar tersedia untuk orang lain, penelitian ini melakukan model deployment menggunakan Streamlit.

Malware remains one of the most critical threats in the digital ecosystem, targeting both mobile and desktop platforms. Traditional signature-based detection techniques face limitations in identifying polymorphic and zero-day variants. This study proposes a static analysis-based approach using machine learning classifiers, focusing on Random Forest, Decision Tree, and Support Vector Machine (SVM). The dataset was collected from MalwareBazaar, and static features such as PE headers, entropy, and API calls were extracted. Experimental results show that SVM achieved the highest accuracy at 53.2%, while Decision Tree obtained the best F1-score at 61.1%, indicating stronger recall capabilities. Random Forest provided balanced results across all metrics with a shorter training time of 0.23 s, highlighting its efficiency for practical use. These findings demonstrate that while no single classifier dominates across all metrics, Random Forest offers a trade-off between performance and efficiency, making it suitable for large-scale static malware detection systems.

The proliferation of online gambling promotions on social media platforms, particularly YouTube, poses a significant challenge in digital security and regulation. This study evaluates the performance of IndoBERT in detecting online gambling-related spam in YouTube comments. The research utilizes YouTube Data API to collect comments, preprocess the text through cleaning and tokenization, and fine-tune IndoBERT for classification. The model’s performance is assessed using accuracy, precision, recall, and F1-score metrics. IndoBERT achieves outstanding results with an accuracy of 98.26%, proving its effectiveness in detecting online gambling promotion. The confusion matrix analysis highlights a low error rate, with minimal false positives and false negatives. IndoBERT is a promising tool for combating online gambling spam, offering high reliability for automated content moderation. Future improvements should focus on handling implicit promotional language, enhancing dataset diversity, and integrating rule-based filtering. This study contributes to NLP advancements in Indonesian text classification, supporting efforts to maintain a safer digital environment.

The term heart disease refers to a wide range of conditions that impact the heart and blood vessels. It continues to be a major global cause of morbidity and mortality. The narrowing or blockage of blood vessels, which can result in major medical events like heart attacks, angina (chest pain) or strokes, is a common issue linked to heart disease. In order to lower the risk of serious complications and facilitate prompt medical intervention, early diagnosis and prediction are essential. This study developed predictive models that can precisely identify people at risk by applying a variety of machine learning algorithms to a structured dataset on heart disease. Blood pressure, cholesterol, age, gender, and other health-related indicators are among the 13 essential characteristics that make up the dataset. Numerous machine learning models such as Naïve Bayes, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, and others were trained using these features. Using the RapidMiner platform, which offered a visual environment for data preprocessing, model training, and performance analysis, all models were created and assessed. The best-performing model was the Naïve Bayes classifier which achieved an impressive accuracy rate of 90% after extensive testing and comparison of performance metrics like accuracy precision and recall. This outcome shows how well the model can predict heart disease in actual clinical settings. By supporting individualized health recommendations, enabling early diagnosis, and facilitating timely treatment, the effective application of such models can significantly benefit patients and healthcare professionals. Furthermore, heart disease incidence can be considerably decreased by identifying and addressing modifiable risk factors such as high blood pressure, elevated cholesterol, smoking, diabetes, and physical inactivity. In summary, machine learning has the potential to improve the identification and treatment of heart-related disorders. This study highlights the value of data-driven methods in healthcare and indicates that incorporating predictive models into standard medical procedures may enhance patient outcomes, lower healthcare expenses, and improve public health administration.

Predictive analytics has emerged as a powerful tool for improving decision-making in healthcare, particularly in disease prediction and patient management. However, conventional architectures may find it difficult to handle various features of healthcare data, such as high dimensionality and ineffective measures to handle unstructured data. This work examines the shortcomings of the traditional ML strategy by fusing deep learning approaches with the existing models in an improved predictive performance. Specifically, we propose three hybrid models: (1) Random Forest and Neural Networks (RF + NN), (2) XGBoost and Neural Networks (XGBoost + NN), and (3) Autoencoder and Random Forest (Autoencoder + RF). The goal is to compare these models’ ability to predict healthcare outcomes using standard performance metrics, which include the measures of accuracy, precision, recall, and F1-score. An important research gap revealed from the literature review is that most models tend to have higher precision at the cost of recall and vice versa. Our proposed hybrid models combine the strengths of feature selection from traditional algorithms (RF, XGBoost) with the advanced pattern recognition capabilities of Neural Networks (NNs) and autoencoders, aiming for a more balanced predictive performance. The RF + NN model produces the highest accuracy at 96.81%, with precise accuracy at 90.48% and accurate precision at 70.08%. Nevertheless, the accuracy of a slightly lower XGBoost + NN model of 96.75% showed better actual capability of identifying true positives than false positives, with 73.54% recall. From our results it is evident that the best model in terms of precision was the Autoencoder + RF model, with a precision of 91.36%; it was however the worst in recall, with only 66.22%. Accordingly, these findings imply that for the same level of predictive accuracy, the hybrid models are better in handling imbalanced problems and provide directions for better healthcare predictive systems in the future.

The World Health Organization estimates that between 10% and 20% of young people worldwide suffer from mental health problems during adolescence, which is a crucial time for many youngsters. Adolescent mental health cases have increased by 31% as a result of the COVID-19 epidemic, which has made the issue much worse. Due to stigma, restricted access, and challenges in evaluating emotional states, traditional mental health care frequently faces challenges. The goal of this research is to create a chatbot system that uses machine learning and natural language processing (NLP) to identify emotional distress and mental health issues in teenagers at an early age. The study offers a safe environment for teenagers to express their emotions while examining chatbot interactions to find trends suggestive of mental health problems. To improve the efficacy of the chatbot, the methodology integrates quantitative and qualitative techniques, utilizing data from open datasets and mental health services. Sentiment analysis and emotion identification are important strategies that enable the chatbot to react sympathetically. By offering easily available support and highlighting the role of technology in addressing adolescent mental health issues, this project ultimately seeks to enhance youth mental health outcomes.