Analisis Sentimen terhadap Perpanjangan Masa Jabatan Presiden Indonesia Menggunakan Algoritma Naïve Bayes

Media sosial twitter sering digunakan sebagai media untuk menyampaikan pendapat terhadap pemerintahan di Indonesia. Banyak juga hal – hal yang kontoversial di media sosial twitter terhadap pemerintah, seperti halnya pada hari ini sedang terjadi kontrovesi dimana ada sebuah usulan untuk menjadikan masa jabatan Presiden di Indonesia yaitu tiga periode, yang dimana tadinya hanya bisa menjabat sampai dua periode atau sepuluh tahun saja untuk satu orang Presiden. Opini atau sentimen masyarakat yang dalam istilah Twitter biasa disebut sebagai “ciutan” ini dapat berupa opini negatif maupun positif. Akan tetapi jumlah data tersebut cukup banyak sehingga dibutuhkan suatu metode untuk mewujudkannya yaitu dengan menggunakan analisis sentimen. Analisis sentimen dapat dijadikan solusi untuk mengolah opini tersebut dengan menggunakan algoritma Naïve Bayes Classifier. Pada hasil dari teknik naïve bayes memerlukan evaluasi untuk menentukan model terbaik. Pengujian dilakukan dengan tiga model yaitu 70:30, 80:20, dan 90:10 yang kemudian dievaluasi menggunakan confusion matrix. Berdasarkan perbandingan hasil pengujiam evaluasi, skenario terbaik yang untuk model klasifikasi Naïve Bayes yaitu pada skenario atau model pertama ( 90% data training dan 10% data testing ) dengan nilai accuracy sebesar 95% , nilai  precision sebesar 97%, nilai recall sebesar 96%, dan nilai f-measure sebesar 96%

Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms

Dementia is one of the huge medical problems that have challenged the public health sector around the world. Moreover, it generally occurred in older adults (age > 60). Shockingly, there are no legitimate drugs to fix this sickness, and once in a while it will directly influence individual memory abilities and diminish the human capacity to perform day by day exercises. Many health experts and computing scientists were performing research works on this issue for the most recent twenty years. All things considered, there is an immediate requirement for finding the relative characteristics that can figure out the identification of dementia. The motive behind the works presented in this thesis is to propose the sophisticated supervised machine learning model in the prediction and classification of AD in elder people. For that, we conducted different experiments on open access brain image information including demographic MRI data of 373 scan sessions of 150 patients. In the first two works, we applied single ML models called support vectors and pruned decision trees for the prediction of dementia on the same dataset. In the first experiment with SVM, we achieved 70% of the prediction accuracy of late-stage dementia. Classification of true dementia subjects (precision) is calculated as 75%. Similarly, in the second experiment with J48 pruned decision trees, the accuracy was improved to the value of 88.73%. Classification of true dementia cases with this model was comprehensively done and achieved 92.4% of precision. To enhance this work, rather than single modelling we employed multi-modelling approaches. In the comparative analysis of the machine learning study, we applied the feature reduction technique called principal component analysis. This approach identifies the high correlated features in the dataset that are closely associated with dementia type. By doing the simultaneous application of three models such as KNN, LR, and SVM, it has been possible to identify an ideal model for the classification of dementia subjects. When compared with support vectors, KNN and LR models comprehensively classified AD subjects with 97.6% and 98.3% of accuracy respectively. These values are relatively higher than the previous experiments. However, because of the AD severity in older adults, it should be mandatory to not leave true AD positives. For the classification of true AD subjects among total subjects, we enhanced the model accuracy by introducing three independent experiments. In this work, we incorporated two new models called Naïve Bayes and Artificial Neural Networks along support vectors and KNN. In the first experiment, models were independently developed with manual feature selection. The experimental outcome suggested that KNN 3 is the optimal model solution because of 91.32% of classification accuracy. In the second experiment, the same models were tested with limited features (with high correlation). SVM was produced a high 96.12% of classification accuracy and NB produced a 98.21% classification rate of true AD subjects. Ultimately, in the third experiment, we mixed these four models and created a new model called hybrid type modelling. Hybrid model performance is validated AU-ROC curve value which is 0.991 (i.e., 99.1% of classification accuracy) has achieved. All these experimental results suggested that the ensemble modelling approach with wrapping is an optimal solution in the classification of AD subjects

On the Recognition of Emotion from Physiological Data

This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

Contributions to the study of Austism Spectrum Brain conectivity

164 p.Autism Spectrum Disorder (ASD) is a largely prevalent neurodevelopmental condition with a big social and economical impact affecting the entire life of families. There is an intense search for biomarkers that can be assessed as early as possible in order to initiate treatment and preparation of the family to deal with the challenges imposed by the condition. Brain imaging biomarkers have special interest. Specifically, functional connectivity data extracted from resting state functional magnetic resonance imaging (rs-fMRI) should allow to detect brain connectivity alterations. Machine learning pipelines encompass the estimation of the functional connectivity matrix from brain parcellations, feature extraction and building classification models for ASD prediction. The works reported in the literature are very heterogeneous from the computational and methodological point of view. In this Thesis we carry out a comprehensive computational exploration of the impact of the choices involved while building these machine learning pipelines

Classification and Decision Making of Medical Infrared Thermal Images

Medical infrared thermal imaging (MITI) is a technique that allows safe and non-invasive recording of skin surface temperature distribution. The images gained provide underlining physiological information on the blood flow, vasoconstriction/vasodilatation, inflammation, transpiration or other processes that can contribute to skin temperature. This medical imaging modality has been available for nearly six decades and has proved to be useful for vascular, neurological and musculoskeletal conditions. Since the recordings are digital, in the form of a matrix of numbers (image), it can be computationally analyzed by a specialist mainly performing processing and analysis operations manually supported by proprietary software solutions. This limits the number of images that can be processed, making difficult for knowledge to evolve, expertise to develop and information to be shared. This chapter aims to disclose the medical imaging method, along with its particularities, principles, applications, advantages and disadvantages. The chapter introduces all available classification and decision making methods that can be employed using digital information, together with a literature review of their operation in the biomedical applications of infrared thermal imaging.info:eu-repo/semantics/publishedVersio