Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence

Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019

the ENHANCE OBJECT TRACKING ON AUGMENTED REALITY USING HYBRID CONVOLUTIONAL NEURAL NETWORK AND FAST CORNER DETECTION

Markerless augmented reality (AR) is utilized in applications that do not require anchoring to the real world and do not require the use of physical markers (fiducial markers). Augmented object displays not only float but also allow for the automatic placement of 3D augmented reality objects on flat surfaces to enhance realism in real time. There are two challenges that need to be addressed in Markerless AR systems: object tracking and registration, as well as the influence of light intensity. Therefore, the objective of this research is to propose the use of Convolutional Neural Networks (CNN) and Features from Accelerated Segment Test (FAST) corner detection for tracking or detecting objects in markerless augmented reality systems. Testing was conducted using three epoch schemes: 10, 50, and 100. The test results were measured using several parameters, including the execution time, testing loss, and testing accuracy. The test results indicated an improvement in the performance of the tested object detection. The accuracy testing results of using the CNN and FAST corner detection methods were superior to those of the CNN-only method and FAST corner detection alone, reaching 98%. However, this method increases the processing time for object detection. Thus, the processing time of the CNN without FAST corner detection was faster

DETECTION OF BULLYING CONTENT IN ONLINE NEWS USING A COMBINATION OF RoBERTa-BiLSTM

This research aims to build a bullying-themed online news classification system with a combined approach of RoBERTa embedding and BiLSTM. RoBERTa is used to generate context-rich text representations, while BiLSTM captures temporal relationships between words, thereby improving classification performance. The research dataset consisted of news from reputable portals such as Kompas.com, Detik.com, and iNews.com, labeled according to keywords relevant to the theme of bullying. The results of the experiment showed that the model achieved 95.2% accuracy, 98.2% precision, 93.6% recall, and 95.8% F1-score. Although there are few prediction errors (false positives and false negatives), this model shows excellent performance in detecting and classifying bullying-themed news. The main contribution of this research is the development of a new approach that combines RoBERTa and BiLSTM for the classification of complex bullying-themed news. This approach not only improves the accuracy of classification but can also be implemented in automated systems to detect negative content. Thus, this research has the potential to support the creation of a healthier digital space and encourage more responsible media practices

Analyzing Blockchain Adoption for Copyright Certification in Lombok's Woven Industry: An Extended TAM Perspective

This research explores the Extended Technology Acceptance Model (TAM) and Partial Least Squares Structural Equation Modelling (PLS-SEM) to investigate the acceptability of blockchain-based digital copyright certification among traditional woven fabric SMEs (Small and Medium Enterprises) in Lombok. This research develops a blockchain-based certification system using NFTs, IPFS, and ECDSA to secure ownership, metadata, and authentication of traditional woven fabrics in Lombok. The problem addressed is the lack of understanding and acceptance of blockchain technology for copyright certification among SMEs, which can impede the protection of their innovations. The aim of this study is to analyze the variables that influence this technology's acceptance and to provide strategies for increasing its adoption. This study explores blockchain-based copyright certification adoption among Lombok's woven fabric SMEs using an Extended TAM with novel variables: Perceived Trust, Privacy, and Government Regulations. Findings from PLS-SEM reveal these, alongside traditional TAM factors, significantly impact adoption. By addressing digital literacy gaps and regulatory challenges, this research provides insights into promoting blockchain adoption through targeted training and outreach, contributing to innovation protection for traditional artisans. A quantitative method was implemented with a validated and reliable surveys distributed both online and offline to SMEs in three main woven villages in Lombok. Data analysis using PLS-SEM revealed significant impacts of perceived usefulness (PU), perceived ease of use (PEOU), Perceived Trust (PT), Government Regulations (GR), Perceived Protection (PP), attitude towards using (ATU), and behavioral intention to use (BITU) on the acceptance of blockchain technology. This study concludes that TAM factors are crucial in evaluating these SMEs' acceptance of blockchain-based copyright certification. Recommendations are provided to enhance SMEs understanding and skills in applying this technology through targeted training and outreach

Geo-Sentiment Analysis of Public Opinion of X Users towards the Documentary Film Dirty Vote using the Bidirectional Long Short-Term Memory Method

Presidential elections held every five years, often generates significant public discourse. The 2024 presidential election saw the release of the documentary Dirty Vote, which raised allegations of electoral fraud and sparked polarized opinions on social media, especially on X. This study aims to analyze public sentiment toward Dirty Vote using geo-sentiment analysis and the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Data were collected from geotagged tweets, with sentiment classified as positive, negative, or neutral. The research explored various data processing techniques, including TF-IDF for feature extraction, FastText for feature expansion, and balancing methods like SMOTE and class weighting to address data imbalance. Results showed that the baseline Bi-LSTM model achieved an accuracy of 71.57% and an F1-Score of 74.05%. When enhanced with TF-IDF and FastText, accuracy increased to 77.07%, though the F1-Score dropped slightly to 72.95%. Applying SMOTE resulted in a decrease in accuracy to 76.45%, but significantly improved the F1-Score to 74.93%. Exploratory data analysis revealed that negative sentiment was most concentrated in Java Island, particularly Jakarta, and peaked during February 2024, coinciding with the documentary's release and the election period. This study significantly contributes to understanding how geographic locations influence public opinion on sensitive political issues. A lack of understanding of geographically-based sentiment patterns can hinder identifying regional needs, leading to poorly targeted policies. By integrating data analysis methods with geographical approaches, this research provides deep insights for designing more effective, data-driven public intervention strategies and supports policymaking that is more responsive to the dynamics of public opinion

OPTIMIZATION OF BACKTRACKING ALGORITHM WITH HEURISTIC STRATEGY FOR LOGIC-BASED SORTING PUZZLE GAME SOLVING

Puzzle Game Sorting is a logic-based puzzle game where players must transfer colored balls into tubes until each tube contains only one color. Although it appears simple, the game becomes increasingly challenging at higher levels, testing players’ logical thinking and patience. This study proposes using the backtracking algorithm combined with optimization strategies, such as conflict heuristics and lookahead, to address players’ challenges at advanced levels. The test results indicate that the optimized backtracking algorithm can solve the game faster and with more efficient steps compared to manual methods. Specifically, heuristic optimization strategies significantly improved performance, reducing execution time by up to 91.4% and the number of steps by up to 76.9% at the most complex levels. These findings demonstrate that combining the backtracking algorithm and optimization strategies is an effective solution for solving puzzles in Sorting, particularly at levels with increasing complexity

TEXT CLASSIFICATION OF BULLYING REPORTS USING NLP AND RANDOM FOREST.

Bullying is a great concern that needs to be dealt with as early as possible, be it in the form of physical, verbal, social or cyber bullying. Using NLP algorithms, this paper intends to classify bullying report using Natural Language Processing in conjunction with Bag of Words. The study employs quantitative methodology. A total of 4671 reports of bullying are in essence categorized into physical, verbal, social, cyber and non-cyber bullying. We split the dataset into 80% training set (3737 reports) and 20% testing set (934 reports). The above model has achieved an accuracy of 94,76%, with good values of recall, precision and F1-score: 94,64%, 95,02% and 94,97% respectively. The dataset is then analyzed using Random Forest algorithm and Report of the Bullying Survey The model is to be effective in automatic Detection of Textual Bullying Reports Automated. While there has been no such effort in our institutions so far, automatic reporting of bullying will prove to be effective. This is because the system will allow a school or institution to have a precise constant monitoring of bullying reports. It will also allow an instantaneous action to be taken to protect the victim without letting the situation escalate

Gastroesophageal Reflux Disease Early Detection using XGBoost Method Classifier

Gastroesophageal reflux disease (GERD) is a clinical condition that occurs when the gastric content within the stomach rises into the esophagus. If left untreated, GERD can result in complications such as esophageal inflammation, ulcers, and even cancer. In this study, the early detection of GERD is performed using the GERD dataset obtained from the Harvard Dataverse online repository and processed with the XGBoost machine learning model. The SMOTE technique was implemented as a solution to address the data imbalance present in the dataset. In addition, this study applied Principal Component Analysis (PCA) and Pearson Correlation to select the most relevant attributes, with the aim of improving computational efficiency. The results demonstrated that feature selection through Pearson correlation and feature extraction using principal component analysis (PCA) yielded the optimal model performance when utilizing 16 attributes and 16 principal components, respectively. The XGBoost model with PCA achieves a macro average F1-score of 0.9615, while the XGBoost model with Pearson Correlation attains a value of 0.9809. Subsequently, the XGBoost model based on the original dataset yielded a macro F1-score value of 0.9568. The findings of this research indicate that the XGBoost model with the Pearson Correlation-based feature selection method has a better f1-score value than the feature extraction method with PCA or based on the original dataset with a difference in value of 0.0194 and 0.0241 respectively in enhancing the performance of the XGBoost model for early detection of GERD in this study

Improved Micro-expression Recognition: An Apex Frame-Based Approach Feature Tracking and KLT

This research develops a real-time facial micro-expression recognition system, focusing on analyzing the onset and apex phases of micro-expression on the Spontaneous Activity and Micro-Movements (SAMM) dataset. Micro- expressions are very brief (0.04 - 0.2 seconds) facial muscle movements that often occur when a person is trying to hide emotions. The developed system aims to improve computation time efficiency and micro-expression recognition accuracy by optimizing feature extraction techniques and selecting more specific facial areas, including facial components such as eyebrows, eyes, and mouth. This research successfully improved the computation time efficiency by 51.96%, almost half the time required by the previous method. In addition, this study shows an increase in efficiency compared to previous studies, with an increase of 34.3% for SVM with Manual Sampling technique and 32.6% for MLP-Backpropagation. In the Random Sampling technique, SVM efficiency increased by 6.1%, but MLP-Backpropagation accuracy decreased by 4.8%. This method achieved 77.9% accuracy for MLP- Backpropagation, which is higher than the previous method. This research contributes to accelerating micro- expression recognition systems and improving accuracy, which opens opportunities for real-time emotion analysis applications such as lie detection or human behavior monitoring in a broader context

A COMPARATIVE STUDY OF MULTI-MASTER REPLICATION OF NOSQL DATABASE SERVER WITH VARYING DATA FORMATS

NoSQL Databases are currently an effective solution for managing large data sets distributed across many Servers. NoSQL Database design is usually based on its usability. Specifically related to the system or application to be built. This research aims to measure the Transfer Rate, CPU usage, Memory usage, query execution time for Create, Insert, Delete and remote replication query bandwidth in the Multi-Master Server replication process using two document stored NoSQL Database applications namely CouchBase and CouchDB by entering three different data models namely JSON, XML and CSV. The experimental results show that the Transfer Rate with CSV data format on CouchBase has the lowest value with an average of 111.41 kbps. CPU usage with XML data format on CouchBase has the lowest value with an average of 13.89%. Memory usage with JSON data format on CouchBase has the lowest value with an average of 1.68%. Query Execution Time Create with XML data format on CouchBase has the lowest value with an average of 1.16 seconds. Query Execution Time Insert on CouchBase with CSV data format has the lowest value with an average of 33.28 seconds. Bandwidth Query Execution Time Insert with CSV data format on CouchBase has the lowest value with an average of 24.78 mb. Query Execution Time Delete with JSON, XML and CSV data formats on CouchDB has the lowest value with an average of 1.5 seconds. Further research recommendations are to test Multi-Master Server Replication using other data formats and parameters or test the performance of data migration to other Databases with different data formats