In an increasingly modern era, machine learning-based technologies offer solutions to everyday problems. Machine learning enables us to build models that process data and train them to provide predictive results. We call predictions with categorical labels ‘classifications’. We commonly use several basic models for classification, including Decision Trees, Support Vector Machines, Linear Regression, and so on. We can combine these basic models to create an ensemble model, consisting of a boosting algorithm and a bagging model. We will test these two types of algorithms on the profile data of the candidates for the HoR West Java in the 2019 legislative elections. In this paper, we compare the Random Forest bagging algorithm and the Gradient Boosting Decision Tree boosting algorithm. The Decision Tree algorithm is also compared as a baseline. Prior to the comparison, we conduct data pre-processing and data exploration to identify the data features that influence the winning label for feature selection. Furthermore, we address the data imbalance by using Borderline-SMOTE before training the model. We compare the test results based on the F1 score. Our findings reveal that the use of selected features and the Borderline-SMOTE1 sampler significantly improved the F1 score. Additionally, the best-performing model for predicting victory with the given data is the Gradient Boosting Decision Tree, with parameter tuning and using feature selection results, which achieved an F1 score of 0.7445

In recent years, measuring and monitoring analyte concentrations continuously, frequently, and periodically has been a vital necessity for certain individuals. We developed a cotton-based millifluidic fabric-based electrochemical device (mFED) to monitor glucose continuously and evaluate the effects of mechanical deformation on the device’s electrochemical performance. The mFED was fabricated using stencil printing (thick film method) for patterning the electrodes and wax-patterning to make the reaction zone. The analytical performance of the device was carried out using the chronoamperometry method at a detection potential of −0.2 V. The mFED has a linear working range of 0–20 mM of glucose, with LOD and LOQ of 0.98 mM and 3.26 mM. The 3D mFED shows the potential to be integrated as a wearable sensor that can continuously measure glucose under mechanical deformation.

Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents significant challenges in management, particularly with limited options for slowing cyst progression, as well as the use of tolvaptan being restricted to high-risk patients due to potential liver injury. However, determining high-risk status typically requires magnetic resonance imaging (MRI) to calculate total kidney volume (TKV), a time-consuming process demanding specialized expertise. Motivated by these challenges, this study proposes alternative methods for high-risk categorization that do not rely on TKV data. Utilizing historical patient data, we aim to predict rapid kidney enlargement in ADPKD patients to support clinical decision-making. We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. The XGBoost model, combined with the Synthetic Minority Oversampling Technique (SMOTE), yielded the best performance. We also leveraged explainable artificial intelligence (XAI) techniques, specifically Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to visualize and clarify the model’s predictions. Furthermore, we generated text summaries to enhance interpretability. To evaluate the effectiveness of our approach, we proposed new metrics to assess explainability and conducted a survey with 27 doctors to compare models with and without XAI techniques. The results indicated that incorporating XAI and textual summaries significantly improved expert explainability and increased confidence in the model’s ability to support treatment decisions for ADPKD patients.

Promotion abuse fraud is promotion abuse by duplicating accounts to gain an advantage over promotional codes fraudulently. This action inflicts a financial loss on the company. Therefore, this study aims to deal with fraud by developing an application to detect promotion abuse fraud. The application development process includes needs analysis, modeling, and application development. The dataset used for modeling comes from an e-commerce company in Indonesia. The dataset collection stage includes calculating the similarity between accounts using the Levenshtein distance similarity algorithm to get additional features, such as the number of accounts that are similar to an account. At the modeling stage, experiments were carried out using the Random Forest algorithm and a risk scoring algorithm based on machine learning, namely FasterRisk. The FasterRisk has better performance than the Random Forest algorithm shown by a higher F1 score and AUC score. The algorithm also has an advantage in terms of interpretability because it has an output in the form of a more understandable risk score model, so users can understand the factors that are fraud indicators. The FasterRisk prediction result is then deployed into a web application based on user needs.

Today, Information System and Information Technology (IS and IT) become a necessity in almost all sectors of life, including health sector or health services. Health sector need very specific IS requirement, also very high availability of IS systems. Organization need to determine the minimum IS and IT that appropriate to fulfill the need while in other hand should consider best risk, cost, and benefit. There is a mandatory process in Health Care environment to be fulfilled by the IS system. This work try to design a Framework of Information System Architecture for Health Service Organization that built based on Collaborative Care Model (CoCM) and focused on collaboration of component process, human resources, information and technology. The framework consist of IS model, description of component as parts of model (including processes), measurement, and the guideline how to apply the framework. The proposed framework can be used as a reference for any parties that want to build Information System for Health Service Environment.