Sinkron : jurnal dan penelitian teknik informatika
Not a member yet
1261 research outputs found
Sort by
Loan Repayment Prediction Using XGBoost and Neural Network in Japan's Technical Internship Training
No full textDelayed repayment of financial aid among participants in Japan’s Technical Internship Training Program presents challenges for training institutions in managing funds efficiently. To address this issue, this study aims to compare the performance of two machine learning models: Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) in predicting the likelihood of delayed loan repayments. The research begins with data preprocessing, including handling missing values, normalization, and feature selection based on a correlation threshold of 0.06, where features with absolute correlation values below this threshold are excluded. Three models are tested: XGBoost Default, XGBoost optimized using GridSearchCV, and MLP. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The XGBoost Default model achieves the highest accuracy at 95% and precision of 95%, although its recall is slightly lower at 83%. Tuning XGBoost improves recall to 84%, albeit with a marginal reduction in accuracy to 94%. In contrast, the MLP model demonstrates the lowest performance, with an accuracy of 92% and recall of 74%, indicating limitations in identifying delayed repayments. XGBoost also outperforms MLP in terms of ROC-AUC, scoring 91% compared to MLP’s 86%. These findings suggest that XGBoost is the more effective model for this predictive task. The results have practical implications for training institutions, enabling better participant selection, reducing repayment delays, and supporting more effective financial aid management
Implementation of an Integrated Cloud-Based Electronic Medical Record System at Community Health Center
No full textKlambir Lima is a village located in Hamparan Perak District, Deli Serdang Regency, North Sumatra Province. It is a small village with a dense population but has only one government community health center (puskesmas). This results in suboptimal patient services. Furthermore, there is no existing application for recording patient data or medical records that could assist in patient data management. Patient medical records are a crucial feature in healthcare services. They are useful for recording or storing a patient's health or illness history, which enables accurate treatment or medication tailored to the patient's needs. Therefore, the Klambir Lima Health Center requires an electronic medical records application based on Cloud Computing. Data will be stored in cloud storage, aiming to minimize damage or loss of data, which is a vital asset. In this research, the author developed the application using the R&D (Research and Development) method. The role and utility of the research were well established to ensure better implementation of the application. The research objective is to create a system capable of recording electronic-based medical records via cloud computing media. This will enable the Klambir Lima Health Center to improve its healthcare services for both BPJS (national health insurance) and non-BPJS patient
Explainable Machine Learning for Poverty Prediction in Central Java Regencies and Cities
No full textPoverty remains a multidimensional challenge in Central Java, necessitating robust data-driven approaches to identify its socioeconomic determinants. This study applied six machine learning models, specifically Extreme Gradient Boosting (XGBoost), Random Forest, CatBoost, LightGBM, Elastic Net Regression, and a Stacking ensemble using district-level data from Statistics Indonesia covering demographics, education, labor, infrastructure, and household welfare. Model evaluation combined an 80:20 hold-out split, 10-fold cross-validation, and noise perturbation tests. Results show that XGBoost achieved the best individual performance (MAE = 2,180.01; RMSE = 3,512.07; R² = 0.931), while the Stacking ensemble surpassed all single learners (MAE = 2,640.99; RMSE = 3,202.79; R² = 0.942). Interpretability was ensured through SHAP (Shapley Additive Explanations), Partial Dependence Plots (PDP), and Accumulated Local Effects (ALE), consistently identifying Number of Households, Per Capita Expenditure, and Uninhabitable Houses as the most influential predictors. Counterfactual simulations indicated that increasing per capita expenditure by 10% could reduce the poverty index by 9.9%, while reducing household size by 10% lowered it by 11.3%. Robustness checks revealed Brebes as an influential district shaping model stability. Overall, the findings demonstrate that boosting and stacking ensembles, when combined with explainable AI tools, not only enhance predictive accuracy but also provide transparent, policy-relevant evidence to strengthen poverty alleviation programs in Central Java. This study contributes both methodological advances in explainable machine learning and practical insights for targeted poverty reduction strategies
Utilizing Mobile Applications for 21st-Century Learning and Digital Preservation of Balinese Script
No full textBalinese Script and other inheritance local languages are facing extinction signalment since their use has already been replaced by their national languages, which are simpler and more practical. Bali province has already had governor regulations to preserve this local wisdom through many efforts in Bali, including conducting compulsory local content subject Balinese language (covering Balinese Script) from primary to high school, requiring institution's nameplates to be written in Balinese Script along with different languages, etc. This collaborative study, a joint effort between language and computer science, has exposed the technological side of support for preservation efforts. Through the mobile application from a smartphone, this study revealed the advantage of using this proposed Information Technology application for 21st-century learning of Balinese Script, including paperless material to conduct green-and-sustainable-oriented learning and efficiency to grab knowledge in real-time related to the transliteration and translation aspects from Latin text input. That effort was considered the main contribution to this research area
Integration of YOLOv8 and FastAPI for Early Detection of Nail Diseases
No full textNails are important indicators of various health conditions, including fungal infections (onychomycosis), autoimmune disorders (psoriasis), and subungual melanoma (black line). However, early detection of these diseases remains limited due to low accessibility and public awareness. This study aims to develop an end-to-end, web-based early detection system for nail diseases by integrating the YOLOv8 object detection algorithm with the FastAPI framework. A total of 600 annotated nail images obtained from Kaggle were categorized into four classes: healthy nail, psoriasis, black line, and onychomycosis. The model was trained using PyTorch on Google Colab with GPU acceleration and evaluated using precision, recall, and mean Average Precision ([email protected]). The model achieved a precision of 93%, recall of 88%, and [email protected] of 89%. Manual testing on 100 images via the deployed web application showed an overall accuracy of 97%. Class-wise accuracy reached 100% for healthy nail and psoriasis, 92% for black line, and 96% for onychomycosis. These results demonstrate that the system performs reliably across various conditions. The main contribution of this study is the implementation of a real-time, web-integrated nail disease detection system that is accessible to both medical professionals and the general public. Future research may focus on expanding the dataset, optimizing model robustness under varied lighting and background conditions, and conducting clinical validation
Assessment Clusterization Teacher Performance with K-Means Algorithm Clustering and Agglomerative Hierarchical Clustering (AHC)
No full textResearch This aims to do clustering evaluation teacher performance with the application of the K-means clustering algorithm and agglomerative hierarchical clustering (AHC). Background study This is based on needs to increase quality teaching through analysis and evaluation and better teacher performance. The methods applied involving assessment data collection performance from teachers in the environment education local, processed using a second algorithm The results of the research show that the silhouette score value for K-means reached 0.364, while AHC produced a value 0.343. With Thus, K-means is proven more effective in grouping assessment data and teacher performance compared to AHC. The conclusion of the study This confirms the importance of implementation of the K-means algorithm to get more insight into good evaluation teacher performance. Author Ready to do repairs or revisions to the manuscript. This is in accordance with comments and suggestions from the reviewer as a condition beginning. For processing more, carry on
Transforming Real Estate: Leveraging TOGAF ADM for Digital Optimization in Enterprise Architecture
No full textIn this research paper, we propose an Enterprise Architecture (EA) design for PT XYZ, a middle up class real estate development company in Indonesia, leveraging the TOGAF ADM framework. The study centers on optimizing five key business processes—commercial leasing, residential sales, hotel banquet rentals, waterpark ticket sales, and parking fee collection—to enhance operational efficiency and support digital transformation. Using ArchiMate modeling for clear visualization, this architecture spans from the Preliminary Phase, Phase A Architecture Vision, Phase B Business Layer, Phase C Information System Architecture (Application Layer) to the Phase D Technology Architecture. It provides a strategic blueprint to address common challenges like data fragmentation, reliance on manual processes and human resources readiness. By implementing this EA, PT XYZ can expect improvements in scalability, flexibility, and overall agility. This approach aims to position PT XYZ as a modern, digitally-driven entity, aligning technology investments with business objectives for long-term success. Future research is recommended to explore later phases of TOGAF ADM (Phase E – Phase H) and potentially integrate additional business areas for a holistic digital transformation
Optimizing Twitter Sentiment Analysis on Tapera Policy Using SVM and PSO
No full textThis study aims to analyse the sentiment of Twitter users towards the Public Housing Savings (Tapera) policy in Indonesia using the Support Vector Machine (SVM) algorithm optimised by Particle Swarm Optimization (PSO). In recent years, social media has emerged as a primary platform for individuals to express their views and opinions on public policies. The government programme, Tapera, which was designed to increase access to housing for the public, attracted considerable attention, with a range of responses, including both positive and negative sentiments. The methodology employed in this study comprised the collection of data from Twitter, the processing of text, and the application of SVM-based classification techniques, reinforced by PSO, with the objective of enhancing the accuracy and efficiency of the model. The results demonstrated that the PSO-optimised SVM model exhibited an accuracy of 85%, accompanied by an Area Under Curve (AUC) value of 0.84 and a ROC curve that indicated the model's notable capacity for differentiating between positive and negative sentiments. These findings indicate the existence of certain sentiment patterns that can be utilised for the evaluation and improvement of Tapera policies. In conclusion, this research is expected to provide a comprehensive picture of the public response to the Tapera policy and present an analytical model that can be applied to evaluate other policies. Further research is recommended to expand data coverage and develop algorithms to achieve more accurate results
Design of Intelligent Model for Text-Based Fake News Detection Using K-Nearest Neighbor Method
No full textText-based fake news detection is a crucial issue considering its negative impacts on society and individuals. One of the main impacts that has a significant and detrimental impact on society is disinformation, where false or misleading information can cause confusion and uncertainty in society. This can lead to misunderstandings and develop into riots in society which can lead to legal problems that are detrimental to society. In order to overcome this problem, a method is needed to detect fake news. This study aims to build a fake news detection method using machine learning, which is a technology widely used by researchers to detect and analyze past data. Various methods have been produced using machine learning, including the K-Nearest Neighbor (K-NN) method which is proposed as an effective solution to detect fake news. K-NN is a machine learning algorithm that works by classifying text based on its proximity to known data in feature space. This method is proposed because of its ability to handle non-linear data and its low complexity. The application of K-NN can increase the accuracy in detecting fake news by utilizing the characteristics of relevant text, thus helping in efforts to filter information and maintain the integrity of news circulating in the community. In a study conducted using the FakeNewsDetection dataset, the model evaluation results showed that KNN produced a Mean Absolute Error (MAE) of 0.011 and a Root Mean Squared Error (RMSE) of 0.077, better than the performance of other methods such as SVM and Neural Network
AHP-SWARA Implementation Method for Evaluation and Selection Employee Promotion
No full textThe evaluation process of employee selection is very important for organizations that want to carry out quality leadership, the purpose of this study is to objectively prove the results of the selection of job promotions that have been evaluated continuously every time leadership occurs. The evaluation results of the leadership selection process become routine, so that the results of leadership promotions can provide improvisation to organizations that are increasingly advancing towards future leadership targets. The proposed method for the evaluation and selection process uses the Analytic Hierarchy Process (AHP) and specifically Stepwise Weight Assessment Ratio Analysis (SWARA). Both of these methods utilize expert intervention in providing input in providing assessments of multi-criteria and alternatives. So that the priority of the criteria is carried out by an index process similar to that owned by the two methods, thus providing more optimal results for decision-making support. The assessment of the results requires seven criteria and twenty-four alternatives. The results obtained require two index processes for both criteria and alternatives. The first rank is determined by the weight of the calculation results of the seven criteria and alternative assessments from experts. The first rank of twenty-six employees was given to K20 with a weight of 0.932 and followed by K2 with a weight of 0.08. Thus, job promotion can be developed with a double index that can provide optimal results in supporting job promotion decision makin