49 hasil untuk machine learning
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Artikel Jurnal
MACHINE LEARNING-BASED METHODS FOR SUPPORTING FAKE NEWS DETECTION
In the context of rapidly disseminating online information, fake news has emerged as a serious threat to information security and public trust. Traditional detection methods based on content analysis or propagation models often face limitations in handling large-scale, heterogeneous, and weakly…
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Artikel Jurnal
Boost machine learning development operations value with innovation management integration
Despite the increasing adoption of machine learning across various sectors, many organizations, especially Small and Medium Enterprises (SMEs), struggle to scale machine learning initiatives into production. This paper proposes integrating Innovation Management into Machine Learning Operations…
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Artikel Jurnal
MACHINE LEARNING-BASED PREDICTION OF CONSTRUCTION COST OVERRUNS USING RANDOM FOREST
Cost overrun is a common issue in construction projects, significantly affecting investment efficiency and project management. As projects become increasingly complex, developing accurate predictive models is essential for effective cost risk control. This study proposes a prediction approach using…
Dari koleksi institusi
- Machine learning dan computational intelligenceWidodo Budiharto · 2016 · UNILAK
- Machine learning (konsep dan implementasi)Yaya Heryadi · 2020 · UNILAK
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Artikel Jurnal
Machine learning and deep learning models applied to identification and classification of mango
…varieties across 2 different growing regions. The research developed machine learning models with open-source programming language Python such as: principal component analysis (PCA) combined with support vector machines (SVM), decision trees (DT), random forests (RF), and artificial neural networks…
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Artikel Jurnal
Soil quality monitoring and evaluation system using machine learning and lorawan wireless communication
…regions. In addition, the collected data can be integrated with machine learning models to analyze soil dynamics, predict future trends, and optimize resource allocation, such as fertilizer and irrigation scheduling. Ultimately, the system contributes to sustainable farming practices by…
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Artikel Jurnal
Machine learning applications for chloride ingress prediction in concrete: insights from recent literature
Chloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML…
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Artikel Jurnal
A STUDY ON MACHINE LEARNING-BASED APPROACHES FOR EARLY DETECTION OF PARKINSON’S DISEASE
…timely intervention before significant neuronal damage occurs. Two machine learning algorithms, XGBoost and Random Forest, were employed to train and evaluate the performance of early PD classification on a dataset comprising 195 samples and 24 features. The features included voice data, early…
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Artikel Jurnal
Applying in machine learning and deep learning in finance industry: A case study on repayment prediction
…for the adoption of sophisticated computational methodologies, including machine learning and deep learning, to analyze borrowers’ behavioral patterns, demographic profiles, and credit histories, thus facilitating the prognostication of loan repayment likelihood. Employed techniques encompass…
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Artikel Jurnal
Machine learning-based estimation of power output in solar photovoltaic systems under real-world conditions
Precise prediction of DC power output from photovoltaic (PV) systems under real conditions is essential for improving efficiency and detecting degradation. This paper presents a machine learning framework to forecast PV power using electrical signals (e.g., voltage and current) and environmental…
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Artikel Jurnal
Development of a machine learning model for in-motion train weight estimation on railway bridges
This paper presents a fast, accurate approach for estimating train weight during bridge crossings by coupling a validated train–track–bridge dynamic interaction model with an XGBoost regressor optimized via Bayesian hyperparameter tuning. A high-speed railway bridge in China is used as a case…
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Artikel Jurnal
DEVELOPING A PRODUCT RATING PREDICTION MODEL BASED ON CUSTOMER TEXT FEEDBACK USING MACHINE LEARNING ALGORITHMS
…linguistic feature extraction, and the construction of various machine learning and deep learning models to evaluate predictive performance. The models compared include Logistic Regression, Decision Tree, Random Forest, CatBoost, LightGBM, XGBoost, FastText, CNN, and LSTM. Experimental results…
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Artikel Jurnal
Applications of machine learning coupled with computer vision, electronic nose and untargeted analysis for food quality control
…discrimination and classification of food samples based on using machine learning and deep learning models coupled with analytical data obtained from spectral measurements, using sensors instead of the nose (electronic- nose), camera and image analysis (computer vision) for food quality control…
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Artikel Jurnal
APPLICATION OF MACHINE LEARNING ALGORITHMS TO EVALUATE THE UCI DATABASE IN THE CLASSIFICATION OF AUTISM SPECTRUM DISORDERS
In this article, we present the results of an evaluation of the autism spectrum disorder classification (ASD) of children in the UCI database. We evaluated the data set with the SVM and Random Forest algorithms and also investigated the Decision Tree, Logistic Regression, K-Nearest-Neighbors, Naïve…
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Artikel Jurnal
A HYBRID LIGHTGBM-LSTM MACHINE LEARNING MODEL FOR SHORT-TERM WATER LEVEL FORECASTING IN THE MEKONG RIVER BASIN
…hybrid forecasting framework that integrates the Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM) networks to improve prediction accuracy for hydrological time series. In the proposed approach, LightGBM is first employed to capture nonlinear relationships between lagged…
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Artikel Jurnal
Comparative evaluation of machine learning models for surface roughness prediction in WEDM of SKD61 under limited data conditions
Surface roughness (Ra) is a key quality indicator in Wire Electrical Discharge Machining (WEDM) of SKD61 tool steel. This study compares three machine learning models, Artificial Neural Networks (ANN), Random Forest Regression (RFR), and Extreme Learning Machine (ELM), for Ra prediction under…
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Artikel Jurnal
Salinity forecasting in the Vietnamese Mekong Delta: Evaluating the predictive power of machine learning approaches using multitemporal lag features
…water resource management. This study evaluates the performance of two machine learning models—Random Forest Regression (RFR) and Support Vector Regression (SVR)—for salinity forecasting using long-term observational data (1996–2023) from 44 monitoring stations in the VMD. To capture temporal…
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Artikel Jurnal
Application of machine learning in predicting quorum sensing inhibition: integration of QSAR modeling, virtual screening, and lead compound development
…This study presents an integrated computational pipeline combining machine learning (ML), molecular docking, and de novo drug design to identify novel QS inhibitors. A dataset of 1,983 compounds was used to train ML models, with the Transformer-CNN-Inception architecture achieving the highest…
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Artikel Jurnal
Development of a machine learning–driven intelligent mobile health framework for diagnosis and personalized treatment recommendation in type 2 diabetes
…early diagnosis and effective disease management. This study proposes a machine learning–driven clinical decision support framework for a three-class classification problem, including normal, prediabetes and T2D. The proposed approach establishes an end-to-end clinical data analysis pipeline…
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Artikel Jurnal
Machine learning based on UV-Vis full spectra for the simultaneous determination of curcumin, demethoxycurcumin, bisdemethoxycurcumin in Curcuma longa L.
This study has developed a rapid and simple method based on the UV-Vis full spectra coupled with machine learning for the quantitative prediction of curcumin, demethoxycurcumin and bisdemethoxycurcumin in turmeric rhizomes. The UV-Vis spectral data and HPLC quantification results of the individual…
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Artikel Jurnal
Development of a wearable device for heart rate monitoring and fall detection using machine learning to analyze and detect early anomaly of cardiovascular conditions
…for potential risks to patients based on data collected using the LSTM machine learning model. The wearable device is compact in size with a long battery life. The information collected from the device can be remotely monitored by doctors through a visual interface on a web server model, and…