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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Muhammad Fadlan Hidayat
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Deep Transfer Learning for Sign Language Image Classification: A Bisindo Dataset Study Ika Dyah Agustia Rachmawati; Rezki Yunanda; Muhammad Fadlan Hidayat; Pandu Wicaksono
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.10621

Abstract

This study aims to identify and categorize the BISINDO sign language dataset, primarily consisting of image data. Deep learning techniques are used, with three pre-trained models: ResNet50 for training, MobileNetV4 for validation, and InceptionV3 for testing. The primary objective is to evaluate and compare the performance of each model based on the loss function derived during training. The training success rate provides a rough idea of the ResNet50 model's understanding of the BISINDO dataset, while MobileNetV4 measures validation loss to understand the model's generalization abilities. The InceptionV3-evaluated test loss serves as the ultimate litmus test for the model's performance, evaluating its ability to classify unobserved sign language images. The results of these exhaustive experiments will determine the most effective model and achieve the highest performance in sign language recognition using the BISINDO dataset.
Machine Learning Approach: A Comparative Analysis of Classifiers in Predicting Obesity Type Jeffrey Tedjasulaksana; Ferry Jaya Dinata; Rafael Krisnadi; Matthew S.W. Reksosamudro; Wilbert Wen; Muhammad Fadlan Hidayat
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.15268

Abstract

Obesity is a growing global public health concern that increases the risk of chronic diseases and significantly affects quality of life. Traditional diagnostic methods such as Body Mass Index (BMI) have limitations in accurately representing body fat distribution and individual health conditions. This study aims to comparatively evaluate the performance of various machine learning and neural network models in predicting obesity levels using a multiclass classification approach. The dataset consists of 2,111 observations with 12 predictor variables and seven obesity categories, obtained from a publicly available source. Data preprocessing included duplicate removal, outlier handling using the interquartile range method, feature scaling, and categorical encoding, followed by a 60:20:20 train–validation–test split. Several classifiers were implemented, including Logistic Regression, Support Vector Classifier, Random Forest, Extra Trees, Gradient Boosting-based models (XGBoost and LightGBM), Multilayer Perceptron, K-Nearest Neighbors, and TabNet. Model performance was evaluated using macro-average F1-score and confusion matrix analysis. The results indicate that LightGBM achieved the highest predictive performance with an F1-score of 0.96, demonstrating strong generalization across obesity categories. XGBoost and Random Forest also showed strong performance, while Support Vector Classifier exhibited consistent results across training, validation, and cross-validation. These findings suggest that ensemble-based models are highly effective for obesity classification, while model selection should consider accuracy, interpretability, and computational constraints.
Co-Authors Ferry Jaya Dinata Ika Dyah Agustia Rachmawati Jeffrey Tedjasulaksana Matthew S.W. Reksosamudro Pandu Wicaksono Rafael Krisnadi Rezki Yunanda Wilbert Wen