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All Journal ILKOM Jurnal Ilmiah IJISTECH (International Journal Of Information System & Technology) IJISTECH
Putra, Fajar Rahardika Bahari
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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Implementation of Machine Learning Classification of Obesity Weight using Dicision Tree Putra, Fajar Rahardika Bahari; Surahmanto, Muhammad; Haris, H
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.354

Abstract

This work presents the application of the Decision Tree algorithm in the classification of obesity status using a machine learning approach. Indonesia is faced with three nutrition problems at once: stunting, wasting, and obesity or overnutrition. Obesity is a condition with excessive accumulation of body fat, which can lead to diseases and reduce quality of life. This study uses a dataset of 500 respondents and aims to classify obesity status early using the Decision Tree algorithm. The findings show that the developed Decision Tree model has an accuracy of 82%, with high precision and recall values, demonstrating the effectiveness of the algorithm in classifying obesity status. In conclusion, this study demonstrates the significant potential of the Decision Tree algorithm in supporting the early detection of obesity and facilitating more focused health interventions.
Implementation of Machine Learning Classification of Obesity Weight using Dicision Tree Putra, Fajar Rahardika Bahari; Surahmanto, Muhammad; Haris, H
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.354

Abstract

This work presents the application of the Decision Tree algorithm in the classification of obesity status using a machine learning approach. Indonesia is faced with three nutrition problems at once: stunting, wasting, and obesity or overnutrition. Obesity is a condition with excessive accumulation of body fat, which can lead to diseases and reduce quality of life. This study uses a dataset of 500 respondents and aims to classify obesity status early using the Decision Tree algorithm. The findings show that the developed Decision Tree model has an accuracy of 82%, with high precision and recall values, demonstrating the effectiveness of the algorithm in classifying obesity status. In conclusion, this study demonstrates the significant potential of the Decision Tree algorithm in supporting the early detection of obesity and facilitating more focused health interventions.
Detection of Curcuma and Turmeric Differences Utilizing Fuzzy Tsukamoto Android-Based CCN Model Putra, Fajar Rahardika Bahari; Setyawan, Muhammad Rizki; Ilham, Ahmad; Suseno, Dimas Adi
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2857.276-291

Abstract

Turmeric and curcuma are herbs that are often used in medicine and cooking. However, their similar shapes and colours make it difficult for people, especially in Southwest Papua, to distinguish between them directly. According to the Central Statistics Agency (BPS) in 2023, turmeric production reached 18,302 units, far higher than turmeric, which only reached 2,950 units. Based on field interviews in Southwest Papua, more than 60% of respondents had difficulty distinguishing turmeric from turmeric. To address this issue, this research develops an Android-based classification system by integrating the Fuzzy Tsukamoto algorithm with Convolutional Neural Network (CNN) models. Five CNN models VGG16, MobileNetV2, NASNetMobile, EfficientNetB2, and EfficientNetB3 were selected based on their balance between computational efficiency (MobileNetV2, NASNetMobile), depth and proven stability (VGG16), and modern scalable architectures (EfficientNetB2 and B3). Each model was combined with fuzzy logic to enhance classification accuracy. he dataset consisted of 800 images of curcuma and turmeric obtained from Kaggle and field collections. The data were divided into training, validation, and testing sets, and augmented through a series of transformations including rescaling to a range of 0 to 1, rotation up to 40 degrees, horizontal shift of 20%, angular distortion (shear) of 20%, zoom up to 30%, horizontal flipping, and brightness adjustment. Empty areas generated during augmentation were filled using the nearest pixel value with the ‘nearest’ mode to preserve image integrity. Training was performed using the AdamW optimizer and fine-tuning. Model evaluation employed accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the VGG16 model performed best, achieving 97% accuracy, 98% precision, 97% recall, and 98% F1-score, as confirmed by the classification report and confusion matrix. This model was also the most stable when tested on the Android system, while EfficientNetB2 and B3 produced less satisfactory outcomes. These findings demonstrate that combining CNN and Fuzzy Tsukamoto improves the classification accuracy of images with high visual similarity. The proposed system has the potential to be applied as a direct plant identification tool in the field and can be further extended to classify other visually similar plants
Co-Authors Haris, H Ilham, Ahmad Muhammad Rizki Setyawan Surahmanto Suseno, Dimas Adi