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All Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Juvinal Ximenes Guterres
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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Klasifikasi Stunting Pada Anak Balita Sebagai Prediktor Kesehatan di Masa Dewasa Maulindar, Joni; Jawahir Che Mustapha Yusuf; Juvinal Ximenes Guterres
TEMATIK Vol. 10 No. 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to investigate the classification of stunting in toddlers as a predictor of health in adulthood. The research issue is related to the impact of stunting on child development, which can influence their health in adulthood. The research's objective is to construct a classification model that can identify stunting in toddlers based on a set of relevant variables. The research methodology involves secondary data analysis from surveys related to the health of toddlers and their adult data. The variables used in this research encompass weight, height, energy consumption, upper arm circumference, Body Mass Index (BMI), access to clean water, sanitation facilities, family medical history, parental education, socioeconomic status, age, gender, and geographical region. The research results demonstrate that the constructed classification model performs exceptionally well in identifying stunting in toddlers. The model achieves 100% accuracy, with high precision, recall, and F1-scores for both classes, i.e., class 0 (without stunting) and class 1 (with stunting). This signifies that the model possesses a strong capability to predict stunting in toddlers based on the utilized variables. Furthermore, specific variables such as weight, height, and BMI appear to have a significant influence on stunting classification. The research findings can serve as a foundation for developing more effective intervention programs to prevent stunting in toddlers. Thus, this research makes a significant contribution to efforts to enhance the health of toddlers and prevent health issues in adulthood resulting from stunting.
Feature Extraction in Eye Images Using Convolutional Neural Network to Determine Cataract Disease Fitra Rizki Ramdhani; Khasnur Hidjah; Muhammad Zulfikri; Hairani Hairani; Mayadi Mayadi; Ni Gusti ayu Dasriani; Juvinal Ximenes Guterres
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5064

Abstract

The eye is one of the vital human senses and serves as the main organ for vision. One of the visual impairments that requires special attention is blindness, and cataracts are a major cause of it. A cataract is a condition in which the eye’s lens becomes cloudy due to changes in the lens fibers or materials inside the capsule. This cloudiness blocks light from entering the eye and reaching the retina, significantly interfering with vision. Early detection of cataracts is essential to prevent blindness. An efficient image-based classification model is needed for cataract detection. This study aims to test the Convolutional Neural Network (CNN) model for early cataract detection by exploring the use of several optimization algorithms: Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMSprop), Adaptive Gradient Algorithm (AdaGrad), and Stochastic Gradient Descent (SGD). The research method follows an experimental approach, where eye image datasets are trained using the same CNN architecture but with different parameter configurations. The results show that the Adam optimizer, with a data split of 70% for training, 15% for validation, and 15% for testing over 50 epochs, produced the best results, achieving accuracies of 94%, 93%, and 93%, respectively. Other optimizers performed reasonably well but could not match Adam's stability and accuracy. The implication of this research is that the choice of optimizer and hyperparameter configuration plays a crucial role in improving the performance of image-based cataract detection models.
Implementasi GridSearch dalam Meningkatkan Kinerja Model Support Vector Regresion (SVR) utuk Prediksi Penjualan Produk (Studi kasus : Meubel Rohman Jaya): Implementation of GridSearch to Improve the Performance of the Support Vector Regression (SVR) Model for Predicting Product Sales at Rohman Jaya Furniture Ahmad Baidowi Eko Fitra Firmanda; Ahmad Hudawi AS; Abu Tholib; Juvinal Ximenes Guterres
EXPLORE IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Vol 16 No 1 (2024): Jurnal Explore IT Edisi June 2024
Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v16i1.5042

Abstract

In the era of digitalization, product sales forecasting plays a crucial role for companies in estimating future demand. Meubel Rohman Jaya, a furniture business established since 2010, requires accurate predictions to optimize stock availability with the variety of products they produce. This research aims to forecast furniture product sales using the Support Vector Regression (SVR) algorithm with GridSearch optimization. Sales data of 11 furniture products over 30 months (January 2021 - June 2023) were processed through data collection and preprocessing. Modeling was performed using SVR without optimization and SVR with GridSearch optimization to obtain the best parameters. Predictions were generated and then evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results showed that SVR without optimization achieved a MAPE of 40.39%, while SVR with GridSearch achieved a MAPE of 0.45%, indicating a significant increase in accuracy. GridSearch optimization has proven effective in improving prediction performance and is highly recommended for implementation in forecasting product sales at Meubel Rohman Jaya.
Co-Authors Abu Tholib Ahmad Baidowi Eko Fitra Firmanda Ahmad Hudawi AS Ayu Dasriani, Ni Gusti Fitra Rizki Ramdhani Hairani Hairani Jawahir Che Mustapha Yusuf Khasnur Hidjah Maulindar, Joni Mayadi Mayadi Muhammad Zulfikri