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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) International Journal of Engineering Continuity JOKI: Journal of Computing and Informatics
Juvinal Ximenes Guterres
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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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.
SMOTE Variants and Random Forest Method: A Comprehensive Approach to Breast Cancer Classification Baiq Candra Herawati; Hairani Hairani; Juvinal Ximenes Guterres
International Journal of Engineering Continuity Vol. 3 No. 1 (2024): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v3i1.147

Abstract

This research focused on using machine learning methods for breast cancer diagnosis, considering that breast cancer is the scariest disease for women because it can cause mortality. Not only that, but there is also an increase in breast cancer death rates in women yearly.  Early prediction is the right solution to increase life expectancy and reduce mortality rates caused by breast cancer. However, breast cancer data has a problem, namely that the data is imbalanced, which harms the performance of the machine learning method itself. In the data, breast cancer had a Benign class (357 instances) more than the Malignant class (212 instances). Therefore, this study aimed to solve the problem of imbalanced data using the Smote variants and Random Forest approaches in breast cancer classification. The results of this study showed that the Smote approach with Random Forest had the best performance compared to Borderline Smote and Random Forest in the case of breast cancer data classification, where Smote with Random Forest produced an accuracy of 97.3%, sensitivity of 96.9%, and specificity of 97.8%. In comparison, Borderline Smote with Random Forest produced an accuracy of 96.4%, sensitivity of 95.6%, and specificity of 96.9%. The results of this study can contribute to predicting breast cancer using the proposed method, because it has been proven to have high accuracy.
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.
Realistic 3D Object Visualization in Early Childhood Educational Games Using Ray Tracing Algorithms Yaqin, Moh. Ainol Yaqin; Abu Tholib; Juvinal Ximenes Guterres
JOKI: Jurnal Komputasi dan Informatika Vol 2 No 1 (2025): June 2025
Publisher : Laskar Karya

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Abstract

Computer graphics plays a crucial role in creating engaging and interactive learning experiences for young children. This study implements ray tracing algorithms to enhance 3D object visualization in educational games designed for early childhood. The objective is to improve visual realism, thereby increasing children's interest and engagement in learning through interactive gameplay. The game was developed using C++ and OpenGL, incorporating ray tracing techniques to simulate light behavior accurately and produce realistic shading and reflections. The research followed a systematic development process, including literature review, game design, algorithm implementation, and user evaluation. The evaluation, involving early learners, showed a significant increase in attention span, comprehension, and enthusiasm among children exposed to ray-traced 3D environments, compared to traditional visualization techniques. These findings suggest that realistic 3D visualization through ray tracing can be a valuable asset in educational media, supporting cognitive development and learning motivation in early childhood education.
Co-Authors Abu Tholib Ahmad Baidowi Eko Fitra Firmanda Ahmad Hudawi AS Ayu Dasriani, Ni Gusti Fitra Rizki Ramdhani Hairani Hairani Hairani Hairani Herawati, Baiq Candra Jawahir Che Mustapha Yusuf Khasnur Hidjah Maulindar, Joni Mayadi Mayadi Muhammad Zulfikri Yaqin, Moh. Ainol Yaqin