Article Per Year (5 Year)
p-Index From 2021 - 2026
1.037
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATEMATIKA Journal of Informatics and Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Abdimas Jurnal Ilmiah Kursor JUITA : Jurnal Informatika Scientific Journal of Informatics Jurnal IPTEK Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Informatika (JUTIF) International Journal of Advances in Artificial Intelligence and Machine Learning
Helmie Arif Wibawa
The Informatics Departement, Faculty Of Mathemathics And Sciences, Diponegoro University
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Cardiovascular Disease Risk Prediction Using Random Forest, RFECV Feature Selection, and SHAP with Multisource Clinical Data Integration Fania, Dea; Waspada, Indra; Wibawa, Helmie Arif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5744

Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality in Indonesia, highlighting the urgent need for effective preventive strategies, including the development of risk prediction systems based on population health data. A major challenge in developing CVD prediction models is the limited availability of local medical data that adequately represent the Indonesian population. This study aims to develop a CVD risk prediction model using the Random Forest algorithm by integrating two data sources: private clinical data from cardiology outpatients at RSUD M. Yunus Bengkulu and a publicly available dataset. Data integration was conducted to address the limited size of private data and to improve model performance. The research was conducted through three experimental settings. Shapley Additive Explanations (SHAP) were employed to analyze the contribution of each feature, while Recursive Feature Elimination with Cross-Validation (RFECV) was applied for feature selection. The results indicate that Scenario 3 in the Experiment on Data Integration achieved the best performance, with an accuracy of 73.57%, recall of 81.44%, and F1-score of 77.06%. SHAP analysis identified blood pressure and age as the most influential predictors of CVD risk. These findings demonstrate that integrating limited private data with public datasets can significantly improve model performance while providing clinically interpretable insights, particularly in settings with constrained local data availability.
Deep Learning-Based Recognition of Indonesian Sign Language (BISINDO) Alphabetic Gestures Using Skeletal Feature Extraction and LSTM Afwan, Teuku M Arief; Gernowo, Rahmat; Wibawa, Helmie Arif
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5337

Abstract

Communication is a fundamental aspect of human life, and for the deaf community, sign language serves as the primary medium of interaction. In Indonesia, the Indonesian Sign Language (BISINDO) is widely used, however, research on automatic BISINDO recognition remains limited due to the scarcity of representative datasets. This study presents the development of a BISINDO recognition system based on deep learning by integrating the Long Short-Term Memory (LSTM) architecture with the MediaPipe Holistic framework. To address data limitations, a custom dataset comprising 866 BISINDO alphabetic gesture videos was collected, involving recordings from both expert and non-expert signers to capture stylistic variations. Extracted skeletal landmark features were processed through a three-layer LSTM network followed by dense layers for sequential modeling and classification. Experimental results show that the proposed model achieved a validation accuracy of approximately 93%, outperforming static image–based methods and demonstrating the effectiveness of skeletal features in representing dynamic gestures. The model also exhibited real-time applicability with promising performance, although challenges such as misclassification of visually similar gestures and dataset imbalance remain. This study contributes to the underexplored field of BISINDO recognition by providing a baseline system and dataset, and further advances the domains of computer vision and human–computer interaction within informatics through an inclusive, data-driven framework for Indonesian Sign Language recognition and future AI-assisted accessibility technologies.
Co-Authors Afwan, Teuku M Arief Anneta Shifa Ichwani Aris Sugiharto Dhemma Ratanajaya Fania, Dea Fouziya, MD Ichwani, Anneta Shifa Indra Waspada Indra Waspada Mochammad Hosam Muhamad Subhan Efendi Panji Wisnu Wirawan Prathina , Paila Prima Yusuf Budiarto Priyo Sidik Sasongko Ragil Saputra Rahmat Gernowo Ratanajaya, Dhemma Retno Kusumaningrum RISMIYATI RISMIYATI Rithika, Madas Satriyo Adhy Sruthi, Avula Sukmawati Nur Endah Sukmawati Nur Endah Sukmawati Nur Endah Sushma, Parsha Sutikno Sutikno Sutikno Sutikno Sutikno Sutikno Syaakir Ni’am