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All Journal International Journal of Artificial Intelligence Research Jurnal Penelitian Pendidikan IPA (JPPIPA) Journal of Information Systems and Informatics Jurnal Teknik Informatika (JUTIF)
Andini, Dwi Yana Ayu
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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Expert System for Diagnosis of Lung Disease from X-Ray Using CNN and SVM Zulkifli, Zulkifli; Soeprihatini, Retno Ariza; Sfenrianto, Sfenrianto; Wiyanti, Zulvi; Bintoro, Panji; Fitriana, Fitriana; Sukarni, Sukarni; Putri, Nopi Anggista; Andini, Dwi Yana Ayu
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.870

Abstract

The lung disease diagnosis expert system utilizes human knowledge to diagnose various conditions affecting the lung. Diseases caused by fungal or bacterial infection in the organ can cause inflammation as well as death when it is not detected on time. A standard method to diagnose these conditions is the use of a chest X-ray (CXR), which requires careful examination of the image by an expert. In this study, several CNN and SVM architectural models were proposed to classify CXR images to diagnose whether a person has COVID-19, Viral Pneumonia, Bacterial Pneumonia, Tuberculosis (TB), and Normal. The experiment showed that InceptionV3 had the best results compared to other CNN architectures and SVM. Classification accuracy, precision, recall, and f1-score of CXR images for COVID-19, Viral Pneumonia, Bacterial Pneumonia, TB, and Normal were 0.86, 0.91, 0.91, and 0.91, respectively. This study was based on a deep learning system with different CNN and SVM architectures that can work well on the CXR images dataset for diagnosing lung disease.
Agile Digital Transformation in Local Government: An Extreme Programming Approach to Public Service Mall Applications Andini, Dwi Yana Ayu; Rizki, Fahlul; Yulia, Aviv Fitria
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1083

Abstract

The development of the web-based Public Service Mall (MPP) application aims to enhance the quality, efficiency, and accessibility of public services in Pringsewu Regency. Utilizing the Extreme Programming (XP) methodology, which focuses on iterative and collaborative software development, the application follows five main phases: planning, design, coding, testing, and release. Key features of the application include a service search function, a booking code-based queue system, service history tracking, and a user dashboard for seamless interaction. The implementation results demonstrate that the application significantly simplifies access to various public services, reduces physical queues, and improves transparency throughout the service process. System testing confirms that the application operates according to specifications, with a user satisfaction rate of 87% and a notable improvement in service response times. Therefore, this application serves as an effective digital solution that supports the transformation of modern public services, making them more responsive and accessible to the community's needs.
Optimizing Type 2 Diabetes Classification with Feature Selection and Class Balancing in Machine Learning Wantoro, Agus; Yuliana, Aviv Fitria; Andini, Dwi Yana Ayu; Awaliyani, Ikna; Caesarendra, Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5166

Abstract

Type 2 Diabetes (T2DM) is a crucial factor in patient survival and treatment effectiveness. Errors in diabetes detection lead to disease severity, high costs, prolonged healing time, and a decline in service quality. Additionally, a major challenge in developing Machine Learning (ML)-based detection decision support systems is the class imbalance in medical data as well as the high feature dimensionality that can affect the accuracy and efficiency of the model. This research proposes an approach based on feature selection (FS) and handling class imbalance to improve performance in type 2 diabetes. Several feature selection techniques such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), Chi-Square (CS), Relief-F, and FCBF can perform feature selection based on weighting ranking. Furthermore, to address the imbalanced class distribution, we utilize the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as Support Vector Machine (SVM), Gradient Boosting (GB), Tree, Neural Network (NN), Random Forest (RF), and AdaBoost were tested and evaluated based on the confusion matrix including accuracy, precision, recall, and time. The experimental results show that the combination of strategies for handling imbalanced classes significantly improves the predictive performance of ML algorithms. In addition, we found that the combination of feature selection techniques IG+AdaBoost consistently demonstrates optimal performance. This study emphasizes the importance of data preprocessing and the selection of the right algorithms in the development of machine learning-based T2DM detection systems. Accurate detection can reduce the severity of disease, lower treatment costs, speed up the healing process, and improve healthcare services.
"Perbandingan Kinerja Rendering EEVEE dan Siklus di Blender 3.5 dalam Konteks Visual Interaktif untuk Animasi 3D" Yulia, Aviv Fitria; Zulkifli; Bintoro, Panji; Andini, Dwi Yana Ayu; Triloka, Joko
Jurnal Penelitian Pendidikan IPA Vol 10 No 7 (2024): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i7.5910

Abstract

This research aims to compare the performance of two rendering engines, namely EEVEE and Cycles, available in Blender 3.5, in the context of developing interactive visual 3D animation. The rendering engine is a key element in 3D animation production, and the right choice between EEVEE and Cycles can have a significant impact on the final animation result. In this research, we conducted a series of experiments and analyzes to evaluate rendering speed, the quality of the resulting images, and the ability to achieve the visual effects desired by the animator. The results of this research provide deep insight into the strengths and limitations of each rendering engine in interactive 3D animation scenarios. These findings can help animators, game developers, and similar creative professionals make more informed choices when choosing a rendering engine that suits their project needs. Thus, this research contributes to the development of rendering techniques in the growing 3D animation industry.
Co-Authors Agus Wantoro Aviv Fitria Yulia Bintoro, Panji Fitriana Fitriana Heni Pujiastuti Ikna Awaliyani Nopi Anggista Putri Rizki, Fahlul Sfenrianto, Sfenrianto Soeprihatini, Retno Ariza Sukarni Sukarni Triloka, Joko Wahyu Caesarendra Wiyanti, Zulvi Zulkifli