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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JUITA : Jurnal Informatika Journal of Computing Theories and Applications TSURAYYA: Journal of Education, Economy and Religia
Abdul Karim
Hallym University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest Hindayati Mustafidah; Suwarsito Suwarsito; Rahmat Setiawan; Abdul Karim
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.27358

Abstract

Accurate identification of freshwater fish species plays a vital role in aquaculture, particularly in determining appropriate feed strategies to optimize fish growth. Visual similarities among species—such as color, shape, and surface texture—often hinder novice farmers from correctly recognizing fish types. This study proposes an image-based classification system using the Random Forest algorithm to identify six freshwater fish species: pomfret (bawal), gourami (gurame), catfish (lele), barb (melem), tilapia (nila), and Java barb (tawes) and provide automated feed recommendations. A total of 120 fish images were used as the dataset, collected from various sources, including online repositories and field documentation. Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. Evaluation results show that the system achieved a classification accuracy of 83.33%, with a precision of 83.53%, recall of 83.33%, and an F1-score of 82.86%. Notably, catfish, barb, and tilapia classes achieved perfect classification, while pomfret and gourami showed room for improvement due to overlapping visual features. The findings indicate that the integration of Random Forest with multi-domain image features offers an effective, affordable, and practical solution to support the digital transformation of small and medium scale aquaculture systems through intelligent species recognition and feed guidance
Combination of binary particle swarm optimization and random forest for stroke disease prediction Sutikno Sutikno; Rismiyati Rismiyati; Khadijah Khadijah; Abdul Karim
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2290-2299

Abstract

Stroke is a leading cause of death and disability worldwide, making early risk prediction critical for prevention. Machine learning methods such as random forest (RF) have shown strong predictive performance, but accuracy can be further improved through effective feature selection. This research proposes an integrated model that combines binary particle swarm optimization (BPSO) for feature selection with RF for stroke risk classification. Experiments were conducted on two public datasets: the stroke prediction dataset (SPD) and the brain stroke dataset (BSD). Data preprocessing included handling missing values, normalization, and the synthetic minority oversampling technique (SMOTE) to mitigate the minority and majority classes. BPSO was employed to select the most informative features, followed by RF for classification. The BPSO-RF model delivered superior accuracies of 96.13% on the SPD and 96.07% on the BSD, outperforming competing classifiers and feature selection techniques. Important features such as gender, age, work type, residence type, average glucose level, body mass index (BMI), and smoking status were consistently identified as key predictors. These results indicate that integrating swarm intelligence with ensemble learning can effectively improve stroke risk prediction and support clinical decision-making.
An Explainable Multimodal Framework for Chest X-Ray Alert Classification Using Radiology Reports and Images Edy Winarno; Indah Manfaati Nur; Abdul Karim; Saeful Amri; Ismi Elya Wirdati; Prajanto Wahyu Adi
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16023

Abstract

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.
INNOVATIVE CURRICULUM DESIGN FOR SUSTAINABLE EDUCATION: BRIDGING LOCAL WISDOM AND GLOBAL CHALLENGES Asmarita; Fitri Yuniarti; Ahmad Afendi; Abdul Karim; Multazom; Suprapno; Moh. Syarif Hidayat
TSURAYYA: Journal Education, Economy and Religia Vol. 1 No. 2 (2026): May: TSURAYYA: Journal Education, Economy and Religia
Publisher : PT. Cadas Insan Madani

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

Abstract

In the era of globalization and rapid technological advancement, education systems are increasingly challenged to develop curricula that are responsive to both local contexts and global issues. This study explores innovative curriculum design for sustainable education by integrating local wisdom with global challenges. Local wisdom represents valuable cultural knowledge, traditions, and practices that have been developed by communities over generations and contribute to social cohesion, environmental stewardship, and cultural identity. Meanwhile, global challenges such as climate change, digital transformation, social inequality, and sustainable development require learners to possess critical thinking, problem-solving, collaboration, and global citizenship competencies. Through a conceptual and literature-based approach, this paper examines strategies for curriculum innovation that harmonize indigenous knowledge with international educational frameworks, particularly the Sustainable Development Goals (SDGs). The findings indicate that a curriculum grounded in local wisdom while addressing global concerns can foster contextual learning, strengthen cultural preservation, and enhance students’ readiness to participate in an interconnected world. Furthermore, the integration of local and global perspectives promotes sustainable education by encouraging environmental awareness, social responsibility, and lifelong learning. Therefore, innovative curriculum design serves as a strategic framework for creating relevant, inclusive, and future-oriented educational systems that contribute to sustainable development at both local and global levels.
Co-Authors . Suwarsito Ahmad Afendi Amri, Saeful Asmarita Edy Winarno Fitri Yuniarti Hindayati Mustafidah Indah Manfaati Nur Ismi Elya Wirdati Khadijah Khadijah Moh. Syarif Hidayat Multazom Prajanto Wahyu Adi Rahmat Setiawan Rismiyati Rismiyati Suprapno Sutikno Sutikno