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This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) SISFOTENIKA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Angkasa: Jurnal Ilmiah Bidang Teknologi Compiler CSRID (Computer Science Research and Its Development Journal) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi
Dony Ariyus
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Computer Science & IT Education

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Classification of Cat Skin Diseases Using MobileNetV2 Architecture with Transfer Learning Saputra Aji, Dian; Ashari, Wahid Miftahul; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11469

Abstract

Skin diseases in cats often present similar visual symptoms across different conditions, making early and accurate diagnosis challenging for pet owners and veterinarians. This study develops a classification model for cat skin diseases: Fungal Infection, Flea Infestation, Scabies, and Healthy, using the MobileNetV2 architecture with a transfer learning approach. A total of 1,600 RGB images were collected from public datasets and divided into 1,280 training and 320 validation samples. The dataset underwent preprocessing, normalization, and data augmentation techniques such as rotation, shear, zoom, and flipping to enhance model generalization and reduce overfitting. Several experiments were conducted to analyze the impact of input size and learning rate adjustments on model performance. The optimal configuration was achieved using an input size of 224×224 pixels, a learning rate of 0.001, and augmentation applied to the training data. The resulting model achieved a validation accuracy of 91.8%, with an average precision, recall, and F1-score of 91%, demonstrating balanced performance across all classes. These results indicate that the MobileNetV2 architecture, combined with appropriate hyperparameter tuning and augmentation, provides a reliable and computationally efficient method for automatic identification of cat skin diseases. This approach can support early diagnosis, improve animal welfare, and serve as a foundation for the development of practical veterinary diagnostic applications.
Optimizing Email Spam Detection through Handling Class Imbalance with Class Weights and Hyperparameter Using GridSearchCV Nursyam, Muhammad Ridho; Koprawi, Muhammad; Ariyus, Dony
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12060

Abstract

Email spam is a major problem in digital communication that can disrupt productivity, burden network resources, and pose a security threat. This research focuses on optimizing spam email detection using a machine learning approach by addressing class imbalance through class weighting and hyperparameter tuning using GridSearchCV. To improve model accuracy and sensitivity, a combination of diverse datasets is applied to provide a wider scope of training data. The models used in this study include Support Vector Machine (SVM), Random Forest, Multinomial Naive Bayes (MNB), and XGBoost. Evaluation is carried out based on metrics such as accuracy, precision, recall, and F1-score, before and after hyperparameter tuning. The experimental results show that SVM produces the highest accuracy after tuning, reaching 97.10%, compared to 96.73% before hyperparameter tuning. In addition, Random Forest, MNB, and XGBoost also show significant improvements, with each model achieving better performance after tuning. Overall, this study shows that dataset merging and class weight adjustment can significantly improve the model's ability to detect spam, as well as provide a basis for implementing the model in a more effective email spam detection system.
Co-Authors Ahmad Rifki Harir Amalina, Inna Nur Ashari, Wahid Miftahul Banu Santoso Deuis Nur Astrida Eka Yulia Sari I Wayan Pandu Swardiana Irma Darmayanti Julia Kurniasih Koprawi, Muhammad Muhammad Saifulloh Neno, Friden Elefri Norhikmah Norhikmah Nursyam, Muhammad Ridho Patmawati Hasan Prasetyo, Rafli Ilham Profesi, Dwinda Etika Saputra Aji, Dian Tetik, Yulius Nahak Yuliana Yuliana Yunita, Selviana