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All Journal Jurnal Simetris Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Upgris JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Locus Penelitian dan Pengabdian Scientific Journal of Informatics
Soeleman, M. Arief
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Social Sciences

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Augmentation Strategy and Hyperparameter Optimization Using Optuna for Potato Leaf Disease Classification in Uncontrolled Environment Rofiqi, Harri Kurniawan; Noersasongko, Edi; Winarno, Sri; Soeleman, M. Arief
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.2026.7.2.4898

Abstract

Image-based classification of potato leaf diseases presents a significant challenge, particularly when data are collected in uncontrolled field environments. While Convolutional Neural Networks (CNNs) and Computer Vision have been widely used for plant disease identification, most previous studies relied on laboratory datasets with uniform lighting and backgrounds, limiting their real-world applicability. This study proposes an integrated framework that combines data augmentation, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and automated hyperparameter optimization through Optuna to enhance the robustness and accuracy of CNN-based models. A total of 3,076 high-resolution potato leaf images representing seven disease classes were evaluated across five CNN architectures and three training scenarios. The MobileNetV3-Large model achieved the best baseline performance with an accuracy of 0.863 and F1-score of 0.868, while Optuna-based optimization further improved performance to 0.895 accuracy, 0.913 precision, 0.906 recall, and 0.904 F1-score, demonstrating the effectiveness of adaptive optimization in improving model generalization. The integration of augmentation, SMOTE, and Optuna resulted in an intelligent and efficient system resilient to environmental variability, showing strong potential for automatic early detection of potato leaf diseases in real agricultural settings. This research contributes to the advancement of Informatics and Artificial Intelligence by promoting adaptive computer vision approaches for smart agriculture and real-world image-based diagnostic systems.
Enhancing Diagnostic Accuracy of Polycystic Ovary Syndrome Classification in Ultrasound Images Using a Hybrid Deep Learning Model of VGG16 and AlexNet Maisarah, Hj.; Soeleman, M. Arief; Pujiono, Pujiono; Firdaus, Iqbal; Firdaus, Gusti Aditya Aromatica
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.2026.7.2.4932

Abstract

Diagnosis of Polycystic Ovary Syndrome (PCOS) using ultrasound (USG) imaging still faces a major challenge in the form of inter-observer variability, which can lead to inconsistent diagnostic outcomes and increase the risk of misclassification. This limitation highlights the urgent need for an automated artificial intelligence (AI)–based system capable of performing ultrasound image classification with greater objectivity, accuracy, and consistency. This study aims to develop an automated PCOS classification model based on a hybrid Convolutional Neural Network (CNN) architecture that integrates VGG16 and AlexNet through a feature concatenation mechanism, following preprocessing and data augmentation steps to enhance model generalization. The model’s performance was evaluated using accuracy, precision, recall, F1-score, and specificity as key metrics. Experimental results demonstrate that the VGG16–AlexNet hybrid model achieved the best performance, with an accuracy of 98.26%, precision of 97.90%, recall of 97.90%, F1-score of 97.90%, and specificity of 98.52%. These results outperform other hybrid configurations such as VGG16–MobileNetV2, VGG16–ResNet50, and VGG16–InceptionV3, each of which achieved accuracies above 96%. These findings confirm that combining the feature depth of VGG16 with the computational efficiency of AlexNet enables more comprehensive extraction of spatial and textural patterns in ultrasound images. Consequently, the proposed hybrid model offers a promising AI-driven diagnostic support system that not only enhances the accuracy of PCOS detection but also assists clinicians in making faster, more objective, and consistent medical decisions.
Perbandingan Efisiensi dan Akurasi Model Jaringan Saraf Konvolusional untuk Deteksi Objek pada Lingkungan dengan Gangguan Visual Widhiariandoko, Adhitya; Syukur, Abdul; Soeleman, M. Arief
Jurnal Informatika UPGRIS Vol 11, No 2 (2025): DESEMBER 2025
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jiu.v11i2.25645

Abstract

Model deteksi objek berbasis Jaringan Saraf Konvolusional (JSK) seringkali mengalami penurunan performa signifikan pada kondisi visual dunia nyata yang mengandung gangguan. Penelitian ini bertujuan untuk menganalisis dan membandingkan efisiensi dan akurasi dari tiga arsitektur JSK yang berbeda—Faster R-CNN (dua tahap), SSD (satu tahap klasik), dan YOLOv8s (satu tahap modern)—terhadap gangguan visual. Model-model tersebut dilatih dari awal (from scratch) pada subset dataset COCO dan diuji pada empat skenario: citra bersih, gangguan Gaussian, motion blur, dan kombinasi keduanya. Evaluasi performa menggunakan metrik mean Average Precision (mAP) untuk akurasi dan Frames Per Second (FPS) untuk efisiensi. Hasil eksperimen menunjukkan bahwa YOLOv8s tidak hanya menjadi model tercepat (476.19 FPS), tetapi juga mencapai akurasi tertinggi pada kondisi bersih (mAP 0.2870) dan menunjukkan ketahanan (robustness) terbaik terhadap semua jenis gangguan yang diuji. Sebaliknya, Faster R-CNN terbukti menjadi yang paling rentan, dengan penurunan akurasi mencapai lebih dari 97% pada kondisi gangguan Gaussian. Kesimpulannya, arsitektur modern YOLOv8s menawarkan keseimbangan terbaik antara efisiensi, akurasi, dan ketahanan, menjadikannya pilihan yang superior untuk aplikasi di lingkungan yang tidak dapat diprediksi.
Co-Authors Abdul Syukur Abi Senoprabowo Affandy Affandy Affandy Arie Nugroho, Arie Dliyauddin, Muhammad Edi Noersasongko Elkaf Rahmawan Pramudya Firdaus, Gusti Aditya Aromatica Firdaus, Iqbal Guruh Fajar Shidik Maida, Mamay Maisarah, Hj. Marjuni, Aris Mega Fatimah Rosana Muslih - Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Putri Novitasari, Hestiana Rahmatullah, Muhammad Rifqi Fadhlan Ricardus Anggi Pramunendar Rofiqi, Harri Kurniawan Rosita Sari, Sifa Ayu Sari Ayu Wulandari Sri Winarno Supriyono Asfawi Triyotomo, Triyotomo Tutik Khotimah Widhiariandoko, Adhitya Zami , Farrikh Al