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All Journal JMM (Jurnal Masyarakat Mandiri) Jurnal Informatika: Jurnal Pengembangan IT Jurnal Teknik Elektro
Mutasodirin, Mirza Alim
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Computer Science & IT Electrical & Electronics Engineering Other

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Efficient Weather Classification Using DenseNet and EfficientNet Mutasodirin, Mirza Alim; Falakh, Faiq Miftakhul
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.7539

Abstract

Classifying images of weather conditions using deep learning models is a challenging task due to the computational intensity and resource requirements. To deploy AI models on resource-constrained devices like smartphones and IoT devices, compact and computationally lightweight models are necessary. Efficient deep learning models for weather classification are essential to reduce energy consumption and costs, making AI more accessible and sustainable. To the best of our knowledge, there are limited studies comparing MobileNet, DenseNet, and EfficientNet as efficient models and did not report any hyperparameter optimization. Our study contributes by investigating efficient models with hyperparameter optimization. Firstly, we measured the inference speed of 14 models, namely MobileNet, MobileNetV2, MobileNetV3, EfficientNetB0, EfficientNetV2B0, NASNetMobile, DenseNet121, VGG16, Xception, InceptionV3, ResNet50, ResNet50V2, ConvNeXtTiny, and InceptionResNetV2. Then, the top-7 fast models, which are MobileNet, MobileNetV2, MobileNetV3, EfficientNetB0, EfficientNetV2B0, NASNetMobile, and DenseNet121, were benchmarked for their accuracy. The models were compared by a small dataset having four classes: cloudy, rain, shine, and sunrise. Batch size and learning rate for each model were optimized by grid search method. It turns out that DenseNet121 achieved the best and the most balanced validation and testing accuracy, 0.9821 and 0.9837, followed by EfficientNetB0 with 0.9821 and 0.9740 respectively. This study is important to find efficient models with optimal comparison.
Cat Skin Disease Diagnosis Using EfficientNetV2 for Lightweight Processing on Low-Resource Devices Aminah, Fadila Rizka Nur; Mutasodirin, Mirza Alim; Hidayattullah, Muhammad Fikri
Jurnal Teknik Elektro Vol. 17 No. 2 (2025)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v17i2.29764

Abstract

Skin diseases are among the most common health issues in domestic cats. However, access to veterinarians is often limited, especially in low-resource settings. Automated image-based detection offers a fast and affordable alternative for early intervention. This paper presents a lightweight approach for diagnosing feline skin diseases using EfficientNetV2 optimized for low-resource devices. A balanced custom dataset consisting of 720 images across nine classes, namely Healthy, Mild/Severe Ringworm, Mild/Severe Acne, Mild/Severe Flea, and Mild/Severe Scabies, was compiled from Kaggle, Roboflow, and Google Images, ensuring ethical use of publicly available data. The images were augmented through rotations (0°, 90°, 180°, 270°) and horizontal flips, resulting in 5,760 images, to enhance model generalization. Five CNN architectures were benchmarked: DenseNet121, MobileNetV2, MobileNetV3, EfficientNetB0, and EfficientNetV2B0. Training was conducted with grid searches over batch sizes {64, 32, 16, 8} and learning rates {1e-3, 5e-4, 2e-4, 1e-4, 5e-5} for up to 300 epochs, and with the Adam optimizer and Reduce-LR-on-Plateau (decay factor 0.5). Early stopping (patience = 10) was used to mitigate overfitting. The best model was selected based on highest validation accuracy. The experiments were conducted on an Intel Xeon 6 CPU (2.2 GHz, 2 vCPUs) in Google Colab without GPU to simulate low-resource deployment. EfficientNetV2B0 achieved the best performance with 99.62% validation accuracy and 99.79% test accuracy, with an average inference latency of 78 ms/frame. Compared to previous studies focusing on heavyweight models or conventional ML using handcrafted features, this work highlights the feasibility of deploying an accurate real-time diagnostic pipeline on edge devices.
PENGGUNAAN IMPERATIVE SENTENCE BERBAHASA INGGRIS SEBAGAI PROMPT PADA WEBSITE BERBASIS ARTIFICIAL INTELIGENCE TEXT TO IMAGE Firmansyah, Muchammad Sofyan; Mutasodirin, Mirza Alim
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 4 (2025): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i4.32600

Abstract

Abstrak: Kemajuan teknologi kecerdasan buatan (AI) menuntut generasi muda untuk memiliki keterampilan baru yang mengintegrasikan aspek linguistik dan teknologi. Salah satu teknologi terkini, yaitu AI text-to-image, memerlukan kemampuan dalam menyusun perintah dalam bentuk kalimat imperatif berbahasa Inggris yang jelas dan efektif. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan softskill (kemampuan linguistik) dan hardskill (penggunaan teknologi AI) siswa SMK Muhammadiyah 1 Adiwerna dalam menyusun kalimat imperatif berbahasa Inggris sebagai prompt pada website berbasis Artificial Intelligence (AI). Metode kegiatan yang digunakan meliputi ceramah, praktik langsung, serta diskusi kelompok (FGD). Kegiatan ini melibatkan 30 siswa kelas XI jurusan Animasi sebagai peserta. Evaluasi dilakukan dengan pemberian pre-test dan post-test berupa soal pilihan ganda untuk mengukur peningkatan pemahaman dan keterampilan peserta. Hasil evaluasi menunjukkan peningkatan kemampuan siswa dalam menyusun kalimat imperatif secara signifikan, dengan rata-rata peningkatan sebesar 40% berdasarkan hasil perbandingan pre-test dan post-test. Kegiatan ini terbukti efektif dalam meningkatkan kemampuan peserta baik secara linguistik maupun teknologis, serta membekali mereka untuk siap menghadapi tantangan industri kreatif berbasis AI.Abstract: The advancement of Artificial Intelligence (AI) technology demands that young generations acquire new skills that integrate linguistic competence with technological proficiency. One such emerging technology, AI text-to-image generation, requires the ability to compose clear and effective imperative sentences in English as prompts. This community service program aimed to enhance both the soft skills (linguistic ability) and hard skills (AI application usage) of students at SMK Muhammadiyah 1 Adiwerna by training them to compose English imperative sentences as prompts for AI-based websites. The methods used included lectures, hands-on practice, and Focus Group Discussions (FGDs). The activity involved 30 eleventh-grade students from the Animation Department as participants. Evaluation was conducted using pre-tests and post-tests consisting of multiple-choice questions to measure improvements in understanding and skills. The evaluation results showed a significant improvement in the students' ability to construct imperative sentences, with an average skill increase of 40%, based on the comparison between pre-test and post-test scores. This program effectively enhanced the participants' linguistic and technological competencies and prepared them to face the demands of the AI-driven creative industry.
Co-Authors Aminah, Fadila Rizka Nur Falakh, Faiq Miftakhul Firmansyah, Muchammad Sofyan Muhammad Fikri Hidayattullah, Muhammad Fikri