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Fido Rizki
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Jalan Jendral Besar H.M Soeharto Kelurahan Lubuk Kupang Kecamatan Lubuklinggau Barat I Kota Lubuklinggau Provinsi Sumatera Selatan
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JUSIM (Jurnal Sistem Informasi Musirawas)
Published by Universitas Bina Insan Lubuklinggau
ISSN : 2541190X     EISSN : 26148706     DOI : https://doi.org/10.32767/jusim.v6i2
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
JUSIM (Jurnal Sistem Informasi Musirawas) diterbitkan oleh LPPM Universitas Bina Insan dalam dua kali setahun dengan No ISSN Online : 2614-8706 dan ISSN Print : 2541-190X Terakreditasi SINTA 4 DIKTI Nomor 36/E/KPT/2019 . JUSIM (Jurnal Sistem Informasi Musirawas) terbit 2 (dua) nomor dalam setahun, yaitu bulan Juni dan Desember. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit. JUSIM (Jurnal Sistem Informasi Musirawas) telah terindeks Sinta Ristekbrin, Google Scholar, Garuda Ristekbrin, OneSearch.id Perpusnas, Crosreef, PKP Index, Dimensions, ROAD dan terus akan diupdate mengikuti perkembangan. JUSIM (Jurnal Sistem Informasi Musirawas) merupakan jurnal ilmiah dalam bidang Sistem Informasi dan Komputer yang berfokus pada Perancangan, Evaluasi dan Tata Kelola Sistem Informasi. Sistem Informasi merupakan kombinasi antara aktivitas manusia dan penggunaan teknologi untuk mendukung manajemen dan kegiatan operasional untuk mencapai tujuan dalam sebuah perusahaan atau organisasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 252 Documents
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IMPLEMENTASI TRANSFER LEARNING DALAM KLASIFIKASI KEMATANGAN PEPAYA MENGGUNAKAN METODE DESNET 1.2.1 Muhammad Syariffudin; Joni Karman; Ahmad sobri
JUSIM (Jurnal Sistem Informasi Musirawas) Vol. 11 No. 3 (2026): September
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v11i3.3004

Abstract

Manual determination of papaya fruit ripeness is still widely practiced by farmers and traders, relying solely on visual observation. This approach is prone to errors caused by subjectivity and the limitations of human perception. This study aims to implement the Transfer Learning method using the DenseNet121 architecture to automatically classify papaya fruit ripeness levels based on digital images. The dataset used is divided into three ripeness classes: unripe, semi-ripe, and ripe. The research stages include dataset collection, image preprocessing using ImageDataGenerator, data division into training, validation, and testing sets, and model training using the Adam optimizer with a learning rate of 0.001, batch size of 32, and 50 epochs. The model was built using the Python programming language with the TensorFlow and Keras libraries. Furthermore, model performance was evaluated using a confusion matrix, classification report, and ROC curve. The results show that the DenseNet121 model was able to learn the visual characteristics of papaya ripeness levels very well. Based on the evaluation, the model achieved an accuracy of 97.96%, with high precision, recall, and F1-score values for each ripeness category. In addition, the ROC curve yielded an Area Under Curve (AUC) value close to 1.00, indicating excellent classification performance. These findings demonstrate that the application of Transfer Learning with the DenseNet121 architecture is effective in classifying papaya fruit ripeness levels. This method has the potential to be developed into a decision support system for farmers and agricultural businesses to improve the efficiency and accuracy of the fruit sorting process.
SISTEM CERDAS BERBASIS WEB UNTUK KLASIFIKASI GAMBAR AI MULTIKELAS MENGGUNAKAN FUSI FITUR CNN-VIT DAN BLS Nopalia Nopalia; Raniyah Ayu Lestari; Dina Dalilah
JUSIM (Jurnal Sistem Informasi Musirawas) Vol. 11 No. 2 (2026): JUSIM : Jurnal Sistem Informasi Musi Rawas Juni
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v11i2.3420

Abstract

The rapid advancement of Generative Artificial Intelligence (AI) technology poses significant challenges in digital forensics, particularly in distinguishing authentic images from synthetic ones. The limitations of current binary detection methods drive the need for a more comprehensive identification system. This study aims to develop an intelligent web-based system for multiclass AI image detection. The proposed approach utilizes a hybrid model integrating a Convolutional Neural Network (EfficientNet-B0) for local spatial feature extraction and a Vision Transformer (ViT-Base) to capture global visual context. Features from both architectures are combined through a feature fusion mechanism and efficiently classified using a Broad Learning System (BLS) to distinguish six categories: real/AI humans, real/AI animals, and real/AI objects. Evaluation conducted on 900 validation images demonstrates that the hybrid model achieves an overall accuracy of 96.56%, with an average precision, recall, and F1-score of 97%. The system is deployed as an interactive web application utilizing a Streamlit interface and a FastAPI backend. Functional testing proves that the platform can process inferences stably and responsively in real-time. In conclusion, the integration of CNN-ViT and BLS offers superior accuracy alongside optimal computational efficiency for practical digital image authentication.

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