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Andri Anto Tri Susilo, Andri Anto
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PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3033

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture
PENDEKATAN COMPUTER VISION BERBASIS FUSION CNN DAN GRAD-CAM UNTUK IDENTIFIKASI PENYAKIT DAUN CABAI Andri Anto Tri Susilo; Lukman Sunardi; Budi Santoso
Jurnal Teknologi Informasi Mura (JTI) Vol. 18 No. 2 (2026): Jurnal Teknologi Informasi Mura JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jti.v18i2.3033

Abstract

The rapid advancement of Artificial Intelligence (AI) technology has created new opportunities for modernizing the agricultural sector, particularly in the early detection and classification of plant diseases based on digital images. A Computer Vision-based approach has emerged as an effective solution, as it enables the automation of visual analysis that was previously reliant on manual observation. In this study, a method based on Fusion Convolutional Neural Networks (CNN) is proposed, combining the strengths of ResNet and DenseNet architectures to produce more robust and discriminative feature representations. In addition, this research integrates an Explainable Artificial Intelligence (XAI) technique using Grad-CAM to provide visual interpretations of the model’s decisions, thereby enhancing user trust in the developed system. The dataset used consists of three main classes of chili leaf conditions: Bacterial Spot, Curl Virus, and Healthy. Experimental results demonstrate that the proposed model achieves excellent performance, with an accuracy of 98%. Further analysis through the classification report indicates that the Healthy class attains perfect performance, with precision, recall, and f1-score all reaching 1.00. Meanwhile, the Bacterial Spot class achieves a recall of 1.00 and an f1-score of 0.97, indicating the model’s capability to correctly identify all samples in this class. The Curl Virus class also shows strong performance, with a precision of 1.00, recall of 0.95, and f1-score of 0.97. Overall, the macro average and weighted average f1-scores both reach 0.98, reflecting the model’s stability and consistency across all classes. Furthermore, the implementation of Grad-CAM is able to highlight specific regions on chili leaves that contribute to the model’s predictions, providing deeper insight into the disease patterns recognized by the model. This not only enhances interpretability but also supports visual validation by users. Therefore, this study demonstrates that the combination of Fusion CNN and Grad-CAM is not only effective in improving classification accuracy but also ensures transparency in the decision-making process, making it highly suitable for intelligent decision-support systems in precision agriculture
Penerapan Metode Composite Performance Index (CPI) Pada Pemilihan Hotel Di Kota Lubuklinggau Andri Anto Tri Susilo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 1 No 3 (2017): Desember 2017
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (382.62 KB) | DOI: 10.29207/resti.v1i3.79

Abstract

Seiring dengan kemajuan zaman, kemajuan teknologi informasi juga semakin berkembang pesat. Perkembangan teknologi informasi, memiliki dampak besar pada berbagai bidang kehidupan masyarakat baik dari segi sosial, ekonomi, pendidikan, pembangunan, dan pariwisata. Salah satu unsur penting yang mendukung sektor pariwisata adalah adanya hotel. Hotel sebagai sarana akomodasi umum sangat membantu wisatawan yang berkunjung dengan menyediakan layanan penginapan. Keragaman hotel sering membuat para wisatawan kesulitan dalam menentukan hotel yang akan dipilih sebagai tempat menginap. Sistem pendukung keputusan adalah model yang dibangun untuk memecahkan masalah terstruktur. Composite Performance Index (CPI) adalah metode yang umum digunakan dalam proses pengambilan keputusan adalah). Metode CPI menggunakan pemecahan masalah dengan sistem Multiple Criteria Decision Making (MCDM) yang menentukan urutan atau prioritas dalam analisis multikriteria. Hasil akhir dari penelitian ini adalah terciptanya sistem pendukung keputusan yang menghasilkan informasi mengenai peringkat hotel yang dapat dijadikan tempat referensi untuk tetap memperhatikan beberapa kriteria, termasuk tarif kamar, jarak ke pusat kota, fasilitas dan layanan.
Co-Authors Anita Maharani Aprillian, Alif Armanto Armanto Armanto, Armanto aviani, tri hasanah bimastari Budi Santoso Bunga Intan Bunga Intan Dwi Budi Santoso Elmayati, Elmayati Harma Oktavia Lingga Wijaya Hidayat, Asep Toyib Intan, Bunga Lestari, Novi Lingga Wijaya, Harma Oktafia Lukman Hakim Lukman Lukman LW, Harma Oktavia Martadinata, A. Taqwa Maya Sari, Wisdalia Melizah, Melizah Mona Anggrelyka Putri Muhammad Cahaya Saputra nansyah, Satria Nelly Khairani Daulay Nur Alamsyah, Muhammad Putra, Andriansah Rohani, Dewi Salsabila, Amanda Sari, Wisdalia Maya sobri, ahmad Sunardi, Lukman Susanto Susanto Susanto Susanto Syamsul Arifin, M. Agus Veithzal Rivai Zainal Wijaya, Harma Oktavia Lingga Wisdalia Maya Sari Wisdalia Maya Sari Wiwit Lestari Wulandari, Cindi Yuliani Waruwu