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All Journal Informatics for Educators and Professional : Journal of Informatics SMARTICS Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat IPTEKS JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Generation Journal Kontribusia : Research Dissemination for Community Development Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Community Development Journal: Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Sistem Informasi dan Sistem Komputer Jurnal Computer Science and Information Technology (CoSciTech) JIWAKERTA: Jurnal Ilmiah Wawasan Kuliah Kerja Nyata BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer MIMBAR INTEGRITAS JUSTIFY : Jurnal Sistem Informasi Ibrahimy J-ENSITEC (Journal of Engineering and Sustainable Technology) Journal Of Humanities Community Empowerment Indonesian Journal of Innovation Science and Knowledge
Rahman, Miftahur
Universitas Muhammadiyah Jember
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Penerapan Algoritma Convolution Neural Network untuk Klasifikasi Jenis Cabai Berdasarkan Warna dan Bentuk buah Rizal Abdur Rohman; Moh. Dasuki; Lutfi Ali Muharom; Miftahur Rahman
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2024): September
Publisher : Universitas Wahid Hasyim

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Abstract

Chili is one of the main agricultural commodities in Indonesia with significant economic value. Various types of chili, such as large chili, bird's eye chili, and green chili, are often difficult to distinguish manually due to their physical similarities. To support advancements in the agricultural sector, this study utilizes artificial intelligence technology, specifically the Convolutional Neural Network (CNN) with VGG-16 architecture, to automatically identify chili types through image analysis based on color and shape. This study aims to measure the accuracy, sensitivity, and specificity of the model in classifying chili types. The results show that the VGG-16 architecture achieved 100% accuracy in training data testing, indicating the model’s ability to detect and classify chili types optimally. In the model evaluation (fold 5), the accuracy was 91.8%, sensitivity was 88%, and specificity was 93.8%. This study confirms that CNN with VGG-16 is effective for image classification, especially when test data shares similar characteristics with training data. This system offers significant potential for application in the agricultural sector, particularly in improving the efficiency and accuracy of identifying other agricultural commodities.
Co-Authors Abdul Azis Adhif Aditama Adisiswanto, Alissa Ernawati Afandi, Abidah Wafiyah Ramadhaniah Ahadini Izzatus Zahro Ahmad Fawaiz An’ars, M. Ghufroni Ari Eko Wardoyo Ashrifa, Diba Azmi, M. Abid Ulil Daryanto Daryanto Dasuki, Moh. Faishol, Dimas Erisma Faridha, Noor Fitriyah, Nur Qodariyah Ginanjar Abdurrahman Heni Pujiastuti Intan Ayu Islami Irawan, Dudi Istikomah Istikomah Kahar Haerah Luluk Handayani Lusiana, Dewi Maryam Zakiyyah, Amalina Moh. Dasuki Moh. Dasuki Moh. Iqbal Maulidani Muhammad Ali Ridla Muhammad Fauzen Adiman Muhammad Khoirur Roziqin Muhammad Maulana Akbar Muhammad Rivansyah Muhammad Yuwan Safri Nacikit Muharom, Lutfi Ali Nina Sa'idah Fitriyah Nirwana Hendrastuty Nur Azise Nurhalimah Nurhalimah Oktavianto, Hardian Pater, Dewi Lusiana Prayitno, Moh. Asy Syam Iriansyah pribadi, kukuh Putri Nur Apriliyanti Qurrota A’yun Rahma Wahyuningsih Rahmadianti, Fitrah Amalia Ravi Budi Handwika Rega Sukmawati Resti Sutianingtyas Rizal Abdur Rohman Rohman, Rizal Abdur Saifudin, Ilham Setiawansyah Setiawansyah Suharso, Wiwik Sulistyo, Henny Wahyu Syamsul Arifin Syarif Hidayatullah Tiara Puspita Arin Triawan Adi Cahyanto Umilasari, Reni Very Hendra Saputra Wardiyanto, Dedep Vicky Wijaya, Guruh ZAINUL ARIFIN Zakiyyah, Amalina Maryam