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All Journal Nuansa Informatika JEJARING JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Buffer Informatika Empowerment : Jurnal Pengabdian Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Aplikasi Pelayaran dan Kepelabuhanan Balong International Journal of Design Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Teknologi Informatika dan Komputer Jurnal Pengabdian kepada Masyarakat Nusantara Indonesian Community Service and Empowerment Journal (IComSE) Jurnal Computer Science and Information Technology (CoSciTech) Abditeknika - Jurnal Pengabdian Kepada Masyarakat Aptekmas : Jurnal Pengabdian Kepada Masyarakat Nuansa Informatika Buffer Informatika ORAHUA : Jurnal Pengabdian Kepada Masyarakat
Tito Sugiharto
Universitas Kuningan
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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

 1 
KLASIFIKASI JENIS TANAMAN ALPUKAT BERDASARKAN BENTUK DAUN MENGGUNAKAN ALGORITMA CNN Pratama, Agum; Tito Sugiharto; Panji Novantara
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9474

Abstract

The avocado plants is a popular horticultural commodities in Indonesia, especially in Java, due to their health benefits and high economic value. However, differences in leaf shape across avocado varieties often make identification difficult for both buyers and sellers, which can lead to transaction errors and losses. Manual identification requires specialised skills that are not always available, especially in areas such as Kuningan Regency. To answer these problems, this research aims to develop an Android-based application that is able to classify avocado varieties, namely alligator, kendil, and butter, based on leaf images automatically. This application uses Convolutional Neural Network (CNN) algorithm with SSDMobileNetV2 FPNLite pre-trained model implemented through TensorFlow framework. The dataset used consists of 4,800 avocado leaf images divided for training, validation, and testing processes. The test results show that the model is able to achieve an accuracy rate of 99%. For the alligator class, the precision and recall values were 1.00 and 0.98 respectively; for the kendil class, 1.00 and 0.99; and for the butter class, 0.99 and 1.00. These findings prove that the CNN algorithm is effective in classifying avocado varieties based on visual characteristics of the leaves. Thus, this application has the potential to become a fast, accurate, and practical tool in the process of identifying avocado varieties, both for commercial and educational purposes.
Implementasi CNN untuk identifikasi penyakit daun jagung Gumelar, Gilang; Tito Sugiharto; Iwan Lesmana
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9462

Abstract

Maize is an important commodity in Indonesia's agricultural sector. However, disease attacks on the leaves can reduce the quality and quantity of the harvest. At SMK Negeri 1 Kuningan, disease identification is still done manually, so there is a risk of errors. This research aims to design and build an Android application to automatically detect corn leaf diseases using the Convolutional Neural Network (CNN) algorithm. The development method used is Rapid Application Development (RAD), with a CNN model based on MobileNetV2 architecture trained using a dataset of diseased and healthy corn leaf images. Evaluation using test images resulted in an accuracy of 96.2%. The model was able to detect five categories: leaf spot, downy mildew, leaf blight, leaf rust, and healthy leaves. The F1-Score is 94% Leaf Spot, 96% Leaf Blight, 96% Healthy Leaf, 97% Leaf Blight, and 96% Leaf Rust, respectively. The precision and recall values of all classes are above 94%. These results show that the integration of CNN in mobile applications is effective in helping the automatic identification of corn leaf diseases in an educational environment.
KLASIFIKASI JENIS TANAMAN PHILODENDRON BERDASARKAN CITRA DAUN MENGGUNAKAN ALGORITMA CNN Alif, Muhammad Alif Fathan; Tito Sugiharto; Iwan Lesmana
Computer Science and Information Technology Vol 6 No 2 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i2.9484

Abstract

Accurate identification of ornamental plants is becoming important as public interest in tropical plant collections increases, one of which is from the Philodendron genus. This ornamental plant has many varieties that are often difficult to distinguish due to visual similarities in the shape and pattern of their leaves. This research aims to develop a system for Philodendron type classification based on leaf images using the Convolutional Neural Network (CNN) algorithm to help the identification process. The method used is with a dataset of 5000 leaf images of five Philodendron species, which are divided into 80% training data, 10% validation data, and 10% test data. A CNN model with MobileNetV2 FPNLite SSD architecture was implemented and trained for 50,000 steps, then optimised for mobile devices using TensorFlow Lite. Performance analysis was conducted using confusion matrix to evaluate accuracy, precision, recall, and F1-Score metrics. The results show that the developed model is able to accurately classify leaf images, both in the form of static images and in real-time. This system has been successfully implemented in an Android application that is expected to be a practical identification tool for general users and ornamental plant enthusiasts.
Co-Authors Aah Sumiah Abdurohman, Ali Aji Permana Aji Permana, Aji Aldrine Barindra N.W Alif, Muhammad Alif Fathan Amalia Asikin, Nida Arisma, Neno Asep Jejen Jaelani, Asep Jejen Cecep Juliansyah Abbas Dadan Ramdoni Danu Pradana Dede Irawan Elfrida Ratnawati Elpan Januar Erik Kurniadi Faturohman, Firman Ferdiansyah, Vidy FX Adi Purwanto Gumelar, Gilang Heri Herwanto Heru Budianto Iwan Lesmana Kusumo Setyo Aryogi Lutfi Slamet Riyadi Maitsa, Elsa Mochamad Abdul Gofur Mochammad Nur Fikri Denintyo Denintyo Mohammad Irwansyah Muhammad Abd Azis Muhsin . Nunu Nugraha Nur Alamsyah Nura'isyah, Ninda Panji Novantara Poli, Sofyan Pratama, Agum Priguna, Gentur Rika Nugraha Riki Yuspika RIO ANDRIYAT KRISDIAWAN Rio Andriyat. K Rio Priantama Rohmawati, Lutfi Roni Nursyamsu Somantri, Mohammad Irwansyah Sugeng Supriyadi Sulistiani, Mirna Sutarbi, Tata Tata Sutabri Yanti, Pani Puja Yanto, Riki Yati Nurhayati Yulyanto Yulyanto