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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknik Informatika dan Teknologi Informasi
Gasim Gasim
Universitas Indo Global Mandiri
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Pengaruh Ukuran Cropping Terhadap Tingkat Akurasi Pengenalan Jenis Bibit Jeruk Melalui Citra Urat Daun dengan Metode JST Menggunakan Fitur GLCM Muhammad Zulfikar; Gasim Gasim; Zaid Romegar Mair
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 5 (2025): Oktober 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i5.9752

Abstract

Abstrak − Penelitian ini mengevaluasi pengaruh variasi ukuran cropping citra terhadap tingkat akurasi pengenalan jenis bibit jeruk berdasarkan tekstur urat daun menggunakan metode Jaringan Saraf Tiruan (JST) dengan fitur Gray Level Co-occurrence Matrix (GLCM). Citra daun dari lima varietas jeruk (manis, nipis, lemon, bali, sunkist) diolah dengan lima ukuran cropping (160×120, 320×240, 400×300, 480×360, dan 640×480 piksel) melalui konversi ke grayscale, ekstraksi fitur GLCM (kontras, korelasi, homogenitas, energi), dan klasifikasi menggunakan JST. Total 1000 citra (800 latih, 200 uji) digunakan dalam penelitian. Hasil menunjukkan bahwa ukuran cropping 480×360 piksel menghasilkan akurasi tertinggi sebesar 76,0%, sedangkan ukuran 160×120 piksel menghasilkan akurasi terendah (54,0%). Resolusi cropping menengah terbukti optimal dalam mempertahankan detail tekstur tanpa menambah kompleksitas komputasi. Penelitian ini menegaskan pentingnya pemilihan ukuran cropping dalam meningkatkan efektivitas sistem pengenalan citra berbasis JST dan GLCM.Kata Kunci: Pengenalan Jenis Bibit Jeruk; Ukuran Cropping; GLCM; JST; Tekstur Urat Daun; Abstract − This study evaluates the effect of varying image cropping sizes on the accuracy of citrus seedling type recognition based on leaf vein texture using the Artificial Neural Network (ANN) method with Gray Level Co-occurrence Matrix (GLCM) features. Leaf images from five citrus varieties (sweet orange, lime, lemon, pomelo, and sunkist) were processed with five cropping sizes (160×120, 320×240, 400×300, 480×360, and 640×480 pixels) through grayscale conversion, GLCM feature extraction (contrast, correlation, homogeneity, energy), and classification using ANN. A total of 1000 images (800 training, 200 testing) were used. Results show that the 480×360 pixel cropping size achieved the highest accuracy of 76.0%, while the 160×120 pixel size yielded the lowest accuracy of 54.0%. Medium-resolution cropping proved optimal in retaining texture details without increasing computational complexity. This study highlights the importance of selecting appropriate cropping sizes to enhance the effectiveness of image-based recognition systems using ANN and GLCM.Keywords: Citrus Seedling Recognition; Cropping Size; GLCM; ANN; Leaf Vein Texture;
Pengaruh TIngkat Skala Keabuan Terhadap Akurasi Klasifikasi Jenis Ikan Melalui Citra Sisik Ikan Menggunakan Jaringan Syaraf Tiruan Gilang Hadi Ramadhan; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5796

Abstract

This study was conducted to examine the effect of grayscale image variations on the accuracy of fish species recognition by utilizing fish scale images through the Artificial Neural Network (ANN) method. Automatic fish species identification plays a crucial role in the fisheries sector, both for research purposes, marine resource monitoring, and trade processes. One factor that can influence recognition accuracy is the quality of image representation, including the grayscale level used. Therefore, this study aims to analyze how much grayscale level variations affect fish species classification results. This research method uses a dataset consisting of 180 scale images for each fish species. Of these, 150 images are used as training data and 30 images as test data. The feature extraction process is carried out using the Gray Level Co-occurrence Matrix (GLCM) method, which utilizes contrast, energy, homogeneity, correlation, and entropy parameters. These features are then used as input to the ANN for the classification process. The analysis was conducted by comparing the accuracy results of various grayscale levels, namely 16, 32, 64, 128, and 256 levels. The results showed that variations in grayscale significantly influenced the accuracy level of fish species recognition. The highest accuracy was obtained at a scale of 256 levels with a value of 96%, followed by a scale of 128 levels at 95%, 64 levels at 92.5%, 32 levels at 84.2%, and the lowest at 16 levels with an accuracy of only 82.5%. In conclusion, the higher the variation in grayscale levels used, the better the recognition accuracy obtained. Thus, the use of images with 256 grayscale levels is recommended for research on fish scale image classification using the ANN method because it is able to provide the most optimal results.
Pengaruh Tingkat Pencahayaan Pemotretan Urat Daun terhadap Tingkat Akurasi Pengenalan Jenis Bibit Mangga Menggunakan Metode Pengenalan JST-PB dan Fitur LBP Suci Aulia Ramadhani; Gasim Gasim; Mustafa Ramadhan
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 1 (2025): April: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i1.5854

Abstract

Mango (Mangifera indica L.) is one of the most important tropical fruits with high nutritional value and significant economic potential. However, manual identification of mango seedlings remains less accurate due to the similarities in leaf shape and size among different varieties, which often leads to misclassification. This study aims to develop an automated system to recognize five types of mango seedlings—Harum Manis, Indramayu, Golek, Madu, and Gedong Gincu by utilizing leaf vein textures as the main distinguishing features. The methodology employed the Local Binary Pattern (LBP) technique for feature extraction and a Backpropagation Neural Network (BPNN) as the classification model. The dataset consisted of 250 training images and 125 testing images with a resolution of 100×100 pixels, captured under varying lighting conditions ranging from one to five lamps. The experimental results indicate that lighting conditions significantly affect classification accuracy. The highest accuracy was achieved under four-lamp lighting conditions, reaching 91.20%, followed by two lamps (89.60%), three lamps (87.20%), five lamps (76.80%), and one lamp (67.20%). Furthermore, a BPNN configuration with 12 hidden neurons consistently demonstrated reliable recognition performance. These findings suggest that the combination of LBP and BPNN is effective for automatic classification of mango seedlings. The implementation of this system has the potential to assist farmers and seedling institutions by improving efficiency, accuracy, and reliability in seedling identification, thereby supporting the advancement of technology-based agriculture.
Co-Authors Gilang Hadi Ramadhan Muhammad Zulfikar Mustafa Ramadhan Suci Aulia Ramadhani