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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Riset Informatika Jurnal Informatika Global bit-Tech Jurnal Media Informatika Brilliance: Research of Artificial Intelligence Jurnal Pendidikan Sains dan Komputer Jurnal INFOTEL Jurnal Teknik Informatika dan Teknologi Informasi
Gasim
Universitas Indo Global Mandiri
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Materials Science & Nanotechnology Mathematics Neuroscience Physics Transportation Other

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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.
Improving the Accuracy of Concrete Mix Type Recognition with ANN and GLCM Features Based on Image Resolution Gasim Gasim; Rudi Heriansyah; Shinta Puspasari; Muhammad Haviz Irfani; Evi Purnamasari; Indah Permatasari; Samsuryadi Samsuryadi
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1201

Abstract

Concrete is an essential construction material that is often used due to its strength and durability, but its mix type identification often relies on conventional methods that are less efficient and accurate. This research aims to evaluate the effect of image resolution on the accuracy of concrete mix type recognition using Artificial Neural Network (ANN) and Gray-Level Co-Occurrence Matrix (GLCM) features. The method used involves analysing concrete images at various resolutions: 200 x 200, 300 x 300, 400 x 400, 500 x 500, 600 x 600, and 700 x 700 pixels. The experimental results show that higher image resolutions tend to improve recognition accuracy. all types of image sizes using 1,250 training data and 250 test data. Image sizes of 200 x 200 and 300 x 300 pixels give low accuracy of 42% and 45% respectively, while sizes of 400 x 400 and 500 x 500 pixels show an increase in accuracy to 60.5% and 62.5%. The higher resolutions of 600 x 600 and 700 x 700 pixels produced the highest accuracy of 68% and 70%, respectively. These results indicate that larger image resolutions are able to capture more details and characteristics required for more accurate concrete mix type recognition. This research has implications for improving efficiency and consistency in concrete inspection in the construction industry through the use of AI-based image recognition methods.
Perbandingan Akurasi Jarak Potret Untuk Pengenalan Jenis Bibit Mangga Metode JST-PB Dan Fitur GLCM Farhan Ramadhani; Gasim; Nazori Nazori
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2303

Abstract

Penelitian ini bertujuan untuk membandingkan tingkat akurasi dalam mengenali jenis bibit mangga berdasarkan tekstur urat daun menggunakan metode Jaringan Syaraf Tiruan Propagasi Balik (JST-PB) dan fitur Gray Level Co-occurrence Matrix (GLCM). Identifikasi jenis bibit mangga menjadi tantangan karena variabilitas genetik yang tinggi, yang mempengaruhi kemampuan dalam mengenali bibit mangga secara otomatis. Dalam penelitian ini, citra daun mangga diambil dengan tiga variasi jarak potret (5 cm, 10 cm, dan 15 cm) untuk menilai pengaruh jarak terhadap akurasi pengenalan. Proses pertama dilakukan dengan ekstraksi tekstur urat daun menggunakan GLCM, yang mengubah citra RGB menjadi citra grayscale dan mengambil empat fitur utama (kontras, homogenitas, korelasi, dan energi). Setelah itu, model JST-PB diterapkan untuk melatih jaringan saraf dengan menggunakan data citra yang telah diproses. Hasil penelitian menunjukkan bahwa jarak potret 15 cm menghasilkan akurasi tertinggi, yaitu 34%, dengan 22 dari 64 citra uji berhasil dikenali dengan benar. Jarak potret 5 cm dan 10 cm menunjukkan akurasi yang lebih rendah, masing-masing 23% dan 17%. Penggunaan satu hidden layer dengan 10 neuron dalam JST-PB terbukti memberikan performa terbaik pada jarak potret 15 cm. Penelitian ini diharapkan dapat memberikan kontribusi dalam meningkatkan akurasi pengenalan bibit mangga secara otomatis, serta memberikan wawasan mengenai pengaruh jarak potret dalam aplikasi pengolahan citra pertanian.
Identification of Ginger Varieties Using Manhattan Distance on Image Pixel Vectors and Histograms Rauditha Putri Cahyani; Rudi Heriansyah; Gasim Gasim
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3019

Abstract

The integration of digital image processing and pattern recognition has opened new opportunities for improving agricultural product classification. This study focuses on the identification of three economically important ginger varieties red ginger, elephant ginger, and Emprit ginger through an image-based classification system. Unlike conventional manual inspection, which is prone to subjectivity and error, the proposed method applies a distance-based similarity measure to enhance consistency and reliability. Central to this approach is the use of the Manhattan Distance metric, chosen for its computational efficiency and robustness in high-dimensional data spaces. Two types of image features were explored: global intensity histograms and pixel vector representations. Comparative evaluation demonstrates that histogram-based classification achieves an accuracy of 86.6%, substantially outperforming the pixel vector approach at 76.6%. Novelty this research lies in demonstrating that lightweight, interpretable techniques can deliver competitive accuracy while avoiding the data and computational demands of more complex machine learning or deep learning models. This makes the system particularly suitable for smallholder farmers, local cooperatives, and resource-limited agricultural environments. Moreover, the study highlights the potential of histogram-based representation as a practical solution to variability in lighting and texture, offering improved robustness over traditional visual inspection or pixel-level methods. By contributing a simple yet effective framework, this research advances the field of agricultural informatics and supports the development of low-cost, automated tools for crop identification. Beyond academic significance, the findings have practical implications for supply chain management, post-harvest quality control, and precision agriculture, fostering transparency and value optimization in ginger production and distribution
Optimizing Bit Depth for Longan Seedling Identification Using ANN and GLCM Rizki Andika; Gasim Gasim; Zaid Romegar Mair
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6811

Abstract

Accurate identification of longan (Dimocarpus longan) seedling varieties is essential for agribusiness to select cultivars meeting market and environmental needs, but manual identification is error-prone due to similar leaf textures. This study optimizes grayscale image bit depth using Artificial Neural Networks (ANN) and Gray Level Co-occurrence Matrix (GLCM) to enhance longan seedling classification accuracy, addressing a gap in texture-based identification efficiency. Leaf images from five longan varieties (Itoh, Pingpong, Merah, Matalada, Diamond River) were captured with a USB digital microscope and converted to grayscale at bit depths of 4 (0–15), 5 (0–31), 6 (0–63), 7 (0–127), and 8 (0–255). Texture features (contrast, correlation, energy, homogeneity, entropy, standard deviation) were extracted using MATLAB. An ANN model, trained with the traingdx algorithm on 800 training and 200 test images, classified the varieties. The 6-bit and 4-bit depths yielded the highest accuracy (84.5%), followed by 7-bit (84.0%), 5-bit (83.5%), and 8-bit (82.0%), with Matalada achieving 90.0% accuracy. The 8-bit depth introduced texture noise, reducing performance. A 6-bit depth is optimal for longan leaf texture classification, though distinguishing similar varieties like Itoh and Pingpong remains challenging. Future research should incorporate color or morphological features to improve agricultural image processing.
Pengaruh Resize Citra terhadap Pengenalan Sidik Jari dengan Pendekatan Klasifikasi SVM: The Effect of Image Resizing on Fingerprint Recognition with the SVM Classification Approach Surya Ario Pratama; Gasim Gasim; Indah Permatasari
Jurnal Pendidikan Sains dan Komputer Vol. 6 No. 02 (2026): Call for Papers Juni 2026
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v6i02.8711

Abstract

Fingerprint recognition systems on resource-limited devices often face the challenges of aggressive image dimension compression (resizing) and natural scan tilt variations. This research does not aim to design a commercial identification system, but rather to specifically analyze the limitations of image resolution reduction (64x64, 96x96, 128x128, and 256x256 pixels) and to evaluate the effectiveness of synthetic rotation augmentation in compensating for Support Vector Machine (SVM) classification performance. The test uses a primary dataset (100 images, 20 classes) partitioned stratified (80:20) to prevent data leakage, where the augmentation process produces a total of 1,600 training images. In comparison, 20 test images are retained as pure unseen data. The stage continues with feature extraction using the Rotation Invariant Local Binary Pattern (LBP-RoR, radius 1). The experimental results show that a 64x64-pixel size is the threshold for structural failure, at which the ridge topology is fatally damaged, leading to a test accuracy of 10%. The model exhibited the highest overfitting phenomenon at 128x128 pixel resolution (training accuracy 79.17%, testing 40%). The best generalization equilibrium point was achieved at 256x256 pixels with a testing accuracy of 50%. This maximum achievement, which was stuck at 50%, demonstrates the vulnerability of the LBP and linear SVM margin methods to pixel-artifact distortion (aliasing) caused by digital rotation. This study concludes that spatial data augmentation cannot fully substitute the need for a physical finger alignment module (fingerprint alignment) in the preprocessing stage.
INFLUENCE OF LEAF IMAGING DISTANCE ON WATER GUAVA CLASSIFICATION USING NEURAL NETWORK WITH GRAY LEVEL CO-OCCURRENCE MATRIX FEATURES Muhammad Haviz Irfani; Gasim; Andika Afrianto
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.419

Abstract

The development of Computer Vision technology has made a significant contribution to the agricultural sector, particularly in the identification of plants based on visual characteristics. Water guava (Syzygium aqueum) is one of the fruit commodities widely cultivated in Indonesia; however, its seedling varieties are often difficult to distinguish visually. Conventional methods relying on human observation tend to have low accuracy, highlighting the need for an accurate and efficient identification system from the early stages. This study aims to analyze the effect of varying imaging distances on the extraction results of leaf vein texture features using the Gray Level Co-occurrence Matrix (GLCM) method and to evaluate how this parameter influences the classification performance of water guava seedlings using the Backpropagation Artificial Neural Network (ANN). Unlike previous GLCM–ANN plant classification studies that primarily focused on lighting or species variation, this work systematically investigates imaging distance as a key factor in optimizing texture feature stability and improving model accuracy. Experiments were conducted using five imaging distances—7 cm, 9 cm, 11 cm, 13 cm, and 15 cm—with 2,500 images used for training data and 500 images for testing data. The results show that an imaging distance of 13 cm yielded the best performance, achieving 80% accuracy, where 80 out of 100 test images were correctly classified, supported by balanced precision, recall, and F1-score values indicating stable and reliable classification performance.
Identifikasi Kualitas Batik Berdasarkan Jarak Pengambilan Gambar Menggunakan K-Nearest Neighbor Muhammad Alief Faza Nujjiya; Gasim Gasim; Zaid Romegar Mair
Jurnal Media Informatika Vol. 6 No. 6 (2025): Edisi Desember 2025
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v6i6.7130

Abstract

Penelitian ini bertujuan untuk mengevaluasi pengaruh jarak pengambilan gambar terhadap akurasi identifikasi kualitas kain batik menggunakan metode K-Nearest Neighbor (K-NN) dengan ekstraksi fitur tekstur Gray Level Co-occurrence Matrix (GLCM). Data yang digunakan berupa citra lima motif batik tradisional yang diambil pada enam variasi jarak: 10, 20, 30, 40, 50, dan 60 cm. Tahapan pra-pemrosesan meliputi auto-cropping citra ke ukuran 500×500 piksel, konversi ke grayscale, dan kuantisasi 8 level. Empat fitur utama GLCM contrast, energy, homogeneity, dan entropy diekstraksi dan digunakan sebagai masukan bagi K-NN dengan nilai k=1. Hasil penelitian menunjukkan bahwa jarak 30 cm memberikan akurasi tertinggi sebesar 100%, sedangkan jarak 20 cm dan 10 cm mencapai 80%, jarak 40–50 cm turun menjadi 60%, dan jarak 60 cm hanya 40%. Temuan ini menegaskan bahwa jarak pemotretan berpengaruh signifikan terhadap kualitas identifikasi berbasis tekstur. Kesimpulannya, jarak optimal untuk akuisisi citra batik adalah rentang 20–40 cm, khususnya 30 cm, yang direkomendasikan sebagai standar pada sistem identifikasi kualitas batik berbasis citra.
Co-Authors Andika Afrianto David Ricardo Dini Amputri Edgar Utama Evi Purnamasari Farhan Ramadhani Fika Rusilawati Fikario Yapputra Gilang Hadi Ramadhan Hardianing Wahyu Kinasih Indah Permatasari Indah Permatasari Jesslyn K Muhammad Alief Faza Nujjiya Muhammad Ezar Al Rivan Muhammad Haviz Irfani Muhammad Haviz Irfani Muhammad Zulfikar Mulia Octavia Mustafa Ramadhan Nazori Nazori Rauditha Putri Cahyani Rizani Teguh Rizki Andika Rudi Heriansyah Rudi Heriansyah Rusbandi Rusbandi Samsuryadi Samsuryadi Shinta Puspasari Siti Nadra Suci Aulia Ramadhani Sudiadi Sudiadi Sudiadi, Sudiadi Surya Ario Pratama Zaid Romegar Mair