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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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Journal : bit-tech

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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
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