IMPLEMENTASI IMAGE PROCESSING BERBASIS PYTHON UNTUK OTOMATISASI ANALISIS METALOGRAFI KUANTITATIF

*Muhammad Reza Phahlevi  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Toni Prahasto  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia
Eflita Yohana  -  Department of Mechanical Engineering, Universitas Diponegoro, Jl. Prof. Sudarto, SH, Tembalang, Semarang, Indonesia 50275, Indonesia

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Abstract

Analisis metalografi kuantitatif menyediakan hubungan fundamental antara struktur mikro material seperti dislokasi, fasa, dan ukuran grain dengan sifat mekaniknya. Namun, metode evaluasi konvensional sering kali mengandalkan teknik manual atau komparatif sehingga memakan waktu dan menimbulkan subjektivitas karena bergantung pada keahlian operator. Mengatasi hal tersebut, penelitian ini bertujuan mengembangkan dan memvalidasi metode otomatis menggunakan bahasa pemrograman Python, didukung oleh library seperti OpenCV dan Scikit-Image, dalam melakukan ekstraksi kuantitatif fitur total area dan fraksi area dari gambar mikrostruktur dislokasi, fasa, serta grain. Metode ini diimplementasikan mengikuti alur kerja image processing secara sistematis, dimulai dengan kalibrasi, konversi skala abu-abu (grayscale), dan median filtering sebagai peredam noise. Segmentasi objek, sebuah langkah penting dalam mengisolasi fitur, diterapkan menggunakan metode thresholding Otsu. Proses validasi dilakukan dengan menguji hasil metode Python terhadap ImageJ, software yang telah menjadi standar acuan oleh komunitas ilmiah. Perbandingan tersebut dikuantifikasi menggunakan metrik mean absolute percentage error (MAPE) untuk mengukur deviasi. Hasil validasi menunjukkan akurasi yang tinggi, dengan nilai MAPE secara konsisten berkisar antara 0% hingga 0,828%. Analisis pada gambar dislokasi dan grain bahkan menunjukkan data identik dengan ImageJ. Studi ini membuktikan bahwa Python dapat berfungsi sebagai alternatif yang valid, andal, dan efisien. Keunggulan utamanya mencakup potensi otomatisasi, peningkatan objektivitas analisis, dan kapabilitas dalam memproses dataset berskala besar.

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Keywords: image processing; metalografi kuantitatif; otomatisasi; python; segmentasi
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