IDENTIFIKASI KEMATANGAN BUAH ALPUKAT (AVOCADO) MENGGUNAKAN ALGORITMA ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) - Nurfiyah Nurfiyah; Hendarman Lubis; Ratna Salkiawati (Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta, 2026)
Avocado (Persea americana) is a horticultural commodity with high economic value but is climacteric, so the ripening process occurs quickly after harvest. Determining the ripeness level is still done conventionally (visually and manually) often results in subjective assessments and damages the fruit. This study aims to develop a non-destructive avocado ripeness identification system (unripe, ripe, and overripe) using the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm. The input parameters used are based on the Red-Green-Blue (RGB) color features and Gray Level Co-occurrence Matrix (GLCM) texture features extracted from digital images of avocados. The test results show that the combination of the ANFIS network architecture with a Gaussian membership function is able to recognize the ripeness level of avocados with an accuracy of up to 93.3% on the test data. This system is expected to be a technological solution for farmers and distributors in the process of sorting fruit objectively and quickly.