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All Journal JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) DEVICE
IQBAL GIFFARI RITONGA
UNIVERSITAS POTENSI UTAMA
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Mechanical Engineering

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ANALISIS ALGORITMA ADAPTIVE NEURO FUZZY INFERENCE SYSTEM PADA PENGENALAN POLA IKAN KOI MENGGUNAKAN RED, GREEN, BLUE, DAN HUE, SATURATION, VALUE IQBAL GIFFARI RITONGA; Rika Rosnelly; Pius Deski Manalu; Teresa Tamba; Kristine Wau
Device Vol 12 No 2 (2022): November
Publisher : Fakultas Teknik dan Ilmu Komputer (FASTIKOM) UNSIQ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/device.v12i2.3998

Abstract

ANFIS adalah algoritma yang menggabungkan sistem fuzzy dengan sistem jaringan syaraf tiruan. ANFIS dapat membuat nilai masukan menjadi keluaran berdasarkan nilai yang sudah dilatihkan dalam bentuk fuzzy. ANFIS dapat digunakan dalam klasifikasi jenis ikan koi dengan melatih nilai red, green, blue, serta hue, saturation, value, dan biner untuk menghapus nilai background citra ikan koi. Pada penelitian ini digunakan 3 jenis dari ikan koi yaitu kohaku, sanke, dan showa. Data latih pada algoritma ini menggunakan 10 citra ikan koi kohaku, 10 citra ikan koi sanke, dan 10 citra ikan koi showa serta 6 data uji yang diambil dari 2 data latih dari setiap jenis ikan koi tersebut. Hasil Akurasi dari data latih menghasilkan 100% dan hasil Akurasi dari data uji menghasilkan 100%.
Klasifikasi Citra Cuaca Menggunakan Inception-V3 dan K-Nearest Neighbors Iqbal Giffari Ritonga; Rika Rosnelly; Pius Deski Manalu; Teresa Tamba; Kristine Wau
JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Vol. 6 No. 2 (2023): Jutikomp Volume 6 Nomor 2 Oktober 2023
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jutikomp.v6i2.4052

Abstract

Weather imagery has a crucial role in various sectors, such as aviation, maritime and agriculture. Weather conditions have a big impact on activities in these fields and greatly influence operations. Classifying weather images can be done by analyzing weather image data, which can be used to predict the type of weather that may occur. The results of these weather predictions have significant value in daily decision making in these various sectors. One method for classifying weather images can be done by first extracting weather image features using Inception-V3 which is then calculated using the K-Nearest Neighbors method. This research uses 1748 weather images with 4 categories to carry out training which produces a model with Accuracy 91%, F1 91%, Recall 91%, Precision 91%, and uses 8 weather images with 4 categories to carry out testing which produces classifications with all correct values. every image.
Co-Authors Kristine Wau Pius Deski Manalu Rika Rosnelly Teresa Tamba