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All Journal Jurnal Pengolahan Hasil Perikanan Indonesia Jurnal Sains dan Teknologi
Nurjannah, Farah
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Klasifikasi Jenis Kendaraan (Helikopter, Mobil, Motor) Menggunakan Metode K-Means Clustering pada Pengolahan Citra Nurjannah, Farah; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3631

Abstract

Digital image-based vehicle type classification still faces obstacles because the identification process is generally done manually, so it takes a long time and has the potential to result in object recognition errors. This condition indicates the need for an image processing-based automation system that is able to recognize vehicle types accurately and efficiently. This study aims to develop a vehicle image classification system (helicopters, cars, and motorcycles) using the K-Means Clustering method to improve identification accuracy based on visual characteristics. This study was conducted with a quantitative approach through four main stages, namely image preprocessing (RGB to LAB conversion and size normalization), segmentation using the K-Means Clustering algorithm, extraction of shape features (metric, eccentricity) and texture (contrast, correlation, energy, homogeneity) based on Gray Level Co-occurrence Matrix (GLCM), and evaluation of accuracy using a confusion matrix. The research dataset consists of 30 vehicle images divided equally for each class. The results show that the combination of the K-Means Clustering method and GLCM feature extraction is able to classify three types of vehicles with an accuracy level reaching 100%. These findings prove that the K-Means method is effective for vehicle image recognition automation, and can be used as a basis for developing artificial intelligence-based visual identification systems in the future.
Profil asam lemak minyak dari jeroan ikan nila dan mas dengan rasio pelarut yang berbeda: Fatty acid profiles of fish oil from tilapia and carp with different solvent ratio Suseno, Sugeng Heri; Pari, Rizfi Fariz; Ibrahim, Bustami; Ramadhan, Rizki Tri Kurnia; Listiana, Desi; Nurjannah, Farah; Adha, As Syaffa Amalia
Jurnal Pengolahan Hasil Perikanan Indonesia Vol. 26 No. 3 (2023): Jurnal Pengolahan Hasil Perikanan Indonesia 26 (3)
Publisher : Department of Aquatic Product Technology IPB University in collaboration with Masyarakat Pengolahan Hasil Perikanan Indonesia (MPHPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17844/jphpi.v26i3.45781

Abstract

Extraction of fish oil from the by-products of tilapia and carp can be achieved using an appropriate extraction process. The purpose of this study was to determine the proportion of acetone and fish oil solvents in relation to the oxidation parameters, iodine number, and concentration profile of omega-3 fatty acids present in tilapia and goldfish oil. This study utilized a completely randomized design, in which the treatment ratios of acetone and oil solvent were varied as 3:1, 4:1, 5:1, 6:1, and 7:1 (v/v). The following parameters were examined: the chemical composition, free amino acids, acid value, peroxide value, p-anisidine, total oxidation, iodine value, and fatty acid profile.The omega-3 levels in tilapia and carp were 1.20% and 1.53%, respectively. An Iodine Number Test was employed to identify the most effective solvent treatment, revealing a 5:1 (v/v) ratio as the optimal solution. The increase in the value of omega-3 in tilapia oil amounted to 1.76%, whereas that in carp oil increased from 0.78% to 2.96%, resulting in an average increase of 2.31%.
Co-Authors Adha, As Syaffa Amalia Bustami Ibrahim Fariz Pari, Rizfi Listiana, Desi Ramadhan, Rizki Tri Kurnia Ramadhanu, Agung Sugeng Heri Suseno