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All Journal Techno.Com: Jurnal Teknologi Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Pertiwi, Yuliana
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Computer Science & IT Economics, Econometrics & Finance Education Engineering Social Sciences

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Implementation of Extreme Learning Machine Based on HSV Color Features for Marine Animal Image Classification Hidayati, Dzil; Pertiwi, Yuliana; Ramadhanu, Agung
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13490

Abstract

Recognizing sea animals is a significant challenge in digital image recognition. This is due to the diverse visual characteristics of marine animals, including morphological shapes, body surface colors, and textures displayed in images. Environmental factors also influence image quality, such as underwater lighting conditions, water turbidity, and other external elements. To address these classification challenges, one proposed approach is the use of the Extreme Learning Machine (ELM) method, which can be implemented by utilizing HSV (Hue, Saturation, Value) color features as the main input. The HSV color space is chosen because it more closely resembles the way humans perceive colors. In this model, color is separated into three main components: hue represents the type of color, saturation indicates the intensity or purity of the color, and value refers to its brightness or darkness. The dataset consists of several classes of marine animals such as clams, squids, and shrimp, collected from high-resolution image datasets. Test results show that the ELM model can classify images with competitive accuracy, achieving up to 83% accuracy in a much shorter training time compared to traditional learning methods. This study demonstrates that combining HSV color features with the ELM algorithm can be an efficient approach for classifying marine animal images.   Keywords - Shell, Squid, Shrimp, ELM,HSV
PENERAPAN LOGIKA FUZZY MAMDANI DENGAN TIGA VARIABEL INPUT UNTUK PREDIKSI GRADE AKADEMIK SISWA SMA Hidayati, Dzil; Idris, Muhammad; Pertiwi, Yuliana
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4913

Abstract

Abstract: This research aims to design and implement a Fuzzy Inference System (FIS) using the Mamdani method as an efficient machine learning solution for predicting Estimated GPA (4.0 Scale) for Senior High School students. The model addresses the inherent uncertainty in academic assessment by utilizing three crucial input variables from the Students Performance Dataset (Kaggle): Weekly Study Time (x), Number of Absences (y), and Parental Support (z). The parameters for the membership functions (Trimf) and the Rule Base (27 rules) were determined through descriptive analysis of the secondary dataset and academic heuristic knowledge, ensuring the validity and objectivity of the design. The system integrates fuzzification, inference, and defuzzification (Centroid of Area) to yield a continuous GPA value (0.004.00). Simulation results demonstrate that the model is effective in mapping qualitative factors to precise quantitative outputs, providing a practical contribution as an efficient and reliable diagnostic tool for educational institutions.Keyword: Fuzzy Mamdani; GPA Prediction; Academic Grade PredictionAbstrak: Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Inferensi Fuzzy (FIS) Mamdani sebagai solusi machine learning untuk memprediksi Perkiraan GPA (Skala 4.0) siswa Sekolah Menengah Atas. Model ini mengatasi ketidakpastian dalam penilaian akademik dengan menggunakan tiga variabel input krusial dari Students Performance Dataset (Kaggle): Waktu Belajar Mingguan (x), Jumlah Absensi (y), dan Dukungan Orang Tua (z). Parameter fungsi keanggotaan (Trimf) dan Basis Aturan (27 aturan) didapatkan melalui analisis deskriptif terhadap dataset sekunder dan pengetahuan heuristik akademik, yang menegaskan validitas dan objektivitas perancangan. Sistem ini menggabungkan fuzzifikasi, inferensi, dan defuzzifikasi (Centroid of Area) untuk menghasilkan nilai GPA kontinu (0.00-4.00) yang lebih realistis. Hasil simulasi kasus menunjukkan bahwa model ini efektif dalam memetakan faktor kualitatif ke output kuantitatif yang presisi, memberikan kontribusi praktis sebagai alat diagnostik yang efisien dan dapat diandalkan bagi lembaga pendidikan.Kata kunci: Fuzzy Mamdani; Prediksi GPA; Prediksi Grade Akademik
Co-Authors Hidayati, Dzil Muhammad Idris Ramadhanu, Agung