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All Journal Syntax: Journal of Software Engineering, Computer Science and Information Technology INOVTEK Polbeng - Seri Informatika
Edison, Nicholas
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Classification of Diabetic Retinopathy Using ShuffleNet V2 and Real-ESRGAN with CLAHE Image Enhancement Edison, Nicholas; Tinaliah
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/g1xj7p28

Abstract

Diabetic retinopathy (DR) is a microvascular complication of diabetes that can lead to blindness if not detected and treated early. Manual DR grading from fundus images is time-consuming and highly dependent on expert availability, motivating the need for automated and efficient decision-support systems. This study proposes a lightweight DR severity classification model using ShuffleNet V2 combined with a preprocessing pipeline consisting of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Real-ESRGAN-based super-resolution. Unlike prior works that mainly employ these enhancement techniques with deeper or computationally expensive networks, this study explicitly investigates their synergistic integration with ShuffleNet V2 to improve lesion visibility while preserving computational efficiency for resource-constrained environments. Experiments conducted on the APTOS 2019 dataset demonstrate that the proposed combination significantly improves classification performance, achieving a best accuracy of 90.70%, with balanced precision, recall, and F1-score when optimized using Adam. Comparative analysis with SGD optimizer further reveals a trade-off between accuracy and inference speed. The results confirm that combining CLAHE and Real-ESRGAN with ShuffleNet V2 offers an effective and efficient solution for automated diabetic retinopathy grading, highlighting its suitability for large-scale screening and low-resource clinical deployment
KEYWORD EXTRACTION KOMENTAR TERHADAP KONFLIK INDIA-PAKISTAN PADA PLATFORM YOUTUBE MENGGUNAKAN TF-IDF DAN COSINE SIMILARITY Edison, Nicholas; Fernando, Kristian; Irsyad, Hafiz; Rahman, Abdul
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 6, No 2 (2025): Desember 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v6i2.6638

Abstract

Konflik antara India dan Pakistan merupakan isu geopolitik yang sering menjadi perhatian global dan menimbulkan diskusi luas di media sosial, termasuk platform YouTube. Penelitian ini bertujuan untuk mengekstraksi kata kunci dari komentar-komentar pengguna YouTube mengenai topik konflik India-Pakistan, serta menganalisis kemiripan makna seluruh komentar antar video menggunakan metode TF-IDF dan Cosine Similarity. Data diperoleh dari kolom komentar tiga video YouTube yang relevan dan diproses melalui tahapan pra-pemrosesan teks, perhitungan bobot kata menggunakan TF-IDF, serta pengukuran similaritas menggunakan Cosine Similarity. Hasil ekstraksi kata kunci menggunakan TF-IDF menunjukkan terdapat 20 kata kunci dengan frekuensi tertinggi, dengan 3 kata kunci tertinggi adalah “india”, “pakistan” dan “perang”. Hasil perhitungan Cosine Similarity menunjukkan bahwa tingkat kemiripan antar komentar video berkisar antara 0,544 hingga 0,695, dimana nilai similarity tertinggi terdapat pada perbandingan Video 1 dan Video 3 (0,695), Video 1 dan Video 2 (0,653), sementara Video 2 dan Video 3 (0,544). Hasil ini menunjukkan bahwa kombinasi metode ini efektif dalam mengidentifikasi topik dominan serta hubungan semantik antar komentar. Visualisasi kata kunci dengan WordCloud juga memperjelas representasi opini publik yang berkembang. Penelitian ini memberikan kontribusi dalam pemetaan diskursus digital secara kuantitatif dan efisien.
Co-Authors Abdul Rahman Fernando, Kristian Hafiz Irsyad Tinaliah, Tinaliah