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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Informatics and Enginering Dedication Buletin Ilmiah Informatika Teknologi
Laksana, Jovansa Putra
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Mechanical Engineering Other

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Analisis Topik Dominan Dalam Paper Ilmu Komputer Menggunakan TF-IDF Dan K-Means Laksana, Jovansa Putra; Shela, Shela; Irsyad, Hafiz; Rahman, Abdul
Buletin Ilmiah Informatika Teknologi Vol. 3 No. 3: Mei 2025
Publisher : AMIK STIEKOM SUMATERA UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58369/biit.v3i3.122

Abstract

The rapid growth of scientific publications in the field of computer science has created a need to understand the distribution and trends of emerging research topics. This study aims to identify and analyze dominant topics in computer science literature using a text mining approach based on Term Frequency–Inverse Document Frequency (TF-IDF) vectorization and the K-Means clustering algorithm. A total of 1,222 publication titles from Semantic Scholar (2020–2025) were processed through language normalization, text preprocessing, TF-IDF feature extraction, optimal cluster determination, and cluster quality evaluation using Silhouette Score and Davies-Bouldin Index (DBI). The results reveal that topics such as cybersecurity, artificial intelligence, and machine learning are the most prevalent. While some clusters show good internal cohesion, the overall evaluation yielded a Silhouette Score of 0.0585 and a DBI of 4.387, indicating overlapping topics and limited cluster separation. These findings suggest that although the TF-IDF and K-Means approach can highlight general topic trends, it has limitations in capturing semantic context. Future research is encouraged to explore more contextual representation and clustering techniques to improve topic analysis quality.
PENGENALAN TEKNOLOGI RFID DALAM SISTEM ABSENSI OTOMATIS BERBASIS KARTU FLAZZ DI SMA XAVERIUS 3 PALEMBANG Suparto, Adrian; Clement, Michael Joy; Laksana, Jovansa Putra; Pratama, Brilliant Chandra; Feliansyah, Fernando; Pribadi, Muhammad Rizky; Widiyanto, Eka Puji
FORDICATE Vol 4 No 3 (2025): November 2025
Publisher : Universitas Multi Data Palembang, Fakultas Ilmu Komputer dan Rekayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/fordicate.v4i3.11704

Abstract

Abstrak: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memperkenalkan konsep dan implementasi sistem absensi otomatis berbasis teknologi RFID di lingkungan sekolah menengah. Tujuan utama kegiatan adalah memberikan pemahaman praktis kepada siswa mengenai cara kerja sistem absensi tanpa kontak dan manfaatnya dalam meningkatkan efisiensi administrasi. Metode yang digunakan meliputi sosialisasi langsung, penyampaian materi visual, serta demonstrasi aplikasi prototipe yang dikembangkan menggunakan antarmuka berbasis web dan pemindai kartu RFID. Hasil kegiatan menunjukkan respons positif dari siswa terhadap penggunaan teknologi tersebut. Demonstrasi berhasil memperlihatkan proses pencatatan kehadiran secara otomatis menggunakan kartu RFID dan bagaimana data disimpan dalam basis data lokal. Meskipun sistem belum diadopsi oleh pihak sekolah, aplikasi ini menunjukkan potensi sebagai solusi digital yang dapat diimplementasikan di masa mendatang. Kegiatan ini memberikan manfaat edukatif sekaligus mendorong kesadaran akan pentingnya transformasi digital dalam tata kelola sekolah.
Feature-Level Fusion of DenseNet121 and EfficientNetV2 with XGBoost for Multi-Class Retinal Classification Laksana, Jovansa Putra; Yohannes
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15670

Abstract

Accurate and efficient classification of retinal fundus images plays a critical role in supporting the early diagnosis of ocular diseases. However, models relying on a single deep learning backbone often struggle to capture the multi-scale and heterogeneous characteristics of retinal lesions, leading to unstable performance across visually similar disease classes. To address this limitation, this study proposes a novelty feature-level fusion framework that integrates complementary representations from DenseNet121 and EfficientNetV2-s, followed by classification using XGBoost. The fusion pipeline extracts 1024-dimensional features from DenseNet121 and 1280-dimensional features from EfficientNetV2-s, which are concatenated into a unified 2304-dimensional feature vector. Experiments were conducted on a dataset of 10,247 retinal fundus images spanning six categories: Central Serous Chorioretinopathy, Diabetic Retinopathy, Macular Scar, Retinitis Pigmentosa, Retinal Detachment, and Healthy. The proposed fusion model achieved an accuracy of 91.60%, outperforming DenseNet121 XGBoost (91.31%) and EfficientNetV2-s XGBoost (89.70%). Moreover, the fusion strategy demonstrated improved class-level stability, particularly for visually similar retinal disorders where single-backbone models exhibited higher misclassification rates. This study contributes a lightweight yet effective multi-backbone feature-level fusion approach that enhances discriminative representation and classification stability without increasing model complexity. In addition, the use of XGBoost introduces a tree-based decision mechanism that is inherently more interpretable than conventional fully connected layers, offering potential advantages for clinical analysis. Overall, the results highlight the effectiveness of multi-backbone feature fusion as a reliable strategy for automated retinal disease classification.
Co-Authors Abdul Rahman Clement, Michael Joy Eka Puji Widiyanto Feliansyah, Fernando Hafiz Irsyad Muhammad Rizky Pribadi Pratama, Brilliant Chandra Shela, Shela Suparto, Adrian Yohannes