Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY INOVTEK Polbeng - Seri Informatika
Setiawan, Christofer Evan
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Implementasi TF-IDF dan Cosine Similarity untuk Penyaringan Dokumen Berita Program Makan Siang Gratis Pemerintah Indonesia Tanuwijaya, William; Setiawan, Christofer Evan; Irsyad, Hafiz; Rahman, Abdul
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.6724

Abstract

Penelitian ini menerapkan metode Information Retrieval (IR) dalam menyaring berita yang relevan terkait program makan siang gratis yang diselenggarakan oleh pemerintah Indonesia, sebuah program yang ditujukan untuk meningkatkan gizi pelajar dan mencegah terjadinya stunting, namun juga menampilkan data berita dari berbagai media nasional, preprocessing data (termasuk case folding, tokenisasi, stopword removal dan stemming), pembobotan kata menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF), serta menggunakan pengukuran tingkat relevansi menggunakan Cosine Similarity. Dataset terdiri dari lima berita dengan topik terkait, yang IR mampu menyaring dokumen secara efektif. Dari lima Berita, empat di antaranya terdeteksi relevan dan satu tidak relevan. Evaluasi model menghasilkan akurasi sebesar 80%, precision 100%, recall 80% dan f1-score 89%. Nilai-nilai ini menunjukkan bahwa sistem dapat mengidentifikasi relevansi konten Berita terhadap topik yang terutama dalam kasus judul Berita yang bersifat clickbait. Penelitian ini juga memberikan kontribusi terhadap pengembangan sistem penyaringan informasi yang lebih efisien dan akurat dalam konteks isu publik.
Application of EfficientNet Deep Learning with Wiener Filter for Freshwater Fish Disease Image Classification Setiawan, Christofer Evan; 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/k1xeb958

Abstract

Challenges pertaining to the timely and accurate diagnosis of diseases in freshwater fish have adversely impacted the productivity of the aquaculture industry. Image classification using deep learning techniques has the potential to overcome such challenges. However, this potential has not been realized due to such problems as image noise, motion blur, and small dataset sizes. Most prior studies in this area employ the same Convolutional Neural Network (CNN) architectures and, while using the same or similar techniques, are generic to the studies to preprocess the images. The focus of this study is to compare and benchmark the image classification performance of the EfficientNet architectures (B0 to B7) using the Wiener Filter as a preprocessing technique for the classification of diseases in freshwater fish. The experiments used a publicly available dataset of 1,750 images of seven diseases in fish while maintaining identical training parameters to yield sixteen different experimental configurations. Metrics such as accuracy, precision, recall, and F1-score were exercised while evaluating model performance. The data show that medium-scale architectures surpass both smaller and larger size variants. The optimal performance was achieved by EfficientNet-B4 and the Wiener Filter with an accuracy of 94.89%, a precision of 95.15%, a recall of 94.92%, and an F1-score of 94.89%. The results confirm that preprocessing with the Wiener filter improves performance on classification tasks using medium-sized models and further elucidate the applicable value of the model developed in this study in aquaculture and its related interventions.
Co-Authors Abdul Rahman Hafiz Irsyad Tanuwijaya, William Tinaliah, Tinaliah