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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah Sistem Informasi
Octavian, Octavian
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Computer Science & IT Engineering

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Enhancing Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification Octavian, Octavian; Badruzzaman, Ahmad; Muhammand Yusuf Ridho; Trisedya, Bayu Distiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5669

Abstract

Deep learning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant disease classification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble prediction technique using a weighted soft-voting method. Instead of assigning a generalized weight to each CNN model, our approach emphasizes giving weights to each label's prediction within every individual model. We employed three respected CNN architectures for our experiments: DenseNet201, InceptionV3, and Xception focus on classifying various diseases that affect grapes. By harnessing transfer learning coupled with end-to-end fine-tuning, we achieved a streamlined and efficient training process. In particular, the f1-score for each grape disease class was used as a parameter for weight determination and as a metric for the final evaluation. In our study, the newly proposed method was tested across various datasets and ensemble scenarios, demonstrating its effectiveness by not only outperforming the conventional soft-voting and prevalent weighted soft-voting methods, which achieved best scores of 95.68% and 95.81% respectively, but also by achieving a remarkable accuracy of 96.56%. The efficacy of this method is enhanced when the ensemble models exhibit distinct characteristics; the more varied the model characteristics, the more enhanced the ensemble results.
Analisis Penggunaan Fitur Video Interaktif pada Aplikasi Cookpad terhadap UMKM Kuliner Menggunakan TAM dan SUS Malolo, Andi Muh. Ibnu Hibban Bagoes; Sampetoding, Eliyah Acantha Manapa; Octavian, Octavian; Gomantara, Jeriko
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 3 (2025): November: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/k7ta4t87

Abstract

Cookpad sebagai platform resep digital populer menghadirkan fitur video interaktif yang memudahkan pengguna memahami proses memasak secara visual. Fitur ini berpotensi mendukung UMKM kuliner dalam meningkatkan keterampilan dan daya saing. Penelitian ini bertujuan menganalisis penerimaan teknologi dan tingkat usability fitur tersebut menggunakan Technology Acceptance Model (TAM) dan System Usability Scale (SUS). Metode penelitian yang digunakan adalah survei kuantitatif dengan responden 195 pelaku UMKM kuliner di Kota Makassar. Instrumen penelitian berupa kuesioner TAM (9 indikator) dan SUS (10 item), kemudian dianalisis menggunakan SPSS melalui uji validitas, reliabilitas, dan analisis deskriptif. Hasil penelitian menunjukkan bahwa semua item instrumen valid dan reliabel. Analisis TAM mengungkapkan bahwa perceived usefulness dan perceived ease of use dinilai tinggi, sedangkan skor SUS berada pada kategori acceptable, yang menandakan usability baik. Temuan ini menunjukkan bahwa fitur video interaktif Cookpad diterima secara positif oleh pelaku UMKM. Penelitian ini memberikan kontribusi akademis mengenai evaluasi teknologi pembelajaran visual, sekaligus implikasi praktis bagi pengembang aplikasi dan UMKM kuliner dalam penguatan daya saing berbasis digital.
Co-Authors Badruzzaman, Ahmad Eliyah Acantha Manapa Sampetoding Gomantara, Jeriko Malolo, Andi Muh. Ibnu Hibban Bagoes Muhammand Yusuf Ridho Trisedya, Bayu Distiawan