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All Journal Jurnal Pendidikan dan Teknologi Indonesia INOVTEK Polbeng - Seri Informatika
Saputra Edika, Nelson
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Computer Science & IT Education Engineering

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Perancangan UI/UX Pada Aplikasi Mobilindo Menggunakan Design Thinking Wijaya, Daniel; Saputra Edika, Nelson; Wilcent, Wilcent; Hendrawan, Malvin; Saputra, Adi; Pribadi, Muhammad Rizky
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 2 (2024): JPTI - Februari 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.395

Abstract

Sebuah studi yang mengadopsi pemikiran desain untuk mewujudkan antarmuka pengguna (UI) yang menarik secara visual dan memperkaya pengalaman pengguna (UX) untuk aplikasi pembelian mobil bertema "Mobilindo". Perjalanan kami melalui berbagai tahapan pemikiran desain membawa kami ke dalam jurang terdalam kebutuhan, keinginan, dan tantangan pengguna saat mereka mengarungi keruhnya proses pembelian mobil. Kami memulai dengan empati: wawancara dan observasi selama fase ini memberikan wawasan tentang calon pengguna dan konteks aplikasi mereka. Beralih ke definisi, kami memaparkan masalah yang ada serta kebutuhan pengguna; ini akan menjadi bintang utara kami selama upaya desain.
Classification of Tomato Fruit Ripeness Level Using Convolutional Neural Network–Support Vector Machine Based on Digital Image Saputra Edika, Nelson; Hartati, Ery
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: 10.35314/1r0wh197

Abstract

Tomato ripeness classification is an important task in post-harvest quality management, as the ripeness level directly influences taste, shelf life, and market value. Conventional ripeness assessment methods based on manual visual inspection are inherently subjective and often yield inconsistent results. To address this limitation, this study proposes an image-based tomato ripeness classification model using a hybrid Convolutional Neural Network–Support Vector Machine (CNN–SVM) approach. In the proposed model, a pretrained ResNet-50 architecture is employed as a fixed feature extractor to derive deep visual representations, while a Support Vector Machine with a Radial Basis Function kernel is utilized for final classification. The model is evaluated using a publicly available tomato image dataset, with the analysis limited to unripe and ripe categories. Image preprocessing procedures include resizing, normalization, and data augmentation, followed by an 80:20 train–test split strategy. Experimental results demonstrate that the proposed CNN–SVM model achieves strong and balanced performance, with an accuracy of 96.56%, a weighted precision of 96.80%, a recall of 96.56%, and an F1-score of 96.57%. These findings indicate that integrating deep feature extraction with an SVM classifier provides an effective and robust solution for tomato ripeness classification, particularly under limited data conditions.
Co-Authors Adi Saputra Daniel Wijaya Hartati, Ery Hendrawan, Malvin Muhammad Rizky Pribadi Wilcent, Wilcent