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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF SCIENCE AND SOCIAL RESEARCH
Tri Aristi Saputri
Universitas Dharma Wacana
Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Classification of Paddy as Visual Anomaly in Rice Piles Using MobileNetV2-Based Convolutional Neural Network Barokah Saadah; Tri Aristi Saputri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 3 (2026): Article Research July 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i3.16129

Abstract

Rice is a strategic food commodity whose quality is assessed based on the visual appearance of the grains, including the presence of unhusked rice as an undesirable element in piles of milled rice. Manual inspection is subjective, time-consuming, and prone to errors, necessitating a more objective automated approach. To address this issue, this study applies a MobileNetV2-based Convolutional Neural Network with transfer learning to classify unhusked grains as visual anomalies in rice piles into two classes: normal rice and anomalous grains. In terms of methodology, the dataset consists of 1,000 self-acquired images stratified into three groups with a 70:15:15 ratio. Image preprocessing was performed via background removal using the rembg library and random background simulation with five background color variations. Training was conducted in two phases: Phase 1 (transfer learning with a frozen base model) and Phase 2 (fine-tuning by opening the last 30 layers of the base model). The evaluation results on the test data showed an accuracy of 90.67%, a macro precision of 0.9213, a macro recall of 0.9067, and a macro F1-score of 0.9058. The false positive rate across all tests was 0. Phase 1 was selected as the best model because it produced more stable performance compared to Phase 2. Grad-CAM visualizations confirmed that the model focuses its attention on the visual features of the objects, not background patterns. These findings demonstrate that a combination of preprocessing, transfer learning, and data augmentation is effective for binary image classification when dealing with limited datasets.
PERANCANGAN VIDEO MOTION GRAPHIC PENCEGAHAN KEBOCORAN DATA PRIBADI DI MEDIA SOSIAL Usep Saprudin; Eka Gustinasari; Tri Aristi Saputri; Seiji Lian Wibowo; Ristiana Dewi
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.7075

Abstract

The rapid advancement of information and communication technology has significantly increased social media engagement among the public. However, high social media activity is accompanied by growing cyber risks, particularly personal data leaks caused by a lack of user awareness regarding sensitive information disclosure. This study aims to design a motion graphic video as an engaging visual educational medium to enhance public awareness regarding personal data protection and prevention strategies on social media. The Multimedia Development Life Cycle (MDLC) framework was adopted, comprising six phases: concept, design, material collecting, assembly, testing, and distribution. Visual animation was crafted using Alight Motion for 2D flat-design elements, combined with CapCut for audio integration and final rendering. The output is an 84-second motion graphic video (12 scenes) covering basic personal data definitions, cyber threats (phishing, malware, social engineering), risk impacts, and practical security measures such as strong passwords and Two-Factor Authentication (2FA). Testing results demonstrate that the motion graphic serves as an effective, highly accessible, and visually appealing educational tool for improving digital literacy and cyber safety practices among social media users.
Co-Authors Barokah Saadah Eka Gustinasari Ristiana Dewi Seiji Lian Wibowo Usep Saprudin