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All Journal Journal of Applied Data Sciences
Tri Basuki Kurniawan
Magister Program, Universitas Bina Darma, Palembang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Convolutional Neural Network Based Deep Learning Model for Accurate Classification of Durian Types Diana Diana; Tri Basuki Kurniawan; Deshinta Arrova Dewi; Mashal Kasem Alqudah; Musab Kasim Alqudah; Mohd Zaki Zakari; Eyna Fahera Binti Eddie Fuad
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.480

Abstract

Durian recognition is significant among fans of the durian community since many people tend to get confused, especially if they are not familiar with durian species, which can lead them to be involved in durian fraud. The development of this prototype can detect and classify durian fruits into three categories, including Musang King, Black Thorn, and D24, which can significantly benefit consumers. The prototype in this research involves training using a dataset of durian images, specifically in Musang King, Black Thorn, and D24 varieties. Preprocessing techniques such as resizing and scaling data are applied to enhance the quality and consistency of the dataset. The models chosen to develop this prototype include VGG-16 and Xception, and each model is compared according to its accuracy percentage. The accuracy outcomes of VGG-16 and Xception models are 56.64% and 92%, respectively. The models used a total of 1,372 images of durian with three classifications. Based on the findings, further enhancement of the CNN models for durian classification can be done by implementing different architectures, techniques, and methods. Moreover, future models can consider real-time image capture and processing capabilities to enhance the practicality of the system for durian consumers. The prototype developed in this study demonstrates the feasibility of using deep learning techniques for accurate and efficient durian classification, paving the way for future advancements in automated fruit grading and quality control systems in the durian industry.
Data Science Approaches to Analyzing Aesthetic Strategies in Contemporary Presidential Campaigns Isnawijaya Isnawijaya; Siti Rahayu Pratami Lexianingrum; Dwi Muhammad Taqwa; Deshinta Arrova Dewi; Tri Basuki Kurniawan
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.609

Abstract

In today’s digital political landscape, social media platforms play a critical role in shaping voter engagement, especially among youth. This study investigates how aesthetic political strategies were applied in Prabowo Subianto’s 2024 presidential campaign on TikTok and Instagram. It focuses on decoding voter sentiment, optimizing content delivery, and identifying visual elements that resonate with the public. Using machine learning models tailored to various data types, the research analyses over 50,000 comments and 30 million engagements. A BERT-based sentiment analysis model achieved 88% accuracy, revealing 60% positive, 25% neutral, and 15% negative sentiment, reflecting broad public approval. Meanwhile, a Gradient Boosting engagement prediction model reached 85% accuracy in forecasting post performance based on content format, timing, and hashtag use. Posts with videos and trending hashtags had a 78% chance of high engagement, while static images without hashtags scored only 45%. Evening posts performed best, with a 25% higher likelihood of engagement. The findings highlight the value of AI-driven insights in political communication, emphasizing that emotionally and visually rich content—particularly patriotic and relatable themes—enhances audience connection. This study offers a practical framework for political actors to develop adaptive, data-informed strategies that align with voter preferences in an increasingly fragmented and fast-paced digital media environment.
Co-Authors Deshinta Arrova Dewi Deshinta Arrova Dewi Diana Diana DWI MUHAMMAD TAQWA Eyna Fahera Binti Eddie Fuad Isnawijaya Isnawijaya Mashal Kasem Alqudah Mohd Zaki Zakari Musab Kasim Alqudah Siti Rahayu Pratami Lexianingrum