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All Journal JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) DIGITAL BUSINESS INSIGHTS JOURNAL
Fanisha Juliananda Putri
Universitas Islam Indragiri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

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SISTEM KASIR PENYIMPANAN DATA TRANSAKSI DAN BARANG MASUK PADA TOKO RZ KIDS TEMBILAHAN BERBASIS CLOUD Zulrahmadi; Fanisha Juliananda Putri; Cici Nurmalayuni
Digital Business Insights Journal Vol 1 No 2 (2025): DIGITAL BUSINESS INSIGHTS JOURNAL
Publisher : Fakultas Ekonomi dan Bisnis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/bidi.v1i2.4454

Abstract

This study aims to design and build a web-based cashier application equipped with transaction data storage features to facilitate the sales process and data management at the RZ Kids Store. The system development method used is waterfall, starting from the needs analysis stage, design, implementation, to testing. This application is built using the Python programming language with the Flask framework, and uses SQLite as a data storage medium. The results of the test show that the application can run well, record every sales transaction automatically, and produce daily and monthly sales reports that are neatly stored. With this application, the transaction process becomes faster, more efficient, and has minimal errors, and helps shop owners in making data-based decisions.
Optimization of a Hybrid CNN-SVM Model for Pineapple Ripeness Classification Fanisha Juliananda Putri; Muh. Rasyid Ridha; Fitri Yunita
JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Vol. 11 No. 2 (2026): JUSTINDO
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/justindo.v11i2.5890

Abstract

Pineapple ripeness classification plays an important role in maintaining fruit quality and supporting the sorting process. Conventional ripeness assessment is commonly performed manually, which may lead to subjectivity and inconsistent results. This study aims to develop a Hybrid Convolutional Neural Network (CNN) and Support Vector Machine (SVM) model for classifying pineapple ripeness levels based on digital images. The dataset consisted of 3,745 pineapple images categorized into five ripeness classes: unripe, under-ripe, half-ripe, ripe, and rotten. Before model training, the images underwent preprocessing and data augmentation using rotation, horizontal flip, zoom out, brightness adjustment, and darkness adjustment techniques to increase dataset diversity. CNN was employed as a feature extractor to capture the visual characteristics of pineapples, while SVM was used as the classifier to determine the ripeness class. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results showed that the proposed Hybrid CNN-SVM model achieved an accuracy of 92%, outperforming CNN (87%), K-Nearest Neighbor (86%), Random Forest (84%), and SVM (71%). These findings indicate that the combination of CNN and SVM effectively improves pineapple ripeness classification performance. The proposed model has the potential to be implemented as an automated, fast, and objective fruit-sorting support system.
Co-Authors Cici Nurmalayuni Fitri Yunita Muh. Rasyid Ridha Zulrahmadi