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Damar Pratama Ristadias Hariyanto
Universitas Mercu Buana
Computer Science & IT Engineering

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Implementation of the ResepKita Application as an Innovative Platform for Creating and Sharing Food and Beverage Recipes Damar Pratama Ristadias Hariyanto; Ahmad Yani; Arya Restu Pratama; Syamil Syamil; Yaya Sudarya
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.31252

Abstract

This research focuses on developing a web-based application designed to improve the management of food and beverage recipes in canteens. The application is built using PHP for the backend, MySQL for database management, and Bootstrap for a responsive user interface. The system helps canteen managers efficiently manage recipes, track inventory, and analyze menu preferences to optimize resource usage. Key features of the application include the ability to search for recipes based on ingredients, generate reports on popular and less popular menu items, and provide recommendations for stock management. The application was evaluated by testing its functionality with real-world data from canteen operations, showing its effectiveness in increasing operational efficiency and customer satisfaction. Future development will focus on integrating real-time inventory monitoring and expanding the application to support mobile devices for enhanced accessibility.
Using Tensorflow for Clean and Messy Room Image Classification with Python Gilas Adi Saputra; Damar Pratama Ristadias Hariyanto
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27274

Abstract

Image classification is a fundamental computer vision task that can support automated visual monitoring in domestic, educational, and workplace environments. This study develops a transparent baseline pipeline using TensorFlow 2.x, Keras, and Python to distinguish clean and messy room images. The dataset contains 192 training images, with 96 images in each class, and 20 validation images, with 10 images in each class. All images are resized to 150 x 150 pixels and normalized to a 0-1 range. Rotation, horizontal flipping, and shearing are applied only to the training data, while validation images are normalized without random transformation. The sequential convolutional neural network contains four convolution-pooling blocks, a fully connected layer, and a sigmoid output for binary classification. Qualitative testing with two external images produced labels that were consistent with visual observation: the cluttered room was classified as messy and the organized room as clean. These demonstrations confirm that the pipeline operates from image input to class prediction, but they do not establish broad generalization or perfect accuracy. The main contribution is a reproducible small-data workflow that documents dataset distribution, preprocessing, augmentation, model parameters, validation procedures, and prediction thresholds. The study is limited by the small validation set, the absence of a large independent test set, and the lack of direct comparison with pretrained models. Future studies should evaluate transfer learning, larger datasets, repeated trials, and metrics such as precision, recall, F1-score, and confusion matrices.
Co-Authors Ahmad Yani Arya Restu Pratama Gilas Adi Saputra Syamil Syamil Yaya Sudarya