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All Journal Jurnal Ilmiah Teknik (JUIT) Horizon: Indonesian Journal of Multidisciplinary
Ari Rosemalatriasari
Universitas Gunadarma
Agriculture, Biological Sciences & Forestry Humanities Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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IMPLEMENTASI METODE AGILE PADA PEMBUATAN E – REPORT LITTLE CLOUD BERBASIS WEB MENGGUNAKAN FRAMEWORK CODEIGNETER Waliya Rahmawanti; Ari Rosemalatriasari
Jurnal Ilmiah Teknik Vol. 2 No. 2 (2023): Mei : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v2i2.609

Abstract

This website-based E – Report Little Cloud is specifically for the kindergarten level first, with restrictions on several access rights depending on the user who will use it. The output provided from the website-based Little Cloud E-Report is in the form of a report card which can be downloaded independently by the student's guardian at any time. The purpose of making the website-based Little Cloud E-Report is expected to make it easier for teachers in the process of filling out report cards because everything is computerized properly so that it can minimize the time for filling out report cards. In addition, it makes it easier for parents and students to view report cards anywhere and anytime. The Little Cloud e-Report is created using the Agile method. The agile method is a conceptual framework with an iterative and incremental development approach [7]. The programming language used for the creation of the Little Cloud e-Report is PHP, for database management it uses MySql, and this application uses the CodeIgniter framework.
Implementation of Coffee Bean Roasting Level Classification System Using CNN and Knn Models with Web-Based Real-Time Camera Andini Sintawati; Ira Windarti; Ari Rosemalatriasari; Muhammad Alan Darma Saputra
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i3.6545

Abstract

Manual coffee bean roasting assessment is still susceptible to operator subjectivity, variations in lighting conditions, and inconsistencies in results between assessors. This study aims to develop a real-time web-based coffee bean roasting classification system by integrating Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN) models. The study uses a quantitative experimental approach with a dataset of coffee bean images collected independently and expanded to 6,470 images, which are grouped into five classes: GreenRoasting, LightRoasting, MediumRoasting, DarkRoasting, and Unknown. All images are processed through a pre-processing stage including resizing to 160 × 160 pixels, normalization, data augmentation, and splitting training and test data with a ratio of 80:20 in stages. MobileNetV2 is used as a feature extractor in CNN, while KNN with a value of k = 7 and a cosine distance metric is applied for feature vector classification. The final prediction was obtained using a weighted ensemble method with a composition of 60% CNN and 40% KNN, then implemented in a Flask-based web application with support for real-time image upload and camera. Test results showed the ensemble model achieved an accuracy of 85.67% with an average response time of 1,247 ms. This system has the potential to support faster, more consistent, and objective coffee roasting level assessments, especially for small to medium-scale coffee businesses.
Co-Authors Andini Sintawati Dhian Sweetania Ira Windarti Muhammad Alan Darma Saputra Puji Sularsih Rahmawanti, Waliya Remi Senjaya