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All Journal Jurnal Informatika Kaputama (JIK) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Haqqi, Matsnan
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Implementation Of Machine Learning To Identify Types Of Waste Using CNN Algorithm Haqqi, Matsnan; Rochmah, Lailatur; Safitri, Arisanti Dwi; Pratama, Rizki Adhi; Tarwoto
JURNAL FASILKOM Vol. 14 No. 3 (2024): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v14i3.8116

Abstract

Waste management remains a significant challenge globally, particularly in Indonesia, where the annual waste generation reached 24.67 million tonnes in 2021, with only 50.43% properly managed. To address the issue of mixed organic and inorganic waste and the lack of public awareness regarding waste separation, this study applied machine learning, specifically the Convolutional Neural Network (CNN) algorithm, to classify waste types. The research aimed to develop an effective automated waste classification model to improve waste management processes. The research involved collecting a dataset of 2,848 images representing six waste categories: glass, cardboard, paper, metal, organic, and plastic. Preprocessing techniques such as cropping, noise reduction with Gaussian filters, and data augmentation were applied to enhance data quality. The dataset was divided into training, validation, and testing subsets in a 70:20:10 ratio. The CNN model employed feature extraction through convolution, activation, and pooling layers, followed by classification using a fully connected layer and a softmax function. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved an overall accuracy of 95%, with an average precision, recall, and F1-score of 0.95 across all classes. These results demonstrate the CNN model’s ability to reliably classify waste types. Compared to previous studies, this research achieved higher accuracy through the use of enhanced preprocessing and CNN optimization. This study highlights the potential of CNN-based models for automated waste classification, contributing to sustainable waste management practices and fostering environmental awareness in the future research.
Evaluasi Kepuasan Pengguna Aplikasi E-Book-Ku Universitas Amikom Purwokerto Dengan Metode UTAUT Susilo, Deni Dwi; Pratama, Riski Adhi; Mubarok, Rifqi; Haqqi, Matsnan; Setiawan, Ito
Jurnal Informatika Kaputama (JIK) Vol 9 No 1 (2025): Volume 9, Nomor 1, Januari 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v9i1.923

Abstract

This research aims to analyze user experience on the level of user satisfaction of the application E-Book-Ku application at Amikom Purwokerto University, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Theory of Acceptance and Use of Technology (UTAUT) model. This research using a quantitative approach with data collection through a questionnaire distributed to students of Amikom Purwokerto University as users of the E-Book-Ku application. E-Book-Ku application. The variables studied consisted of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioral Influence (BC), and Behavioral Behavior (BC). (SI), Facilitating Conditions (FC), Behavioral Intention (BI), and Use Behavior (UB). Data were analyzed using validity test, reliability, and hypothesis testing to test the relationship between variables using SmartPLS. using SmartPLS. The results showed that all hypotheses accepted, where Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions have a positive and significant effect on Behavioral Intention. and significant on Behavioral Intention. In addition, Behavioral Intention is proven to have a positive influence on Use Behavior. This finding indicates that user satisfaction in using the E-Book-Ku application is influenced by ease of use. E-Book-Ku application is influenced by the ease of use of the application, social support from the surrounding environment, and the availability of facilitating conditions. environment, and the availability of conditions that facilitate the use of application. This research contributes to application managers to improve the aspects that influence user experience in order to increase the level of satisfaction and use of the application among students. Keywords: User Satisfaction, Application E-Book-Ku, UTAUT Method, SmartPLS.
Co-Authors Mubarok, Rifqi Pratama, Riski Adhi Pratama, Rizki Adhi Rochmah, Lailatur Safitri, Arisanti Dwi Setiawan, Ito Susilo, Deni Dwi Tarwoto