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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia).
Arjuna Subject : -
Articles 3 Documents
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Search results for , issue "Vol. 9 No. 3 (2024): December, 2024" : 3 Documents clear
Car Price Prediction Using Artificial Neural Networks: A Data-Driven Approach Taiwo, Abass; Ogundele, Lukman; Ayo, Femi; Ejidokun, Adekunle
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 3 (2024): December, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Used cars suffer from depreciation and require reevaluation from time to time to ascertain the actual price at which the car can be purchased or sold by buyers and sellers. Car price prediction is important because of the increase in the rate of purchase of used car compared with that of new cars due to inflation, fluctuation in exchange rates, currency devaluation and so on. To address the issues of accuracy and error rate, this work suggests a hybrid feature selection approach that extracts the most crucial properties from the dataset. The most important attributes in the dataset were then used as input for the prediction phase using deep learning approach. The deep learning model's output is contrasted with that of other machine learning techniques to identify the most effective approach. In comparison to the Decision Tree and Support Vector Machine (SVM) models, which performed at 87.8% and 88.3%, respectively, the suggested hybrid feature selection using deep learning model attained an accuracy of 96.9%, according to the evaluation data. However, the other two classifiers indicate a lower error rate as compared to the ANN model.
UI/UX Redesign Using User-Centered Design (UCD) Method on Fatsecret Website Asep Pujiyono, Putra Wira Pratama Ramadhan; Muhamad Irsan
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 3 (2024): December, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.3.977

Abstract

Weight loss websites have become a primary source of information for individuals looking to change their diet and achieve a healthy weight by offering a variety of features, content, and UI/UX redesigns to help users achieve their health goals using User-Centered Design (UCD). This study aims to find out trends and characteristics using the Fatsecret Website. FatSecret is a website that has a food calorie counter feature used to calculate the number of calories eaten each day. This research analyzes the improvement of the website's quality in terms of User Interface (UI) and User Experience (UX) by redesigning it using the UCD method. This study aims to produce analysis and design of the UI and UX on the Fatsecret Website. The method used is User-Centered Design, divided into 4 stages. The results of the UI/UX design include solutions to problems found, such as adding a workout feature, redesigning the reminder feature, and placing it outside to make it easier to see. The addition of a workout feature makes it easier for users to lose weight by exercising and managing calories simultaneously. The main colors of the Fatsecret logo, white and green, inspired the prototype's color scheme.
Evaluating Non-Negative Matrix Factorization and Singular Value Decomposition for Skincare Recommendation Systems Ahmad Indra Nurfauzi; Agung Toto Wibowo
Indonesian Journal on Computing (Indo-JC) Vol. 9 No. 3 (2024): December, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.3.983

Abstract

Facial skincare plays a crucial role in maintaining clean, healthy, and radiant skin. Recommendation systems, such as Collaborative Filtering and Content-Based Filtering, can help users discover suitable skincare products based on their preferences and reviews. This study compares two Matrix Factorization techniques Non-Negative Matrix Factorization (NMF) and Singular Value Decomposition (SVD) to enhance the accuracy and relevance of skincare product recommendations. The results reveal that the SVD model outperforms NMF, achieving a Mean Absolute Error (MAE) of 0.7190, Root Mean Squared Error (RMSE) of 1.0104, Precision of 0.8054, Recall of 0.8144, and an F-1 score of 0.8099. In contrast, the NMF model produced an MAE of 0.7074, RMSE of 1.1052, Precision of 0.7865, Recall of 0.7987, and an F-1 score of 0.7926. These findings demonstrate that both models provide accurate recommendations, with SVD offering more precise and relevant predictions for skincare product recommendations.

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