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All Journal (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Putu Fajar Tapa Mahendra
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Computer Science & IT Engineering

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Klusterisasi Fitur Tanya Dokter Pada Website Alodokter Menggunakan Metode Latent Dirichlet Allocation I Putu Fajar Tapa Mahendra; I Gusti Ngurah Anom Cahyadi Putra
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JLK.2024.v12.i04.p14

Abstract

Digital advancements have change information-seeking behaviors, particularly in health inquiries for the people. The Alodokter website's "Tanya Dokter" feature facilitates an easy connections with medical experts to ask question regarding health. The posed questions tend to mirror evolving health trends and public misunderstandings regarding health issues. Manual analysis of data in “Tanya Dokter” features proves challenging, prompting the use of Latent Dirichlet Allocation (LDA) topic modeling. This research categorizes Alodokter topics, unveiling common health issues. The optimal model reveals 8 clusters with diverse topic distributions. Validation metrics using coherence score with 0.258481 as the highest value affirm the model's efficacy. Optimal outcomes stem from combination of parameter such as 8 topics, alpha 0.02, and beta 0.02. This study may offers Alodokter and healthcare providers an informed perspective on an accessible approach to categorize health questions effectively using Topic Modelling Latent Dirichlet Allocation.
Klasifikasi Kualitas Buah dengan Menggunakan Convolutional Neural Network (CNN) Studi Kasus Dataset Fresh and Rotten Classification I Gede Diva Dwijayana; I Putu Fajar Tapa Mahendra; Ivan Luis Simarmata; Gst. Ayu Vida Mastrika Giri
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 2 (2024): JNATIA Vol. 2, No. 2, Februari 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i02.p24

Abstract

This research aims to develop a deep learning model for fruit quality classification using Convolutional Neural Network (CNN) with the Fresh and Rotten Classification dataset. Two CNN models are compared, with the first model serving as the baseline and the second model resulting from parameter tuning based on the first model. The results indicate that increasing the number of epochs improves the model accuracy, as evidenced by the first model achieving 91% accuracy with 10 epochs and 93% accuracy with 15 epochs. Similar patterns are observed in the second model, with 87% accuracy at 10 epochs and 90% accuracy at 15 epochs. Despite the second model involving the addition of layers and parameters, its accuracy tends to be lower compared to the first model. The research emphasizes that increasing the number of epochs enhances model performance, while adding layers does not always lead to significant improvements, depending on the model's complexity and dataset characteristics. The first model, trained with 15 epochs, demonstrates the highest accuracy, approaching results from similar previous studies. This evaluation provides valuable insights for developing a CNN-based fruit classification model on the Fresh and Rotten Classification dataset. 
Co-Authors Gst. Ayu Vida Mastrika Giri I Gede Diva Dwijayana I Gusti Ngurah Anom Cahyadi Putra Ivan Luis Simarmata