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Shafirawati, Fitri
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Computer Science & IT

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Optimasi Algoritma Machine Learning Menggunakan Teknik Bagging Pada Klasifikasi Diagnosis Kanker Payudara Pramudita, Rully; Muis, Saludin; Safitri, Nadya; Shafirawati, Fitri
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1928

Abstract

Classification algorithms have a very important role in Machine Learning, but not all algorithms have the same performance in every case. Algorithm performance can be affected by the type of data used, differences in problem characteristics, and the parameters used. Additionally, ensemble learning techniques such as Bagging can affect algorithm performance. Therefore, the problem arises of how to choose the most suitable algorithm for a particular classification task and how to optimize the performance of the algorithm. This research aims to carry out a comparative analysis and optimization of classification algorithms in Machine Learning. Classification algorithms that will be evaluated include Support Vector Machine (SVM), Neural Network, Logistic Regression, Decision Tree, and K-Nearest Neighbors (K-NN). Evaluation of the performance of these algorithms will be carried out using the confusion matrix, Receiver Operating Characteristic (ROC) Curve, and Area Under Curva (AUC). The result of this research is a comparative analysis of the optimization of classification algorithms using the bagging technique. After carrying out the evaluation process using the confusion matrix and ROC curve, it was found that the algorithm optimization using the bagging technique only had an effect on the Decision Tree (DT) and K-Nearest Neighbors (KNN) algorithms. . The accuracy of the DT algorithm increased by 0.6% while the accuracy of KNN increased by 1.3%. The AUC value for the DT algorithm increased by 1.4% and the KNN algorithm increased by 0.3%.
Model Prediksi Kepadatan Pariwisata Jawa Barat Menggunakan Metode Long Short-Term Memory with Temporal Attention Nadya Safitri; Pramudita, Rully; Muis, Saludin; Shafirawati, Fitri; Anggoro, Muhammad Seno
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2086

Abstract

This study aims to apply the Long Short-Term Memory Networks (LSTM) with Temporal Attention method in predicting tourism density in West Java tourist destinations. The problem faced is the uncertainty in estimating tourist density at various locations and times, which makes the management of tourism resources and facilities difficult. Therefore, this study is important to provide a tool that can help make more effective decisions in the tourism sector in West Java. The urgency of this study lies in the need for accurate and real-time tourist density predictions to support the management and development of tourist destinations in West Java. With the right prediction model, related parties can regulate capacity, optimize services, and avoid negative impacts such as excess capacity and crowds that have the potential to endanger visitors and the environment. The purpose of this study is to develop a tourism density prediction model that combines the distinctive features of LSTM with a temporal attention mechanism. This model aims to provide accurate and dynamic tourist density estimates, taking into account the temporal patterns of tourist visits in West Java. The model evaluation methods used in this study are RMSE and MAE, and the results of the model testing are that it has an RMSE value of 32208867.139 and an MAE value of 5099.219, and it is hoped that there will be a dataset with a long period after the covid mass where the dataset is free from abnormal events so that a more appropriate model is obtained.
Co-Authors Anggoro, Muhammad Seno Muis, Saludin Nadya Safitri Nadya Safitri, Nadya Rully Pramudita