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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JURNAL PENGABDIAN KEPADA MASYARAKAT Information System for Educators and Professionals : Journal of Information System Jurnal Mahasiswa Bina Insani SMARTICS Journal Jurnal Pengabdian Pada Masyarakat Jurnal ICT : Information Communication & Technology JATI (Jurnal Mahasiswa Teknik Informatika) Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Media Jurnal Informatika Jurnal Buana Pengabdian INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat)
Rully Pramudita
Universitas Bina Insani
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Tematik : Jurnal Teknologi Informasi Komunikasi

 1 
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.
Studi Komparatif Algoritma Machine Learning dengan Teknik Bagging dan AdaBoost pada Klasifikasi Kanker Payudara Pramudita, Rully; Safitri, Nadya; Nazah, Vina Zahrotun
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2435

Abstract

This study aims to evaluate and compare the performance of five Machine Learning classification algorithms—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbors (KNN)—in detecting breast cancer using the Breast Cancer Wisconsin (Diagnostic) dataset. The evaluation was conducted in stages, beginning with baseline model training, followed by optimization using Bagging ensemble techniques, and further enhanced with the Adaboost algorithm. Model performance was assessed using accuracy, confusion matrix, ROC curve, and Area Under Curve (AUC) metrics. The results show that Logistic Regression demonstrated the most consistent performance, achieving 97.1% accuracy and the highest AUC of 99.7% after Adaboost was applied. Decision Tree also showed noticeable improvement in both accuracy and AUC. In contrast, Neural Network and KNN models were found incompatible with Adaboost. These findings highlight that the effectiveness of ensemble techniques is highly dependent on the nature of the base algorithm. This research contributes to a better understanding of how to select and combine classification algorithms with appropriate optimization strategies to improve the accuracy of breast cancer diagnosis.  
Co-Authors Anggoro, Muhammad Seno Ari Nurul Alfian Ayu Medina Prameswari Chusyairi, Ahmad Didik Setiyadi Endang Retnoningsih Febriyanti Valentina Herwin Simanjuntak Ismail Marzuki Kevin Setyawan Kristiana Widiawati Muhammad Dhedy Dwi Putra Muhammad Rizal Muis, Saludin Nada Aura Wansa Nadya Safitri Nadya Safitri Nadya Safitri Nadya Safitri, Nadya Nazah, Vina Zahrotun Pela, Maria Febrianti Pramana Saoni Aprilian Restu Amalia Novita Rita Wahyuni Arifin Saludin Muis Saludin Saludin Samhana Suryati Subari Shafirawati, Fitri Shilka Dina Anwariya Solikin Solikin Wira Hadi Kusuma, Mochamad Wulan Nabilla, Dwi