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All Journal Jurnal Penelitian Komunikasi dan Opini Publik Sisforma: Journal of Information Systems Jurnal Ilmiah KOMPUTASI Jurnal Dimensi Pendidikan dan Pembelajaran JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Tekno Kompak Jurnal Pengabdian Masyarakat Teknik Jurnal Teknik Informatika (JUTIF) Brilliance: Research of Artificial Intelligence Jurnal Algoritma
Eka Angga Laksana
Universitas Widyatama
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Journal : Jurnal Algoritma

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Pendekatan Transfer Learning dengan InceptionResNetV2 dan Augmentasi MixUp untuk Peningkatan Klasifikasi Tumor Otak Mahendra, Randa; Laksana, Eka Angga; Sukenda, Sukenda
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2194

Abstract

Diagnosis of brain tumors such as Glioma, Meningioma, and Pituitary using MRI still faces challenges, including reliance on manual interpretation, long evaluation times, and the potential for human error. To address these issues, deep learning-based approaches offer efficient and accurate solutions. This study aims to develop a brain tumor classification model based on deep learning using the InceptionResNetV2 architecture with MixUp augmentation to improve model accuracy and generalization. The model was trained on 7,023 MRI images (Glioma: 1,621; Meningioma: 1,645; Pituitary: 1,757; No-tumor: 2,000), with MixUp proven effective in reducing overfitting and handling data imbalance. The proposed model achieved a highest accuracy of 99.70%, surpassing other models such as CNN with Image Enhancement (97.84%) \[1], Xception (98.00%) \[2], EfficientNet (98.00%) \[3], and ResNet50 (98.47%) \[4]. Evaluation was conducted using metrics including precision, recall, F1-score, as well as MSE, RMSE, and MAE, showing strong performance. These results support the use of transfer learning for medical image classification with limited datasets. This research demonstrates clinical application potential, particularly in improving diagnostic accuracy, speeding up evaluation processes, and reducing human error. Future recommendations include using more diverse datasets, real-world evaluation, and integration into Clinical Decision Support Systems (CDSS).
Optimalisasi Parameter Support Vector Machine dengan Algoritma PSO untuk Tugas Klasifikasi Sentimen Ulasan IMDb Adrian, Mochammad Ilham; Laksana, Eka Angga
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2306

Abstract

In this study, the LinearSVC algorithm from the Support Vector Machine (SVM) method was used for sentiment analysis on IMDb review data. Feature extraction was carried out using the TF-IDF Vectorizer. The main challenge lay in determining the value of the hyperparameter, particularly the regularization parameter (C), which greatly influences the quality of the prediction results. To address this issue, the study employed the Particle Swarm Optimization (PSO) algorithm to find the optimal value of C. Experiments showed that without optimization, the SVM model achieved an accuracy of only 89.48%, but after applying PSO, the optimal C value of 0.1612 was found, which increased the model’s accuracy to 92.03% on the test data. Additionally, other evaluation metrics also showed significant improvements, with a Precision of 91.29%, Recall of 92.92%, and F1-Score of 92.10%. The significance of this improvement indicates that the PSO method consistently outperforms conventional approaches that rely on manual hyperparameter selection or grid search, which are often slower and less accurate in finding the optimal value. The results of this study demonstrate that hyperparameter optimization using PSO can significantly enhance SVM performance in sentiment classification. This approach is not only relevant for analyzing IMDb reviews but can also be applied to various other NLP tasks, such as public opinion analysis and product reviews, making it an efficient solution for improving text classification accuracy.
Co-Authors -, Sunjana Adrian, Mochammad Ilham Ardhian Ekawijana Ase Suryana Azizah Zakiah Benny Yustim Fahrezi, Marchel Maulana Heri Heryono Herlistiono, Iwa Ovyawan Madsu, Yosi Malatta Mahendra, Randa Maulana, Rafli Iqbal Nafysah, Mutiara Sukenda . Viddi Mardiansyah