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All Journal Jurnal Pseudocode Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Informatika: Jurnal Pengembangan IT
Vitri Tundjungsari
Esa Unggul University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Materials Science & Nanotechnology Physics

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Explainable Brain Tumor Classification Using EfficientNet-B2 and Grad-CAM on MRI Dataset　 Tino Saputra; Wahyu Purnama Magribi; Vitri Tundjungsari
Jurnal Penelitian Pendidikan IPA Vol 12 No 3 (2026)
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v12i3.14179

Abstract

Brain tumors are life-threatening central nervous system disorders requiring early and accurate diagnosis for effective clinical management. Although MRI is the standard modality for detection, manual interpretation remains prone to inconsistency, particularly for complex cases such as glioma. This study proposes an explainable deep learning framework integrating EfficientNet-B2 with a threshold-based two-stage classification scheme and Grad-CAM interpretability analysis. In the first stage, a one-versus-rest binary classifier with an optimized threshold (τ = 0.20) performs glioma detection; the second stage classifies remaining cases into meningioma, pituitary tumor, or normal. The dataset comprises 7,023 MRI images across four classes from a public Kaggle repository. Preprocessing includes CLAHE contrast enhancement, normalization, and augmentation. EfficientNet-B0 serves as the baseline. EfficientNet-B2 achieves 97.9% overall accuracy, outperforming the baseline (96.7%), with a glioma F1-score of 0.988 at the optimal threshold. Grad-CAM visualizations confirm the model focuses on anatomically relevant regions, enhancing transparency and clinical trustworthiness. The proposed framework demonstrates that combining architectural capacity, threshold-based inference, and explainability yields a reliable system for computer-aided brain tumor diagnosis.
Implementasi Content-Based Filtering Menggunakan TF-IDF dan Cosine Similarity untuk Rekomendasi Buku Akademik Mahasiswa Yunisa Salsabila Anggraeni; Septiana Dewi Saputri; Tania Azzahra; Aloysius Gonzaga Verrel; Vitri Tundjungsari
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10055

Abstract

The abundance of digital book collections in academic libraries creates information overload, making it difficult for students to find relevant references. This study aims to design and evaluate an academic book recommendation system based on Content-Based Filtering (CBF) using TF-IDF for text feature weighting and Cosine Similarity for measuring inter-content similarity. The dataset consists of 10 simulated academic book entries from Kaggle, covering title, author, category, and description attributes across Computers, Technology Engineering, and Mathematics domains. Methodological stages include text preprocessing, TF-IDF feature extraction, similarity matrix construction, and top-N recommendation selection. Evaluation was conducted through subjective satisfaction testing involving 10 student respondents and quantitative evaluation using Precision@K and Recall@K metrics. Satisfaction results showed recommendation relevance (87%), system speed (90%), ease of use (85%), and overall satisfaction (86%). Quantitative evaluation revealed limitations with Mean Precision@5 of 0.0921, Mean Recall@5 of 0.0772, Mean Precision@10 of 0.0815, and Mean Recall@10 of 0.1136, exhibiting a consistent precision-recall trade-off. The system is concluded to be functionally effective but semantically limited. Future development is recommended to integrate word embedding techniques such as Word2Vec or BERT alongside hybrid filtering to substantially improve system performance.
Analisis Prediksi Probabilitas Otomatisasi Pekerjaan Tahun 2030 Menggunakan Algoritma Linear Regression Dan Gradient Boosting Nicholas Leonardo; Ahmad Zidane Arrasyid; Muhammad Arif Billah Natagama; Hafidz Muhammad Dzaky; Vitri Tundjungsari
Jurnal Pseudocode Vol 13 No 1 (2026): Volume 13 Nomor 1 Februari 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.13.1.69-74

Abstract

The rapid development of artificial intelligence and automation is expected to significantly impact future employment. This study aims to predict job automation probability in 2030 using supervised learning methods. A public dataset containing job types, education levels, and automation probabilities was utilized. Linear Regression and XGBoost Regressor were employed to build and compare predictive models. The research process included data preprocessing, training–testing data split, model training, and performance evaluation using Root Mean Square Error (RMSE) and coefficient of determination (R²). Experimental results indicate that XGBoost outperforms Linear Regression by achieving lower RMSE and higher R² values. This study provides insights into automation risks and may support workforce skill development planning.
Co-Authors Ahmad Zidane Arrasyid Aloysius Gonzaga Verrel Hafidz Muhammad Dzaky Muhammad Arif Billah Natagama Nicholas Leonardo Septiana Dewi Saputri Tania Azzahra Tino Saputra Wahyu Purnama Magribi Yunisa Salsabila Anggraeni