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All Journal Jurnal Penelitian Fisika dan Aplikasinya (JPFA) TELKOMNIKA (Telecommunication Computing Electronics and Control) Abdimas Lembaran Ilmu Kependidikan Scientific Journal of Informatics Jurnal Kajian Informasi & Perpustakaan JURNAL PROFESI KEGURUAN Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Journal of Innovation in Educational and Cultural Research Journal of Soft Computing Exploration International Journal of Engineering, Science and Information Technology Journal of Advances in Information Systems and Technology Proceeding of International Conference on Science, Education, and Technology Recursive Journal of Informatics Scientific Journal of Informatics Jurnal Abdi Negeri Recursive Journal of Informatics
Aji Purwinarko, Aji
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Arts Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Scientific Journal of Informatics

 1 
Integrating C4.5 and K-Nearest Neighbor Imputation with Relief Feature Selection for Enhancing Breast Cancer Diagnosis Purwinarko, Aji; Budiman, Kholiq; Widiyatmoko, Arif; Sasi, Fitri Arum; Hardyanto, Wahyu
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.21673

Abstract

Purpose: Breast cancer remains a significant cause of mortality among women, requiring accurate diagnostic methods. Traditional classification models often face accuracy challenges due to missing values and irrelevant features. This investigation advances the classification of breast cancer through the amalgamation of the C4.5 algorithm with K-Nearest Neighbor (KNN) imputation and Relief feature selection methodologies, thereby augmenting data integrity and enhancing classification efficacy. Methods: The Wisconsin Breast Cancer Database (WBCD) was the core reference for evaluating the proposed methodology. KNN imputation addressed missing values, while Relief selected the most relevant features. The C4.5 algorithm executed training by utilizing data segregations in the corresponding proportions of 70:30, 80:20, and 90:10, with its efficiency gauged through a range of metrics, particularly accuracy, precision, recall, and F1-score. Result: This innovative methodology achieved the highest classification accuracy of 98.57%, surpassing several existing models. Particularly noteworthy, the strategy being analyzed exhibited remarkable success relative to PSO-C4.5 (96.49%), EBL-RBFNN (98.40%), Gaussian Naïve Bayes (97.50%), and t-SNE (98.20%), demonstrating associated advancements of 2.08%, 0.17%, 1.07%, and 0.37%. These results confirm its effectiveness in handling missing values and selecting relevant features. Novelty: Unlike prior studies that addressed missing values and feature selection separately, this research integrates both techniques, enhancing classification accuracy and computational efficiency. The findings suggest that this approach provides a reliable breast cancer diagnosis method. Future work could explore deep learning integration and validation on larger datasets to improve generalizability.
Improving Sentiment Analysis with a Context-Aware RoBERTa–BiLSTM and Word2Vec Branch Hardyanto, Wahyu; Aryani, Nila Prasetya; Andestian, Defin; Sugiyanto; Setyaningrum, Wahyu; Mardiansyah, M Fadil; Islam, Muhamad Anbiya Nur; Purwinarko, Aji
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.35918

Abstract

Purpose: We improve the accuracy of Twitter/X sentiment analysis with a hybrid model combining Word2Vec and the Robustly Optimized BERT Pretraining Approach (RoBERTa). However, Twitter/X text is noisy (slang/OOV) and ambiguous, so the performance of the pre-trained transformer decreases. Word2Vec is also limited to local contexts. Integrative studies of both are still limited. The idea is that Word2Vec is strong for slang/novel vocabulary (distributional semantics), while RoBERTa excels in contextual meaning; combining the two mitigates each other's weaknesses. Methods: The Sentiment140 dataset contains 1.6 million balanced tweets. The split is stratified; Word2Vec is trained solely on the training data. RoBERTa is pretrained (frozen in the first stage, then fine-tuned with some layers in the second stage). The Word2Vec and RoBERTa vectors are concatenated and processed using Bidirectional Long Short-Term Memory (BiLSTM) with sigmoid activation. Training utilizes TensorFlow and the Adam optimizer, incorporating dropout and early stopping. The decision threshold is optimized during the validation process. Result: The hybrid model achieved an accuracy of 88.09%, an F1-score of 88.09%, and an Area Under the Curve (AUC) ≈ 95.19% on the Receiver Operating Characteristic (ROC). No overfitting was observed, and the hybrid model outperformed both single baselines. The confusion matrix and ROC curve corroborate the findings. Novelty: The novelty lies in the fusion of distributional and contextual representations with a structured fusion mechanism. Limitations: Computational requirements and hyperparameter tuning are not yet extensive. Further directions: Systematic hyperparameter search and cross-validation across other large sentiment datasets to assess generalization.
Co-Authors Adi, Mahargjo Hapsoro Aditya Marianti Akhmad Munawar Alfa Narendra Amalia Langgundi, Fitri Amidi Amidi Amin Retnoningsih Amirul Mukminin Andestian, Defin Anggyi Trisnawan Putra Annindya Ardiansari Ardhi Prabowo Arif Widiyatmoko, Arif Arsyadani, Fathan Aryani, Nila Prasetya Aryono Adhi, Aryono Bambang Sugiantoro Budi Prasetiyo, Budi Budiman, Kholiq Cahyani, Rima Ayu Damayanti, Dela Rista Dimarti, Safira Chairani Edi Subkhan, Edi Fianti Fianti, Fianti Fitri Arum Sasi, Fitri Arum Fridiyanto, Fridiyanto Hakim, M. Faris Al Hapsari, Renita Arianti Hasan Mukhibad Hasani, Azharu Alfi Ian Yulianti, Ian Indah Urwatin Wusqo Insan , Shiva Maulana Khoiru Islam, Muhamad Anbiya Nur Kasinyo Harto Krishandhie, Syuja Zhafran Rakha Langlang Handayani Lutfia Nur Hadiyanti, Lutfia Nur Mahargjo Hapsoro Adi Majidah Majidah Mardiansyah, M Fadil Martien Herna Susanti Melati, Inaya Sari Melissa Salma Darmawan MHD. Reza M.I. Pulungan Muhamad Taufiq Munawar, Akhmad Mutoriq, Alfat Nor Hafizah Ngajikin Nuni Widiarti Nur Kusuma Dewi Putra, Ngurah Made Darma Rafli, Alfandy Himawan Bagus Rahmawati, Sinta Kurnia Ririanti, Novia Puji Sri Siswati Subagja, Mona Subhan Subhan SUGIYANTO Sugiyarta Stanislaus, Sugiyarta Sujarwata Sujarwata Sukestiyarno Sukestiyarno Susilo Susilo Syukron Salam, Syukron Tris Sugiarto Tsabit Azinar Ahmad Utis Sutisna Wahyu Hardyanto Wahyu Setyaningrum Widiyanto Widiyanto Woro Sumarni Yanuarto, Alfath YB. Praharto Yuliantika, Atika