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Journal : Jurnal Computer and Technology

SISTEM PAKAR DIAGNOSA PENYAKIT MANDUL PADA PRIA MENGGUNAKAN METODE CERTAINTY FACTOR BERBASIS WEBSITE Giardi, Muh Hamzah Andung; Imran, Bahtiar; Suryadi, Emi
Journal Computer and Technology Vol. 1 No. 1 (2023): Juli 2023
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v1i1.82

Abstract

Penyakit mandul pada pria merupakan kondisi medis yang mempengaruhi kemampuan pria untuk memproduksi sperma atau memiliki keturunan. Diagnosa dini dan tepat sangat penting dalam pengelolaan penyakit ini agar dapat memberikan perawatan yang tepat dan meningkatkan peluang keberhasilan reproduksi. Untuk mengatasi tantangan tersebut, sebuah sistem pakar berbasis website dikembangkan menggunakan metode Certainty Factor. Sistem pakar ini dirancang dengan antarmuka pengguna yang sederhana dan mudah digunakan, memungkinkan pengguna untuk memasukkan gejala dan riwayat kesehatan mereka. Kemudian, sistem akan melakukan proses diagnosa berdasarkan basis pengetahuan yang telah diimplementasikan dalam sistem dan memberikan rekomendasi diagnosa berdasarkan tingkat keyakinan.
CYBER BULLYING SENTIMENT ANALYSIS BASED ON SOCIAL CATEGORIES USING THE CHI-SQUARE TEST Hadi, Zulpan; Suryadi, Emi; Akbar, Ardiyallah; Zaenudin; Muslim, Rudi
Journal Computer and Technology Vol. 2 No. 1 (2024): Juli 2024
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v2i1.144

Abstract

This research evaluates various machine learning models in classifying sentiment in cyberbullying data across six categories: not_cyberbullying, gender, religion, other_cyberbullying, age, and ethnicity. Using a Bag of Words approach combined with Chi-Square feature selection (1000 features), models tested include SVM, Logistic Regression, Naïve Bayes, KNN, and Random Forest. Results show SVM and Logistic Regression achieving the highest accuracy at 83%, indicating their effectiveness in prediction. Naïve Bayes performed the poorest with 62% accuracy, suggesting a mismatch with the data or need for further tuning. KNN and Random Forest showed good performance with 75% and 81% accuracy respectively, though not as high as SVM and Logistic Regression. This multi-algorithm approach provides insights into each model's effectiveness and behavior on diverse data characteristics, essential for understanding the unique nuances of each cyberbullying category. Model selection should consider accuracy, interpretability, computational cost, and suitability to specific problem characteristics. This research aims to deepen understanding of cyberbullying to support more effective mitigation strategies.
FAKE REVIEW DETECTION ON DIGITAL PLATFORMS USING THE ROBERTA MODEL: A DEEP LEARNING AND NLP APPROACH Hadi, Zulpan; Nurkholis, Lalu Moh.; Imran, Bahtiar; Riadi, Selamet; Suryadi, Emi
Journal Computer and Technology Vol. 3 No. 1 (2025): Juli 2025
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v3i1.355

Abstract

Fake reviews have emerged as a serious threat to the integrity of digital platforms, particularly in e-commerce and online review sites. This study explores the application of RoBERTa (Robustly Optimized BERT Approach), a transformer-based architecture optimized for natural language processing (NLP), in automatically detecting fake reviews. The methodology includes data collection from online platforms, contextual feature extraction using RoBERTa embeddings, model training through supervised learning, and evaluation using classification metrics such as accuracy, precision, recall, and F1-score. The training results indicate a significant convergence trend in the training loss, while the validation loss remains relatively unstable, reflecting challenges in model generalization. Nevertheless, experimental results demonstrate that RoBERTa outperforms other approaches such as Logistic Regression PU, K-NN with EM, and LDA-BPTextCNN, achieving an accuracy of 86.25%. These findings highlight RoBERTa's strong potential in detecting manipulative content and underscore its value as an essential tool in building a transparent and trustworthy digital ecosystem.