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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Sains, Aplikasi, Komputasi dan Teknologi Informasi
Septiani, Riska Kurnia
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering

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Implementasi Algoritma BERT Pada Komentar Layanan Akademik dan Non Akademik Universitas Terbuka di Media Sosial Fatmasari, Rhini; Septiani, Riska Kurnia; Pinem, Tuahta Hasiolan; Fabiyanto, Dedik; Gata, Windu
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 2 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i2.13915

Abstract

Media sosial TikTok dan Twitter (X) merupakan dua media sosial yang memiliki banyak pengguna di Indonesia. Berdasarkan lembaga survei We Are Social pengguna TikTok di Indonesia mencapai 109.9 juta sedangkan pengguna Twitter (X) di Indonesia mencapai 24 juta. Media sosial TikTok dan Twitter (X) seringkali menjadi tempat untuk menyampaikan pendapat atau komentar terhadap suatu hal. Universitas Terbuka merupakan suatu kampus yang memiliki media sosial TikTok dan Twitter (X) dengan ribuan pengikut. Penelitian ini dilakukan untuk mengetahui tingkat layanan bidang akademik dan non-akademik Universitas Terbuka. Data yang dianalisis sebanyak 685 data komentar pada media sosial TikTok dan Twitter (X) dengan kata kunci Universitas Terbuka. Metode yang digunakan adalah analisis menggunakan model pre-trained BERT. Pada model ini diperoleh nilai akurasi sebesar 90% dengan proporsi data latih dan data uji 80:20.
Ensembled Voting Techniques for Advanced Breast Cancer Prediction Septiani, Riska Kurnia; Rianto, Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13004

Abstract

Breast cancer is the most common type of cancer affecting women worldwide, with a significant increase in incidence rates each year. Information and Communication Technology (ICT) has made substantial contributions to the medical field, particularly through the use of Big Data and machine learning algorithms to enhance diagnostic accuracy and healthcare efficiency. This research aims to assess the performance of five breast cancer classification algorithms: Support Vector Machine (SVM), Decision Tree (C4.5), k-Nearest Neighbors (k-NN), Logistic Regression, and Ensembled Voting, using the Breast Cancer Wisconsin (Diagnostic) dataset. The study findings indicate that all models achieved high levels of accuracy, precision, recall, and F1-Score, with Ensembled Voting reaching the highest accuracy of 98.57%. This study confirms that machine learning algorithms, particularly Ensembled Voting, can be relied upon to improve breast cancer diagnosis accuracy, thereby significantly contributing to better healthcare outcomes.
Co-Authors Fabiyanto, Dedik Gata, Windu Pinem, Tuahta Hasiolan Rhini Fatmasari, Rhini Rianto, Yan