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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Computer Science and Informatics Engineering Cosmic Jurnal Teknik
Rivaldi Prima Nanda
Universitas Islam Negeri Sumatera Utara
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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Klasifikasi Berita detik.com Terkait Teknologi Informasi Menggunakan TF-IDF dan Naive Bayes Nur Bainatun Nisa; Rivaldi Prima Nanda; Zahra Humaira Kudadiri; Bagus Ageng Alfahri; Mhd Furqan
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9171

Abstract

Abstrak – Penelitian ini membahas tentang klasifikasi berita Detik.com terkait teknologi informasi dengan menerapkan metode Term Frequency-Inverse Document Frequency (TF-IDF) sebagai ekstraksi fitur dan algoritma Naive Bayes sebagai model klasifikasi. Tujuan dari penelitian ini adalah untuk mengelompokkan berita-berita yang dimuat pada situs Detik.com ke dalam beberapa kategori utama di bidang teknologi informasi, seperti kecerdasan buatan, keamanan siber, gadget, dan aplikasi. Proses penelitian diawali dengan pengumpulan 1.050 data berita dari Detik.com menggunakan search query ‘teknologi informasi’ pada rentang Maret hingga April 2025. Data kemudian diproses melalui tahapan text preprocessing, meliputi case folding, tokenizing, stopword removal, dan stemming. Selanjutnya, fitur teks diubah menjadi representasi numerik menggunakan TF-IDF, lalu dilakukan pelatihan model klasifikasi dengan algoritma Naive Bayes. Evaluasi kinerja model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa kombinasi TF-IDF dan Naive Bayes efektif dalam mengklasifikasikan berita teknologi informasi, dengan akurasi model mencapai 85%. Temuan ini menunjukkan bahwa pendekatan klasifikasi berbasis machine learning dapat membantu pengelompokan dan identifikasi topik utama secara otomatis dalam berita teknologi informasi di Detik.com.Kata Kunci: TF-IDF; Naive Bayes; Klasifikasi; Detik.com; Teknologi Informasi.Abstract – This study discusses the classification of Detik.com news related to information technology by applying the Term Frequency-Inverse Document Frequency (TF-IDF) method as a feature extraction and the Naive Bayes algorithm as a classification model. The purpose of this study is to group news published on the Detik.com site into several main categories in the field of information technology, such as artificial intelligence, cybersecurity, gadgets, and applications. The research process began with the collection of 1,050 news data from Detik.com using the search query 'information technology' in the range of March to April 2025. The data was then processed through the text preprocessing stage, including case folding, tokenizing, stopword removal, and stemming. Furthermore, text features were converted into numeric representations using TF-IDF, then training a classification model with the Naive Bayes algorithm. Model performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that the combination of TF-IDF and Naive Bayes was effective in classifying information technology news, with a model accuracy reaching 85%. This finding suggests that a machine learning-based classification approach can help automatically cluster and identify key topics in information technology news on Detik.com.Keywords: TF-IDF; Naive Bayes; Classification; Detik.com; Information Technology.
Aplikasi Simulasi Enkripsi Menggunakan Metode Caesar Cipher Berbasis Website Fiqri Dian Priyatna Sinaga; Khoiratul Azmi; Afri Yunda Nasution; Rivaldi Prima Nanda; Ibnu Rusydi
Cosmic Jurnal Teknik Vol 2 No 4 (2025): November
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosmic.v2i4.1466

Abstract

Cryptography is a fundamental field in data security; however, it is often considered difficult for beginner students due to its abstract and mathematical nature. Previous studies indicate that learning Caesar Cipher is commonly conducted through theoretical approaches or desktop-based applications with limited interactivity and accessibility. Therefore, this study aims to develop a web-based Caesar Cipher encryption simulation application as an interactive learning medium. The system was developed using the prototype method, while functional testing was conducted using the black box testing approach. The effectiveness of the application was evaluated through primary data collected through questionnaires distributed to students. The results show that before using the application, 82.4% of students were at low to moderate levels of understanding, whereas after using the application, 100% of students reached a high level of understanding. These findings indicate that the web-based Caesar Cipher simulation application is effective in improving students’ understanding of fundamental cryptographic concepts prior to studying more advanced data security algorithms
Obesity Level Classification Using the Gaussian Naive Bayes and 10-Fold Cross-Validation Rivaldi Prima Nanda; Muhammad Siddik Hasibuan; Mhd. Ikhsan Rifki
Journal of Computer Science and Informatics Engineering Vol 5 No 4 (2026)
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i4.1855

Abstract

Obesity classification is an important task in health-related data analysis for identifying obesity levels based on individual characteristics. This study aims to implement the Gaussian Naïve Bayes algorithm for obesity level classification and evaluate differences in performance estimates obtained using a 70:30 train-test split and 10-Fold Cross Validation. The dataset consisted of 1,000 observational records containing five predictor variables: age, height, weight, body mass index (BMI), and physical activity level. Obesity levels were categorized into four classes: Underweight, Normal, Overweight, and Obese. The model was implemented using Python and evaluated through two validation scenarios. Experimental results showed that the Gaussian Naïve Bayes model achieved an accuracy of 95.33% using the train-test split approach. Evaluation using 10-Fold Cross Validation produced an average accuracy of 96.30%, precision of 96.66%, recall of 96.45%, and F1-score of 96.48%. These findings indicate that different validation strategies can produce different performance estimates. On the dataset used in this study, Gaussian Naïve Bayes demonstrated high classification performance, while 10-Fold Cross Validation provided performance estimates across multiple data partitions, offering a broader assessment of model performance. This study provides empirical evidence regarding the use of different validation strategies for obesity level classification
Co-Authors Afri Yunda Nasution Bagus Ageng Alfahri Fiqri Dian Priyatna Sinaga Ibnu Rusydi Khoiratul Azmi Mhd Furqan Mhd Ikhsan Rifki Muhammad Siddik Hasibuan Nur Bainatun Nisa Zahra Humaira Kudadiri