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All Journal Journal of Embedded Systems, Security and Intelligent Systems Indonesian Technology and Education Journal Journal of Progressive Information, Security, Computer and Embedded System
Dewi Fatmawati Surianto
Universitas Bakrie
Computer Science & IT Education Engineering

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Ensemble-Based Clickbait Detection in Indonesian Online News Dewi Fatmawati Surianto; Diny Anggriani Adnas
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i4.11251

Abstract

Clickbait has become a pervasive issue in online news media, particularly in the Indonesian digital information ecosystem, where sensational headlines are frequently used to attract user attention at the expense of content accuracy. This phenomenon not only degrades information quality but also contributes to the spread of misinformation. To address this challenge, this study proposes an ensemble-based machine learning approach for detecting clickbait in Indonesian-language news articles by jointly analyzing headlines and full article content. The proposed method employs Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction with extended n-gram configurations to capture both lexical and contextual patterns characteristic of clickbait. Three baseline classifiers, Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine are integrated into a hard voting ensemble framework to leverage their complementary strengths. The experiments were conducted on the CLICK-ID dataset, consisting of annotated Indonesian news articles, using an 80:20 train–test split. Experimental results demonstrate that the proposed ensemble model outperforms all individual baseline classifiers, achieving an overall accuracy and F1-score of 93%. The ensemble approach shows notable improvements in recall for the clickbait class, indicating its effectiveness in minimizing false negatives. Furthermore, qualitative analysis using word cloud and bigram visualization reveals distinct linguistic patterns between clickbait and non-clickbait articles, supporting the discriminative capability of the extracted features. These findings confirm that combining TF-IDF with ensemble learning provides a robust and effective solution for clickbait detection in Indonesian online news. The proposed model contributes to the development of more reliable content filtering systems and supports efforts to improve information quality in digital media environments.
Evaluasi Kinerja Karyawan PT. XYZ dengan Pendekatan Metode Fuzzy Mamdani Muharni Muharni; Widiarti Awaliah; Dewi Fatmawati Surianto
Indonesian Technology and Education Journal Vol. 3 No. 1 (2025): Februari
Publisher : Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/itej.v3i1.567

Abstract

Employee performance evaluation is vital for organizational effectiveness, ensuring fair and objective assessments. PT. XYZ, a cement distributor, struggles with subjective evaluation criteria, prompting the need for a more accurate approach. This study applies the Fuzzy Mamdani method to improve performance assessment. Data from 31 employees were collected through literature reviews, interviews, and observations. The system uses salary, age, and years of service as input variables, while the output variable represents employee performance. The Fuzzy Mamdani method processes data through fuzzification, fuzzy inference, and defuzzification to handle uncertainty and enhance evaluation fairness. The results show that 29 employees fall under the "Good" performance category, while 2 employees are classified as "Very Good." This demonstrates that the method provides more precise and consistent assessments compared to traditional techniques. Implementing this approach enables companies to make better-informed human resource decisions and promote employee growth. This study underscores the potential of fuzzy logic in refining performance evaluations, contributing to a more transparent and equitable assessment system
Sistem Pengambilan Keputusan Untuk Menentukan Tingkat Kecanduan Game Online Menggunakan Metode Weighted Product Muh. Hadal Ali Sam; Muhammad Miftah Farid; Dewi Fatmawati Surianto
Progressive Information, Security, Computer, and Embedded System Vol. 3, No. 1 Maret (2025)
Publisher : Sakura Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/pisces.v3i1.700

Abstract

Playing online games is one of the activities that many people do to entertain themselves in the midst of their busy daily lives. The existence of several genres in online games certainly makes the game more exciting and entertaining. However, excessive and unlimited use as a means of entertainment can have a negative impact, such as online game addiction. In addition, not everyone realizes that they have developed this type of addictive behavior towards the game. As a result, a person who experiences online game addiction tends to be less interested in other activities, feels restless when not playing online games, decreased academic achievement, social relationships and health. For this reason, the utilization of a decision-making system with the weighted product method is very necessary to determine the level of online game addiction. Therefore, the purpose of this research is to develop a decision-making system to determine the level of online game addiction using the weighted product (WP) method. This research consists of several assessment criteria, namely playing time, frequency of play, level of satisfaction playing games, financial expenses, social interactions and physical problems. in this study, manual calculations were carried out with excel and also used a system to determine the level of online game addiction from several alternatives.
Co-Authors Diny Anggriani Adnas Muh. Hadal Ali Sam Muhammad Miftah Farid Muharni Muharni Widiarti Awaliah