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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Information Systems and Informatics Jurnal Algoritma
Purwadi
Universitas Amikom Purwokerto
Computer Science & IT

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DETECTION OF MICRO-VIRAL CONTENT ON TIKTOK THROUGH SOCIAL LISTENING AND MACHINE LEARNING Ratih Anggraeni; Purwadi; Pungkas Subarkah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7472

Abstract

The phenomenon of micro-virality on TikTok illustrates how content can rapidly spread on a small scale before reaching broader virality. Understanding its driving factors is essential for supporting digital marketing strategies, managing content creators, and analyzing social media trends. This study aims to detect and predict the potential of micro-virality in TikTok videos by integrating a social listening approach with machine learning techniques. The dataset consists of approximately 4,000 TikTok posts enriched with 20 features across five categories, including user metadata (author popularity, follower ratio), temporal features (posting time and day), network features (hashtags and mentions), content features (text length and keywords), and contextual elements (trending music and video duration). To ensure objective labeling, a quantile-based threshold was applied, categorizing videos in the top 25% of view counts (≥ 26,300,000 views) as viral, resulting in a class distribution of 24.74% viral and 75.26% non-viral. To address this imbalance, the SMOTENC technique was used to oversample the minority class and enhance data representativeness. Three machine learning algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN) were implemented. Experimental results show that Random Forest improved from 88% to 92%, XGBoost maintained strong performance at 95%, and ANN increased significantly from 92% to 93% after SMOTENC application. These findings indicate that SMOTENC effectively improves model generalization and reduces bias toward majority classes, supporting more reliable early-stage virality prediction. Overall, the study enriches social media analytics research and provides practical insights for optimizing TikTok content strategies and early trend detection.
A Hybrid Feature-Enriched IndoBERT Framework for Sentiment Analysis of Ride-Hailing Service Reviews in Indonesia Puas Triawan; Imam Tahyudin; Purwadi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1587

Abstract

This study examines sentiment classification for Indonesian ride-hailing user reviews, which often contain informal expressions, ambiguity, and strong contextual dependency. Existing studies commonly rely on either traditional machine learning or transformer-based models, while limited attention has been given to integrating heterogeneous feature representations. To address this gap, this study proposes a feature-level hybrid integration strategy combining TF-IDF and IndoBERT embeddings. This approach enables the model to capture statistical term importance and contextual semantic meaning within a unified representation. A quantitative experimental design was applied to approximately 20,000 reviews collected from Gojek, Grab, and Maxim. Sentiment labels were generated through rating-based mapping and manually validated for consistency. The dataset, which was relatively balanced across positive, neutral, and negative classes, was divided into training and testing sets using an 80:20 split. Model performance was evaluated on the test set using accuracy, precision, recall, and F1-score. The proposed hybrid model achieved the highest accuracy of 93.5%, outperforming IndoBERT (91.8%) and traditional machine learning models (78.4%–87.6%). The results show that feature-level integration improves sentiment classification performance, although neutral sentiment remains challenging due to contextual ambiguity.
Perancangan dan Evaluasi Usability Aplikasi Talentgo Untuk Karier Bidang Informatika Menggunakan Metode Design Thinking Solihatun Havidah; Purwadi; M. Syaiful Amin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3161

Abstract

This study aims to develop a prototype of the TalentGo application as a career mapping and job search platform specifically for the Informatics field, capable of providing relevant job vacancy recommendations, competency-based career pathways, and a user-friendly interface. The development process uses the Design Thinking approach through the stages of empathize, define, ideate, prototype, and test to ensure that the design aligns with user needs and behavior. The resulting prototype is evaluated using the System Usability Scale (SUS), the User Experience Questionnaire (UEQ), and a five-point Likert scale–based questionnaire to assess interface aspects, user experience, satisfaction, and user loyalty. The results show that the user flow on both the applicant and company sides runs efficiently with minimal obstacles, and key features such as one-click apply and application status tracking are considered helpful in the job search process. Quantitative evaluation produces average scores above 4.00 across all assessment aspects, indicating that the application interface is attractive, navigation is easy to understand, and features are relevant to user needs. Overall, the TalentGo prototype meets modern UI/UX standards and is suitable for further development as a digital recruitment platform for users in the Informatics field.
Co-Authors Imam Tahyudin M. Syaiful Amin Puas Triawan Pungkas Subarkah Ratih Anggraeni Solihatun Havidah