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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Komputasi dan Teknologi Informasi
Ratih Anggraeni
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.
Pendeteksian Steganografi pada Gambar Digital Menggunakan StegSolve dan HxD dalam Investigasi Digital Forensik Ratih Anggraeni; Noer Fotin Octavia; Didit Suhartono
Jurnal Nasional Komputasi dan Teknologi Informasi Vol. 9 No. 2 (2026): April, 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/cfz31c83

Abstract

Abstrak - Steganografi merupakan teknik penyembunyian informasi yang memanfaatkan media digital seperti gambar, audio, maupun video sehingga keberadaan pesan tidak terdeteksi oleh pengamat. Dalam ranah kejahatan siber, steganografi digunakan untuk menyamarkan komunikasi rahasia, menyembunyikan instruksi malware, hingga menyelundupkan data ilegal. Penelitian ini bertujuan menganalisis indikasi steganografi pada file citra berformat JPEG menggunakan kombinasi StegSolve dan HxD dalam konteks investigasi digital forensik. Penelitian dilakukan terhadap dua sampel citra, yaitu satu file JPEG asli dan satu file JPEG termodifikasi yang diberi payload teks secara appended payload untuk mensimulasikan skenario barang bukti. Analisis dilakukan melalui visualisasi bit-plane dengan StegSolve dan pemeriksaan struktur biner dengan HxD berdasarkan tahapan identifikasi, pengumpulan, pemeriksaan, analisis, dan pelaporan yang mengacu pada NIST SP 800-86 serta DFRWS. Hasil penelitian menunjukkan bahwa StegSolve mampu menampilkan anomali pada bit-plane rendah, sedangkan HxD mengidentifikasi adanya byte tambahan setelah marker EOI (End of Image) serta string ASCII yang tidak termasuk dalam struktur standar file JPEG. Temuan tersebut menunjukkan bahwa kombinasi kedua tool efektif sebagai prosedur pemeriksaan awal untuk mendeteksi indikasi steganografi sederhana berbasis manipulasi bit-level dan appended payload pada citra digital. Kata kunci : Steganografi; StegSolve; HxD; Digital Forensik; Bit-Plane; Hex Analysis;   Abstract - Steganography is an information-hiding technique that utilizes digital media such as images, audio, and video to ensure that the existence of a message remains undetected by observers. In the realm of cybercrime, steganography is used to conceal secret communications, hide malware instructions, and smuggle illegal data. This study aims to detect steganography in images using two methods: bit-plane analysis with StegSolve and binary structure analysis with HxD. The research methodology includes acquiring image samples, examining bit-planes, analyzing pixel patterns, inspecting header–footer structures, and identifying additional data located after the End of File. The results show that StegSolve can reveal modification patterns in the bit-plane layers indicating the use of the Least Significant Bit (LSB) technique, while HxD successfully identifies extra bytes after the IEND chunk as well as unusual ASCII patterns that indicate the presence of a hidden payload. The combination of these two techniques provides a comprehensive approach to the steganalysis process and supports digital forensic investigations of modified image files. Keywords: Steganography; StegSolve; HxD; Digital Forensics; Bit-Plane; Hex Analysis;
Co-Authors Didit Suhartono Noer Fotin Octavia Pungkas Subarkah Purwadi