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Analyzing the Impact of Social Media and Influencer Endorsements on Game Revenue using Multiple Linear Regression in the Metaverse Dewi, Deshinta Arrova; Kurniawan, Tri Basuki
International Journal Research on Metaverse Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v2i2.29

Abstract

The gaming industry, particularly within the metaverse, has seen significant transformations driven by the integration of social media, influencer marketing, and player engagement metrics. These elements are crucial in shaping the success and revenue generation of games. This study explores the role of social media mentions and influencer endorsements in influencing game revenue, applying DBSCAN clustering to segment player engagement into distinct groups. By analyzing the "Gaming Trend 2024" dataset, which includes key metrics such as social media mentions, influencer endorsements, in-game purchases, and game revenue, we identify patterns in player behavior that directly impact revenue generation. The DBSCAN clustering method was employed to group players based on their social media interactions and influencer influence, identifying key segments that contribute to game success. The results reveal that certain clusters, characterized by higher social media engagement and influencer endorsements, are associated with increased game revenue. In contrast, other segments showed lower engagement and contributed less to overall revenue. The clustering analysis highlights the power of social media and influencers in driving player behavior, which in turn drives financial outcomes for game developers. This research provides insights into how targeted marketing strategies, personalized influencer campaigns, and tailored engagement efforts can enhance game revenue. This study offers practical applications for game developers and marketers in the metaverse, emphasizing the need to leverage clustering insights to optimize marketing strategies and increase revenue. Future research could expand on these findings by integrating sentiment analysis of social media mentions, exploring alternative clustering methods like hierarchical clustering, and developing hybrid models that combine clustering with predictive analytics to forecast game revenue trends.
Stacked LSTM with Multi Head Attention Based Model for Intrusion Detection Praveen, S Phani; Panguluri, Padmavathi; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Efrizoni, Lusiana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.764

Abstract

The rapid advancement of digital technologies, including the Internet of Things (IoT), cloud computing, and mobile communications, has intensified reliance on interconnected networks, thereby increasing exposure to diverse cyber threats. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats; however, traditional signature-based and rule-based methods fail to detect unknown or complex attacks and often generate high false positive rates. Recent studies have explored machine learning (ML) and deep learning (DL) approaches for IDS development, yet many suffer from poor generalization, limited scalability, and an inability to capture both spatial and temporal dependencies in network traffic. To overcome these challenges, this study proposes a hybrid deep learning framework integrating Convolutional Neural Networks (CNN), Stacked Long Short-Term Memory (LSTM) networks, and a Multi-Head Self-Attention (MHSA) mechanism. CNN layers extract spatial features, stacked LSTM layers capture long-term temporal dependencies, and MHSA enhances focus on the most relevant time steps, improving accuracy and reducing false alarms. The proposed model was trained and evaluated on the UNSW-NB15 dataset, which represents modern attack vectors and realistic network behavior. Experimental results show that the model achieves state-of-the-art performance, attaining 99.99% accuracy and outperforming existing ML and DL-based intrusion detection systems in both precision and generalization capability.
Edukasi Kesehatan tentang Virus Covid-19 pada Masa Pandemi oleh Pengajian Shollihah dan Rumah Cinta Quran Fafifa Saputri, Nurul Adha Oktarini; Misinem, Misinem; Kurniawan, Tri Basuki; Nirwana, Nirwana
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 10 (2025): Desember
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i10.3547

Abstract

Pandemi Covid-19 telah memberikan dampak signifikan terhadap berbagai aspek kehidupan masyarakat, termasuk dalam hal kesehatan, sosial, dan ekonomi. Kurangnya pemahaman masyarakat tentang virus Covid-19 serta cara pencegahannya menjadi tantangan besar dalam menekan angka penyebaran. Kegiatan pengabdian ini bertujuan untuk memberikan edukasi kesehatan kepada masyarakat, khususnya anggota Pengajian Keluarga Shollihah dan Rumah Cinta Quran (RCQ) Fafifa Palembang, mengenai pentingnya protokol kesehatan, gejala Covid-19, serta langkah-langkah pencegahan yang efektif. Metode pelaksanaan berupa penyuluhan langsung, pembagian media edukatif, serta sesi tanya jawab interaktif. Hasil dari kegiatan ini menunjukkan peningkatan pengetahuan dan kesadaran peserta terhadap pentingnya menjaga kesehatan selama masa pandemi. Kegiatan ini juga menjadi sarana untuk memperkuat peran komunitas dalam mendukung upaya pemerintah dalam penanganan Covid-19. Edukasi yang berkelanjutan di tingkat komunitas terbukti efektif dalam membentuk perilaku hidup sehat di masa krisis kesehatan global.
Co-Authors - Kurniawan, - Adi Wijaya Agus Riyanto Alde Alanda, Alde Alqudah, Mashal Kasem Alqudah, Musab Kasim Andri Andri Antoni, Darius Armoogum, Sheeba Armoogum, Vinaye Asro Asro Astried, Astried Aziz, RZ. Abdul Azmi, Nurhafifi Binti Bappoo, Soodeshna Batumalay, Malathy Bidul, Winarsi J. Bujang, Nurul Shaira Binti Chandra, Anurag Dedy Syamsuar Devi Udariansyah Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Diana Diana Edi Surya Negara Efrizoni, Lusiana Eko Risdianto Fadly Fadly Fatoni, Fatoni Febriyanti Panjaitan Firosha, Ardian Fuad, Eyna Fahera Binti Eddie Habib, Shabana Hadi Syahputra Hanan, Nur Syuhana binti Abd Hasibuan, M.S. Henderi . Hendra Kurniawan Herdiansyah, M. Izman Hidayani, Nieta Hisham, Putri Aisha Athira binti Irianto, Suhendro Y. Irwansyah Irwansyah Ismail, Abdul Azim Bin Isnawijaya, Isnawijaya Joan Angelina Widians, Joan Angelina Kijsomporn, Jureerat Kurniawan, Dendi Lexianingrum, Siti Rahayu Pratami M Said Hasibuan Madjid, Fadel Muhammad Maizary, Ary Mantena, Jeevana Sujitha Mashal Alqudah Melanie, Nicolas Misinem, Misinem Mohd Salikon, Mohd Zaki Motean, Kezhilen Muhamad Akbar Muhammad Islam, Muhammad Muhammad Nasir Muhayeddin, Abdul Muniif Mohd Nathan, Yogeswaran Nazmi, Che Mohd Alif Nirwana, Nirwana Oktariansyah Oktariansyah, Oktariansyah Onn, Choo Wou Panguluri, Padmavathi Periasamy, Jeyarani Prahartiningsyah, Anggari Ayu Pratiwi, Ayu Okta Praveen, S Phani Puspitasari, Novianti Qisthiano, M Riski R Rizal Isnanto Rahmi Rahmi RR. Ella Evrita Hestiandari Saksono, Prihambodo Hendro Saputri, Nurul Adha Oktarini Saringat, Zainuri Singh, Harprith Kaur Rajinder Sirisha, Uddagiri Sri Karnila Sugiyarto Surono, Sugiyarto Sulaiman, Agus Sunda Ariana, Sunda Suriani, Uci Syaputra, Hadi Taqwa, Dwi Muhammad Thinakaran, Rajermani Triloka, Joko Usman Ependi Wibaselppa, Anggawidia Yeh, Ming-Lang Yesi Novaria Kunang Yorman Yupika Maryansyah, Yupika Yusuf, Abi daud Zakari, Mohd Zaki Zakaria, Mohd Zaki