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All Journal Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JOIV : International Journal on Informatics Visualization Emerging Science Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Informasi MURA Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Information Systems and Informatics APTIKOM Journal on Computer Science and Information Technologies (CSIT) IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences Jurnal Locus Penelitian dan Pengabdian Jurnal Bina Komputer Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bangsa Jurnal Teknologi Informasi Mura International Journal of Advances in Artificial Intelligence and Machine Learning Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Kurniawan, Tri Basuki
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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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.
Deep Learning-Based Loan Approval Prediction Using Artificial Neural Network (ANN) and Feature Importance Analysis Armoogum, Sheeba; Dewi, Deshinta Arrova; Armoogum, Vinaye; Melanie, Nicolas; Kurniawan, Tri Basuki
Journal of Digital Market and Digital Currency Vol. 3 No. 1 (2026): Regular Issue March 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v3i1.55

Abstract

The increasing demand for efficient and objective credit evaluation has motivated the adoption of artificial intelligence in financial decision-making. This study proposes a deep learning-based loan approval prediction model using an Artificial Neural Network (ANN) combined with feature importance analysis to enhance interpretability. The dataset, consisting of 2,000 loan application records with both financial and demographic attributes, was preprocessed through normalization and one-hot encoding to ensure consistent feature representation. The ANN model was trained using three hidden layers (64–32–16 neurons) with the ReLU activation function and optimized using Adam with early stopping to prevent overfitting. Experimental results demonstrate that the proposed ANN model achieves an accuracy of 92%, with a precision of 0.91, a recall of 0.93, and a ROC-AUC of 0.95, indicating excellent classification capability. The Permutation Feature Importance analysis revealed that Credit Score, Income, and Loan Amount are the most significant predictors influencing loan approval decisions. These findings confirm that the ANN model can capture complex non-linear relationships among financial attributes while maintaining transparency through explainable AI techniques. The proposed approach contributes both theoretically and practically by combining predictive power with interpretability, offering a reliable and explainable framework for automating loan evaluation in modern financial institutions.
Constraint-Aware Machine Learning for Ensuring Feasible Predictions in Operational Data Science Shukun, Wu; Kurniawan, Tri Basuki; Esad Kuloğlu, Muhammet
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 1 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i1.652

Abstract

Background: Machine learning models deployed in operational environments often demonstrate high predictive accuracy during benchmark evaluation. However, their practical reliability is frequently compromised when predictions violate domain-specific operational constraints.Aims: This study aims to address the problem of infeasible predictions by proposing a unified framework that integrates operational constraints directly into the learning and inference processes.Methods: The CALF framework incorporates operational constraints through a dual mechanism consisting of correction-based learning and regularization-based penalty functions. These mechanisms are embedded directly within the training and inference objectives, allowing the model to learn constraint-compliant predictions during optimization. The framework was evaluated by comparing predictive error and operational feasibility against an unconstrained baseline model. Sensitivity analysis was also conducted to examine the stability and flexibility of the constraint penalties under varying operational thresholds.Result: Experimental results demonstrate that CALF achieved predictive error levels comparable to the unconstrained baseline while maintaining full operational feasibility. The framework reached 100% operational compliance, indicating that all generated predictions satisfied the defined constraints. Sensitivity analysis further showed that the regularization penalties operated within acceptable thresholds, allowing the model to maintain predictive flexibility while enforcing constraint adherence.Conclusion: The findings highlight the importance of integrating operational constraints directly into machine learning model design. By embedding feasibility constraints within the optimization process, the CALF framework ensures that predictive outputs remain both accurate and operationally compliant. This approach repositions operational constraints as intrinsic components of predictive modeling and contributes to the development of reliable and deployable AI systems in real-world environments.
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 Esad Kuloğlu, Muhammet 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 Shukun, Wu 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