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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Terpadu JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Technologia: Jurnal Ilmiah Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informasi dan Teknologi Prosiding National Conference for Community Service Project G-Tech : Jurnal Teknologi Terapan Jurnal Teknimedia: Teknologi Informasi dan Multimedia Telcomatics Journal of Information System and Technology (JOINT) Conference on Business, Social Sciences and Technology (CoNeScINTech) Social Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Firmansyah, Muhamad Dody
Univeristas Internasional Batam
Humanities 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 Mathematics Other

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Acquiring Knowledge from Data Analytics and Performance-Boosting on Multimedia Content Jed Wan; Hendi Sama; Muhamad Dody Firmansyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In gaining meaningful and actionable insights from complex and diverse multimedia content, many studies have applied data analytics approaches—particularly data mining and machine learning—to uncover patterns, relationships, and hidden knowledge. This systematic literature review synthesizes 26 studies conducted over the past decade on acquiring knowledge from multimedia content using data analytics and performance-boosting techniques. Across domains such as social media, education, healthcare, e-commerce, and public safety, most works integrate text–image or audio–video pairs and increasingly adopt attention-based architectures and transformer models with early fusion strategies. To ensure comparability, each study’s evidence is recorded by considering the reported performance improvement over the authors’ baseline using the same dataset and evaluation metric. The most frequently used metrics include Accuracy, the F1-score (a harmonic mean of Precision and Recall), Precision, Recall, and the Area Under the Receiver Operating Characteristic Curve (AUC), which provides a threshold-independent measure of classification quality. The most common challenges identified include modality integration and alignment, data noise and quality, limitations of datasets and benchmarks, and domain shift, with fewer studies reporting class imbalance, computational cost, and interpretability or privacy issues. At the same time, promising opportunities emerge in the development of standardized multimodal benchmarks, efficient transformer-based and hybrid fusion pipelines, integration of external knowledge, domain-robust learning, and privacy-preserving explainable multimodal artificial intelligence. Overall, this review contributes a consolidated map of modalities, methods, and metrics, a performance-gain versus baseline table for quick comparability, a quantified challenge landscape, and a practical roadmap for guiding future research in multimodal sentiment analysis and related fields.
Gamification Design in Online Risk Management to Enhance Employee Awareness Firmansyah, Muhamad Dody; Jonathan, Jonathan; Wibowo, Tony
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 02 (2026): MAY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i02.2566

Abstract

Organizations increasingly require effective approaches to strengthen employee awareness of operational risks, yet conventional training methods often lack engagement and fail to support sustained learning. This study aimed to design and evaluate a gamified risk-management learning prototype that integrates interactive features with structured instructional content. The research focused on early-stage development and formative evaluation because existing workplace learning research has not sufficiently explored the feasibility of gamification in risk-related training. The study employed the first three stages of the 4D R&D model: Define, Design, and Develop to create the prototype, followed by a descriptive quantitative evaluation involving 82 participants. Data were collected using an online questionnaire adapted from validated instruments measuring usability, content clarity, interactivity, and user satisfaction. The results showed consistently high mean scores across all constructs, indicating positive user perceptions of the prototype’s interface, clarity of information, and engagement generated by gamified elements. Cronbach’s Alpha (0.985) confirmed excellent internal consistency, and correlation analysis demonstrated strong relationships among clarity, interactivity, and satisfaction. Overall, the findings suggest that gamification can serve as a feasible and engaging approach for risk-management learning in organizational contexts. The study provides early empirical evidence supporting further refinement, broader implementation, and more extensive testing of gamified learning systems for workplace risk awareness.
Efektivitas Learning Management System terhadap Hasil Belajar Siswa SMP Plus Al Kaffah Firmansyah, Muhamad Dody; Guntara, Muhammad Arif; Siahaan, Mangapul
Technologia : Jurnal Ilmiah Vol 17, No 2 (2026): Technologia (April)
Publisher : Universitas Islam Kalimantan Muhammad Arsyad Al Banjari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v17i2.22363

Abstract

Pembelajaran berbasis teknologi semakin menuntut penggunaan sistem yang mampu mendukung proses evaluasi secara terukur. Penelitian ini mengkaji pemanfaatan sistem manajemen pembelajaran dalam meningkatkan hasil belajar siswa melalui penerapan metode pretest dan posttest. Penelitian dilakukan dengan pendekatan kuantitatif menggunakan desain eksperimen satu kelompok pada siswa kelas VII SMP Plus Al Kaffah Batam. Tahapan penelitian meliputi pengukuran kemampuan awal siswa, pelaksanaan pembelajaran berbasis sistem manajemen pembelajaran, serta pengukuran hasil belajar setelah perlakuan diberikan. Data dianalisis secara statistik untuk melihat perbedaan capaian belajar sebelum dan sesudah pembelajaran. Temuan penelitian menunjukkan adanya peningkatan hasil belajar siswa setelah penerapan sistem manajemen pembelajaran, sehingga sistem tersebut berpotensi mendukung proses pembelajaran dan evaluasi hasil belajar di tingkat sekolah menengah pertama.
Universitas Internasional Batam ANALISIS KEAMANAN SISTEM INFORMASI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE TERHADAP PENGGUNA SHOPEE Muhamad Dody Firmansyah; Christopher Christopher; Mangapul Siahaan
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2026): June 2026
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v7i1.344

Abstract

The expansion of e-commerce in Indonesia has made information system security a crucial concern, especially on sites like Shopee that see a lot of user activity and transaction volumes. Potential security hazards, such as account misuse, unauthorized access, and suspicious activity, are increased by the volume of online transactions. Therefore, in order to comprehend the elements linked to security threats based on user characteristics and behavioral patterns, an analytical approach is necessary. The purpose of this study is to apply machine learning to examine security risk tendencies among Shopee users. A standardized questionnaire addressing demographic factors, usage frequency, security awareness levels, and experiences with questionable activity was used to gather data from 101 active users. Data cleaning, label encoding, Min–Max normalization, and feature selection were among the steps in the data processing procedure. The classification model used was the Support Vector Machine (SVM) technique with a Radial Basis Function (RBF) kernel. The creation of a security risk analysis model based on user perceptions and behavioral aspects rather than system log or transactional data is what makes this study unique. By using non-technical indications as predictive factors in e-commerce platforms, this method provides an alternate viewpoint for spotting possible security threats.
Co-Authors Anderson, Atnan Ari Bong, Valenteeno Bryan Aditya Chanda Vedalla Putra Chandra, Jefriyanto Christian Christian Christopher Christopher Christopher Khomali Dhafa Firgana Ellwan Edy Wei Fredian Simanjuntak Guntara, Muhammad Arif Hafizh akmal Herman Herman Jecelyn Jed Wan Jevin Leon Jonathan Jonathan Kelven Liong Kelven LIong Kelvin Kelvin Kelvin Melati, Dini Sari Mungkap Mangapul Siahaan Nabila Arinov Mey Nagano Nagano Nathania Jocelyn Rince Monica Hutagalung Ryandy Sonata Saaya, Zurina Sama, Hendi Sama, Hendi Shermay Syaeful Anas Aklani, Syaeful Tito Adrian Pribadi Tony Wibowo, Tony Valenteeno Bong WAN, Jed Wan Wirren Chang Zhilla Syauqina Hafsa Zulkarnain Zulkarnain