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All Journal JOIN (Jurnal Online Informatika) West Science Interdisciplinary Studies West Science Interdisciplinary Studies Journal of Computer Science and Information Technology
Muslimah, Dinda Desmonda
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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A Bibliometric Analysis of Virtual Influencer Muslimah, Dinda Desmonda; Sunengsih, Ayu
West Science Interdisciplinary Studies Vol. 1 No. 12 (2023): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v1i12.421

Abstract

In the current digital era, Virtual Influencers have become a trending phenomenon in social media. This research utilizes bibliometric analysis to explore the developments in Virtual Influencer-related studies, with a focus on the involvement of Artificial Intelligence (AI) technology in their creation. The study also identifies the most influential works, active researchers, and journals at the forefront of publication. Additionally, it unveils the evolution of the concept of virtual influencers from an AI perspective. The research aims to analyze how AI influences marketing strategies through virtual influencers, enabling the optimization of AI-based virtual influencer usage in creating authentic and relevant experiences for today's digital consumers. This study enhances our understanding of AI's role in shaping the future of influencer marketing and opens opportunities for further innovation in virtual influencer research. The research also provides insights into virtual influencers and their role in shaping social media trends and consumer culture transformation in the digital era.
PENDETEKSIAN OBJEK MENGGUNAKAN OPENCV DAN METODE YOLOv4-TINY UNTUK MEMBANTU TUNANETRA Randy Moh Yusup; Aldof Faris Anugrah; Muslimah, Dinda Desmonda; Permana, Sri Mentari Widya Ningrum; Shindi Yuliani
Journal of Computer Science and Information Technology Vol. 1 No. 2 (2024): Maret
Publisher : Yayasan Nuraini Ibrahim Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jcsit.v1i2.532

Abstract

Deteksi objek adalah tugas mendasar dalam computer vision dan memiliki banyak aplikasi di berbagai bidang seperti autonomous vehicles, sistem pengawasan, dan robotika. Jurnal ini menyajikan studi komprehensif tentang deteksi objek menggunakan kombinasi dari OpenCV dan YOLOv4-Tiny, yang merupakan sebuah algoritma pembelajaran mendalam yang canggih. OpenCV adalah perpustakaan computer vision sumber terbuka secara luas yang dikenal dengan koleksi fungsi dan algoritma yang luas. Di sisi lain , YOLOv4 Tiny adalah varian ringkas dari algoritma deteksi objek YOLO (You Only Look Once), yang dirancang untuk mencapai performa waktu nyata tanpa mengurangi akurasi. Dalam studi ini, kami memanfaatkan kemampuan OpenCV dan YOLOv4-Tiny untuk mengembangkan sistem deteksi objek yang kuat. Pertama, kami memberikan tinjauan mendetail tentang arsitektur YOLOv4-Tiny, barpusat pada komponen utamanya, termasuk backbone network, feature pyramid, dan detection layers. Kesimpulannya, jurnal ini memberikan eksplorasi komprehensif tentang deteksi objek menggunakan OpenCV dan YOLOv4 Tiny. Studi tersebut menyoroti keuntungan dari kombinasi ini dalam hal kecepatan dan akurasi dan menghadirkan implementasi praktis dari sistem tersebut. Hasilnya menampilkan potensi sistem untuk aplikasi deteksi objek real-time, berkontribusi pada kemajuan visi komputer dan berbagai domainnya. Selain itu, kami mengevaluasi kinerja sistem kami pada kumpulan data tolok ukur standar, seperti COCO (Common Objects in Context), untuk menilai akurasi pendeteksian dan efisiensi komputasinya.
A Bibliometric Analysis of Virtual Influencer Muslimah, Dinda Desmonda; Sunengsih, Ayu
West Science Interdisciplinary Studies Vol. 1 No. 12 (2023): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v1i12.421

Abstract

In the current digital era, Virtual Influencers have become a trending phenomenon in social media. This research utilizes bibliometric analysis to explore the developments in Virtual Influencer-related studies, with a focus on the involvement of Artificial Intelligence (AI) technology in their creation. The study also identifies the most influential works, active researchers, and journals at the forefront of publication. Additionally, it unveils the evolution of the concept of virtual influencers from an AI perspective. The research aims to analyze how AI influences marketing strategies through virtual influencers, enabling the optimization of AI-based virtual influencer usage in creating authentic and relevant experiences for today's digital consumers. This study enhances our understanding of AI's role in shaping the future of influencer marketing and opens opportunities for further innovation in virtual influencer research. The research also provides insights into virtual influencers and their role in shaping social media trends and consumer culture transformation in the digital era.
A Bibliometric Analysis of Virtual Influencer Muslimah, Dinda Desmonda; Sunengsih, Ayu
West Science Interdisciplinary Studies Vol. 1 No. 12 (2023): West Science Interdisciplinary Studies
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/wsis.v1i12.421

Abstract

In the current digital era, Virtual Influencers have become a trending phenomenon in social media. This research utilizes bibliometric analysis to explore the developments in Virtual Influencer-related studies, with a focus on the involvement of Artificial Intelligence (AI) technology in their creation. The study also identifies the most influential works, active researchers, and journals at the forefront of publication. Additionally, it unveils the evolution of the concept of virtual influencers from an AI perspective. The research aims to analyze how AI influences marketing strategies through virtual influencers, enabling the optimization of AI-based virtual influencer usage in creating authentic and relevant experiences for today's digital consumers. This study enhances our understanding of AI's role in shaping the future of influencer marketing and opens opportunities for further innovation in virtual influencer research. The research also provides insights into virtual influencers and their role in shaping social media trends and consumer culture transformation in the digital era.
Maleo-Short: An "In-the-Wild" Indonesian Dataset for Speaker Diarization Mardiana, Ardi; Muslimah, Dinda Desmonda; Bastian, Ade; Irawan, Eka Tresna
JOIN (Jurnal Online Informatika) Vol 11 No 1 (2026)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v11i1.1781

Abstract

Speaker diarization (SD), the task of partitioning an audio stream into speaker-homogenous segments, is fundamental for analyzing multi-speaker recordings. Its application to “in-the-wild” data, such as content from the YouTube platform, poses significant challenges, including overlapped speech, ambient noise, and rapid speaker turns, thereby constituting an active research area. While numerous SD datasets are available, they predominantly focus on English and other high-resource languages. A notable scarcity of publicly accessible datasets exists for the Indonesian language, as extant corpora are primarily engineered for Automatic Speech Recognition (ASR). To address this resource deficit, this research introduces Maleo-Short, a new Indonesian multi-speaker dataset derived from YouTube. The dataset comprises 110 short conversational clips, with a total duration of 1 hours 32 minutes. A reliable ground truth was established through a meticulous manual annotation process using ELAN to generate precise speaker segmentation and transcription files. To validate its utility and assess its complexity, the dataset was evaluated using pre-trained baseline models. The empirical results confirm its status as a challenging benchmark, with the most effective models achieving a Diarization Error Rate (DER) of 32.64% and a Word Error Rate (WER) of 33.78%. Maleo-Short is presented as a valuable, publicly accessible resource intended to catalyze advancements in Indonesian speaker diarization research by facilitating the development and rigorous evaluation of SD systems on acoustically complex and realistic conversational data. Maleo-Short is available at https://doi.org/10.57967/hf/7944.  
Co-Authors Ade Bastian Aldof Faris Anugrah Ardi Mardiana Irawan, Eka Tresna Permana, Sri Mentari Widya Ningrum Randy Moh Yusup Shindi Yuliani Sunengsih, Ayu