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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Telematics and Informatics Jurnal Penelitian Pendidikan IPA (JPPIPA) Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Inotera Martabe : Jurnal Pengabdian Kepada Masyarakat Krea-TIF: Jurnal Teknik Informatika Informatik : Jurnal Ilmu Komputer Jurnal Abdimas Bina Bangsa Jurnal Abdimas Kartika Wijayakusuma Formosa Journal of Computer and Information Science ABDIKOM Jurnal Rekayasa Informasi
Neny Rosmawarni
Fakultas Ilmu Komputer, Universitas Pembangunan Nasional Veteran Jakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Transformers in Machine Learning: Literature Review Thoyyibah T; Wasis Haryono; Achmad Udin Zailani; Yan Mitha Djaksana; Neny Rosmawarni; Nunik Destria Arianti
Jurnal Penelitian Pendidikan IPA Vol 9 No 9 (2023): September
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v9i9.5040

Abstract

In this study, the researcher presents an approach regarding methods in Transformer Machine Learning. Initially, transformers are neural network architectures that are considered as inputs. Transformers are widely used in various studies with various objects. The transformer is one of the deep learning architectures that can be modified. Transformers are also mechanisms that study contextual relationships between words. Transformers are used for text compression in readings. Transformers are used to recognize chemical images with an accuracy rate of 96%. Transformers are used to detect a person's emotions. Transformer to detect emotions in social media conversations, for example, on Facebook with happy, sad, and angry categories. Figure 1 illustrates the encoder and decoder process through the input process and produces output. the purpose of this study is to only review literature from various journals that discuss transformers. This explanation is also done by presenting the subject or dataset, data analysis method, year, and accuracy achieved. By using the methods presented, researchers can conclude results in search of the highest accuracy and opportunities for further research.
Microservices-Based Open-Source Video Conference Deployment for Optimized Online Learning Infrastructure Davy Putra Ananda; Muhammad Fadhil Ramadhan Wicassono; Farhah Safrila Diva; Abdullah Rasyid; Juwita Istiqomah Trahira; Neny Rosmawarni
Formosa Journal of Computer and Information Science Vol. 5 No. 1 (2026): March 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i1.16470

Abstract

The rapid advancement of information technology has fundamentally shifted the interaction paradigm in education from conventional methods to hybrid learning models. In this context, the availability of stable, real-time communication platforms has become crucial for maintaining the effectiveness of knowledge transfer. This study evaluates the implementation of Apache OpenMeetings v9.0.0 using Docker and WSL2 to provide efficient video conferencing. Using an experimental methodology, system performance was monitored during active sessions. Results show high resource efficiency with a stable CPU utilization of 4.94% and memory usage of 1.339 GiB. The system achieved a rapid startup velocity of 11.1 seconds, proving that containerization offers optimal isolation with minimal overhead. The study concludes that this architecture provides a lightweight, portable, and cost-effective solution for independent communication infrastructure in educational institutions.
Perbandingan Kinerja Random Forest Dan Smote Random Forest Dalam Mendeteksi Dan Mengukur Tingkat Stres Pada Mahasiswa Tingkat Akhir Vionota Oktaviani; Neny Rosmawarni; M. Panji Muslim
Informatik : Jurnal Ilmu Komputer Vol 20 No 1 (2024): April 2024
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v20i1.9158

Abstract

Dalam kehidupan sehari-hari manusia, stres merupakan masalah nyata sehingga menjadi bagian yang tidak terpisahkan. Ketidaksiapan individu dalam menghadapi tuntutan akademis dapat mengakibatkan stres sebagai salah satu gangguan psikologis. Dalam hal ini, stres akademik merupakan stres yang dialami oleh mahasiswa, terutama mahasiswa tingkat akhir. Adanya banyak tekanan baik dari masalah ekonomi, akademik maupun kondisi sosial dapat menjadi pemicu stres bagi mahasiswa tingkat akhir. Penelitian ini bertujuan untuk mengklasifikasikan diagnosa tingkat stress mahasiswa tingkat akhir dengan membandingkan kinerja yang terbaik antara Random Forest dengan SMOTE Random Forest. Data yang diolah dalam penelitian ini merupakan data yang dihasilkan oleh kuesioner yang berisi 14 pertanyaan yang ditujukan pada mahasiswa tingkat akhir yang sedang melaksanakan skripsi. Adapun hasil dari penelitian ini, disimpulkan bahwasannya metode Random Forest dengan menggunakan SMOTE mampu mempengaruhi dan meningkatkan evaluasi klasifikasi studi kasus klasifikasi diagnosa mahasiswa tingkat akhir dengan akurasi sebesar 71%, precision sebesar 72% dan recall sebesar 71% pada pembagian 80% data latih, 20% data uji dengan nilai K=5.
Co-Authors Abdullah Rasyid Achmad Udin Zailani Catur Nugrahaeni Puspita Dewi Davy Putra Ananda Dimas_20 Setiadi Edyana, Fajar Erliza Hambali Fajar Edyana Farhah Safrila Diva Haryono, Wasis Jayanta Juwita Istiqomah Trahira M. Octaviano Pratama M. Panji Muslim Marhaeni Marhaeni Muhammad Fadhil Ramadhan Wicassono Muhammad Faiz Azam Musthofa Galih Pradana Nindy Irzavika Niqotaini, Zatin Nunik Destria Arianti Nurul Afifah Arifuddin Putri, Marella Azaria Rafa Karimah Rahma Syabania Ridwan Raafi'udin Rifka Dwi Amalia Rizki Harwisantoso Siti Nurmiati Taufik Djatna Tedja Diah Rani Octavia Thoyyibah T Vionota Oktaviani Yan Mitha Djaksana Yani Nurhadryani Zaidiah, Ati