Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal Jurnal Pengabdian Masyarakat Nian Tana Switch: Jurnal Sains dan Teknologi Informasi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan
Tri Dewi Yuni Utami
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Health Professions

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Analisis dan Perbandingan Media Interaktif Kahoot dan Quizizz dalam Kemudahan Pembelajaran Aditya Ahmad Fauzi; Fithriawan Nugroho; Wahyu Putra; Yossa Agung Pratama; Andria Rezki; Tri Dewi Yuni Utami
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 1 (2025): Februari: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i1.726

Abstract

Technological advancements have significantly impacted education, including more interactive learning methods. Kahoot and Quizizz are two platforms that utilize gamification to enhance student engagement in learning. This study aims to analyze and compare these interactive media in terms of ease of use, student engagement, and effectiveness in providing feedback. Using a qualitative method and a case study approach, data was collected through interviews and observations of teachers and students utilizing both platforms. The study's findings indicate that Kahoot is more effective in boosting student motivation through a competitive system, whereas Quizizz excels in independent learning flexibility and providing more detailed result reports. Therefore, the utilization of these platforms should be adjusted according to the learning objectives to be achieved..
Pelatihan Penggunaan Classpoint Sebagai Penunjang Kegiatan Pembelajaran di Kelas pada SMA Negeri 1 Parittiga Aditya Ahmad Fauzi; Fithriawan Nugroho; Wahyu Putra; Yossa Agung Pratama; Andria Rezki; Tri Dewi Yuni Utami
Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan Vol. 2 No. 1 (2025): Februari : Pelayanan Unggulan : Jurnal Pengabdian Masyarakat Terapan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/unggulan.v2i1.1205

Abstract

The use of technology in education is increasingly important in creating effective and interactive learning experiences. This community service activity aims to enhance teachers' skills at SMA Negeri 1 Parittiga in utilizing ClassPoint, a Microsoft PowerPoint add-in that enables more dynamic and participatory learning. The method used includes training, demonstrations, hands-on practice, and evaluation of the technology's effectiveness. The results indicate an improvement in teachers' understanding of ClassPoint features, increased student participation in learning, and the creation of a more engaging and collaborative classroom environment. Some technical challenges, such as device limitations and internet access issues, were successfully addressed through direct assistance. This training has had a positive impact on improving teachers' digital skills and teaching effectiveness. It is expected that the implementation of this technology will contribute to the continuous improvement of learning quality.
Workshop Peningkatan Kompetensi Pemrograman Web bagi Siswa Peserta Lomba Kompetensi Siswa SMK Adisuputra Adisuputra; Aditya Ahmad Fauzi; Ditra Liandaputra; Fitriyanti Fitriyanti; Tri Dewi Yuni Utami; Dzalfa Tsalsabila Rhamadiyanti
JPMNT JURNAL PENGABDIAN MASYARAKAT NIAN TANA Vol. 4 No. 3 (2026): Juli: Jurnal Pengabdian Masyarakat Nian Tana
Publisher : Fakultas Ekonomi & Bisnis, Universitas Nusa Nipa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59603/jpmnt.v4i3.1427

Abstract

The Student Competency Competition (LKS) in the field of Web Technologies for Vocational High Schools (SMK) demands a high level of web programming expertise aligned with the requirements of the modern digital industry. However, most participating students often face constraints in implementing clean code architecture, optimal performance, and W3C standardization validation. This community service activity aims to improve the technical web programming competence of vocational students who are prospective LKS participants through an intensive workshop delivery method. The partners for this service involve vocational students majoring in Software Engineering along with their mentoring teachers. The implementation method is systematically organized into three main phases, including prerequisite needs analysis, intensive training based on LKS jury standard modules, and independent project simulation evaluation. The results of the workshop implementation showed a significant increase in the technical capabilities of the participants, where the understanding of modern web architecture, basic security implementation, and compliance with code writing regulations experienced optimal improvement. Documentation of activities and students' portfolio work proves that the LKS jury criteria-based workshop approach is effective in preparing the mental and technical competence of the participants. The implication of this activity is expected to serve as a sustainable coaching model for schools to increase the competitiveness of vocational graduates in both regional and national competition events.
Analisis Komparatif Metode Machine Learning dalam Klasifikasi Risiko Penyakit Jantung Berbasis Data Kaggle Adisuputra Adisuputra; Aditya Ahmad Fauzi; Ditra Liandaputra; Fitriyanti Fitriyanti; Tri Dewi Yuni Utami
Switch : Jurnal Sains dan Teknologi Informasi Vol. 4 No. 4 (2026): Juli : Switch : Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v4i4.952

Abstract

Heart disease remains one of the leading causes of the highest mortality rates worldwide, requiring a fast and accurate early detection system to minimize the risk of fatality. This study aims to test, compare, and analyze the performance of two popular machine learning methods, Support Vector Machine (SVM) and Naive Bayes, in classifying the risk of heart attacks. The research method applied is quantitative experimental, utilizing structured secondary data from the Kaggle repository, which includes 79,583 patient medical records. The data preprocessing stages involve handling missing values, feature normalization using the MinMax Scaler technique, and dataset splitting with a proportion of 80% training data and 20% testing data. The research findings indicate that the SVM architecture significantly dominates global performance, achieving an accuracy rate of 0.9847, a precision of 0.9594, and an F1-score of 0.8745. On the other hand, the Naive Bayes algorithm records the highest sensitivity (recall) value of 0.9017, compared to SVM, which only reaches 0.8034. The implications of this study confirm that although SVM is highly superior in the aggregate and accurate in suppressing false-positive rates, Naive Bayes demonstrates better characteristics for initial screening scenarios due to its high sensitivity in minimizing the risk of undetected critical patients.
Co-Authors Adisuputra, Adisuputra Aditya Ahmad Fauzi Andria Rezki Ditra Liandaputra Dzalfa Tsalsabila Rhamadiyanti Fithriawan Nugroho fitriyanti fitriyanti Wahyu Putra Yossa Agung Pratama