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All Journal JOURNAL EDUCATIONAL OF NURSING(JEN) Jurnal Informatika Dan Tekonologi Komputer
Rizky Rizaldi
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Computer Science & IT Control & Systems Engineering Nursing Other

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THE EFFECTIVENESS OF SPLINTING SKILLS EDUCATION IN CLOSED FRACTURE CASES USING THE SEMINAR AND SELF DIRECT VIDEO METHODS ON THE KNOWLEDGE LEVEL OF GRADE 12 STUDENTS OF SMAN 4 JAKARTA Rizky Rizaldi; Bahreni Yusuf; Hendik Wicaksono
JOURNAL EDUCATIONAL OF NURSING(JEN) Vol 9, No 1 (2026): Journal Educational of Nursing (JEN)
Publisher : STIKes RSPAD RSPAD Gatot Soebroto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37430/jen.v9i1.340

Abstract

Background: Fractures resulting from traffic accidents are injuries that have the potential to cause disability if not treated appropriately. One technique used is splinting to prevent bone displacement, reduce pain, and protect surrounding tissue. Students' knowledge of splinting techniques is still low, requiring effective educational methods, such as seminars and self-directed videos. Objective: To determine the effectiveness of splinting skills education for closed fractures using seminars and self-directed videos on the knowledge level of 12th-grade students at SMAN 4 Jakarta. Methods: This study used a Quasi-Experimental design with a Pretest-Posttest with Control Group Design. 144 students were selected using simple random sampling and divided into an intervention group (self-directed video) and a control group (seminar), each with 72 respondents. The research instrument consisted of pretest and posttest questionnaires. Data analysis used the Wilcoxon Signed Rank Test. Results: There was an increase in knowledge in both groups with a p-value <0.05, where the posttest score was higher than the pretest. The positive rank score for the intervention group was 61 and for the control group, 55. Conclusion: Education on splinting skills for closed fractures using seminars and self-directed video was equally effective in improving the knowledge of 12th-grade students at SMAN 4 Jakarta. However, the self-directed video method yielded higher improvement than the seminar method, thus concluding that the self-directed video method is more effective in improving student knowledge.
Penerapan Algoritma Support Vector Machine dan XGBoost Dalam Mengklasifikasikan Sentimen Opini Publik Terhadap Aplikasi Uber Rizky Rizaldi; M Ridho; Arraihan Tahta Ainullah; Lusiana Efrizoni; Rahmaddeni Rahmaddeni; M Fahrel Dea Putra
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 1 (2025): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i1.5735

Abstract

The development of application-based transportation services such as Uber has driven an increase in the number of public opinions distributed through various digital platforms. Sentiment analysis of this public opinion is important to understand user perceptions of Uber services. This study applies the Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms to classify public opinion sentiment, by optimizing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The data used comes from Uber reviews on public platforms, which are grouped into positive, negative, and neutral sentiments. The experimental results show that the SVM algorithm has superior performance with an accuracy of 94%, while XGBoost experienced an increase in accuracy of up to 93% after applying SMOTE. This study provides insight into the effectiveness of machine learning algorithms in sentiment analysis and its implementation in the development strategy of application-based transportation services. Abstrak: Perkembangan layanan transportasi berbasis aplikasi seperti Uber telah mendorong peningkatan jumlah opini publik yang disalurkan melalui berbagai platform digital. Analisis sentimen terhadap opini publik ini menjadi penting untuk memahami persepsi pengguna terhadap layanan Uber. Penelitian ini menerapkan algoritma Mesin Vektor Pendukung (SVM) dan Peningkatan Gradien Ekstrem (XGBoost) untuk mengklasifikasikan sentimen opini publik, dengan mengoptimalkan ketidakseimbangan data menggunakan Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan berasal dari ulasan Uber di platform publik, yang dikategorikan ke dalam sentimen positif, negatif, dan netral. Hasil eksperimen menunjukkan bahwa algoritma SVM memiliki performa lebih unggul dengan akurasi mencapai 94%, sementara XGBoost mengalami peningkatan akurasi hingga 93% setelah penerapan SMOTE. Penelitian ini memberikan wawasan mengenai efektivitas algoritma pembelajaran mesin dalam analisis sentimen serta implikasinya terhadap strategi pengembangan layanan transportasi berbasis aplikasi.  
Co-Authors Arraihan Tahta Ainullah Bahreni Yusuf Hendik Wicaksono Lusiana Efrizoni M Fahrel Dea Putra M Ridho Rahmaddeni Rahmaddeni