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Implikasi Penegakan Hukum bagi Pengemudi Motor dan Pemilik Mobil dalam Insiden Lalu Lintas Marshanti, Lina; Sari, Tri Wulan; Mahmud, Mahmud
Journal of Modern Social and Humanities Vol. 1 No. 6: Journal of Modern Social and Humanities, November 2025
Publisher : Gema Cendekia Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/jmsh.v1i6.258

Abstract

Penelitian ini bertujuan untuk menganalisis implikasi penegakan hukum terhadap pengemudi motor dan pemilik mobil dalam insiden lalu lintas. Fokus utama penelitian adalah menilai efektivitas sanksi hukum, tanggung jawab pengemudi dan pemilik kendaraan, serta peran teknologi dan pendekatan restorative justice dalam meningkatkan kepatuhan dan keselamatan jalan. Penelitian menggunakan pendekatan kualitatif dengan metode hukum empiris dan yuridis-normatif, meliputi pengumpulan data melalui wawancara semi-terstruktur dengan aparat kepolisian dan pelaku kendaraan, observasi lapangan, serta studi dokumen hukum dan putusan pengadilan. Analisis data dilakukan secara tematik dan normatif untuk mengidentifikasi faktor penyebab pelanggaran, implementasi sanksi, serta dampaknya terhadap perilaku pengemudi dan pemilik kendaraan. Hasil penelitian menunjukkan bahwa pengemudi motor cenderung melakukan pelanggaran akibat kelalaian atau rendahnya kepatuhan terhadap aturan, sedangkan pemilik mobil memiliki tanggung jawab hukum terkait kondisi kendaraan yang tidak laik jalan. Kombinasi sanksi pidana, sanksi administratif, dan penerapan teknologi tilang elektronik efektif meningkatkan kepatuhan pengemudi, meskipun faktor sosial dan persepsi risiko tetap memengaruhi efektivitas hukum. Pendekatan restorative justice memberikan alternatif penyelesaian konflik yang humanis dan memperkuat tanggung jawab sosial. Penelitian ini menekankan pentingnya integrasi antara kepatuhan pengemudi, tanggung jawab pemilik kendaraan, sanksi hukum yang konsisten, teknologi, dan restorative justice untuk menciptakan keselamatan lalu lintas yang berkelanjutan.
Evaluation of Civil Engineering Students’ Academic Performance Using Fuzzy C-Means Clustering Saputra, Jonathan; Edistria, Ega; Wacono, Sidiq; Sari, Tri Wulan; Adyan, Faqih Al
Jurnal Pendidikan Teknik Sipil Vol. 7 No. 2 (2025): November
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jpts.v7i2.89018

Abstract

Background: Students’ academic performance is a crucial indicator of their mastery of core competencies obtained throughout the learning process in higher education. These competencies become an essential benchmark, not only for academic evaluation, but also for the industry that expects graduates to meet professional standards. Therefore, an objective and data-driven evaluation method is needed to identify students’ academic performance and support academic decision-making. Methods: This study employs the Fuzzy C-Means (FCM) clustering method as an educational data mining technique to classify civil engineering students based on their academic results. Three key competency areas are used in this study, i.e., Structure and Material (SM), Geometry and Transportation (GT), and Construction Management (CM). A total of 221 students were analysed, exceeding the minimum sample size. The clustering process was performed using multiple cluster models (three, four, and five clusters), and the silhouette coefficient was used to evaluate the quality and accuracy of the clusters. Results: The findings reveal that the three-cluster model provides the most representative structure, showing the highest silhouette coefficient value compared with others. This indicates that three clusters offer the most appropriate grouping for evaluating academic performance. Cluster 1 represents students with excellent academic achievement, cluster 2 consists of students with good performance, and cluster 3 represents students with concerning academic performance requiring additional academic support. Conclusion: Overall, the study concludes that the three-cluster model, consisting of an excellent, good, and concerning performance group, offers the most accurate and representative evaluation of civil engineering students’ academic performance. These results provide valuable insights to design targeted interventions, enhance learning support, and optimize curriculum alignment to ensure that students achieve the competencies required before entering the professional field.