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All Journal SOSCIED Sciening : Science Learning Journal Charm Sains : Jurnal Pendidikan Fisika Lontar Physics Today
Lahope, Kenny Setiawan
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Investigasi Kesulitan Mahasiswa dalam Pembelajaran Berbasis Riset pada Perkuliahan Fisika Lingkungan Marianus, Marianus; Lahope, Kenny Setiawan
Lontar Physics Today Vol 4, No 2 (2025): June 2025
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/lpt.v4i2.23654

Abstract

Penelitian ini menginvestigasi kesulitan-kesulitan yang dihadapi mahasiswa fisika dalam pembelajaran berbasis riset pada mata kuliah Fisika Lingkungan. Studi ini mengadopsi pendekatan kualitatif deskriptif untuk mengidentifikasi akar permasalahan serta faktor-faktor yang berkontribusi terhadap hambatan belajar tersebut. Metode pengumpulan data meliputi wawancara mendalam dan observasi kelas untuk memperoleh pemahaman komprehensif mengenai pengalaman belajar mahasiswa. Penelitian dilaksanakan pada enam mahasiswa dan dua dosen pengampuh mata kuliah fisika lingkungan yang menerapkan pembelajaran berbasis riset. Data dalam penelitian ini dikumpulkan melalui wawancara yang dilakukan langsung dengan mahasiswa dan dosen pengampu mata kuliah Fisika Lingkungan, observasi partisipatif saat proses pembelajaran berbasis riset berlangsung, serta studi dokumentasi terhadap laporan riset mahasiswa dan materi perkuliahan terkait. Teknik analisis data dilakukan menggunakan Model Miles dan Huberman yang melibatkan pengumpulan data, reduksi data, penyajian data, dan penarikan kesimpulan untuk mengidentifikasi pola-pola kesulitan yang muncul secara sistematis. Temuan yang didapati menunjukkan kurangnya kemampuan mahasiswa dalam menuliskan argumen-argumen ilmiah, pemilhan metode, pegumpulan data, dan analisis data menggunakan program yang tepat; dimana para mahasiswa masih perlu mengikuti pelatihan atau workshop mengenai teknik menulis karya ilmiah atau artikel riset agar menjadi lebih mahir dalam menganalisis data, serta perlu adanya peningkatan literasi digital.
Intensity of Artificial Intelligence (AI) Use for Physics Learning in High Schools Rende, Jeane Cornelda; Lahope, Kenny Setiawan
Lontar Physics Today Vol 4, No 3 (2025): November 2025
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study investigates the intensity of Artificial Intelligence utilization in Physics education at a public high school in Manado City, Indonesia. The research aimed to understand the patterns and extent of AI integration, as well as the influencing factors, through a qualitative case study approach. The methodology involved in-depth interviews, classroom observations, and document analysis with six Physics teachers, students, and school management selected via purposive sampling. Data were analyzed using Miles and Huberman's model and thematic analysis. Key parameters examined included the forms of AI usage, intensity levels (low, medium, high), frequency, depth of utilization, and both supporting (teacher digital competence, school policy support, student enthusiasm) and hindering factors (lack of formal pedagogical training, infrastructure limitations, cultural resistance). Important findings indicate that AI adoption is predominantly at a medium level, primarily utilizing interactive simulations like PhET Interactive Simulation for abstract concept visualization, which significantly enhances student engagement and comprehension. However, comprehensive AI integration for adaptive, personalized learning remains rare. While school facilities are generally supportive, teachers' digital readiness varies, and AI usage frequency averages one to two times per week, the depth of its application often remains instrumental rather than transformational. In conclusion, AI adoption in Physics learning is still nascent, not yet fully optimizing AI's potential as an adaptive intelligent learning system, largely due to variations in teacher competency, school policies, and infrastructure readiness.
Effects of Integrating Deep Learning with a Project-Based Learning Model on Thermodynamics Learning Outcomes Makahinda, Tineke; Lahope, Kenny Setiawan; Kuron, Meidy Atina
Lontar Physics Today Vol 4, No 3 (2025): November 2025
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/lpt.v4i3.25521

Abstract

This study investigates the difference in Thermodynamics learning outcomes between students taught using a Project-Based Learning (PBL) model integrated with deep learning and those taught using the PBL model alone.. The research employed an experimental method with a quasi-experimental design, utilizing a Nonequivalent Pretest-Posttest Control Group Design. The study was conducted in the Physics Department of Manado State University for Physics Education and Physics programs during the Odd Semester of the 2025/2026 Academic Year. The research population included all active students from these programs, with a random sample of 40 students divided into an experimental class and a control class, each consisting of 20 students. Data were collected through essay-type tests, administered as pretests and posttests, and subsequently analyzed statistically using descriptive and inferential techniques with Python programming. The findings indicate that the experimental class, which implemented deep learning integrated with the Project Based Learning model, achieved a higher average Thermodynamics learning outcome (80.28) compared to the control class (72.35), demonstrating better data consistency (standard deviation of 7.93 versus 9.71). Shapiro-Wilk normality tests for both classes confirmed a normal distribution of data (p-value for experimental class is 0.3147 while control class is 0.0638), and Levene's homogeneity test confirmed homogeneous variances (p-value 0.2529). Furthermore, the independent sample t-test results showed a t-statistic of 2.8289 and a p-value of 0.0074, which is less than 0.05. This leads to the conclusion that there is a statistically significant difference in Thermodynamics learning outcomes between the experimental and control classes. These findings suggest that the integration of deep learning with the Project Based Learning model is effective in enhancing Thermodynamics learning outcomes.
Co-Authors Alfrie Musa Rampengan Alfrits Komansilan Antameng, Johan Olden B. Dj Tulandi Farly Reynol Tumimomor Jeane V. Tumangkeng Jeferson Polii Kuron, Meidy Atina M Marianus Marianus Marianus Mulyono, Maulana Muya, Angelika Birene Patricia Mardiana Silangen Rende, Jeane Cornelda Rolles Nixon Palilingan Rondonuwu, Anneke T Satyano W Mongan Silangen, Patricia Merdiana Sindim, Excelcis Deo Christianto Tineke Makahinda