Jurnal Kumparan Fisika

PENERAPAN PROBLEM BASED LEARNING DENGAN FLIPPED CLASSROOM TERHADAP KEMAMPUAN PEMECAHAN MASALAH DAN SELF EFFICACY PADA MATERI FLUIDA STATIS

2025-08-29T03:58:43+00:00

ABSTRAK

Rendahnya kemampuan siswa dalam memecahkan masalah dan tingkat kepercayaan diri (self-efficacy) dalam pembelajaran fisika disebabkan oleh kurangnya keterlibatan aktif siswa dan ketidaksesuaian metode pembelajaran. Oleh karena itu, diperlukan pembelajaran yang efektif untuk meningkatkan kemampuan pemecahan masalah (KPM) dan self-efficacy. Penelitian ini menggunakan desain one group pretest posttest untuk mengeksplorasi penerapan model Problem Based Learning (PBL) dengan pendekatan flipped classroom dalam meningkatkan KPM dan self-efficacy siswa. Sampel penelitian terdiri dari 34 siswa kelas XI SMA di Kota Bandung yang diambil menggunakan teknik purposive sampling. Data dikumpulkan melalui tes kemampuan pemecahan masalah dalam bentuk esai yang terdiri dari 6 soal dan angket untuk mengukur self-efficacy yang di adaptasi dari physics learning Self Efficacy yang terdiri atas 30 pernyataan. Hasil analisis menunjukkan adanya peningkatan signifikan dalam kemampuan pemecahan masalah dan self-efficacy siswa setelah penerapan model tersebut, dengan nilai signifikansi 0,001 yang lebih kecil dari taraf signifikansi 0,05 berdasarkan uji paired sample t-test. Selain itu, analisis N-Gain menunjukkan rata-rata peningkatan kemampuan pemecahan masalah sebesar 0,77, yang termasuk dalam kategori tinggi, dan rata-rata N-Gain self-efficacy sebesar 0,56, yang berada dalam kategori sedang. Temuan ini menunjukkan bahwa penerapan model Problem Based Learning dengan pendekatan flipped classroom efektif dalam meningkatkan kemampuan pemecahan masalah dan self-efficacy siswa, sehingga dapat dijadikan acuan untuk inovasi dalam pembelajaran yang mendukung pengembangan keterampilan abad ke-21.

Kata kunci : Problem based Learning, flipped classroom, kemampuan pemecahan masalah siswa, self efficacy

ABSTRACT

Students' low problem-solving skills and self-efficacy in physics learning are caused by a lack of active student involvement and inappropriate learning methods. Therefore, effective learning is needed to improve problem-solving skills and self-efficacy. This study uses a one-group pretest-posttest design to explore the application of the Problem-Based Learning (PBL) model with a flipped classroom approach in improving students' problem solving skilland self-efficacy. The research sample consists of 34 eleventh-grade high school students in Bandung City, selected using purposive sampling. Data were collected through a problem-solving ability test in the form of an essay consisting of 6 questions and a questionnaire to measure self-efficacy, adapted from the Physics Learning Self-Efficacy scale, which consists of 30 statements. The analysis results showed a significant increase in students' problem-solving ability and self-efficacy after the implementation of the model, with a significance value of 0.001, which is smaller than the significance level of 0.05 based on the paired sample t-test. Additionally, N-Gain analysis showed an average increase in problem-solving ability of 0.77, which falls into the high category, and an average N-Gain self-efficacy of 0.56, which falls into the moderate category. These findings indicate that the implementation of the Problem-Based Learning model with a flipped classroom approach is effective in enhancing students' problem-solving skills and self-efficacy, thereby serving as a reference for innovative learning approaches that support the development of 21st-century skills.

Keywords : Problem based Learning, flipped classroom, problem solving skills, self efficacy

EVALUATION OF THE PERFORMANCE OF VARIOUS TYPES OF LDR SENSORS AS LUXMETERS THROUGH EXPONENTIAL AND POWER REGRESSION CALIBRATION

2025-10-30T00:41:33+00:00

This study aims to evaluate the performance of various types of Light Dependent Resistor (LDR) sensors as alternative luxmeters based on Arduino using exponential and power regression calibration methods. Four LDR types—GL5506, GL5528, GL5537, and GL5539—were tested under controlled lighting conditions using a dimmable smart bulb with light intensity variations from 5% to 100%. A commercial GM1030C luxmeter was used as the calibration reference. The measured data were analyzed using statistical parameters, including the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean percentage error, to determine the accuracy and stability of each sensor. The results show that all sensor types achieved R² values ranging from 0.9772 to 0.9992, indicating that both regression models effectively represent the nonlinear relationship between sensor output and actual light intensity. The GL5506 sensor exhibited the best accuracy with R² = 0.9962, RMSE = 13.13 lux, MAE = 11.1 lux, and an average error of 2.89% using the power regression model. The power regression model performed better for sensors with fast and linear responses (GL5506 and GL5528), while the exponential regression model was more suitable for sensors with gradual nonlinear responses (GL5537 and GL5539). With overall errors below 7%, all LDR sensors tested are suitable for use as economical and reliable Arduino-based luxmeters for educational and basic research applications.

ANALISIS HUJAN LEBAT DI CILACAP MENGGUNAKAN MODEL ECMWF, CITRA SATELIT, DAN DATA OBSERVASI

2025-11-21T13:14:53+00:00

ABSTRAK

Kejadian hujan sangat lebat di Kabupaten Cilacap pada 8–9 Oktober 2022 yang memicu banjir pada 9 Oktober 2022 menunjukkan adanya interaksi berbagai skala atmosfer yang kompleks dan belum banyak dibahas dalam studi sebelumnya. Penelitian ini menganalisis mekanisme penguatan konveksi pada skala global, regional, dan lokal dengan mengintegrasikan indeks NINO 3.4, IOD, MJO, suhu permukaan laut, pola aliran angin, data ECMWF, serta citra satelit Himawari-8 band 13. Hasil analisis menunjukkan bahwa kombinasi La Niña dan IOD negatif yang diikuti anomali suhu permukaan laut positif di selatan Jawa membentuk latar kondisi basah yang signifikan, namun fase 4 MJO yang secara klimatologis cenderung melemahkan konveksi justru tidak menghambat perkembangan awan. Ketidaksesuaian ini dijelaskan oleh adanya penguatan konvergensi regional dan pendinginan suhu puncak awan hingga −76,9 °C, yang menandakan pertumbuhan awan konvektif secara intensif. Pada skala lokal, indeks stabilitas atmosfer menunjukkan kondisi labil yang memicu terbentuknya hujan sangat lebat pada 8 Oktober 2022 antara pukul 18.00–21.00 UTC. Temuan ini menegaskan bahwa hujan ekstrem di Cilacap bukan hanya dipicu oleh kondisi globa yang basah, tetapi oleh hubungan antara anomali termal regional dan ketidakstabilan lokal yang mampu meniadakan efek pelemahan konveksi dari MJO fase 4. Studi ini memberikan pemahaman yang lebih komprehensif mengenai dinamika berbagai skala pemicu hujan ekstrem di pesisir selatan Jawa dan dapat berkontribusi pada peningkatan akurasi peringatan dini banjir berbasis kondisi atmosfer aktual.

Kata kunci : Hujan Lebat, Ecmwf, Citra Satelit, Observasi, Cilacap

ABSTRACT

The extreme rainfall event in Cilacap Regency on 8–9 October 2022, which triggered flooding on 9 October 2022 reflects the interaction of complex atmospheric processes across multiple scales and that has not been extensively explored in previous studies. This research investigates the mechanisms that enhanced convection at global, regional, and local scales by integrating the NINO 3.4 index, IOD, MJO, sea surface temperature, wind patterns, ECMWF data, and Himawari-8 band 13 satellite imagery. The analysis shows that the combination of La Niña and a negative IOD accompanied by positive sea surface temperature anomalies south of Java established a markedly moist background environment. However, despite the climatological tendency of MJO phase 4 to suppress convection, it did not inhibit cloud development during this event. This apparent inconsistency is explained by strengthened regional scale convergence and cloud top cooling reaching −76.9 °C, indicating vigorous convective growth. At the local scale, atmospheric stability indices reveal highly unstable conditions that triggered the very heavy rainfall observed on 8 October 2022 between 18.00–21.00 UTC. These findings demonstrate that the extreme rainfall in Cilacap was driven not only by moist global conditions but also by the interplay between regional thermal anomalies and local instability, which effectively counteracted the convective suppression typically associated with MJO phase 4. This study provides a more comprehensive understanding of the multiscale dynamics that trigger extreme rainfall along the southern coast of Java and contributes to improving the accuracy of flood early-warning systems based on real-time atmospheric conditions.

Keywords : Heavy Rainfall, Ecmwf, Satellite Imagery, Observation, Cilacap

EFEKTIVITAS MODEL PEMBELAJARAN TARL BERBANTUAN AI GEMINI TERHADAP KEMAMPUAN BERPIKIR KRITIS DAN PEMECAHAN MASALAH PADA MATA KULIAH HIDRODINAMIKA

2025-11-25T05:19:01+00:00

ABSTRAK

Teknologi artificial intelligence (ai) berpotensi meningkatkan kemampuan berpikir kritis dan pemecahan masalah pada mahasiswa, khususnya pada mata kuliah hidrodinamika yang menuntut keterampilan analitis dan pemecahan masalah. Penelitian ini bertujuan menganalisis efektivitas pembelajaran berbantuan gemini dibandingkan pembelajaran konvensional. Metode penelitian menggunakan desain pretest–posttest dengan membandingkan n-gain berpikir kritis dan pemecahan masalah pada tiga kategori kemampuan awal (low, medium, high). Hasil menunjukkan bahwa kelas kontrol dengan pembelajaran konvensional hanya mengalami peningkatan rendah hingga sedang, baik pada kemampuan berpikir kritis maupun pemecahan masalah (n-gain 0,16–0,43). Sebaliknya, kelas eksperimen yang menggunakan ai-gemini mengalami peningkatan signifikan dengan n-gain berada pada kategori sedang hingga tinggi (0,47–0,76). Seluruh kelompok mencapai nilai post-test pada kategori tinggi–sangat tinggi. Temuan ini mengindikasikan bahwa integrasi ai gemini mampu memberikan umpan balik cepat, penjelasan adaptif, dan dukungan konseptual yang memperkuat pengembangan berpikir kritis dan kemampuan pemecahan masalah mahasiswa. Pembelajaran berbasis ai direkomendasikan sebagai alternatif inovatif dalam perkuliahan hidrodinamika.

Kata kunci: Artificial Intelligence, Gemini, Hidrodinamika, berpikir kritis, pemecahan masalah.

ABSTRACT

The use of artificial intelligence (ai) technology has the potential to improve critical thinking and problem-solving skills in students, especially in hydrodynamics courses that require analytical and problem-solving skills. This study aims to analyze the effectiveness of gemini-assisted learning compared to conventional learning. The research method used a pretest–posttest design by comparing n-gain critical thinking and problem solving in three categories of initial abilities (low, medium, high). The results show that the control class with conventional learning only experienced low to moderate improvement in both critical thinking and problem solving abilities (n-gain 0.16–0.43). In contrast, the experimental class that used ai-gemini experienced a significant increase with n-gain in the moderate to high category (0.47–0.76). All groups achieved post-test scores in the high to very high category. These findings indicate that the integration of ai gemini is capable of providing rapid feedback, adaptive explanations, and conceptual support that strengthen the development of students' critical thinking and problem-solving skills. Ai-based learning is recommended as an innovative alternative in hydrodynamics lectures

Keywords: Artificial Intelligence, Gemini, Hydrodynamics, critical thinking, problem solving.