PENERAPAN PENDEKATAN “5 M” \nUNTUK MENINGKATKAN HASIL BELAJARSISWA \nPADA MATA PELAJARAN IPA \nTENTANG SIFAT – SIFAT CAHAYA \n \n:Penelitian Tindakan Kelas di Kelas V Sekolah Dasar Negeri Limusnunggal 01 \nKecamatan Cileungsi Kabupaten Bogor Tahun Pelajaran 2013/2014:
Bibliographic record
Abstract
Implementation Approach "5M" To Improve Student Results in Science Lesson About Properties - properties in Class V Light SDN Limusnunggal 01 Sub Cullinan Bogor, academic year 2013-2014. \nThe process of action research is done two cycles, the first cycle consisted of three meetings while the second cycle consists of one session and each consists of four main actions: planning, action, observation, and reflection. End of each cycle tests performed using instruments about. Then the results obtained at the first meeting and the second meeting were averaged to be the end result of each cycle. The results showed that the average value of learning outcomes in prasiklus 53 with a percentage of success (25%) based on the KKM 70, in the first cycle while scoring 70 second cycle gain value of 84.13. Percentage of successful first cycle (65%) and second cycle (90%). Similarly, the observation of students showed an increase in learning motivation, cooperation, and student activity. This study concludes that the application of the "5M" Observe, inquire, gather information / experiment, Associate / process information, and Communicating) \nYang menjadilatarbelakangdaripenelitianiniadalahkurangnyakreativitassiswadan guru dalammetodepembelajarantersebutmenyebabkanpembelajaranpasifdanhasiltestulisrendah, sebagianbesarsiswamemperolehnilai yang belummencapai KKM matapelajaranIlmuPengetahuanAlam di SDN Limusnunggal 01 yang telahditetapkanyaitu 70. Hal initerbuktidengan data yang menunjukkanbahwaberdasarkanhasilulanganpelajaran IPA di kelas V D tahun 2014, tentang“Sifat – sifatcahaya”menunjukkanhasil yang tidakmemuaskan.Hanya10 siswaatau 25% yang mampumencapainilai di atas KKM dansebanyak30 orang siswaatau 75 % hasiltessiswamasihberada di bawah KKM. Dalam proses pembelajaranmenyebutkansumber- sumbercahayadanmenemutunjukkansifat- sifatcahaya, dan penggunaan alatperagamerupakan hal yang paling menentukanprestasi belajar agar paling tidak 75% siswadiharapkanmampumencapainilai di atas KKM. Untukmemecahkanmasalahtersebutditerapkan model pembelajaran “5 M” dan model tersebutmempunyaikelebihanyaitu rasa ingin tau, mengamati, mencoba, menanya, menyaji, menalardanmenciptakaryasederhanaakantumbuhdalam proses pembelajaran, kerjasamasesamasiswaterwujuddengandinamis, munculnyadinamikagotongroyong yang merata di seluruhanakdidik. SemogadenganmenggunakanMetodeDemontrasiinidapatmenjadikanmasukkanbagipara guru yang kreatifdaninteraktifsehinggapembelajaranmenjadimenyenangkanuntukparasiswa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".