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Record W3041147905 · doi:10.47647/jsh.v3i1.234

PENINGKATAN MOTIVASI DAN KEMAMPUAN MENGINSTALASI SISTEM OPERASI JARINGAN MELALUI METODE DISCOVERY LEARNING PADA SISWA KELAS XI-TKJ SMK NEGERI 1 SIGLI

2020· article· id· W3041147905 on OpenAlexaff
Nuraidah Nuraidah

Bibliographic record

VenueJurnal Sosial Humaniora Sigli · 2020
Typearticle
Languageid
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsDiscovery Centre
Fundersnot available
KeywordsHumanitiesPhysicsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

Rendahnya motivasi belajar siswa dipengaruhi oleh beberapa faktor diantaranya adalah penggunaan metode pembelajaran yang kurang tepat sehingga dapat berpengaruh terhadap motivasi dan kemampuan siswa. Penelitian ini bertujuan untuk meningkatkan motivasi dan kemampuan menginstalasi sistem operasi jaringan melalui metode discovery learning pada siswa kelas XI-TKJ SMK Negeri 1 Sigli. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) dengan prosedur pelaksanaannya melalui tahapan: perencanaan, pelaksanaan, pengamatan, dan refleksi. Penelitian ini dilaksanakan pada semester ganjil tahun pelajaran 2019/2020. Subjek dalam penelitian adalah siswa kelas XI TKJ SMK Negeri 1 Sigli yang berjumlah 28 orang. Teknik pengumpulan data melalui observasi, tes dan dokumentasi. Analisis data secara deskriptif kualitatif. Hasil penelitian menunjukkan: ketuntasan hasil belajar siswa secara klasikal pada kondisi awal 46,4%. Pada siklus I, ketuntasan siswa meningkat 14,3% dari sebelumnya yang mencapai 60,7% sehingga ketuntasan belajar secara klasikal dikatakan tidak tuntas. Pada siklus ke II terjadi peningkatan yang signifikan yaitu meningkat 35,7% sehingga menjadi 96.4%. Maka disimpulkan bahwa penggunaan metode discovery learning dapat meningkatkan motivasi dan kemampuan menginstalasi sistem operasi jaringan pada siswa kelas XI TKJ SMK Negeri 1 Sigli.Kata Kunci: Motivasi, Kemampuan, Menginstalasi, dan Metode Discovery Learning.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.288
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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