PENINGKATAN MOTIVASI BERPRESTASI DAN HASIL BELAJAR MATEMATIKA SISWA KELAS XI AKL 1 SMK KHOIRIYAH MELALUI PEMBELAJARAN MODEL MURDER BERBANTUAN MEDIA MICROSOFT EXCEL DAN LKPD
Abstract
Penelitian ini bertujuan untuk mengetahui sejauhmana penerapan pembelajaran model MURDER berbantuan media Power Point dan LKS meningkatkan motivasi berprestasi dan hasil belajar matematika siswa. Penelitian ini merupakan Penelitian Tindakan Kelas (PTK) yang dilaksanakan dalam 2 siklus, masing-masing siklus terdiri atas empat tahapan yaitu: perencanaan, tindakan, observasi, dan refleksi. Subjek dalam penelitian tindakan ini adalah siswa kelas XI AKL 1 Semester Gasal SMK Khoiriyah Sumobito tahun pelajaran 2019/2020 dengan jumlah siswa sebanyak 32 orang.
Teknik pengumpulan data menggunakan teknik observasi, angket, catatan lapangan, dan tes. Analisis data dilakukan dengan model analisis interaktif yang terdiri dari proses pengumpulan data, penyajian data, dan verifikasi data. Hasil dari penelitian ini menunjukkan bahwa motivasi berprestasi dan hasil belajar siswa pada aspek pengetahuan dan keterampilan meningkat. Hal ini ditunjukkan dengan meningkatnya skor rata-rata motivasi berprestasi siswa pada tahap Pra Silus, Siklus I dan II adalah 53,19; 57,25 dan 62,72. Pada aspek pengetahuan, nilai rata-rata kelas pada tahap Pra Siklus, Siklus I dan II adalah 63,97; 69,50 dan 73,97Pada aspek keterampilan nilai rata-rata kelas pada tahap Pra Siklus, Siklus I dan II adalah 64,69 dan 74,06; dan 76,41.
Berdasarkan hasil tindakan dan analisis, penelitian ini menyimpulkan bahwa penerapan pembelajaran model MURDER berbantuan Microsoft Excel dan LKPD dapat meningkatkan motivasi berprestasi dan hasil belajar pada mata pelajaran matematika siswa kelas XI AKL 1 SMK Khoiriyah Sumobito tahun pelajaran 2019/2020.
Downloads
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with DIDAKTIKA: Jurnal Pemikiran Pendidikan agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
Licensing for Data Publication
Open Data and Software Publishing and Sharing
The journal strives to maximize the replicability of the research published in it. Authors are thus required to share all data, code or protocols underlying the research reported in their articles. Exceptions are permitted but have to be justified in a written public statement accompanying the article.
Datasets and software should be deposited and permanently archived inappropriate, trusted, general, or domain-specific repositories (please consult http://service.re3data.org and/or software repositories such as GitHub, GitLab, Bioinformatics.org, or equivalent). The associated persistent identifiers (e.g. DOI, or others) of the dataset(s) must be included in the data or software resources section of the article. Reference(s) to datasets and software should also be included in the reference list of the article with DOIs (where available). Where no domain-specific data repository exists, authors should deposit their datasets in a general repository such as ZENODO, Dryad, Dataverse, or others.
Small data may also be published as data files or packages supplementary to a research article, however, the authors should prefer in all cases a deposition in data repositories.
