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/><br /><img style="float: left; width: 176px; margin-top: 8px; margin-right: 10px; border: 2px solid #184b80;" src="https://gaung.dialeks.id/public/journals/3/cover_issue_9_en_US.jpg" alt="" width="199" height="246" /></p> <table style="font-size: 0.875rem;" cellpadding="2"> <tbody align="top"> <tr> <td width="100px">Journal Title</td> <td><strong>GAUNG: Jurnal Ragam Budaya Gemilang</strong></td> </tr> <tr> <td>ISSN</td> <td><strong>2985-945X</strong> (online) | <strong>2985-9123</strong> (print)</td> </tr> <tr> <td>DOI Prefix</td> <td><strong>Prefix 10.55909 by</strong><img 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/><strong style="font-size: 0.875rem;"><img src="blob:https://kpd.ejournal.unri.ac.id/2148b56b-9ad6-4f7e-a873-10d2bba52522" alt="" /></strong><img style="font-size: 0.875rem;" src="blob:https://kpd.ejournal.unri.ac.id/2148b56b-9ad6-4f7e-a873-10d2bba52522" alt="" /><strong style="font-size: 0.875rem;"><img src="blob:https://kpd.ejournal.unri.ac.id/06eb0c4d-05d1-40c1-bcfe-4f75e3b9d5df" alt="" /><img src="blob:https://kpd.ejournal.unri.ac.id/ceaa0919-956d-430c-95d2-96440a0275b9" alt="" /></strong><strong><br /></strong></td> </tr> <tr> <td>Editor in Chief</td> <td><strong>Nurul Ardiani</strong></td> </tr> <tr> <td>Publisher</td> <td> <p>Raja Zulkarnain Education Foundation</p> </td> </tr> <tr> <td valign="top">Frequency</td> <td><strong>3 issues in a year</strong></td> </tr> </tbody> </table> <p align="justify"><img src="data:image/jpeg;base64,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" /><strong><a href="https://gaung.dialeks.id">GAUNG: Jurnal Ragam Budaya Gemilang</a></strong> <strong> 2985-945X (online) | 2985-9123 (print)</strong> is published by Raja Zulkarnain Education Foundation, Riau Province, Indonesia. <br /><strong><a href="https://gaung.dialeks.id">GAUNG: Jurnal Ragam Budaya Gemilang</a></strong> is published three times a year. The first issue begins in January 2023. Subsequent issues in May and September; articles can be written in Indonesian or English.<br /><strong><a href="https://gaung.dialeks.id">GAUNG: Jurnal Ragam Budaya Gemilang</a></strong> contains several coverage topics. These include: language, teaching and learning language, literature, teraching and learning literature, culture, teaching and learning culture, art, teaching and learning art, the nature of activity based culture orientation, the nature of time based culture orientation, the nature of environment based culture orientation, the human relations based culture orientation. </p> <p align="justify"><strong><a href="https://gaung.dialeks.id">GAUNG: Jurnal Ragam Budaya Gemilang</a></strong> was indexed in:</p> <p><a style="background-color: #ffffff; font-size: 0.875rem;" href="https://garuda.kemdikbud.go.id/journal/view/34982" target="_blank" rel="noopener"><img 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" 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" 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" 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" 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</div> </div> </div> </div> Raja Zulkarnain Education Foundation en-US GAUNG: Jurnal Ragam Budaya Gemilang 2985-9123 Prosedur Penggunaan Uji t Satu Sampel dan Sampel Berpasangan untuk Data Prates dan Postes Cerpen Profetik https://gaung.dialeks.id/index.php/aj/article/view/127 <p>Penelitian ini bertujuan untuk mendeskripsikan: 1) prosedur input data prates dan psotes ke sheet SPSS; 2) prosedur uji normalitas data prates dan postes; 3) prosedur uji homogenitas data prates dan postes; 4) prosedur uji t satu sampel data prates dan data postes menemukan tema dan amanat cerpen profetik; 5) prosedur uji t sampel berpasangan data prates dan postes menemukan tema dan amanat cerpen profetik. Populasi penelitian ini adalah 39 siswa kelas X SMA Negeri 12 Pekanbaru yang mengikuti prates dan postes atas pembelajaran menemukan tema dan amanat cerpen profetik menggunakan bahan ajar khusus berbasis model Problem-Based Learning. Sampel ditarik secara random sebanyak 36 siswa. Data yang terkumpul dianalisis menggunakan uji t satu sampel dengan mean pembanding 10,50 untuk data prates dan 15,48 untuk data postes. Data perbandingan hasil prates dan postes dianalisis menggunakan uji t sampel berpasangan. Hasil penelitian: 1) prosedur input data: buka aplikasi SPSS, pilih variable view lalu ketik prates di kolom Name baris-1 dan ketik postes di kolom Name baris-2 serta ubah kolom Decimal menjadi 0, klik tombol Data View di kiri bawah sehingga tampil layar baru, salin data prates dan postes yang sudah disiapkan di excel; 2) prosedur uji normalitas adalah klik secara berturut-turut: tombol descriptive statistics, explore, tempatkan prates dan postes di kotak dependent list; tombol plots, normality plots with test, countinue, OK; 3) prosedur uji homgenitas adalah secara berturut-turut: klik tombol compare means, One-way Anova, tempatkan variabel prates ke kotak dependent list, variabel postes di kotak factors, option, homogeinity of variance test, countinue, OK; 4) prosedur uji t satu sampel data prates, klik analyze, compare means, one-sample t test, pindahkan variabel prates ke kotak test variable(s) via anak panah, option, ketik 10,50 pada kotak test value, option, countinue, OK; 5) prosedur uji t satu sampel variable postes, klik analyze, compare means, one-sample t test, pindahkan varibel postes ke kotak test variable(s) ke kanan via anak panah, option, ketik 15,48 pada kotak test value, option, countinue, OK.</p> Abdul Razak Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-04-10 2026-04-10 4 2 81 96 10.55909/gj.v4i2.127 Orang Patut”: Studi Kritis Karakter Kepemimpinan dalam Naskah Gurindam Dua Belas https://gaung.dialeks.id/index.php/aj/article/view/132 <p>Krisis karakter dalam kepemimpinan kontemporer menuntut adanya penggalian kembali nilai-nilai kearifan lokal sebagai instrumen penguatan integritas. Penelitian yang berjudul “Orang Patut”: Studi Kritis Karakter Kepemimpinan dalam Naskah Gurindam Dua Belas ini bertujuan untuk menganalisis secara kritis konsep “orang patut” dalam naskah Gurindam Dua Belas karya Raja Ali Haji. Kajian ini didasari oleh rangkaian penelitian terdahulu, dimulai dari Hamidy (2001) dan Mulyadi (2018) yang menyinggung relevansi birokrasi secara deskriptif-administratif di Kepulauan Riau, diikuti oleh Hidayat (2018) yang memfokuskan pada aspek religiusitas sebagai landasan perilaku sosial masyarakat Melayu, hingga Elmustian (2025) yang mengonstruksi identitas dan kekuasaan dalam kepemimpinan Melayu tradisional. Meskipun demikian, terdapat celah riset (research gap) mengenai bedah teoretis yang menempatkan terminologi “orang patut” sebagai sebuah model kepemimpinan kritis-etis yang aplikatif. Menggunakan metode kualitatif dengan pendekatan hermeneutika, penelitian ini berhasil menjawab tiga rumusan masalah utama sebagai temuan sekaligus novelty: (1) konsep “orang patut” merupakan representasi kepemimpinan performatif yang menyatukan integritas batin (budi) dan kompetensi sosial (bahasa); (2) kriteria etis dan spiritual pemimpin bersumber dari fondasi transendensi yang diderivasi ke dalam manajemen hati dan pengendalian indera; serta (3) relevansi konsep ini sebagai instrumen integritas internal yang mampu menutup kelemahan hukum positif melalui sistem kontrol moral profetik. Penelitian ini menyimpulkan bahwa “orang patut” adalah model kepemimpinan otentik yang mampu menjawab tantangan dekadensi moral di era modern.</p> Elmustian Hermandra Irwanto Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-05-14 2026-05-14 4 2 97 106 10.55909/gj.v4i2.132 Analisis Nilai Religius dalam Budaya Melayu pada Film Negeri 5 Menara https://gaung.dialeks.id/index.php/aj/article/view/144 <p>Penelitian ini bertujuan untuk menganalisis budaya Melayu dalam film Negeri 5 Menara dengan fokus pada nilai-nilai religius yang terkandung di dalamnya. Penelitian menggunakan pendekatan kualitatif dengan metode deskriptif. Sumber data penelitian berupa dialog, adegan, tindakan tokoh, dan unsur visual dalam film yang mencerminkan budaya Melayu dan nilai religius. Teknik pengumpulan data dilakukan melalui metode simak dan catat dengan cara menyimak film secara berulang, kemudian mencatat bagian-bagian yang relevan dengan fokus penelitian. Teknik analisis data dilakukan melalui tahap reduksi data, penyajian data, dan penarikan kesimpulan. Hasil penelitian menunjukkan bahwa budaya Melayu dalam film Negeri 5 Menara tercermin melalui nilai kesopanan, penghormatan kepada guru dan orang tua, kebersamaan, persaudaraan, serta penghargaan terhadap pendidikan dan ilmu pengetahuan. Nilai-nilai tersebut berkaitan erat dengan ajaran Islam yang menjadi dasar kehidupan masyarakat Melayu. Selain itu, ditemukan pula nilai religius berupa keimanan dan ketakwaan, disiplin dalam menjalankan ibadah, kerja keras, semangat menuntut ilmu, kesabaran, dan keikhlasan dalam menghadapi berbagai ujian kehidupan. Kehidupan pesantren dalam film memperlihatkan hubungan yang kuat antara budaya Melayu dan nilai keagamaan melalui perilaku para tokoh serta pola interaksi sosial yang menjunjung tinggi adab dan moralitas. Dengan demikian, film Negeri 5 Menara tidak hanya berfungsi sebagai media hiburan, tetapi juga sebagai sarana pendidikan karakter dan pelestarian budaya Melayu yang berlandaskan nilai religius.</p> Elmustian Yusron Hadi Falah Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-06-25 2026-06-25 4 2 107 114 10.55909/gj.v4i2.144 Penerapan Teknik Tugas Menyalin dan Teknik Tes di Bahan Ajar Khusus dalam Pembelajaran Keragaman Budaya https://gaung.dialeks.id/index.php/aj/article/view/46 <p>Penelitian ini bertujuan untuk mendeskripsikan: 1) kategori hasil prates keragaman budaya; 2) prosedur penerapan teknik tugas menyalin dan teknik tes dalam bahan ajar khusus dalam pembelajaran keragaman budaya 3) keaktifan siswa belajar keragaman budaya menggunakan bahan ajar khusus; 4) kategori hasil postes keragaman budaya setelah menerapkan teknik tugas menyalin dan teknik tes dalam bahan ajar khusus; 5) efektivitas teknik tugas menyalin dan teknik tes di bahan ajar khusus dalam pembelajaran keragaman budaya. Penelitian dilakukan di SD Negeri Normal. Kegiatan penelitian berlangsung di awal semester ganjil tahun pelajaran 2025/2026. Populasi penelitian ini adalah para siswa kelas 5 yang mengikuti tes pembelajaran keragaman budaya baik sebelum maupun sesudah menggunakan teknik tugas menyalin dan teknik tes di bahan ajar khusus. Jumlah populasi hanya 11. Sampel sebanyak 11 juga karena menggunakan sampel total. Data hasil belajar dikumpulkan menggunakan instrumen tes pilihan ganda 3 opsi yang disusun mengikuti prosedur objektif dan sistematis. Data dianalisis menggunakan statistik deskriptif yakni mean, simpangan baku, skor minimum, skor maksimum, dan persen. Teknik tugas menyalin di bahan ajar khusus diyatakan efektif terhadap pembelajaran keragaman budaya jika mean postes lebih besar minimal 20,00 persen dari mean prates. Hasil penelitian: 1) hasil belajar keragaman budaya sebelum menerapkan teknik tugas menyalin dan teknik tes di bahan ajar khusus berkategori rendah; 2) prosedur pembelajaran keragaman budaya melibatkan 4 kegiatan awal, 16 kegiatan inti, dan 4 kegiatan akhir; 3) hasil belajar keragaman budaya setelah menerapkan teknik tugas menyalin dan teknik tes di bahan ajar khusus berkategori tinggi; 4) penerapan teknik tugas menyalin dan teknik tes di bahan ajar khusus terbukti efektif dalam pembelajaran keragaman budaya di kelas 5 SD Negeri Normal.</p> Aminah Marselina Magal Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-05-30 2026-05-30 4 2 115 128 10.55909/gj.v4i2.46 Pengaruh Teknik Inovasi Menggunakan Bahan Ajar Khusus dalam Pembelajaran Menemukan Tema dan Amanat Cerpen https://gaung.dialeks.id/index.php/aj/article/view/149 <p>Penelitian ini bertujuan untuk mendeskripsikan: 1) hasil prates menemukan tema dan amanat cerpen; 2) hasil postes menemukan tema dan amanat cerpen setelah menerapkan teknik tugas menyalin dan teknik tes pilihan ganda dalam bahan ajar khusus; 3) pengaruh teknik tugas menyalin dan teknik tes pilihan ganda di bahan ajar khusus dalam pembelajaran menemukan tema dan amanat cerpen. Penelitian ini menggunakan metode deskriptif kuantatif. Penelitian dilakukan di SMP Negeri 1 Sebatik yang berlangsung di awal semester genap tahun pelajaran 2025/2026. Populasi penelitian ini adalah para siswa kelas 7A yang mengikuti tes pembelajaran menemukan tema dan amanat cerpen baik sebelum maupun sesudah menggunakan teknik tugas menyalin dan teknik tes pilihan ganda di bahan ajar khusus. Mereka berjumlah 27 siswa. Sampel ditetapkan sebanyak 25 siswa berdasarkan formula statistik. Data hasil belajar dikumpulkan menggunakan instrumen tes pilihan ganda 4 opsi yang disusun mengikuti prosedur objektif dan sistematis. Tujuan-1 dan tujuan-2 dianalisis menggunakan statistik deskriptif yakni mean, persen, simpangan baku, modus, skor minumum, dan skor maksimum. Tujuan-3 dicapai menggunakan analisis statistik inferensial parametrik yakni uji t sampel berpasangan. Hasil penelitian: 1) mean prates menemukan tema dan amanat cerpen berkategori rendah; 2) prosedur pembelajaran menemukan tema dan amanat cerpen menggunakan teknik tugas menyalin dan teknik tes pilihan ganda dalam bahan ajar khusus bagi siswa kelas 7 SMP Negeri 1 Sebatik melibatkan 3 kegiatan awal, 21 kegiatan inti, dan 3 kegiatan akhir; 3) hasil postes menemukan tema dan amanat cerpen berkategori tinggi; 4) teknik tugas menyalin dan teknik tes pilihan ganda dalam bahan ajar khusus terbukti berpengaruh positif terhadap pembelajaran menemukan tema dan amanat cerpen di kelas 7A SMP Negeri 1 Sebatik.</p> Rudianto Sahar Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-05-30 2026-05-30 4 2 129 140 10.55909/gj.v4i2.149 The Theme of Subur Bahasa, Segar Sastera in the Form 3 Malay Language Textbook: A Readability Analysis Using the Modified Fog Index https://gaung.dialeks.id/index.php/aj/article/view/53 <p>This research aims to describe: 1) the readability level of the Malay Language Dignity text in theme 8: Fertile Language, Blooming Literature in the Level 3 Malay Language textbook; 2) the readability level of the text The Benefits of Reading Literary Works in theme 8: Fertile Language, Blooming Literature in the Level 3 Malay Language textbook; 3) the readability level of the Akhbar text in theme 8: Fertile Language, Blooming Literature in Level 3 Malay Language textbooks. This research uses a quantitative descriptive method based on descriptive statistics in the context of calculating readability using numerical formulas. This research uses two types of instruments, namely an observation guide to collect narrative text data and a checklist instrument which is useful for validating the results of calculating the number of words and number of text sentences and validating data from analysis of the readability of narrative texts. Narrative text readability data was analyzed using the modified Fog Index readability formula, namely the Abdul Razak Modified Fog Index Criteria (KIFMAR). Research results: 1) readability of the text Dignity of the Malay Language in theme 8: Fertile Language, Blooming Literature in the Level 3 Malay Language textbook at low school level, which means the text is easy to read by level 3 students; 2) readability of the text Dignity of the Malay Language in theme 8: Fertile Language, Blooming Literature in the Level 3 Malay Language textbook at low school level, which means the text is easy to read by level 3 students; 3) the readability of the Akhbar text in theme 8: Fertile Language, Blooming Literature in the Level 3 Malay language textbook at low school level, which means the text is easy to read by level 3 students.</p> Azlina Ayup Yessi Vanni Deya Noor Hafiza Abdul Razak Copyright (c) 2025 GAUNG: Jurnal Ragam Budaya Gemilang 2026-05-30 2026-05-30 4 2 141 150 10.55909/gj.v3i1.53 Rekonstruksi Nilai Kepemimpinan dalam Tunjuk Ajar Melayu Tenas Effendy: Pendekatan Deskriptif Kualitatif https://gaung.dialeks.id/index.php/aj/article/view/141 <p>Penelitian ini bertujuan untuk: 1) merekonstruksi nilai-nilai kepemimpinan bermartabat yang terkandung dalam Tunjuk Ajar Melayu karya Tenas Effendy; 2) organisasi nilai-nilai kepemimpinan bermartabat yang terkandung dalam Tunjuk Ajar Melayu. Pendekatan deskriptif kualitatif diterapkan dengan analisis isi sebagai metode utama. Sumber data utama adalah Tunjuk Ajar Melayu karya Tenas Effendy. Instrumen nontes digunakan dalam penelitian ini untuk mengumpulkan nilai-nilai kepemimpian bertabat yang terkandung dalam Tunjuk Ajar Melayu. Analisis data nilai-nilai kepemimpinan bermartabat yang terkandung dalam Tunjuk Ajar Melayu menggunakan model kualitatif interaktif Miles, Huberman, dan Saldaña. Keabsahan data nilai-nilai kepemimpinan bermartabat yang terkandung dalam Tunjuk Ajar Melayu menggunakan triangulasi sumber dan telaah sejawat akademik. Hasil penelitian: 1) Penelitian ini mengidentifikasi delapan nilai kepemimpinan bermartabat dalam Tunjuk Ajar Melayu: amanah, bijaksana, adil, berani karena benar, musyawarah, rendah hati, bertanggung jawab, dan berwibawa; 2) nilai-nilai kepemimpinan bermartabat dalam Tunjuk Ajar Melayu diorganisasikan ke dalam tiga dimensi—intrinsik, relasional, dan transendental—serta direkonstruksi melalui model pedagogik tiga tahap yang mencakup identifikasi, interpretasi, dan kontekstualisasi.</p> Hermandra Elmustian Hendri Ramadhan Copyright (c) 2026 GAUNG: Jurnal Ragam Budaya Gemilang 2026-06-25 2026-06-25 4 2 151 160 10.55909/gj.v4i2.141