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All Journal JoEELE Seminar Nasional Hasil Penelitian LP2M UNM
Muh. Miftah Fauzan
Universitas Negeri Makassar
Religion Arts Humanities Chemistry Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Program Kebijakan Makassar Recover dalam Penanggulangan Covid-19 di Kota Makassar Geminastiti Sakkir; Muh. Miftah Fauzan; Muji Iswanty; Andi Muhammad Irawan
Seminar Nasional LP2M UNM SEMINAR NASIONAL 2022 : PROSIDING EDISI 3
Publisher : Seminar Nasional LP2M UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2608.574 KB)

Abstract

Abstrak. Artikel ini merupakan bagian dari hasil penelitian yang berjudul Efektivitas Program “Makassar Recover” bagi Penderita Covid-19 di Kota Makassar. Metode penelitian yang akan digunakan adalah metode Qualitative Method. Penelitian ini akan digunakan untuk menjelaskan efektivitas program Makassar Recover tersebut bagi penderita Covid-19 di kota Makassar. Subjek yang akan dilibatkan adalah Pemerintah Kota Makassar, Dinas Kesehatan Kota Makassar, dan Ikatan Dokter Indonesia sebagai eksekutor Makassar Recover. Teknik pengumpulan data akan dimulai dengan observasi. Kedua, melakukan interview atau wawancara terhadap subjek penelitian untuk menggali informasi lebih rinci dan lengkap. Ketiga, mengumpulkan dokumentasi penelitian dengan cara memperoleh laporan dan dokumen lainnya yang erat hubungannya dengan objek penelitian dan membaca literatur sebagai dasar teori yang akan dijadikan sebagai landasan teoritis dalam penelitian. Analisis data kualitatif dilakukan secara interaktif dan berlangsung terus menerus hingga selesai. Proses pengumpulan data kualitatif pada umumnya memfokuskan pada wawancara dan observasi. Membuat analisis datanya berupa analisis tekstual dari hasil transkrip atau catatan lapangan yang tidak terstruktur. Analisis data yaitu reduksi data, penyajian data dan kesimpulan. Hasil analisis akan berbentuk deskripsi tentang efektivitas program Makassar Recover bagi penderita Covid-19 di kota Makassar. Adapun hasil yang ditemukan dari data analisis adalah program- program yang termasuk dalam kebijakan Makassar Recover terbagi dalam 3 bagian, yaitu: (a) Imunitas Kesehatan; (b) Adaptasi Sosisal; dan (c) Pemulihan Ekonomi. Kata Kunci: Program, Makassar Recover, Covid-19, Kebijakan
Students’ Perceptions of AI Integration in EFL Writing: A Case Study of ChatGPT and Grammarly Use Indra Hidayat; Muh. Tahir; Muh. Miftah Fauzan
Journal of Excellence in English Language Education Vol 5, No 4, October (2026): Journal of Excellence in English Language Education
Publisher : Program Studi Pendidikan Bahasa Inggris FBS UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/joeele.v5i4, October.89948

Abstract

This study examines how English Education students perceive the integration of artificial intelligence tools, specifically ChatGPT and Grammarly, into their EFL writing practice. The researcher used a qualitative case study design and interviewed ten students at Universitas Negeri Makassar. These participants were selected purposively, on the basis that they already used AI regularly for their writing assignments, and the interviews themselves were semi-structured. Coding proceeded in three stages, open, axial, and selective, and four themes emerged from that process. AI serves as a helpful tool for technical writing tasks. Students pick up these tools mainly through watching classmates, not through formal teaching. Extrinsic motivations, speed, accuracy, grades, push most of the usage. And lecturers do warn students about over-reliance, though that warning rarely changes actual behavior. Participants spoke well of AI's help with paraphrasing, grammar, translation, vocabulary, and paragraph structuring. But how they learned to use it matters here: self-exploration and peer observation carried the weight, not instruction. Motivation followed the same shape, since most students cared about finishing quickly and getting a good grade rather than growing as writers. Lecturer warnings about dependence were common too, yet these cautions mostly failed to produce more critical or self-regulated use among students. The gap this leaves is between technical appreciation and deeper cognitive engagement. Students recognize what AI can do for them mechanically, but that recognition doesn't stretch into using these tools to think harder or better. EFL writing courses seem to be missing structured instruction in AI literacy, and the findings here suggest that gap needs closing.
Co-Authors Andi Muhammad Irawan Geminastiti Sakkir Indra Hidayat Muh. Tahir Muji Iswanty