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IMPLEMENTASI EXTREME PROGRAMMING DALAM PENGEMBANGAN APLIKASI MOBILE PENGENALAN ORGANISASI PADA MASA ORIENTASI MAHASISWA Rahman, Muhammad Farhan Fadlu; Darussalam, Kholdi; Saphira, Regita Cahya; Purwani, Fenny
Jurnal Sistem Informasi, Teknologi Informatika dan Komputer Volume 14 No 2, Januari Tahun 2024
Publisher : Universitas Muhammadiyah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24853/justit.14.2.128-132

Abstract

Penelitian ini membahas implementasi extreme programming (XP) dalam pengembangan aplikasi mobile untuk pengenalan organisasi selama masa orientasi mahasiswa di UIN Raden Fatah Palembang. Extreme programming (XP) diterapkan untuk mempercepat pengembangan, meningkatkan kualitas perangkat lunak, dan merespons perubahan kebutuhan pengguna. Studi kasus menggambarkan langkah-langkah extreme programming (XP) dalam perencanaan, perancangan sistem, desain, coding, dan pengujian. Hasilnya menunjukkan bahwa extreme programming (XP) memungkinkan fleksibilitas yang tinggi dan komunikasi yang kuat antara pengembang dan pemangku kepentingan. Dalam fase perencanaan, peran pemangku kepentingan dalam menentukan fitur aplikasi sangat penting. Selama fase pengujian, kesalahan dapat dideteksi dan diperbaiki secara efisien. Hasil ini menggarisbawahi potensi extreme programming (XP) dalam pengembangan aplikasi mobile orientasi mahasiswa.
Data Mining Analysis for Assessing Students Proficiency in Scientific Writing Fahruddin; Saphira, Regita Cahya; Testiana, Gusmelia
Indonesian Research Journal in Education |IRJE| Vol. 8 No. 2 (2024): IRJE |Indonesian Research Journal in Education|
Publisher : Universitas Jambi, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/irje.v8i2.35403

Abstract

A good understanding of the material and clear writing are important for success in academic and professional careers. However, not all students are equally skilled at writing scientific articles. This research aims to classify the levels of student understanding in writing scientific articles. This study classifies college students' understanding of scientific writing across four universities in Palembang with a sample of 108 students selected through random sampling. Data were collected via questionnaires, and the quantitative method used data mining with the C4.5 algorithm. Testing with RapidMiner software yielded a model accuracy of 74.58%. The study found that the C4.5 algorithm's accuracy in classifying students’ understanding of scientific writing falls into the Fair category, meaning the model treats all individuals or groups equally. The findings of this research should be a particular concern for higher education institutions to support and assist students in better understanding how to write scientific articles.