Pengembangan Aplikasi Prediksi Kemampuan Siswa pada Pembelajaran Berbasis Proyek dalam Perkuliahan Rekayasa Perangkat Lunak

Denny Kurniadi

doi:10.24036/voteteknika.v12i1.127511

Pengembangan Aplikasi Prediksi Kemampuan Siswa pada Pembelajaran Berbasis Proyek dalam Perkuliahan Rekayasa Perangkat Lunak

Denny Kurniadi⁽¹⁾,
(1) Universitas Negeri Padang

Indonesia

Corresponding Author

DOI : https://doi.org/10.24036/voteteknika.v12i1.127511

Full Text:

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Abstract

ABSTRAK

Artikel ini menyajikan sebuah studi kasus tentang implementasi Pembelajaran Berbasis Proyek (PBL) dalam konteks pendidikan Rekayasa Perangkat Lunak, dengan fokus pada pengembangan aplikasi untuk memprediksi kemampuan siswa menggunakan metode Naive Bayes. Proyek ini mengikuti metodologi Waterfall untuk proses pengembangannya. Evaluasi aplikasi menunjukkan keberhasilannya dalam memprediksi kemampuan siswa dengan akurasi tinggi, mencapai 100% presisi dan review untuk kelas yang dikategorikan sebagai 'prediksi sangat baik' dan 'butuh bimbingan', serta 80% untuk kelas 'baik' dalam prediksi. Pengujian kedua metodologi PBL dan Waterfall menghasilkan hasil positif, dengan pengujian Waterfall mencapai antara 95% hingga 100% dalam verifikasi kebutuhan, kesesuaian desain sistem, pengujian unit, dan integrasi komponen sistem. PBL juga terbukti berhasil dalam meningkatkan pemahaman konsep Rekayasa Perangkat Lunak, keterampilan praktis, dan memfasilitasi pendekatan pembelajaran aktif. Sebagai kesimpulan, aplikasi yang dikembangkan menawarkan solusi yang efektif dan efisien untuk memprediksi kemampuan siswa, dengan dampak signifikan dalam meningkatkan kualitas pembelajaran dan penilaian dalam mata kuliah Rekayasa Perangkat Lunak.

Kata kunci : pengembangan aplikasi, prediksi kemampuan siswa, rekayasa perangkat lunak, pembelajaran berbasis proyek.

This article presents a case consider on the usage of Project-Based Learning (PBL) within the setting of Program Building instruction, centering on the advancement of an application for anticipating understudy capacities utilizing the Naive Bayes strategy. The extend takes after the Waterfall strategy for its improvement handle. The assessment of the application illustrates its victory in anticipating understudy capacities with tall precision, accomplishing 100% exactness and review for classes categorized as 'excellent prediction' and 'guidance needed', and 80% for 'good' expectation classes. Testing both PBL and Waterfall techniques yielded positive comes about, with Waterfall testing accomplishing between 95% to 100% in prerequisites confirmation, framework plan compliance, unit testing, and framework component integration. PBL too demonstrated fruitful in improving understanding of Computer program Designing concepts, commonsense aptitudes, and cultivating an dynamic learning approach. In conclusion, the created application offers an viable and productive arrangement for anticipating understudy capacities, altogether affecting the quality of learning and evaluation in Program Designing courses.

Keywords: application development, student ability prediction, software engineering, project-based learning.

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