Integration of Business Intelligence and Predictive Analytics for Student Success Based on Blockchain: Integrasi Business Intelligence dan Analitik Prediktif untuk Keberhasilan Mahasiswa Berbasis Blockchain

Untung  Rahardja; Mohamad  Rakhmansyah; Surta Wijaya; Sheila Aulia Anjani; Mary  Davies

doi:10.33050/tmj.v10i1.2389

Penulis

Untung Rahardja Universitas Raharja https://orcid.org/0000-0002-2166-2412 (tidak diautentikasi)
Mohamad Rakhmansyah Universitas Raharja https://orcid.org/0009-0007-9392-3777 (tidak diautentikasi)
Surta Wijaya Universitas Raharja https://orcid.org/0009-0007-1238-5584 (tidak diautentikasi)
Sheila Aulia Anjani Universitas Raharja https://orcid.org/0009-0007-9121-1151 (tidak diautentikasi)
Mary Davies Eduaward Incorporation , Eduaward Incorporation https://orcid.org/0000-0002-4248-3552 (tidak diautentikasi)

DOI:

https://doi.org/10.33050/tmj.v10i1.2389

Abstrak

Di era digital, dunia pendidikan mengalami transformasi signifikan, di mana analitik prediktif menjadi pendekatan penting untuk meningkatkan keberhasilan mahasiswa. Kemajuan teknologi yang pesat memungkinkan institusi untuk mengumpulkan dan menganalisis berbagai jenis data, namun tantangan dalam menjamin akurasi, transparansi, dan keandalan data masih tetap ada. Penelitian ini mengeksplorasi integrasi teknologi blockchain untuk mengatasi tantangan integritas data, dengan fokus pada penerapannya dalam analitik prediktif. Tujuannya adalah meningkatkan keandalan data terkait mahasiswa serta efektivitas prediksi terhadap kinerja akademik. Secara khusus, penelitian ini mengkaji hubungan antara Academic Performance Metrics (APM), Student Engagement Data (SED), Socioeconomic Factors (SEF), Blockchain-Enabled Data Integrity (BDI), dan Predictive Algorithm Efficiency (PAE). Dengan menggunakan metode Partial Least Squares Structural Equation Modeling (PLS-SEM), data dikumpulkan melalui survei terstruktur dan catatan institusi yang melibatkan mahasiswa perguruan tinggi. Validasi konstruk dilakukan melalui pengujian model pengukuran sebelum analisis jalur struktural. Hasil menunjukkan pengaruh signifikan dari faktor sosial ekonomi dan integritas data berbasis blockchain terhadap hasil akademik, sementara keterlibatan mahasiswa dan efisiensi algoritma prediktif juga menunjukkan pengaruh sedang. Studi ini juga mengidentifikasi area yang perlu ditingkatkan dalam model prediktif, khususnya dalam penyelarasan variabel input dengan desain algoritma. Temuan ini menegaskan pentingnya pemanfaatan teknologi untuk mengembangkan strategi pendidikan yang lebih adil dan efektif, serta perlunya peningkatan desain konstruk untuk meningkatkan keandalan dan validitas model. Penelitian ini berkontribusi dalam pengembangan ilmu data pendidikan melalui kerangka kerja analitik prediktif berbasis blockchain.

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