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All Journal Jupiter EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Scientific Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi ARSY : Jurnal Aplikasi Riset kepada Masyarakat Jurnal Informatika dan Kesehatan
Agung Wibowo
Universitas Ngudi Waluyo
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Medicine & Pharmacology Public Health

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Model Penelitian Basis Data untuk Sistem Informasi Skala Besar Ega Seladevi; Desi Ramadani Putri; Agung Wibowo
Jurnal Informatika dan Kesehatan Vol. 2 No. 2 (2025): IKN : Jurnal Informatika dan Kesehatan
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/ikn.v2i2.3804

Abstract

Database management is a crucial aspect of developing large-scale information systems that require high efficiency, scalability, and reliability. This article discusses a research model based on scientific methodology to design and optimize databases for large-scale information systems. The research approach includes exploring database schema design techniques, evaluating performance using large datasets, and implementing optimization strategies such as indexing, data partitioning, and replication. This study also highlights the comparison between relational (SQL) and non-relational (NoSQL) databases in the context of complex information system requirements. The research findings show that applying a systematic methodology can improve data processing efficiency by up to 30% and accelerate system response time. This article provides practical guidelines for developers and researchers in designing reliable database solutions to meet large-scale demands, as well as guidance for information system developers in selecting and implementing the appropriate database model. ABSTRAK Pengelolaan basis data merupakan aspek krusial dalam pengembangan sistem informasi skala besar yang memerlukan efisiensi, skalabilitas, dan keandalan tinggi. Artikel ini membahas model penelitian berbasis metodologi ilmiah untuk merancang dan mengoptimalkan basis data pada sistem informasi skala besar. Pendekatan penelitian mencakup eksplorasi teknik perancangan skema basis data, evaluasi performa menggunakan dataset besar, serta implementasi strategi optimasi seperti indexing, partisi data, dan replikasi. Studi ini juga menyoroti perbandingan antara basis data relasional (SQL) dan non-relasional (NoSQL) dalam konteks kebutuhan sistem informasi yang kompleks. Hasil penelitian menunjukkan bahwa penerapan metodologi yang sistematis mampu meningkatkan efisiensi pengolahan data hingga 30% dan mempercepat waktu respons sistem. Artikel ini memberikan panduan praktis bagi pengembang dan peneliti dalam merancang solusi basis data yang handal untuk memenuhi tuntutan skala besar, serta memberikan panduan bagi para pengembang sistem informasi dalam memilih dan mengimplementasikan model basis data yang tepat.
SMARTGRAD: Prediksi Kelulusan Tepat Waktu Mahasiswa Kampus Merdeka Wibowo, Agung; Pratama, Ade; Setiawan, Dwi
EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Vol 15, No 2 (2025): December
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/expert.v15i2.4605

Abstract

On-time graduation is a primary indicator of student success and serves as a key benchmark for the quality of higher education institutions. This study aims to develop SmartGrad, a prediction model for on-time graduation based on the Naive Bayes algorithm, supported by feature selection using Decision Tree. The model integrates academic variables (semester GPA, average grades) and non-academic variables (types of MBKM, employment status, age) to produce accurate and contextual predictions. The research dataset comprises 313 entries with 17 attributes, processed through feature selection and classification stages. Evaluation results demonstrate the model's excellent performance, with an average accuracy of 88.8%, precision of 90.5%, recall of 97.9%, and an F1-score of 94.0%. The implementation of SmartGrad as an interactive web application based on Streamlit supports transparent and easily comprehensible decision-making. The novelty of this research lies in the integration of MBKM factors and employment status into the prediction model, as well as the application of an interpretable AI approach to support higher education policies and the achievement of Sustainable Development Goal 4 (Quality Education). These findings are expected to serve as a strategic reference for higher education administrators in enhancing academic quality and the effectiveness of the Freedom of Learning Independent Campus program.
Co-Authors Abdul Rohman Ade Pratama Afif Muhamad, Armana Ameyriya Angraeni Danny Manongga Dega Adhi Kusuma Desi Ramadani Putri Diva Riyandini Ega Seladevi Eko Nur Hermansyah Hindriyanto Dwi Purnomo Idham Renaldi Kustiyono Kustiyono Lailatul Fadhila Ricky Darmawan Rohman, Abdul Setiawan, Dwi Syafitri, Ajeng Ulya, Zahrotun Diyana Yayi Suryo Prabandari Yoannes Romando Sipayung