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All Journal Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research DBESTI: Journal of Digital Business and Technology Innovation
Faralita Faisal
Sekolah Tinggi Teknologi Terpadu Nurul Fikri, Depok
Computer Science & IT Economics, Econometrics & Finance Education Engineering Library & Information Science

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Digitalisasi Sistem Manajamen Inventaris Koperasi Berbasis Aplikasi Odoo Kamilah, Shofiyyah Nur; Faisal, Faralita; Amalia, Amalia
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 2 (2025): November, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i2.2273

Abstract

This study discusses the implementation of the Inventory module in the Odoo-based Enterprise Resource Planning (ERP) system as a digital solution for inventory management at the Bogor Madani Sejahtera Cooperative. The use of Previous manual recording often led to data discrepancies, late reports, and difficulties in monitoring goods availability in real time. This research aims to improve the efficiency and accuracy of stock recording through a more integrated system. The approach used is a case study with a qualitative method. Data collection methods include observation, interviews, and documentation. Data analysis is carried out descriptively by comparing conditions before and after system implementation. The system was built using Odoo version 16 with a focus on the Inventory module, and tested using the Blackbox Testing method and User Acceptance Testing (UAT) involving the admin and cooperative employees. The results of the study show that the system meets the cooperative's needs, especially in recording transactions, monitoring stock, and reporting. In general, this system can improve operational efficiency, enhance data accuracy, and facilitate decision-making. Thus, digitalization through Odoo is considered effective in supporting the overall management of cooperative inventory.
Analisis Perbandingan Kinerja Cloud Amazon Web Services dan Google Cloud Platform untuk Learning Management System Menggunakan Metode Analytical Hierarchy Process Reza Maulana; Faralita Faisal; Salman Fathy Shiroth; Hany Hidianti
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1210

Abstract

The digital transformation of education demands scalable and reliable LMS infrastructure, yet institutions often struggle to choose the right cloud platform because decisions rely only on catalog prices or features without empirical performance evidence. This study compares the performance of Amazon Web Services and Google Cloud Platform at the Infrastructure as a Service layer through five stages: provisioning two equivalent virtual machines, deploying Moodle with its database and monitoring stack via Docker containerization, executing JMeter load tests at 100, 250, and 500 concurrent users, collecting performance metrics, and evaluating them with the Analytical Hierarchy Process across four criteria of performance, reliability, cost, and integration. GCP excels in error containment (as low as 0.12%) and OS-level stability and is 27.7% cheaper, while AWS leads in throughput up to 11.7 requests per second with consistent maximum response times. AHP yields scores of 0.768 for AWS and 0.762 for GCP with a consistency ratio of 0.041. The contribution of this research is the first integrated evaluation framework combining empirical JMeter load testing on LMS workloads with multi-criteria AHP decision making, together with a portable Docker-based testing architecture replicable on both platforms.
Systematic Literature Review Desain UI untuk Pengurangan Beban Kognitif Pada Pembelajaran Anak Autisme dan Disleksia Faralita Faisal; Putri Dayanti; Amalia Rahmah
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 8 (2026): JPTI - Agustus 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1806

Abstract

Penelitian ini bertujuan untuk mengkaji dan mensintesis prinsip desain antarmuka pengguna (UI) yang berkontribusi terhadap pengurangan beban kognitif pada aplikasi pembelajaran digital bagi anak dengan autisme dan disleksia. Karakteristik kognitif dan sensorik yang beragam pada pengguna neurodivergen menuntut penerapan desain UI yang inklusif dan adaptif agar proses pembelajaran berlangsung secara efektif. Metode yang digunakan adalah Systematic Literature Review (SLR) terhadap artikel ilmiah terbitan tahun 2021–2025 yang diperoleh dari basis data Google Scholar, PubMed, IEEE Xplore, Crossref, dan ScienceDirect. Proses seleksi menghasilkan 25 studi primer yang memenuhi kriteria inklusi dan digunakan sebagai dasar analisis. Analisis dilakukan berdasarkan empat research question (RQ1–RQ4) untuk mengidentifikasi pola dan prinsip desain yang konsisten. Hasil sintesis menunjukkan bahwa desain UI berperan penting dalam menurunkan beban kognitif pengguna. Prinsip desain yang efektif meliputi kesederhanaan visual, navigasi terstruktur, konsistensi interaksi, personalisasi konten, pendekatan multisensorik, serta penggunaan tipografi dan warna yang ramah neurodivergen. Pada anak dengan autisme, desain yang memperhatikan kebutuhan sensorik, instruksi yang jelas, dan umpan balik langsung terbukti meningkatkan fokus dan pengalaman belajar. Pada anak dengan disleksia, desain yang menekankan keterbacaan teks, segmentasi materi, dan dukungan multimedia efektif dalam meningkatkan pemahaman dan motivasi belajar. Temuan penelitian ini tidak hanya memberikan kontribusi teoretis dalam memahami hubungan antara desain antarmuka dan pengurangan beban kognitif, tetapi juga menawarkan implikasi praktis bagi perancang UI/UX dalam mengembangkan antarmuka yang lebih inklusif, adaptif, dan ramah bagi pengguna dengan kebutuhan kognitif yang beragam.
Co-Authors Amalia Amalia Hany Hidianti Kamilah, Shofiyyah Nur Putri Dayanti Rahmah, Amalia Reza Maulana Salman Fathy Shiroth