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All Journal Jurnal Manajemen Informatika & Teknologi Jurnal Sains Student Research
Ahmad Juan Syahwali
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Computer Science & IT Other

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Perbandingan Kinerja Algoritma Fibonacci Retracement dalam Prediksi Pergerakan Harga Saham Ulung Wira Yuda; Ahmad Juan Syahwali; Tata Sutabri
Jurnal Manajemen Informatika & Teknologi Vol. 5 No. 1 (2025): Mei : Jurnal Manajemen Informatika & Teknologi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/ykp87v45

Abstract

This study explicitly examines the performance of the Fibonacci Retracement algorithm in predicting the direction of stock price movements and compares it with other technical indicators, namely Moving Average (MA) and Relative Strength Index (RSI). Fibonacci Retracement is used to identify potential support and resistance levels based on certain mathematical ratios. The data used are historical stock prices of five financial sector companies listed on the Indonesia Stock Exchange (IDX) during the period 2020 to 2024. The evaluation process is carried out by measuring the level of accuracy of trend direction predictions and the performance of the applied trading strategies. The results of the analysis show that the use of Fibonacci Retracement separately has limitations in determining trend direction. However, when combined with MA and RSI, its predictive ability increases significantly, resulting in a more consistent trading strategy
Pemanfaatan MongoDB dalam Sistem Informasi Akademik untuk Pengelolaan Data Mahasiswa Pada Universitas Bina Darma Ahmad Juan Syahwali; Tata Sutabri
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 2 (2025): Jurnal Sains Student Research (JSSR)
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i2.4335

Abstract

Penelitian ini mengkaji pemanfaatan MongoDB dalam Sistem Informasi Akademik untuk pengelolaan data mahasiswa di Universitas Bina Darma. Latar belakang penelitian menguraikan keterbatasan basis data relasional dalam menangani data akademik yang tidak terstruktur dan berkembang pesat. Metode penelitian menggunakan pendekatan kualitatif dengan analisis studi kasus untuk mengevaluasi kinerja MongoDB dalam skalabilitas, fleksibilitas, dan efisiensi pengambilan data. Hasil penelitian menunjukkan bahwa MongoDB meningkatkan pengelolaan data melalui arsitektur berbasis dokumen, mempercepat pemrosesan kueri, dan adaptasi terhadap struktur data akademik yang dinamis. Kesimpulan penelitian memberikan rekomendasi untuk implementasi MongoDB di lingkungan akademik serupa.
Co-Authors Tata Sutabri Ulung Wira Yuda