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All Journal Jurnal Ilmiah Sinus JUSIFOR : Jurnal Sistem Informasi dan Informatika
Faizah Via Fadhillah
Universitas Pelita Bangsa, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science

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Perbandingan Efektivitas Metode SAW dan AHP dalam Seleksi Penerima Beasiswa di LIPIA Jakarta Clarita, Anggita Risqi Nur; Fadhillah, Faizah Via; Nurhaliza, Zahra; Fatchan, Muhamad; Anshor, Abdul Halim
Jurnal Ilmiah SINUS Vol 23, No 1 (2025): Vol. 23 No. 1, Januari 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i1.877

Abstract

This research discusses the application of SAW and AHP methods in the selection process of scholarship recipients at LIPIA Jakarta. Given the complexity of assessing potential recipients, both methods are evaluated to determine which one provides the most accurate and efficient results. SAW and AHP methods are used to process the assessment of several criteria, such as Arabic Written Test, Arabic Oral Test, Diploma Score, Memorization and Good Behavior which have been given weights and scales according to their importance. The main objective of this research is to compare the effectiveness of the two methods in determining scholarship recipients who meet the criteria at LIPIA. The results of this comparison are expected to provide recommendations regarding the most suitable method to improve objectivity, accuracy, and efficiency in scholarship selection.
Sistem Prediktif Pemeliharaan Hidraulik dengan Pendekatan Algoritma Histogram Gradient Boosting Faizah Via Fadhillah; Muhammad Fatchan; Wahyu Hadikristanto
JUSIFOR : Jurnal Sistem Informasi dan Informatika Vol 5 No 1 (2026): JUSIFOR - Juni 2026
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/jusifor.v5i1.9630

Abstract

Hydraulic systems are widely used in industry due to high power density and precision control; however, failures in components such as coolers, valves, pump leakage, and hydraulic accumulators can cause downtime and high maintenance costs. Conventional corrective and preventive maintenance is limited due to reliance on fixed schedules and manual inspection. Therefore, this study applies machine learning-based predictive maintenance to predict hydraulic component conditions. This study aims to predict the condition of four components (Cooler, Valve, Internal Pump Leakage, and Hydraulic Accumulator), evaluate Histogram Gradient Boosting (HGB) using Accuracy and macro F1-score, and implement the model in a Streamlit application. The Condition Monitoring of Hydraulic Systems dataset (2,205 cycles) is used. Sensor data from .txt files is transformed into tabular features per cycle, standardized using StandardScaler, and split into 70:30 training and testing sets. Four independent HGB models are trained and evaluated using confusion matrix, Accuracy, and macro F1-score. Results show strong performance: Cooler_condition (Accuracy 0.998, F1 0.998), Valve_condition (0.834, 0.786), Internal_pump_leakage (0.968, 0.963), and Hydraulic_accumulator/bar (0.989, 0.987). The trained models are saved as .pkl files and integrated into a Streamlit application for interactive prediction and CSV export.
Co-Authors Abdul Halim Anshor Anggita Risqi Nur Clarita Muhamad Fatchan Muhammad Fatchan Nurhaliza, Zahra Wahyu Hadikristanto