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All Journal Journal of Information Systems and Informatics JISA (Jurnal Informatika dan Sains)
Nitami Lestari Putri
Politeknik Negeri Banjarmasin
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Enhancing Recruitment Transparency Using Simple Additive Weighting in Smart City Governance Rahimi Fitri; Nitami Lestari Putri; Abdul Rozaq; Agus Setiyo Budi Nugroho; Upik Upik; Masyita Ratu Diba
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1396

Abstract

The advancement of digital governance requires municipal recruitment processes that are transparent, accountable, and based on measurable criteria. In many local government environments, recruitment remains manual or semi-structured, increasing subjectivity, reducing efficiency, and limiting the traceability of decision outcomes. Although Decision Support Systems (DSS) using the Simple Additive Weighting (SAW) method are widely applied for candidate ranking, prior work often emphasizes technical scoring accuracy with limited attention to Smart City governance needs such as transparency, auditability, and accountable decision justification. This study develops and evaluates a SAW-based DSS to support objective, transparent, and traceable recruitment decisions within a Smart Governance context. Using a quantitative system development approach, candidate attributes were transformed into numerical scores and assessed through weighted criteria: education, work experience duration, English proficiency, age (cost criterion), and relevance of work experience. The SAW computation produced consistent and interpretable rankings, with the highest preference score reaching 98.462, indicating reduced reliance on unstructured subjective judgment. Usability testing using the System Usability Scale (SUS) yielded an average score of 87.6 (“Excellent”), demonstrating strong acceptance and practical feasibility across stakeholder roles. Overall, the proposed system functions as a governance-support tool that strengthens transparency and accountability in public-sector recruitment.
Short-Term and Long-Term Forecasting of Global Gold Prices Using LSTM and GRU Models Fitria Fitria; Muhammad Syahid Pebriadi; Nitami Lestari Putri
JISA(Jurnal Informatika dan Sains) Vol 9, No 1 (2026): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v9i1.2723

Abstract

Global gold prices exhibit high volatility and complex temporal patterns, making accurate forecasting a challenging task. This study aims to compare the performance of deep learning models for short-term and long-term gold price prediction using daily historical data. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were selected because both models can capture temporal dependencies in financial time-series data, while having different architectural complexities and learning characteristics. Comparing these models is important to identify the most suitable approach for different forecasting horizons. The dataset consists of daily global gold prices denominated in USD obtained from an open financial data source covering the period from 2010 to 2024. The models were evaluated under two forecasting horizons, namely short-term prediction (1 day ahead) and long-term prediction (30 days ahead). Model performance was assessed using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Experimental results indicate that the GRU model outperforms LSTM in short-term forecasting by producing lower prediction errors, while LSTM demonstrates slightly better stability in long-term forecasting. These findings suggest that the effectiveness of deep learning models for gold price prediction is highly dependent on the forecasting horizon.
Predicting Respiratory Conditions Using Random Forest and XGBoost Dhiyaussalam Dhiyaussalam; Ahmad Yusuf; Isna Wardiah; Nitami Lestari Putri
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1124

Abstract

This study examines the performance of Random Forest and XGBoost in predicting the diagnosis and severity of respiratory diseases using a simulated dataset of 2,000 patient records. The models were tested on two classification tasks: identifying disease types (e.g., pneumonia, influenza) and classifying severity levels (mild, moderate, severe). Both models achieved perfect accuracy in severity classification, with 1.0000 ± 0.0000 cross-validation scores, demonstrating strong stability under balanced class distributions. However, in the diagnosis task, Random Forest underperformed on minority classes, particularly pneumonia, with a recall of 0.18 and F1-score of 0.31. XGBoost, on the other hand, achieved superior results across all classes, including minority cases, with 0.9825 ± 0.0170 cross-validation accuracy and perfect test set performance. These findings highlight XGBoost’s robustness in handling imbalanced and multiclass medical data, making it a promising candidate for clinical decision support. Future work should address class imbalance and explore explainability techniques to improve trust and transparency in real-world applications.
Co-Authors Abdul Rozaq Agus Setiyo Budi Nugroho Ahmad Yusuf Dhiyaussalam Dhiyaussalam Fitria Isna Wardiah Masyita Ratu Diba Muhammad Syahid Pebriadi Rahimi Fitri, Rahimi Upik Upik