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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Testing dan Implementasi Sistem Informasi
Dayanti Dayanti
Universitas Patria Artha
Computer Science & IT Engineering

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Design and Evaluation of an AI-Assisted Digital KPI Information System for Employee Performance Monitoring and Recommendation Asnefi Asnefi; Essy Malays Sari Sakti; Dayanti Dayanti; Irwan Syarif
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2342

Abstract

Manual and fragmented KPI-based evaluation often weakens employee performance monitoring by causing delays, input errors, and inconsistent interpretation. This study addresses that problem by designing and evaluating an AI-assisted digital KPI information system that integrates competency assessment, digital KPI records, performance scoring, dashboard-based monitoring, and recommendation support in a single environment. The study contributes by transforming the conventional competency–KPI–performance framework into an operational decision-support artifact with embedded AI classification. Using a prototype-based approach, the system was evaluated with a structured dataset of 340 employee-year records from 2021–2025. Three machine learning models were compared for classifying employee performance into High, Moderate, and Low categories. The results show that the system is functionally feasible and usable for KPI-based performance monitoring, while Random Forest achieved the best classification performance with 0.9853 accuracy and 0.9852 F1-score. The findings indicate that the proposed system can improve the structure of digital KPI monitoring and provide AI-assisted support for managerial review and follow-up actions. The study contributes theoretically by extending KPI-based performance management into an intelligent information system context and practically by offering a feasible model for organizations operating under limited implementation conditions.
Explainable rice yield from Sentinel-1 and Sentinel-2 satellite data for food security Dhimas Tribuana; Usman Sattar; Baharuddin Mide; Dayanti Dayanti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp615-627

Abstract

Reliable, explainable crop-yield estimates are essential for food-security planning in data-sparse regions. We present a transparent pipeline for district-level (regency) rice yield prediction in Indonesia that fuses Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 normalized difference vegetation index (NDVI), and weather/reanalysis features. The system standardizes inputs per province, fixes a 16-day temporal key, and uses a small, auditable ensemble of tree models (gradient boosting+light gradient-boosting machine (LightGBM)). Trained on ≤2023 data and evaluated on a 2024 temporal hold-out, a joint West Java ∪ South Sulawesi model achieves root mean square error (RMSE)≈0.80 t/ha, mean absolute error (MAE)≈0.48 t/ha, and R-squared (R²)≈0.33 at regency scale. Feature importances and Shapley additive explanations (SHAP) confirm that phenology (NDVI peak, integral, green-up/senescence), SAR backscatter (vertical transmit-vertical receive/vertical transmit-horizontal receive (VV/VH)), and wind/pressure are consistent drivers under monsoon conditions. The workflow supports routine, one-click provincial updates and produces parity maps and error bars for actionable diagnostics. These results demonstrate that combining Sentinel-1, Sentinel-2, and basic meteorology delivers accurate, interpretable, and operational yield signals suited to Indonesia’s food security needs, while providing a clear recipe for scaling to additional provinces.
Design and Evaluation of an AI-Assisted Digital KPI Information System for Employee Performance Monitoring and Recommendation Asnefi Asnefi; Essy Malays Sari Sakti; Dayanti Dayanti; Irwan Syarif
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2342

Abstract

Manual and fragmented KPI-based evaluation often weakens employee performance monitoring by causing delays, input errors, and inconsistent interpretation. This study addresses that problem by designing and evaluating an AI-assisted digital KPI information system that integrates competency assessment, digital KPI records, performance scoring, dashboard-based monitoring, and recommendation support in a single environment. The study contributes by transforming the conventional competency–KPI–performance framework into an operational decision-support artifact with embedded AI classification. Using a prototype-based approach, the system was evaluated with a structured dataset of 340 employee-year records from 2021–2025. Three machine learning models were compared for classifying employee performance into High, Moderate, and Low categories. The results show that the system is functionally feasible and usable for KPI-based performance monitoring, while Random Forest achieved the best classification performance with 0.9853 accuracy and 0.9852 F1-score. The findings indicate that the proposed system can improve the structure of digital KPI monitoring and provide AI-assisted support for managerial review and follow-up actions. The study contributes theoretically by extending KPI-based performance management into an intelligent information system context and practically by offering a feasible model for organizations operating under limited implementation conditions.
Co-Authors Asnefi Asnefi Baharuddin Mide Dhimas Tribuana Essy Malays Sari Sakti Syarif, Irwan Usman Sattar