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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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.
Probabilistic Machine Learning Early Warning for Urban PM2.5 in SEA Cities Baharuddin Mide; Dhimas Tribuana; Usman Sattar; Dayanti Dayanti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i7199

Abstract

Air pollution, particularly fine particulate matter (PM₂.₅), poses a critical threat to public health in rapidly urbanizing regions. Reliable early-warning systems are essential for mitigating exposure risks, yet challenges remain in cities with heterogeneous sensor coverage and event frequency. This study aimed to develop and evaluate a probabilistic, portable across cities early-warning framework for PM₂.₅ exceedances in Southeast Asia, focusing on Jakarta, Singapore, and Bangkok. Using a staged experimental design (Exp-A through Exp-E), we integrated regression-based back-casts with classification-based exceedance alerts, applied variant selection across thresholds and horizons, and validated for operational readiness through model freezing, documentation, and online simulation. Results showed that Jakarta achieved near-perfect exceedance prediction up to 6-hour horizons (F1 ≈ 0.99), Singapore maintained strong performance at short horizons (F1 ≈ 0.91 at 2–3 hours), while Bangkok yielded moderate but actionable signals at very short horizons (F1 ≈ 0.62 at 1 hour). Regression components provided stable situational awareness, and online smoothing reduced false alarms by approximately 15–20% without degrading performance. The framework demonstrated that calibrated exceedance probabilities can serve as an effective basis for city-level air quality alerts, with reliability strongly influenced by data density and event prevalence. This work contributes a reproducible, transparent, and computationally efficient approach that bridges machine learning innovation with practical environmental management. The findings emphasize the importance of horizon-specific calibration and adaptive strategies, offering both theoretical insights and practical value for policymakers in urban air quality governance.
Co-Authors Asnefi Asnefi Baharuddin Mide Dhimas Tribuana Dhimas Tribuana Essy Malays Sari Sakti Syarif, Irwan Usman Sattar