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All Journal International Journal of Advances in Applied Sciences Jurnal Testing dan Implementasi Sistem Informasi
Asnefi Asnefi
Universitas Patria Artha
Computer Science & IT Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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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.
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.
Novel bat algorithm for short-term peak load forecasting in Sulselrabar electricity system Muhammad Rais; Rosihan Aminudin; Asnefi Asnefi; Andi Nur Putri; Irwan Syarif; Muhammad Ruswandi Djalal
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1334-1346

Abstract

This study addresses short-term load forecasting (STLF) in the South, Southeast, and West Sulawesi (Sulselrabar) power system, Indonesia, using interval type-2 fuzzy logic (IT2FL) optimized by the proposed novel bat algorithm (NBA). The NBA is employed to optimize the footprint of uncertainty (FOU) of the fuzzy membership functions for both antecedent (X and Y) and consequent (Z) variables. The forecasting model utilizes daily peak load data from the previous four days (d−4 to d−1) to predict the peak load of the forecast day (d). To evaluate the effectiveness of the proposed approach, NBA is benchmarked against particle swarm optimization (PSO), firefly algorithm (FA), and cuckoo search algorithm (CSA). The results demonstrate that the proposed IT2FL–NBA model provides the highest forecasting accuracy among the evaluated methods, achieving a mean absolute percentage error (MAPE) of 1.5650%. In comparison, IT2FL–PSO, IT2FL–FA, and IT2FL–CSA achieve MAPEs of 1.6353%, 1.6451%, and 1.6353%, respectively. For type-1 fuzzy logic (IT1FL), the NBA, PSO, FA, and CSA optimization methods produce MAPEs of 1.6668%, 1.6842%, 1.6805%, and 1.6768%, respectively. These findings demonstrate that optimizing the FOU of IT2FL using the proposed NBA significantly improves forecasting accuracy and provides a robust and reliable approach for STLF in power systems.
Co-Authors Dayanti Dayanti Essy Malays Sari Sakti Muhammad Rais Muhammad Ruswandi Djalal Putri, Andi Nur Rosihan Aminudin Syarif, Irwan