Article Per Year (5 Year)
p-Index From 2021 - 2026
0.517
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Performance
Zakiyyan Zain Alkaf
Universitas Jenderal Soedirman
Computer Science & IT Education

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Evaluating Technopark Service Quality Using The Carter Model And Importance-Performance Analysis (IPA) Telma Anis Safitri; Katon Muhammad; Zakiyyan Zain Alkaf
Performance: Jurnal Personalia, Financial, Operasional, Marketing dan Sistem Informasi Vol 33 No 1 (2026): Performance
Publisher : Faculty of Economics and Business Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32424/1.jp.2026.33.1.19090

Abstract

Technoparks play a pivotal role in fostering regional innovation; however, many in Indonesia face significant challenges, including underutilized facilities and inconsistent service quality, which constrain their overall effectiveness. This study aims to evaluate and enhance the service quality of Technoparks in Java—specifically in Solo, Bandung, and Pekalongan—by implementing the CARTER model, which assesses six dimensions: Compliance, Assurance, Reliability, Tangibles, Empathy, and Responsiveness. A quantitative approach was employed, integrating the Customer Satisfaction Index (CSI) and Importance-Performance Analysis (IPA), further enriched by spatial mapping. The findings reveal varying levels of visitor satisfaction across the locations. Pekalongan Technopark achieved the highest CSI score of 87.41% ("Excellent"), followed by Bandung Technopark at 83.77% ("Good") and Solo Technopark at 82.05% ("Good"). The IPA results highlight that the Empathy and Responsiveness dimensions require significant improvement, particularly at Solo Technopark. Consequently, this study recommends strategic interventions focused on service development tailored to visitor needs, the optimization of underutilized facilities, and the enhancement of human resource quality. These findings serve as a strategic reference for Technopark management to bolster competitiveness and contribute to the sustainable development of local innovation and economic ecosystems.
Interpretable machine learning for digital soil pH mapping using an optimized AdaBoost algorithm Zakiyyan Zain Alkaf; A’isya Nur Aulia Yusuf; Elsa Sari Hayunah Nurdiniyah; Tri Wisudawati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 4: August 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i4.27724

Abstract

Soil pH is a fundamental parameter determining nutrient availability, microbial activity, and crop productivity. Unlike previous studies that often prioritize prediction accuracy over explainability, this study proposes an interpretable machine-learning framework integrating hyperparameter optimized adaptive boosting (AdaBoost) with Shapley Additive exPlanations (SHAP) to unravel the spatial drivers of soil pH. A systematic workflow was implemented to evaluate a diverse set of algorithms, followed by Bayesian optimization to fine-tune the best-performing models. The results demonstrated that the optimized AdaBoost model yielded the largest performance improvement (~7.5%), achieving excellent accuracy on independent test data with a coefficient of determination (R²) of 0.817 and a mean absolute error (MAE) of 0.293. Furthermore, SHAP analysis identified iron (Fe) and calcium carbonate (CaCO₃) as the most influential predictors, revealing that Fe exhibits a strong inverse relationship with pH, while CaCO₃ shows a positive association. This framework successfully balances high predictive accuracy with pedological interpretability, offering a robust tool for digital soil mapping and precision agriculture.
Co-Authors A’isya Nur Aulia Yusuf Elsa Sari Hayunah Nurdiniyah Muhammad, Katon Telma Anis Safitri Tri Wisudawati