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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences
Gerry Firmansyah
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Religion Arts Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Evaluation of the Implementation of E-Government Public Service Aduan Konten Using E-Govqual, Importance Performance Analysis and Heuristic Evaluation (case study: Ministry of Communication and Information, APTIKA directorate) Nila Rusiardi Jayanti; Gerry Firmansyah; Nenden Siti Fatonah; Budi Tjahjono; Habibullah Akbar
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 3 (2022): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i3.6640

Abstract

In the current condition of society that is critical in responding to everything, more public services are needed professional, effective, simple, transparent, timely, responsive. Efforts to improve the quality of public services cannot be separated from service evaluation. In order to improve the quality of public services, the Directorate General of Informatics Applications of the Ministry of Communications and Informatics established the Public Service for Aduan konten at the Directorate of Information Application Control (PAI) as a pilot project. So to evaluate the quality of public services, a bureaucratic reform program is carried out at the PAI Directorate through efforts to develop a zone of territorial integrity free from corruption and a clean bureaucratic area to serve. One of the evaluations carried out is for measuring service performance as mandated in the Regulation of the Minister of Administrative Reform Number 14 of 2017 concerning the Community Satisfaction Survey (SKM) on the Implementation of Public Services. This study aims to determine the service quality of the Content Complaint website using the e-Govqual method, while the IPA and heuristic evaluations are to determine the attributes that are priorities for improving service quality, as recommendations to public service providers for Aduan konten. To assess the service quality of the content complaint website, 6 dimensions and 21 e-Govqual attributes are used. Of the 300 respondents who were used as research samples, this study shows the results of the analysis of the level of conformity of the 6 dimensions are 98.03% (<100%) meaning that the public services provided by the Aduan konten website are not satisfactory to users or still not in accordance with user expectations. The result of the average value of the gap between expectations and performance shows the number -0.05 or < 0. With this gap, it can be said that the quality of public service performance of Aduan konten perceived by the public still does not meet what is expected. Attributes that need improvement are those in quadrant A (3 attributes) and quadrant C (8 attributes). Recommendations are given based on the literature/theory for attributes that need to be improved to improve the quality of public services for Aduan konten.
Evaluation of Academic Service Business Processes through a Business Process Improvement Approach (Case Study: Esa Unggul University Learning Administration Bureau) Sigit Purworaharjo; Gerry Firmansyah
Budapest International Research and Critics Institute-Journal (BIRCI-Journal) Vol 5, No 3 (2022): Budapest International Research and Critics Institute August
Publisher : Budapest International Research and Critics University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33258/birci.v5i3.6207

Abstract

Business Process Improvement is a systematic system framework that helps an organization in advancing or improving business processes. To find out the effectiveness of business processes running in academic service processes, it is necessary to evaluate and model existing business processes. The main objective of BPI is to improve business processes and ensure issues in an organization's business processes are handled properly. The use of BPMN is a tool for describing or modeling business process diagrams based on flow chart techniques, assembled to create graphical models of business operations where there are activities and flow controls that define the work order. The results of this study are to evaluate and improve on important functions in the learning administration bureau of Universitas Esa Unggul.
Loan Repayment Prediction Using XGBoost and Neural Network in Japan's Technical Internship Training Suhendry, Mohammad Roffi; Gerry Firmansyah; Nenden Siti Fatonah; Agung Mulyo Widodo
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14709

Abstract

Delayed repayment of financial aid among participants in Japan’s Technical Internship Training Program presents challenges for training institutions in managing funds efficiently. To address this issue, this study aims to compare the performance of two machine learning models: Extreme Gradient Boosting (XGBoost) and Multi-Layer Perceptron (MLP) in predicting the likelihood of delayed loan repayments. The research begins with data preprocessing, including handling missing values, normalization, and feature selection based on a correlation threshold of 0.06, where features with absolute correlation values below this threshold are excluded. Three models are tested: XGBoost Default, XGBoost optimized using GridSearchCV, and MLP. These models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The XGBoost Default model achieves the highest accuracy at 95% and precision of 95%, although its recall is slightly lower at 83%. Tuning XGBoost improves recall to 84%, albeit with a marginal reduction in accuracy to 94%. In contrast, the MLP model demonstrates the lowest performance, with an accuracy of 92% and recall of 74%, indicating limitations in identifying delayed repayments. XGBoost also outperforms MLP in terms of ROC-AUC, scoring 91% compared to MLP’s 86%. These findings suggest that XGBoost is the more effective model for this predictive task. The results have practical implications for training institutions, enabling better participant selection, reducing repayment delays, and supporting more effective financial aid management.
Metaheuristic-Optimized SVM for Stunting Risk Detection in Pregnancy Wibowo, Yudha; Agung Mulyo Widodo; Gerry Firmansyah; Budi Tjahjono
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14710

Abstract

Stunting is a chronic growth disorder that originates during pregnancy, making early risk detection crucial for effective prevention and long-term child development. This study introduces a stunting risk prediction model based on urine testing, employing a Support Vector Machine (SVM) algorithm enhanced through metaheuristic optimization. Three metaheuristic algorithms—Grey Wolf Optimizer (GWO), Simulated Annealing (SA), and Firefly Algorithm (FA)—were utilized to fine-tune the SVM hyperparameters (C and gamma). Clinical urine samples collected from pregnant women served as the dataset for model training and validation. The results indicate that the SVM model optimized using GWO achieved the highest prediction accuracy at 94.15%, outperforming both the default SVM (88.46%) and the models optimized using SA (94.12%) and FA (85.71%). Additionally, significant improvements were observed in precision, recall, and F1-score metrics, affirming the effectiveness of metaheuristic tuning in enhancing classification performance. These findings highlight the potential of integrating metaheuristic algorithms with SVM for robust medical prediction tasks, especially in the early detection of stunting risks. The proposed model offers a promising and non-invasive diagnostic approach that can be implemented in prenatal care settings, enabling timely interventions to mitigate stunting and improve maternal and child health outcomes.
Co-Authors Agung Mulyo Widodo Budi Tjahjono Habibullah Akbar Nenden Siti Fatonah Nila Rusiardi Jayanti Sigit Purworaharjo Suhendry, Mohammad Roffi Wibowo, Yudha