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Prediksi Kuantitas Penggunaan Obat pada Layanan Kesehatan Menggunakan Algoritma Backpropagation Neural Network Fajrul Khairati; Hasdi Putra
Jurnal Sistim Informasi dan Teknologi 2022, Vol. 4, No. 3
Publisher : Rektorat Universitas Putra Indonesia YPTK Padang

Prediction of the amount of drug use at public health centers is needed to ensure the availability of drugs for patients in service quality management. A good prediction of the amount of medicine needed helps the quality of development planning in the health sector. Scientific developments in the field of Artificial Intelligence (AI) deliver a variety of the best techniques for making predictions. By adopting the workings of neural networks (neurons) in the human brain or Artificial Neural Network (ANN), the Backpropagation Neural Network (BPNN) algorithm is one of the best algorithms in making predictions, including predicting drug use in health services. The problem of this research is how to design the best architectural model such as the number of neurons in the input layer, hidden layer and other parameters so as to produce predictions with optimal accuracy. This study aims to develop an ANN architectural design with the Backpropagation algorithm to predict the need for drug use. The data used is data on drug use reports from 2015 to 2021 at the Andalas Community Health Center (Puskesmas) Padang City. The steps taken to predict are; collect data, pre-process data and perform analysis, design ANN architecture, make predictions. Learning using the backpropagation algorithm through the initial weight initialization process, activation stage, weight training (weight change) and iteration stage. The proportion of the amount of data used for training is 70% data and 30% for testing data. The results of this study indicate that the best ANN architecture is 12-12-1 with an accuracy of predicting the quantity of drug use reaching 97.87% for paracetamol with a Mean Absolute Percentage Error (MAPE) of 2.13%. The prediction results become a reference for the Puskesmas and the Health Office for service planning and development.

Empowering Government Fiscal Efficiency: Usability Evaluation and E-Government Model Refinement Khairati, Fajrul; Putra, Hasdi
International Journal of Management Science and Information Technology Vol. 4 No. 2 (2024): July - December 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v4i2.2775

In the current digital transformation landscape, governments increasingly rely on electronic systems for financial management, yet the effectiveness of these systems often hinges on their usability and adaptability to evolving user needs. This study addresses these challenges by evaluating the usability of existing e-government applications for financial management and refining e-government models to enhance their efficiency, transparency, and accessibility. Through a comprehensive evaluation encompassing Nielsen's five usability categories—Learnability, Memorability, Efficiency, Error, and Satisfaction—alongside methodologies such as user surveys, usability testing, and expert evaluations, the research aims to identify areas for improvement within existing systems and refine e-government models. The expected outcomes include insights into usability challenges, empowerment of governments with more efficient fiscal management tools, and contributions to the broader discourse on optimizing public sector performance in the digital age. These efforts have the potential to significantly impact government fiscal efficiency and transparency, leading to better resource allocation, reduced waste, and increased accountability in public service delivery.

Empowering Government Fiscal Efficiency: Usability Evaluation and E-Government Model Refinement Khairati, Fajrul; Putra, Hasdi
International Journal of Management Science and Information Technology Vol. 4 No. 2 (2024): July - December 2024
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v4i2.2775

In the current digital transformation landscape, governments increasingly rely on electronic systems for financial management, yet the effectiveness of these systems often hinges on their usability and adaptability to evolving user needs. This study addresses these challenges by evaluating the usability of existing e-government applications for financial management and refining e-government models to enhance their efficiency, transparency, and accessibility. Through a comprehensive evaluation encompassing Nielsen's five usability categories—Learnability, Memorability, Efficiency, Error, and Satisfaction—alongside methodologies such as user surveys, usability testing, and expert evaluations, the research aims to identify areas for improvement within existing systems and refine e-government models. The expected outcomes include insights into usability challenges, empowerment of governments with more efficient fiscal management tools, and contributions to the broader discourse on optimizing public sector performance in the digital age. These efforts have the potential to significantly impact government fiscal efficiency and transparency, leading to better resource allocation, reduced waste, and increased accountability in public service delivery.

Enhancing Organizational Control Through Business Intelligence: Monitoring and Automated Alerts Putra, Hasdi; Qatrunnada, Raidha; Rahmadoni, Jefril; Khairati, Fajrul
Electronic Journal of Education, Social Economics and Technology Vol 6, No 1 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i1.222

This study explores the role of Business Intelligence (BI) systems in enhancing organizational control through real-time monitoring, automated alerts, and predictive analytics. By integrating a BI system into operational workflows, this research demonstrates significant improvements in efficiency, decision-making, and responsiveness to emerging challenges. Real-time dashboards enabled stakeholders to monitor key performance indicators (KPIs), such as export volumes, commodity values, and transaction frequencies, providing immediate insights for proactive management. The automated alert system reduced anomaly response times by 42%, enabling timely corrective actions and minimizing operational disruptions. Additionally, the predictive analytics module achieved a 92% accuracy rate in forecasting trends, allowing for better resource allocation and strategic planning. Stakeholder feedback highlighted the system's usability, relevance, and operational impact, with 80% of users reporting enhanced decision-making capabilities and 75% noting increased efficiency in their daily tasks. This study provides a replicable framework for implementing BI systems to support real-time decision-making and improve operational control. While the findings are based on a specific context, they underscore the scalability of BI systems across different sectors and organizational settings. Future research should investigate the long-term impacts of BI systems, explore their integration with emerging technologies such as artificial intelligence, and address challenges related to data quality and user adoption. This research positions BI systems as indispensable tools for fostering agility, efficiency, and strategic alignment in dynamic operational environments.

Reframing Cybersecurity Risk in Social Media through Digital Intimacy and Relational Exposure Khairati, Fajrul; Putra, Hasdi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.6069

Cybersecurity research has traditionally focused on technical vulnerabilities, malicious attacks, and organizational safeguards to protect digital infrastructure. Although socio-technical perspectives have gained attention, everyday digital practices remain insufficiently examined as sources of cybersecurity risk. Concurrently, social media platforms have normalized digitally mediated intimacy, including public displays of affection (digital PDA), which embed emotionally salient and relational data into platform-based information systems. These practices introduce exposure mechanisms that are not adequately captured by conventional, technically bounded, and intent-based cybersecurity models. This article reframes cybersecurity risk by conceptualizing digital PDA as a distinct form of behavioural and relational exposure. Through an integrative conceptual synthesis of cybersecurity risk management, behavioural cybersecurity, and digital intimacy literature, the study demonstrates that digital PDA differs fundamentally from general digital oversharing. Digital PDA is relationally embedded, emotionally driven, cumulative over time, and amplified by platform affordances such as algorithmic recommendation, data persistence, and content resurfacing. As a result, intimacy-driven disclosures generate exposure that extends beyond individual users and evolves independently of the original act of sharing. The article develops conceptual frameworks that position digital PDA across multiple cybersecurity risk domains, including human, procedural, platform, and governance risks. By foregrounding relational exposure, this study advances behavioural cybersecurity beyond awareness and compliance-centric assumptions and contributes to information systems research by clarifying how platform design and governance shape cybersecurity risk. The findings provide a foundation for future empirical research and more context-sensitive cybersecurity and digital governance strategies in social media–driven environments.

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