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Optimizing Decision-Making: Data Analytics Applications in Management Information Systems
No full textThis study delves into integrating data analytics applications within Management Information Systems (MIS), exploring their impact on decision-making processes in organizational settings. The discussion synthesizes qualitative and quantitative methodologies, presenting insights from scholarly literature, surveys, and interviews. Scholarly discourse highlights the transformative potential of data analytics tools in facilitating informed decision-making, aligning with practical applications showcased in empirical studies. However, inherent challenges surface, primarily concerning data quality, as revealed by 62\% of respondents, underscoring the need for organizations to address these obstacles. Despite challenges, substantial adoption rates of data analytics tools (78\%) affirm their growing recognition in decision-making within diverse industries. Reported enhancements in operational efficiency (35\%) and competitive advantage (22\%) among organizations leveraging data analytics validate their efficacy in driving organizational performance metrics within MIS. Further research should address ethical implications, longitudinal analyses of data analytics efficacy, and interdisciplinary collaborations exploring the nexus between data analytics and managerial decision-making. This study is a foundational step, providing empirical evidence and future research trajectories essential for organizations aiming to optimize decision-making through data analytics applications within Management Information Systems
Optimizing Efficiency Through Sustainable Strategies: The Role of Management and Monitoring in Achieving Goals
No full textIn today\u27s rapidly evolving business landscape, efficiency is a critical aspect for organizations aiming to remain competitive and sustainable. This research explores the important roles of management and monitoring in achieving organizational goals while prioritizing sustainable practices. Effective management serves as a primary driver in formulating, implementing, and overseeing initiatives to enhance efficiency while also reducing negative impacts on the environment and society. Strong monitoring enables organizations to assess the effectiveness of sustainable initiatives, identify areas needing improvement, and track progress toward established goals. Through systematic data collection and analysis, monitoring facilitates informed decision-making, thereby enhancing the organization\u27s ability to align actions with its sustainability objectives. The research findings indicate that effective decision-making, accurate performance measurement, good strategic planning, and strong monitoring mechanisms significantly influence organizational productivity and the achievement of company goals. By reinforcing management practices and monitoring mechanisms, organizations can optimize operational efficiency and achieve strategic goals sustainably
International Business Expansion Strategies: A Data-Driven Approach with IBM SPSS
No full textThis paper presents a structural framework to enhance time management proficiency within dynamic work environments. The framework integrates prioritization techniques, task scheduling methods, delegation strategies, and technology utilization to optimize time allocation and productivity. The methodology involves the application of the Eisenhower Matrix, Pareto Principle, and time-blocking techniques, supported by case studies in diverse professional settings. Results indicate a 20% improvement in project completion times, a 25% reduction in project turnaround time, and a 30% increase in project visibility. These findings underscore the framework’s effectiveness in enhancing time management and achieving long-term success. Implications include recommendations for continuous refinement and integration of emerging technologies
Pengaruh Technology Readiness Dan Satisfaction Terhadap Penerimaan Penggunaan Safe Entry Station: The Influence of Technology Readiness and Satisfaction on Acceptance of Use Safe Entry Station
No full textThe development of artificial intelligence technology has brought transformation in various sectors, including the world of health. The integration of AI in the healthcare sector has opened up new opportunities to improve diagnosis, treatment, medical data management and medical research. Safe Entry Station (SES-UR) is one of the newest technologies that has been introduced in the era of technological development that relies on the concept of artificial intelligence as the basis of its functionality, which has emerged as an innovative breakthrough in monitoring health effectively and accurately. However, new technology often involves concepts that may not yet familiar to most users. This can cause uncertainty and discomfort in using the technology. The aim of this research is to ensure that the implementation of SES-UR is successful and sustainable, so a comprehensive approach is needed in assessing the level of Technology Readiness and measuring the level of Satisfaction. The selected research focus is in the medical health sector to improve the quality of Artificial Intelligence-based health services. This research method, using the PLS-Structural Equation Modeling (SEM) method, was adopted to analyze the relationship between complex variables. To achieve accurate analysis results, this research involved the use of 25 instruments and 7 relevant constructs. The results of this research state that individuals who have a high level of Innovativeness tend to have the perception that the Safe Entry Station is easy to use, so they are more likely to accept and use this technology
Understanding Consumer Acceptance of AI in the Leisure Economy: A Structural Equation Modeling Approach
No full textThis research examines the determinants of consumer acceptance of artificial intelligence (AI) in the leisure economy, using a structural equation model to analyze responses from 560 participants. The study focuses on several psychological factors: Perceived Ease of Use (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Value (PV), and Habit (HB), and their impact on Behavioral Intention (BI) to adopt AI technologies. Results indicate significant influence of six constructs (PE, FC, SI, PV, HM, HB) on BI, with the exception of one hypothesis. The research also assesses the role of Personal Innovativeness in enhancing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model\u27s predictive accuracy. This study contributes to understanding AI adoption in leisure, offering valuable insights for AI application development and marketing strategies in this sector
Faktor-Faktor Yang Berhubungan Dengan Tindakan Tidak Aman (Unsafe Action) Pada Pekerja Di Pt. X Jakarta Tahun 2021: Factors Associated with Unsafe Actions in Workers at Pt. X Jakarta in 2021
Full text linkTindakan tidak aman adalah suatu kegagalan (human failure) dalam mengikuti persyaratan dan prosedur kerja yang benar, sehingga berdampak terjadinya kecelakaan kerja. Terdapat faktor internal dan eksternal yang mempengaruhi terbentuknya perilaku. Faktor internal seperti motivasi dan persepsi, serta faktor eksternal seperti pengawasan, ketersediaan APD, komunikasi, peraturan, dan pelatihan K3. Kecelakaan kerja sektor industri paling banyak disebabkan oleh tindakan tidak aman dari manusia dengan persentase sebesar 88%. Penelitian ini bertujuan mengetahui faktor-faktor yang berhubungan dengan tindakan tidak aman pekerja. Jenis penelitian adalah kuantitatif dengan metode observasional yang bersifat analitik dengan pendekatan cross sectional. Sampel sebanyak 95 pekerja. Analisis data yang dilakukan adalah analisis univariat dan analisis bivariat dengan menggunakan uji chi-square. Hasil penelitian ini adalah sebanyak 48,4% pekerja berisiko tinggi melakukan tindakan tidak aman dan sisanya berisiko rendah melakukan tindakan tidak aman (51,6%). Tidak terdapat hubungan antara motivasi (p=1,000) dan komunikasi (p=0,316) dengan tindakan tidak aman dan terdapat hubungan antara persepsi (p=0,000), pengawasan (p=0,000), ketersediaan APD (p=0,000), peraturan (p=0,027), dan pelatihan K3 (p=0,000) dengan tindakan tidak aman pada pekerja di PT. X Jakarta tahun 2021
KUALITAS PELAYANAN PUBLIK BIDANG ADMINISTRASI KEPENDUDUKAN PADA KECAMATAN BATUCEPER KOTA TANGERANG (STUDI PELAYANAN KARTU KELUARGA)
Full text linkPermasalahan penelitian diawali dengan beberapa hal terkait dengan pelayanan yang diselenggarakan oleh Kecamatan Batuceper Kota Tangerang terkait dengan pelayanan administrasi kependudukan khususnya pelayanan kartu keluarga. Tujuan penelitian ingin mengetahui kualitas pelayanan administrasi kependudukan khsusnya pelayanan Kartu Keluarga di Kecamatan Batuceper Kota Tangerang. Metode penelitian yang digunakan menggunakan metode kualitatif dengan indikator yang digunakan untuk mendeskripsikan kualitas pelayanan menggunakan konsep teori Sinambela Ltjian Poltak, yaitu Ketepatan waktu pelayanan, akurasi Pelayanan, kesopanan dan keramahan, tanggung jawab, kelengkapan, kemudahan dalam mendapatkan pelayanan, variasi model pelayanan, pelayanan pribadi dan kenyamanan. Temuan hasil penelitian bahwa Kecamatan Batuceper berupaya menerapkan waktu pelayanan sesuai dengan standar prosedur jenis pelayanan yang diberikan kepada pemohon. Untuk pelayanan kartu keluarga, merupakan pelayanan yang terhubung dengan Dinas Kependudukan dan Catatan Sipil Kota Tangerang, sehingga waktu pelayanan yang diberikan oleh Kecamatan Batuceper Kota Tangerang bergantung pada hasil proses verifikasi dari Dinas Kependudukan dan Catatan Sipil Kota Tangerang.
Kata Kunci: Kualitas pelayanan, administrasi kependudukan, kecamata
Enhancing Machine Learning with Low-Cost P M2.5 Air Quality Sensor Calibration using Image Processing
No full textLow-cost particulate matter sensors, due to their increased mobility compared to reference monitors, are transforming air quality monitoring. Calibrating these sensors requires training data from reference monitors, which is traditionally done through conventional procedures or by using machine learning techniques. The latter outperforms traditional methods, but still requires deployment of a reference monitor and significant amounts of training data from the target sensor. In this study, we present a cutting-edge machine learning-based transfer learning technique for rapid sensor calibration with Co-deployment with reference monitors is kept to a minimum. This approach integrates data from a small number of sensors, including the target sensor, reducing the dependence on a reference monitor. Our studies reveal that In recent research, a transfer learning method using a meta-agnostic model has been proposed, and the results proved to be much more effective than the previous method. In trials, calibration errors were successfully reduced by up to 32\% and 15\% compared to the best raw and baseline observations. This shows the great potential of transfer learning methods to increase the effectiveness of learning in the long term. These results highlight the potential of this innovative transfer learning technique for rapidly and accurately calibrating low-cost particulate matter sensors using machine learning
Flood Water Level Simulation Bringin River, Semarang City By Using The HEC-RAS 6.3.1 Programming
No full textThe Bringin River is a river in West Semarang, Semarang City, with the Tugu Region drainage subsystem. The Bringin watershed covers parts of the area in Tugu District, Ngaliyan District, and Mijen District. The overflow of water caused by high rainfall resulted in river flooding. The cross-section of the Bringin River cannot accommodate the magnitude of the flood discharge. The purpose of this study was to determine the cross-section of the Bringin River which was experiencing an overflow by carrying out a Hydrological Analysis and using the HEC-RAS 6.3.1 Program as a 1-dimensional cross-sectional design. Calculation of the planned flood discharge with periods of 2, 5, 10, 25, and 50 years using the Nakayasu Synthetic Unit Hydrograph method with a peak discharge Q2years : 52.45 m3/second, Q5years : 62.43 m3/second, Q10years: 68.44 m3/second, Q25years : 75.09 m3/second, and Q50years : 79.95 m3/second. The results of the calculation of the design discharge will be used in the HEC-RAS 6.3.1 programming so that from these results it can be seen that several cross-sections of the river have flood overflows that exceed the capacity of the Bringin River under review
Exploring the Impact of Data Quality on Decision-Making Processes in Information Intensive Organizations
No full textThis study investigates the influence of data quality on decision-making processes within organizations that heavily rely on information for their operations. With the increasing digitalization and proliferation of data in today\u27s business landscape, the quality of data has emerged as a critical factor in ensuring accurate and effective decision-making. Through a comprehensive review of existing literature and an empirical analysis, this research aims to shed light on the relationship between data quality and decision-making outcomes. The study employs a mixed-methods approach, utilizing both qualitative interviews and quantitative surveys to gather insights from professionals across various information-intensive sectors. The findings reveal that data quality significantly impacts the accuracy, reliability, and timeliness of decisions made within these organizations. Moreover, the study identifies key challenges that organizations face in maintaining data quality and suggests potential strategies to enhance decision-making processes. The results of this research contribute to a deeper understanding of the pivotal role data quality plays in the success of information-intensive organizations and provide practical implications for managers and decision-makers