Aptisi Transactions on Management
Aptisi Transactions on Management (ATM) adalah jurnal ilmiah yang diterbitkan oleh APTISI (Asosiasi Perguruan Tinggi Swasta Indonesia), guna memfasilitasi hasil jurnal ilmiah Civitas Akademika dalam bidang teknologi informasi, komunikasi, dan manajemen dalam menghadapi era digital di Indonesia. ATM terbit tengah tahunan (2 kali dalam setahun, periode Januari dan Juli).
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<![CDATA[Blockchain Based Certificate Verification System Management ]]>
<![CDATA[Qurotul Aini]]>;
<![CDATA[Eka Purnama Harahap]]>;
<![CDATA[Nuke Puji Lestari Santoso]]>;
<![CDATA[Siti Nurindah Sari]]>;
<![CDATA[Po Abas Sunarya]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.1922
<![CDATA[One of the key responsibilities of the Public Key Infrastructure is revocation management. Additionally, the security of any Public Key Infrastructure (PKI) depends on it. Today's revocation methods are susceptible to a single point of failure when network traffic rises due to the growth in the quantity and size of networks as well as the adoption of new paradigms like the Internet of Things and the use of the web. The author uses the strength and resiliency of blockchain to overcome these issues and present a productive decentralized certificate revocation management and status verification system. The author adds a field that specifies which distribution point the certificate will belong to in the event that it is revoked using the certificate structure extension field. Then, the author carries out a thorough assessment of our plan using performance indicators like execution time and data consumption to show that it can fulfill the demands with high effectiveness and little expense. Additionally, the author contrasts the effectiveness of our strategy with two of the most widely-used revocation approaches, namely the Certificate Revocation List and the Online Certificate Status Protocol. The data collected demonstrate that our suggested strategy works better than the existing scheme.]]>
<![CDATA[Enhancing Machine Learning with Low-Cost P M2.5 Air Quality Sensor Calibration using Image Processing ]]>
<![CDATA[Untung Rahardja]]>;
<![CDATA[Qurotul Aini]]>;
<![CDATA[Danny Manongga]]>;
<![CDATA[Irwan Sembiring]]>;
<![CDATA[Yulia Putri Ayu Sanjaya]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
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DOI: 10.33050/atm.v7i3.2062
<![CDATA[Low-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.]]>
<![CDATA[Effective Government Management of Flood Discharge in Drainage Channels using HEC-RAS 6.3.1 Application ]]>
<![CDATA[Estha Nathanael]]>;
<![CDATA[Wahyu Sejati]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2115
<![CDATA[Flooding remains one of the most significant natural disasters affecting communities around the world. Proper management of flood discharge in drainage channels is essential to mitigate its impacts on public safety, infrastructure, and the environment. In recent years, advancements in hydrodynamic modeling tools, such as the Hydraulic Engineering Center's River Analysis System (HEC-RAS) 6.3.1, have provided governments with powerful tools to analyze and manage flood discharges effectively. This research aims to investigate the role of government-led management strategies in handling flood discharge within drainage channels using the HEC-RAS 6.3.1 application. The study assesses how the application's features and capabilities can be harnessed by governmental agencies to make informed decisions and implement effective flood management plans. The research methodology involves a combination of field surveys, data collection, and computer simulations using the HEC-RAS 6.3.1 software. Geographic information systems (GIS) data, topographical surveys, and historical flood records are utilized to calibrate and validate the model's accuracy. Various flood scenarios are simulated to assess the performance of different government-led management strategies. The findings of this study reveal that the integration of HEC-RAS 6.3.1 with government-led management approaches enhances the understanding of flood dynamics within drainage channels. The application facilitates the identification of critical flood-prone areas, prediction of potential flood events, and evaluation of flood mitigation measures. The government can utilize this valuable information to formulate more effective flood management policies and allocate resources efficiently.]]>
<![CDATA[Sustainable Institutional Entrepreneurial Culture and Innovation For Economic Growth ]]>
<![CDATA[Oryz Agnu Dian Wulandari]]>;
<![CDATA[Desy Apriani]]>;
<![CDATA[yusuf febriansyah]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2127
<![CDATA[This article discusses institutional entrepreneurial culture and the enabling factors for sustainable economic growth. This study aims to identify the key factors contributing to institutional entrepreneurial culture development. The research utilizes a qualitative approach, and the results show that an innovative and entrepreneurial culture can enhance a country's long-term economic prosperity. A strong relationship exists between culture, innovation, and long-term economic success. Therefore, it is important to promote an institutional entrepreneurial culture through several factors, such as supporting government policies and programs, developing supporting infrastructure, education, and training, and cooperation between the public and private sectors. This research provides useful insights for policies and interests in the economic sector to promote innovation and entrepreneurship that can enhance economic growth. As such, this article provides important insights for readers interested in developing a sustainable institutional entrepreneurial culture and economic growth.]]>
<![CDATA[How Job Insecurity Affects Organizational Commitments Through Job Satisfaction ]]>
<![CDATA[Shindy Devyani]]>;
<![CDATA[Lista Meria]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2132
<![CDATA[Work productivity can be optimal if the human resources have high organizational commitment. This study aims to determine the effect of job insecurity on organizational commitment with job satisfaction as a mediation carried out by employees of PT. Pelangi Elasindo. This study uses a type of causal research. The sampling technique used simple random sampling with a sample size of 208 employees and data analysis using SEM-PLS. The results of the study show that there is an influence between job insecurity on job satisfaction and Job Insecurity on organizational commitment. While job satisfaction has no effect on organizational commitment, there is no evidence of a mediating effect between job insecurity and organizational commitment. From the results obtained, it can be seen that job insecurity plays an important role in job satisfaction and organizational commitment. This study has implications for management to reduce job insecurity and strengthen employee commitment.]]>
<![CDATA[Flood Water Level Simulation Bringin River, Semarang City By Using The HEC-RAS 6.3.1 Programming ]]>
<![CDATA[ananda satria perdana]]>;
<![CDATA[wahyu sejati]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2134
<![CDATA[The 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.]]>
<![CDATA[Exploring the Impact of Data Quality on Decision-Making Processes in Information Intensive Organizations ]]>
<![CDATA[Isaac Khong]]>;
<![CDATA[Natasya Aprila Yusuf]]>;
<![CDATA[Arbi Nuriman]]>;
<![CDATA[Ahmad Bayu Yadila]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2138
<![CDATA[This 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's 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.]]>
<![CDATA[Risk Assessment, Risk Identification, and Control in The Process Of Steel Smelting Using the Hiradc Method ]]>
<![CDATA[Untung Rahardja]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2142
<![CDATA[Workplace accidents include illnesses brought on by working conditions, accidents on the way to and from work,and other incidents. Steel smelting, an operation that involves melting steel to produce an iron rod (billet), which issubsequently molded into different materials needed, is one of the work environments where accidents can happenwith risks ranging from low to severe. Using the HIRADC approach, work safety can undoubtedly be evaluated.Using a scale based on the potential outcome, this method can identify the risks, dangers, and assessments that mayarise throughout the steel-smelting process. The HIRADC approach will be used in this study to identify risks andgauge the likelihood of accidents in the Indonesian steel smelting process. As a result, 36 dangers with varyinglevels of low, medium, and high risk exist. Various additional safety tools must be owned because Indonesia's levelof safety control is still quite poor]]>
<![CDATA[Evaluating Organizational Performance Using SmartPLS: A Management Perspective ]]>
<![CDATA[Jason Moscato]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
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DOI: 10.33050/atm.v7i3.2144
<![CDATA[In the face of complex challenges in the global business environment, organizational performance is a major concern. Evaluation of organizational performance plays an important role in modern management. This research integrates the management perspective with a Structural Equation Modeling (SEM) approach using SmartPLS. Through keywords such as leadership, organizational culture, employee motivation, and other variables, this study identifies key factors that affect organizational performance. The results provide an in-depth view of how organizational management can influence and improve their performance. This research has significant practical implications for different types of organizations. This article details the methodology, variables, data analysis, and expected findings, all in the context of combining management and SEM for organizational performance evaluation.]]>
<![CDATA[Enhancing Organizational Efficiency through the Integration of Artificial Intelligence in Management Information Systems ]]>
<![CDATA[Bhima Bhima]]>;
<![CDATA[Achani Rahmania Az Zahra]]>;
<![CDATA[Tio Nurtino]]>;
<![CDATA[M. Zaki Firli]]>
<![CDATA[ APTISI Transactions on Management (ATM) Vol 7 No 3 (2023): ATM (APTISI Transactions on Management: September) ]]>
Publisher : <![CDATA[Pandawan ]]>
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DOI: 10.33050/atm.v7i3.2146
<![CDATA[This research delves into AI's role in enhancing Management Information Systems for organizational efficiency. It employs cross-sector case studies to showcase AI's potential in automating tasks, offering predictive insights from historical data, and bolstering decision-making. While AI promises substantial benefits, it also poses technical and ethical challenges during implementation. AI integration emerges as a game-changer, liberating organizations from mundane tasks through automation. Predictive analytics empowers firms to foresee trends, fostering a competitive edge in decision-making. Yet, obstacles include algorithm compatibility with existing systems and the demand for heightened technical proficiency. Ethical considerations loom large, demanding robust privacy and fairness guidelines in AI data usage. This research underscores the importance of employee AI training and multidisciplinary teams for tackling technical hurdles. Ethical principles should permeate AI development and utilization. The study recommends a three-fold strategy: First, prioritize employee AI training for seamless adoption. Second, establish cross-disciplinary teams to navigate technical complexities. Third, embed ethics in every AI facet to maintain trust. In conclusion, a holistic approach allows organizations to seamlessly integrate AI into Management Information Systems, yielding operational efficiencies, superior decision-making, and a competitive edge in a dynamic business landscape.]]>
