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This study explores the influence of information system quality, information technology sophistication, and personal technical capability on the effectiveness of Accounting Information Systems (AIS) in Village Credit Institutions (LPDs) in Jembrana Regency. The research adopts a quantitative approach, utilizing a survey method. Questionnaires were distributed to 63 employees from 21 LPDs that have already implemented computerized AIS. The study focuses on assessing how these three independent variables contribute to the effectiveness of AIS in the LPDs. Multiple linear regression was employed for data analysis, allowing the researcher to determine the relationship between the independent variables and the effectiveness of AIS. The respondents were selected from LPDs that had successfully integrated technology into their accounting systems. The primary objective was to understand whether system quality, IT sophistication, and personal technical capability played a significant role in improving AIS performance in these institutions. The findings of the study indicate that all three independent variables—system quality, IT sophistication, and personal technical capability—positively influence the effectiveness of AIS. Specifically, high-quality systems, advanced IT infrastructure, and employees with strong technical skills were found to enhance the operational effectiveness of AIS. This underscores the importance of these factors in ensuring the smooth functioning of AIS in LPDs. As such, LPDs are encouraged to invest in improving system quality, upgrading IT systems, and enhancing the technical skills of their staff to optimize AIS performance. Overall, the study contributes to the understanding of how technological factors can be leveraged to improve accounting processes in financial institutions, particularly in rural settings like LPDs in Jembrana Regency.

This study focuses on the development and implementation of “Pasar Kita,” a web-based marketplace system designed to facilitate digital transactions for traditional market sellers and consumers. The system was developed using PHP as the backend programming language and MySQL as the database management system. The purpose of this project is to address the digital gap experienced by traditional market vendors who have limited access to modern e-commerce platforms. The research includes a needs analysis, system design using UML diagrams, implementation of main features such as registration, login, product management, transactions, checkout, and order tracking, followed by system testing. The results show that all major functions operate properly, including user authentication, product uploads, order processing, and payment simulations via transfer or cash-on-delivery (COD). System testing indicates that the platform meets usability, functional, and responsiveness criteria. The findings highlight the system’s capability to support local economic digitalization by providing an accessible and user-friendly marketplace for traditional market vendors and consumers.

This study aims to determine the influence of factors in the fraud hexagon theory on the potential for fraud, which is described through the variables of compensation suitability, competence, organizational culture, effectiveness of internal control systems, and leadership style. Fraud hexagon theory is a development of the fraud triangle and fraud diamond theories, which emphasize six main elements that cause fraud, namely pressure, opportunity, rationalization, capability, arrogance, and collusion. This theory is relevant to describe various internal factors that can encourage fraudulent behavior, especially in the public sector. This study uses a quantitative approach with multiple linear regression analysis methods and is processed using SPSS software version 30. The population in this study are apparatus or village employees who work throughout the Mojoroto District, Kediri City. The sampling technique uses a purposive sampling method, with predetermined criteria. The selection process resulted in 42 respondents, consisting of the Village Head, Village Secretary, and Village Treasurer, who play important roles in administrative and financial management at the village level. Data were collected through closed-ended questionnaires distributed directly to respondents and analyzed to determine the relationship between the independent and dependent variables. The analysis results showed that partially, only the effectiveness of the internal control system and leadership style significantly influenced the potential for fraud. This indicates that strengthening the internal control system and transparent and accountable leadership can minimize fraudulent activities. Meanwhile, the variables of compensation suitability, competence, organizational culture, and unethical behavior did not show significant effects individually. However, simultaneously, all of these variables were proven to significantly influence the potential for fraud. Thus, fraud prevention must be carried out holistically by considering all aspects contained in the fraud hexagon theory.

Profitability represents a bank's capability to evaluate how effectively its management generates profits. This study seeks to explore and determine the influence of liquidity, capital adequacy, operational risk, and credit risk on bank profitability. The research focuses on banking institutions listed on the Indonesia Stock Exchange over the 2021–2023 period. The study utilizes quantitative data obtained from secondary sources, specifically financial statements published by the respective banks. A total of 32 banks were selected as research samples through purposive sampling. The study adopts a non-participant observation approach, and the data were processed using multiple linear regression analysis. The results indicate that liquidity has a positive yet statistically insignificant effect on profitability, capital adequacy has a significant negative effect, operational risk shows a negative but non-significant influence, and credit risk has a significant negative impact on bank profitability.

The Ripple Mill machine is one of the vital components in a Palm Oil Mill (PKS) that functions to separate the palm kernel from its shell. This process is very important because the quality and quantity of the palm kernel produced will directly affect the economic value and production efficiency in the palm oil industry. At PTPN IV Regional II Plantation Unit and Adolina PKS, various technical problems were found that caused a decrease in machine performance, including rotor bars and square bars that experienced wear due to age and improper machine settings. In addition, other damage that often occurs is a broken van belt due to age and excessive tension, feeder motor dysfunction caused by excessive load, and motor overheating that occurs due to age and high workload. To overcome these problems, this study uses the Failure Mode and Effect Analysis (FMEA) method. This method aims to identify various potential failures, assess the severity, frequency of occurrence, and detection capability, so that the Risk Priority Number (RPN) value can be calculated as a basis for repair priorities. The results showed that worn rotor bars and worn square bars had the highest RPN values, each at 280, equivalent to 40.23% of the total identified risks. This indicates that these two components are critical points requiring immediate repair and maintenance attention. Applying FMEA in this context provides tangible benefits, including helping the company formulate a more targeted maintenance strategy, reducing the risk of recurring damage, and minimizing downtime that impacts production. With more systematic maintenance, operational efficiency can be improved while extending the lifespan of the Ripple Mill machine.

PT. XYZ  applies Information Technology in business processes to achieve company goals. achieve company goals. However the use of Technology brings risks that can reduce business effectiveness and efficiency of the business. PT. XYZ faced several problems in its IT department, including network connectivity issues, human error, hardware failure, ERP system integration failure, data security, and human error, hardware failure, ERP system integration failure, data security, data backup failures, system downtime, and power outages. This research applies data collection methods through interviews and risk analysis, evaluating the steps taken by the company in managing IT risks systematically. Research results show that the application of the COBIT 5 Framework is effective in identifying, assessing, and managing IT risks, thus helping the company to minimize losses and ensure smooth operations. This research provides recommendations for improved risk management that is more structured and data-driven risk management, as well as the importance of training for IT staff to improve risk management capabilities in the company. improve risk management capabilities in the company.

Background: Structural Health Monitoring plays a critical role in ensuring the safety, reliability, and sustainability of high performance composite structures used in aerospace, civil infrastructure, and mechanical systems. Conventional externally mounted sensors often face challenges related to environmental interference, maintenance complexity, and long term stability. Objective: This study aims to develop and validate an integrated smart composite monitoring system with embedded sensing capabilities that enhances damage detection accuracy and operational durability under varying mechanical stress conditions. Method: Smart composite specimens were fabricated by embedding fiber optic and piezoelectric sensors within fiber reinforced polymer laminates, followed by tensile, fatigue, and vibration testing. Signal processing techniques including time frequency analysis were applied to extract damage sensitive features, which were then classified using machine learning algorithms to distinguish healthy and damaged structural states. Results: The experimental findings demonstrate high damage detection capability, stable sensor performance under cyclic loading, improved reliability compared to conventional monitoring approaches, and consistent monitoring accuracy throughout the fatigue life of the specimens. The integration of embedded sensing and data driven analytics significantly enhances structural response interpretation and supports predictive maintenance strategies.

Digital image segmentation is essential in image processing, influencing the accuracy of higher-level tasks. Thresholding is widely used, yet identifying optimal threshold values remains challenging. The Firefly Algorithm with Neighbourhood Attraction (FaNA), a metaheuristic approach, is efficient for color image thresholding but underperforms on grayscale images due to suboptimal thresholds. To overcome this, an enhanced version (eFaNA) was developed by integrating a chaotic tent map for population initialization and a Lévy flight-based random walk for improved exploration. eFaNA was compared with FaNA, fuzzy firefly algorithm (FFA), and the standard Firefly Algorithm (FA) in multilevel thresholding of grayscale images. Results demonstrate that eFaNA achieves superior segmentation quality with minimal detail loss, outperforming the others. The average PSNR obtained by eFaNA, FFA, FaNA, and FA was 25.5320 dB, 25.4075 dB, 24.1522 dB, and 24.4506 dB, respectively; average SSIM was 0.8641, 0.8604, 0.8432, and 0.6703; and execution time was 50.5322, 38.7726, 38.7528, and 107.6340 seconds, respectively. This reflects a PSNR improvement of 5.71% over FaNA, 0.49% over FFA, and 4.42% over FA, and an SSIM gain of 2.48% over FaNA, 0.43% over FFA, and 28.92% over FA. While eFaNA lags behind FFA and FaNA in execution time by ~11.8 seconds, it significantly outperforms FA. The performance gain is attributed to the chaotic tent map’s diverse initialization and the Lévy flight’s enhanced search capability. These improvements enable eFaNA to deliver consistently better threshold values and segmentation results. However, its relatively higher computational cost may limit applicability in real-time image processing.

Preparing effective programs is an important aspect in ensuring the success of an organization. This process includes formulating a program editorial that is clear, systematic and easy to understand as well as determining the person in charge in accordance with the competencies and tasks given. This research discusses how to prepare an efficient program editorial, starting from identifying goals to developing appropriate strategies. Apart from that, selecting a person in charge who has the capability to run the program is also a key factor for success. With structured editorial and optimal distribution of responsibilities, the program can run more effectively, efficiently and according to the targets that have been set. The method used in this research is literature study using journals, books and research relevant to the title. The results of the research are to provide understanding or guidance in designing strategies and programs so that they can be implemented optimally in various fields. By studying this article, researchers hope that this research will be able to open the horizons of knowledge for its readers.

This study aims to describe and analyze: How is the Dynamic Capability of the Special Class I Immigration Office of Surabaya in Passport Making Services.  The research method to be used is qualitative descriptive.  Data Collection Techniques include: Interviews, Observations, Documentation. The research informant is the Head of the Suarabaya Special Class I Immigration Office. The data analysis technique using interactive model analysis was developed by Miles et al., (2014), namely data condensation, data presentation, and conclusion drawn. The results of the study show that the dynamic capability of the Surabaya Special Class I Immigration Office in the implementation of passport services is able to produce various positive changes in service, which has an impact on improving the quality of passport services better in the eyes of the public. However, there are still several aspects that need to be improved in the implementation of this dynamic capability, especially in exploring feedback from passport applicants in more depth as part of the thinking again mindset, as well as studying service practices from the private sector or foreign immigration to realize a thinking across mindset. The process of forming dynamic capabilities at the Surabaya Special Class I Immigration Office begins with the leadership role of the head of the immigration office who regularly holds meetings with employees. This encourages employees to create various innovations in service, which is a manifestation of the thinking ahead mindset. Service performance evaluation activities that are carried out regularly bring changes in service policies, as an implementation of the thinking again mindset. In addition, an open attitude to learn service practices from other parties through comparative studies with other immigration offices is also part of the application of a thinking attitude mindset.

ABSTRACT The implementation of Information Technology at XYZ Hospital serves as a key driver in enhancing the efficiency of healthcare services, but it also raises the organization’s vulnerability to significant operational risks. The primary risks identified involve inaccurate data resulting from human mistakes, breakdowns in system interoperability, and the deterioration of hardware infrastructure that may interfere with patient care. This research seeks to assess the governance of IT risk management and determine the organization’s capability level using the COBIT 5.0 framework, focusing on the APO12 (Manage Risk) domain. The study employs qualitative methods, gathering information through interviews and direct observations, and then conducts a gap analysis to compare the current state (As-Is) with the desired future state (To-Be). Findings show that the organization’s capability is currently at Level 2 (Managed Process) with a score of 1.87, which is still below the intended maturity level of 3 (Established Process) at 2.94. These results indicate that risk management activities are still largely reactive and rely on intuition, with no standardized procedures embedded within the institution. Ultimately, the study suggests formalizing a Risk Register, standardizing mitigation procedures through established SOPs, and developing a Disaster Recovery Plan to strengthen risk governance into a more systematic, preventive, and resilient framework.

Human Resource Management (HRM) is a critical determinant of organizational productivity and competitiveness in the era of globalization and digital transformation. This study employs a Systematic Literature Review (SLR) approach to synthesize empirical findings on HRM strategies published between 2019 and 2024. The review identifies six key domains of effective HRM: competency-based recruitment, continuous training and upskilling, transparent performance evaluation, reward and incentive systems, workforce diversification and inclusion, and technology-enabled HRM. Evidence demonstrates that competency-driven recruitment reduces turnover and enhances job fit, while continuous training fosters innovation and adaptability. Transparent evaluation systems increase motivation and fairness, and balanced reward packages combining financial and non-financial recognition improve retention and engagement. Workforce diversification enhances problem-solving and innovation when paired with inclusive leadership practices. Technology integration, particularly HRIS and people analytics, streamlines processes and strengthens decision-making. Importantly, bundled strategies yield stronger productivity outcomes than isolated interventions, with improvements occurring through capability, motivation, and coordination pathways. The findings underscore the need for organizations to align HRM practices with strategic goals and global trends. This study contributes to HRM literature by providing evidence-based insights and offers practical guidance for managers seeking to enhance productivity in competitive environments.

This research focuses on developing a cayenne pepper quality detection system using the YOLO (You Only Look Once) V5 algorithm. The system is designed to address the limitations of manual post-harvest sorting by classifying cayenne peppers into three categories: “good” (ripe), “bad” (rotten or dry), and “raw” (green), based on their visual characteristics. A dataset consisting of 565 images was manually collected, labeled using Roboflow, and pre-processed through resizing and orientation standardization. Model training was conducted over 150 epochs, achieving high detection performance with a mean average precision (mAP) of 99.5%, precision of 99.6%, and recall of 99.9%. Real-time testing demonstrated the system’s capability to detect and classify cayenne peppers with exceptional accuracy. This research is expected to enhance the efficiency and accuracy of the cayenne pepper sorting process, while paving the way for the application of YOLO-based systems to other agricultural commodities. Further research is recommended to expand the dataset and optimize model parameters for improved system performance.

The problem of household waste, which accounts for 50-70% of the total waste in Indonesia, mainly in the form of organic waste such as food and vegetable scraps, is a major challenge in environmental management. Composting is an effective method of managing organic waste by turning it into compost. However, organic waste management in Indonesia is still minimally practiced because it is considered to have no economic value. A 50 kg capacity composter machine equipped with a vertical mixer and using an electric motor can increase composting efficiency. This study aims to analyze the feasibility of composter machines and to analyze the effect of speed and mixing time on composter machines on the homogeneity of organic waste. The research method used is quantitative experimental with factorial experimental design (DOE) to analyze the effect of speed and stirring time on the composter machine on the homogeneity of organic waste. The stirring speed variations tested were 30, 45, and 60 RPM, with mixing times of 20, 30, and 40 minutes. The results showed that the single mixer composter machine proved to be feasible and reliable in producing homogeneous organic waste, with a high process capability value of 5.86. The use of mixing speed from 30 RPM to 60 RPM and mixing time from 20 minutes to 40 minutes significantly increased the homogeneity of organic waste, with the highest value of 97.54% at 60 RPM speed for 40 minutes.  

E-Learning becomes a platform which supports technology-based learning on many aspects. The usage of E-Learning becomes the main choice especially in education world. SMK TI Bali Global Jimbaran, which is a middle level vocational school, utilizes this platform in its learning process. In its implementation process, there needs to be a precise and thorough supervision to ensure that the system runs according to business goals. This mapping is conducted to know the process used for measurements. This mapping is done by framework with COBIT 5 by applying PAM (Process Assessment Model), which is adjusted with the condition on research place. The activity measurement are derived from several domains on COBIT 5 with mapping process based on the business goals and institution goals. The domains used are EDM03, AP003, BAI07, DSS05 and MEA01. The results of governance measurement process on current capability level is on level 4 (predicable process) and the expected capability level is on level 5 (Optimizing Process) which has gap value of 2 level.

Digital transformation (DT) is a necessity for incumbent companies today, including MSMEs, to be able to continue to compete in the era of technological disruption and accelerated by the COVID-19 pandemic. This research aims to design IT governance (ITG) for BPRACo MSMEs using the COBIT 2019 SME focus area framework. The research method used is design science research (DSR) which creates artifacts in the form of ITG solution development methods and systems. The research results revealed that the highest GMO priorities selected were APO10 Managed Vendors, MEA03 Managed Compliance with External Requirements, and APO12 Managed Risk. From the analysis of the selected GMOs, there are gaps and recommendations for improvement in the seven components which are then mapped into three aspects, namely people, process and technology aspects. The results of the research analysis show that the ambidextrous IT governance approach has succeeded in increasing the level of capability in several main components of BPRACo MSMEs in adopting DT. The right ITG strategy can help BPRACo MSMEs operating in the banking industry achieve a successful DT journey.  

The reliability of sterilization equipment, such as autoclaves, is essential to ensure patient safety, infection control, and operational continuity in healthcare facilities. Damage or malfunction of autoclaves may disrupt sterilization processes and pose significant risks to medical services. This study aims to develop an expert system for autoclave damage detection using the fuzzy logic method to support faster and more accurate diagnostic decision-making. The proposed system applies fuzzy inference to evaluate the level of damage based on input symptoms provided by users. By handling uncertainty and varying symptom intensities, the fuzzy logic approach enables proportional assessment rather than rigid rule-based classification. The system was designed through knowledge acquisition from technical experts and implemented using fuzzy membership functions and inference rules to determine damage severity levels. Experimental testing was conducted to evaluate system performance and diagnostic accuracy. The results indicate that the expert system successfully generated diagnosis outputs for all tested scenarios, achieving functional diagnostic accuracy within the defined test cases. The system was also able to calculate a quantified damage severity value of 11.6235981% based on the given symptoms, demonstrating its capability to assess damage levels numerically and objectively. Furthermore, the developed system significantly reduces the time required for damage detection compared to manual diagnostic procedures. Automating the evaluation process, it assists electromedical technicians in identifying faults more efficiently and taking preventive or corrective actions promptly. Overall, the implementation of a fuzzy logic-based expert system provides an effective, accurate, and practical solution for improving autoclave maintenance management and supporting healthcare service reliability.

Unmanned Surface Vessels (USV) have become a major focus in modern maritime technology for mine search and handling. Unmanned vessels are equipped with advanced sensors such as sonar and lidar to detect and map mines below the water's surface. In addition, image processing systems and artificial intelligence are used to analyze sensor data and identify mine locations with high accuracy. Once a mine is detected, the unmanned vessel is equipped with a mine handling system that can safely destroy or deactivate the mine. Thus, unmanned vessels are an effective and efficient solution in searching and handling mines in potentially dangerous waters. This study discusses the selection of unmanned vessels as a solution to search for and neutralize mines. This study was conducted quantitatively using the Analytical Hierarchy Process (AHP) method to determine recommendations for Unmanned Vessels with Mine Countermeasure capabilities to the Indonesian Navy. The results of the quantitative study showed the priority of the criteria that would later narrow down to the priority of selecting an Unmanned Vessel from several alternative choices analyzed by the researcher. Theoretically, all previous theories and studies can be used in this study so that they can be utilized in subsequent studies. While practically this study provides recommendations for the Indonesian Navy regarding the characteristics of the Unmanned Axe and various supporting equipment that can be used to carry out Mining Operations and Mine Countermeasures Operations. The calculation results in this study indicate that the priority of the alternative Unmanned Vessel with the highest Mine Countermeasures capability is obtained by Inspector 125 from France with a weight of 0.37450145.

This study presents the implementation of Transfer learning using the ResNet-18 architecture for classifying 10 musical instrument categories based on visual representations of audio signals. The audio waveform is transformed into image-like inputs appropriate for CNN processing, accompanied by data augmentation and ImageNet-standard normalization. ResNet-18 is utilized due to its efficient feature extraction capability enabled by residual blocks, which help overcome vanishing gradient issues. The model was trained for 10 Epochs using the AdamW optimizer and Cross-Entropy Loss. Experimental results show that the model achieved a maximum validation accuracy of 77.35%, with a stable downward trend in training loss, indicating effective feature learning. However, several misclassification cases were observed, particularly among instruments with similar spectral characteristics, such as drum–violin and tabla–sitar. These findings demonstrate that while ResNet-18 performs reliably for musical instrument classification, further improvements remain possible through deeper architectures like ResNet-50, more comprehensive hyperparameter optimization, and the use of richer audio representations such as Mel-Spectrograms. This research provides an essential foundation for developing automated music analysis systems powered by Deep Learning.

The purpose of this study was to test and determine the effect of stimulus, opportunity, rationalization, capability, ego, and collusion on indications of financial statement fraud. This study uses purposive sampling in determining the sample with secondary data sources in the form of financial reports and annual reports of infrastructure companies listed on the Indonesia Stock Exchange (IDX) and the pages of each company during 2020 - 2022 with the final number of observations totaling 143 data. This study uses panel data regression analysis techniques in the Eviews 13 data processing application. The results of hypothesis testing in this study indicate that stimulus and opportunity have a positive effect on indications of financial statement fraud. While rationalization, capability, ego, and collusion do not affect indications of financial statement fraud. This research has practical implications for companies, investors, governments, and auditors in examining solutions to crucial problems in the stimulus and opportunity variables that indicate financial statement fraud because it is an evaluation of the level of ROA and NOIR proxies..