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Digitalization demands the MSME sector, including barbershops, to adopt technology in reservation management and marketing. Many barbershops still use manual systems, causing problems such as irregular queues, service delays, and suboptimal promotions. This study formulates how to design an information system that can integrate reservation and marketing functions into a single web-based platform. The objective is to produce an efficient, secure, and user-friendly information system design for both barbershop managers and customers. The method used is the Design Science Research approach with a waterfall system development model, involving needs analysis, system design, database design, and user interface design. The design results were validated by experts and users with a satisfaction rate of over 85%, indicating that the designed system aligns with the operational needs of barbershops. The conclusion is that the developed information system can enhance operational efficiency and customer experience, and is suitable for implementation in the barbershop service sector of SMEs.

Inventory management plays a crucial role in ensuring smooth business operations by preventing stockouts that may cause losses. Toko Cat Gani still relies on manual recording, which is prone to data loss, duplicate entries, and delays in stock reporting, thus requiring a systematic solution. This study aims to design and implement a web-based inventory information system using the buffer stock method to overcome these issues. The research method employed is Research and Development (R&D) with a prototype model, consisting of needs analysis, system design, validation, and testing stages. The system was developed using PHP, MySQL, and XAMPP, featuring item data management, supplier management, inbound and outbound transactions, buffer stock calculations, and real-time inventory reports. The implementation results show that the system facilitates transaction recording, minimizes data entry errors, provides notifications when stock reaches the minimum threshold, and generates accurate and timely reports. Expert validation and user testing confirm that the system is feasible and effective in supporting inventory management at Toko Cat Gani. Therefore, the implementation of a web-based inventory information system with the buffer stock method can be considered an efficient and reliable solution to improve accuracy and effectiveness in inventory control.

Chronic kidney disease (CKD) is a progressive and irreversible decline in kidney function that, if left untreated, can lead to serious complications. Ultrasonography (USG) is a widely used imaging modality for detecting CKD, yet its interpretation remains highly dependent on the radiologist’s expertise. This study aims to develop a CKD detection system using a convolutional neural network (CNN) on kidney ultrasound images based on estimated glomerular filtration rate (eGFR), and to evaluate the system’s performance. This research employed a research and development (R&D) approach with an experimental design. The dataset consisted of kidney ultrasound images from CKD and non-CKD patients with corresponding eGFR values. The methodology included image preprocessing, CNN model training, and accuracy evaluation using classification metrics. The results demonstrated that the developed CNN model achieved a total accuracy of 97% on internal test data and 95.8% on external validation. The model’s sensitivity reached 100% for the normal category, 91.67% for CKD stage 4, and 90% for CKD stage 5. Specificity exceeded 96% across all categories, with high precision and F1-scores above 94% for all classes. This system has proven to be effective as a diagnostic support tool for automatically detecting CKD through kidney ultrasound imaging. Its advantages lie not only in accurately classifying CKD from USG images but also in correlating the classification results with patients' eGFR values. This provides more precise clinical information and supports appropriate CKD staging and management planning.

Preeclampsia is one of the most serious complications in pregnancy, characterized by hypertension and proteinuria, and it poses a significant risk of maternal and fetal morbidity and mortality if not detected and managed promptly. Early detection is crucial, yet clinical diagnosis often faces challenges due to the variability of symptoms and uncertainty in medical decision-making. To address this issue, this study aims to develop an expert system for diagnosing preeclampsia by employing the Dempster-Shafer method, which is known for its ability to handle uncertainty and incomplete information in complex domains such as healthcare. A case study was conducted at Bidadari General Hospital, where data on clinical symptoms and patient medical records were collected and analyzed. The development process of the expert system followed systematic stages, including knowledge acquisition from obstetrics specialists, designing the knowledge base, constructing inference rules, and integrating the Dempster-Shafer algorithm for decision support. The system was subsequently tested using real-case scenarios of pregnant women suspected of having preeclampsia. Evaluation results demonstrated that the system achieved an accuracy rate of 92% in differentiating between preeclampsia and eclampsia, based on belief and plausibility measures combined with symptom analysis. These findings indicate that the proposed system can effectively support medical personnel by providing diagnostic recommendations with a high degree of reliability. In addition, the system offers efficiency in the clinical workflow by minimizing diagnostic errors and reducing delays in treatment initiation. Therefore, this expert system has the potential to become a valuable clinical decision support tool for early detection, risk assessment, and management of preeclampsia. Future development may focus on expanding the knowledge base, integrating real-time patient monitoring data, and enhancing usability to ensure broader applicability in diverse healthcare settings.

The Archives and Library Office of West Sumatra Province is a government institution that serves as a center for archive management and public literacy services. This building is visited by various groups, including agency staff, students, university students, and the general public. However, based on preliminary observations, several issues were identified regarding the existing signage system in the building, which was found to be inadequate, lacking in informativeness, and in many cases, unfit for use—making it difficult for visitors to navigate the space. This study aims to design a more effective and communicative signage system using a visual communication design approach. Data collection methods included direct observation and SWOT analysis to identify the strengths, weaknesses, opportunities, and threats of the current signage conditions. The design process employed the Design Thinking method, consisting of five stages: empathize, define, ideate, prototype, and test. The outcome of this process includes five primary media: pictograms, floor plan boards, ground floor information boards, directional signs, and room information signs. Additionally, five supporting media were designed: staff ID cards, lanyards, library membership cards, folder maps, and bookmarks. The developed designs were evaluated through validation and feasibility testing involving two design experts and three visitors as respondents. Based on the assessment results, the design was rated as excellent, with an average score of 4.95 from the experts, and all general respondents gave positive feedback regarding the visual aspects, legibility, and informational function of the designed signage system.

This study aims to examine the regulatory framework concerning Government Employees with Work Agreements (PPPK), particularly in Functional Expert Positions in Indonesia and to identify how the policy implementation of PPPK in Functional Expert Positions is carried out at the Constitutional Court. The research employs an empirical method with a qualitative approach. The types of data used include both primary and secondary data: primary data was obtained through field interviews, while secondary data consists of documents, information, books, and facts sourced from official records and legislation. The findings of this study indicate that the implementation of policies related to Functional Expert Positions at the Constitutional Court still faces regulatory inconsistencies, especially in the selection mechanism, which does not fully adhere to the established rules. Additionally, competency development for PPPK employees is not supported by systematic planning, which risks creating skill gaps. This situation may lead to uncertainty in career progression and reflects that the management of PPPK still encounters several fundamental challenges in its implementation.  

The development of digital technology has encouraged organisations to adopt big data analytics in human resource management (HRM), but there are still challenges in leveraging it for effective decision-making. This study aims to investigate the effect of big data utilisation on the quality of HR decision-making and identify the supporting and inhibiting factors for its implementation. A quantitative method with purposive sampling survey was conducted on 100-150 respondents from companies that have implemented big data-based management information systems and HR analytics. Data were analysed using Structural Equation Modeling (SEM) with AMOS software. The results showed that big data analytics significantly improved the quality of HR decision-making, with HR digital competencies and organisational culture as important mediating factors. However, challenges related to cultural resistance and limited expertise were found to affect the effectiveness of implementation. The practical implications of this research emphasise the importance of HR digital capacity building and ethical data governance to support the transformation of HRM into an adaptive strategic function in the digital era. This research also contributes to the development of data-driven management theory with a holistic approach that integrates technical, human, and ethical aspects.

Indonesian people mostly tend to ignore health, especially dengue fever because the symptoms that arise are similar to common fever, this causes people with common fever symptoms to be reluctant to see a doctor. This study aims to help the public recognize the symptoms of dengue fever early by using the implementation of the fuzzy method in the dengue fever disease diagnosis system that uses specific datasets as a reference. Mamdani's fuzzy method, which is based on linguistic frameworks and fuzzy concepts, allows for the management of knowledge from experts for intuitive decision-making. The test results using the Confusion Matrix, the system showed an accuracy of 92.8%, an average precision of 90.6%, a recall of 95.8%, and an F1-Score of 92.6%, with an effectiveness value of 100%. This study proves that the system is able to provide information about dengue fever, diagnose based on symptoms, and offer solutions for its treatment.

This study aims to analyze the role of forensic audit, investigative audit, auditor competence, spiritual intelligence, and information technology in detecting fraud. Using a qualitative method with a literature review, the results showed that forensic auditing is effective in detecting and proving fraud through investigative techniques, while investigative auditing focuses on identifying modus operandi and collecting evidence. Auditor competence, which includes technical expertise, accounting, and legal understanding, is critical to uncovering fraud. Spiritual intelligence helps auditors maintain integrity, while information technology supports fraud detection through data management systems and audit software. Synergy between all these factors is necessary to improve fraud detection and prevent further losses to the company. This research is expected to contribute to the development of more effective audit methods to reduce the risk of fraud.