Informasi biaya merupakan informasi penting bagi pelaku usaha untuk penentuan harga jual ataupun untuk menentukan target laba. Penelitian ini bertujuan untuk mengeksplorasi mengenai perhitungan biaya pada jasa pemain pengganti game online dalam menentukan harga jual jasa. Penelitian ini berfokus pada perhitungan biaya serta metode yang digunakan untuk penentuan harga jual jasa. Metode penelitian yang digunakan dalam penelitian ini adalah wawancara secara langsung kepada pemilik layanan penyedia jasa pemain pengganti game online dengan mengambil 3 (tiga) genre diantaranya adalah MOBA, RPG, dan FPS. Hasil wawancara menunjukan bahwa metode yang digunakan untuk menghitung biaya masih tergolong sederhana dan belum ada perhitungan khusus. Harga jual jasa yang ditawarkan kepada konsumen lebih berpatokan pada harga pasar yang berlaku dan perhitungan biaya merupakan pertimbangan minor.

This abstract discusses the importance of preventing corruption through effective implementation of the law, especially in relation to stakeholders. Corruption is a serious threat to public trust and the stability of government and private institutions. Strict and fair application of the law is very necessary to create a transparent and accountable environment. By implementing clear rules and heavy sanctions, acts of corruption can be minimized. Active participation of stakeholders, including government, civil society and the private sector, is also very necessary in monitoring and supporting efforts to prevent corruption. This will create good governance and strengthen public trust in institutions.

In the community of Langkuru Village, Pureman District, Alor Regency, there are often illegitimate children who cause problems because they become a topic of discussion in the community. The process of resolving illegitimate children can be carried out with national law or through customary law as is done by the community of Langkuru Village, East Nusa Tenggara (NTT). The settlement of illegitimate children according to customary law in Langkuru Village, Pureman District, Alor Regency, East Nusa Tenggara, is usually resolved in the Customary Warehouse (langwah) because the Customary Warehouse (langwah) is a place of peace for customary problems and never has to get a court decision because basically the residents of Langkuru Village can resolve it by mutual agreement, either a joint agreement with the family or a joint agreement according to custom. The position of illegitimate children in the distribution of inheritance in Langkuru Village remains the same as the distribution of inheritance against legitimate because it adheres to the matrilineal customary system. This type of research uses an empirical legal research type with a sociological legal approach and primary data sources and secondary data are then analyzed descriptively qualitatively.

Marriage is often pursued with the expectation of finding enduring happiness and fulfillment. However, the reality of marital life can deviate from these aspirations. This study investigates the crucial role of marital commitment in shaping marital satisfaction among couples in Bali. Employing survey techniques, data was collected through questionnaires, utilizing a scale likert to measure both commitment and satisfaction levels. The results highlight a significant positive correlation between marital commitment and marital satisfaction. Specifically, higher levels of commitment are associated with increased marital satisfaction, while lower levels of commitment correspond to diminished satisfaction. Pearson product moment parametric correlation analysis substantiates these findings, revealing a correlation coefficient of r = 0.848 and p = 0.000 (p < 0.05). This study underscores the importance of marital commitment in fostering marital satisfaction, offering valuable insights for couples and practitioners alike.
 

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, necessitating robust attack detection mechanisms. This study presents a comprehensive comparative analysis of ten supervised learning algorithms for IoT attack detection and classification, addressing the critical challenge of balancing detection accuracy with practical deployment constraints. Using the CICIoT2023 dataset, encompassing data from 105 IoT devices and 33 attack types, we evaluate Naive Bayes, Artificial Neural Networks (ANN), Logistic Regression (LR), k-NN, XGBoost, Random Forest (RF), LightGBM, GRU, LSTM, and CNN algorithms based on some performance metrics. The comparative test results show superior performance to the traditional ensemble approach, with RF achieving 99.29% accuracy and leading precision (82.30%), followed closely by XGBoost with 99.26% accuracy and 79.60% precision. Deep learning approaches also demonstrate strong capabilities, with CNN achieving 98.33% accuracy and 71.18% precision, though these metrics indicate ongoing challenges with class imbalance. The analysis of confusion matrices reveals varying success across different attack types, with some algorithms showing perfect detection rates for certain attacks while struggling with others. The study highlights a crucial distinction in IoT security: while high precision remains important, the potentially catastrophic impact of missed attacks necessitates equal attention to recall metrics, as evidenced by the varying recall rates across algorithms (RF: 72.19%, XGBoost: 71.69%, CNN: 64.72%). These findings provide vital insights for developing balanced, context-aware intrusion detection systems for IoT environments, emphasizing the need to consider performance metrics and practical deployment constraints.

This study aimed to analyze the performance management strategies implemented at the Public Works and Spatial Planning Office of Tanjungpinang City. The research type used was descriptive research with a qualitative approach. Data collection techniques were carried out through observation and interviews. The sample used in this study was purposive sampling, with the informants being human resources supervisors at the relevant office. Data analysis was conducted to identify the strategies applied in performance management. The research findings showed that the Public Works and Spatial Planning Office of Tanjungpinang City had implemented several performance management strategies, such as human resource development, measurement and evaluation, reward and sanction provision, and the implementation of information and technology. However, there were several challenges, such as the lack of quality human resources. This study recommended the need for improvements in the performance evaluation system, ongoing training, and better utilization of information technology. These measures were suggested to support the implementation of more effective performance management strategies.

The media has a very important role in a learning and teaching activity. Media is divided into several types, including visual media, audio media, audio visual media, and all kinds of media. The media at Arva School of Fashion is often created and developed for use in workshops is prototype media. Prototypes media for materials or coloring topics using the dip dye technique at the Arva School of Fashion are not yet available. Related to this, a prototype media was made from material coloring using the dip dye technique which was applied to making character costumes. The purpose of making this prototype media is of course so that teaching and learning activities can easily achieve something you want, because the media is a good intermediary in communication. This media prototype development research uses the 4D model development method with descriptive data processing methods. The development method with the 4D model approach consists of Define, Design, Development, and Disseminate. The results of this study state the flow of the prototype development process and the results of the prototype.

This community service activity aims to increase awareness and understanding of early childhood children regarding the importance of saving, especially in facing financial challenges in the future. This program is implemented in Gongsol Village, Merdeka District, Karo Regency, and involves children and parents as the main participants. Through an approach that includes socialization, mentoring, and evaluation, this activity focuses on developing savings habits from an early age as part of basic financial literacy. The methods used in this program include initial surveys, interviews, direct socialization, and the creation of educational media in the form of creative piggy banks to motivate children to be more enthusiastic about saving. The results of this activity show a significant increase in children's understanding of the concept of saving, its benefits, and how to do it effectively. In addition, this program also educates parents about the importance of supporting children in forming savings habits from an early age, in order to prepare them for financial challenges in the future. This socialization has been proven to be able to form a more positive mindset regarding financial management in children, teaching them discipline, responsibility, and independence in managing their pocket money. This program is also expected to be integrated into the school curriculum in the village, to strengthen the habit of saving as part of character education and financial literacy. With ongoing support from various parties, including schools and families, the habit of saving from an early age can be the foundation for the financial stability of individuals and society as a whole.

The growing complexity of maritime operations demands deck officers with advanced competencies and a strong understanding of sustainability principles. This study addresses critical gaps in maritime vocational education by exploring the effectiveness of current training programs, the integration of sustainability, and the role of technology-driven approaches. While previous research has focused on technical skills aligned with STCW standards, limited attention has been paid to leadership, communication, and environmental education. The research sought to answer how maritime training can be enhanced to address these gaps. Using a systematic literature review and qualitative analysis of insights from maritime professionals, educators, and graduates, the study synthesized theoretical frameworks with real-world experiences. Data were collected through semi-structured interviews and questionnaires, analyzed using thematic coding, and triangulated with findings from the literature review. The results demonstrate strong technical training aligned with STCW standards but reveal fragmented sustainability integration and inadequate leadership preparation. Simulation-based training was identified as highly effective, though it requires support from mentorship and real-world experiences. The findings provide actionable recommendations for enhancing curricula, fostering industry-academic partnerships, and refining policies to better prepare deck officers for modern maritime challenges. This research offers a practical roadmap for transforming maritime education, ensuring safety, operational efficiency, and sustainability in global maritime operations.

Face spoofing detection is critical for biometric security systems to prevent unauthorized access. This study proposes a deep learning-based approach integrating FaceNet and DenseNet201 to enhance face spoofing detection performance. FaceNet generates identity-based embeddings, ensuring robust facial feature representation, while DenseNet201 extracts complementary texture-based features. These features are fused using the Concatenate function to form a more comprehensive representation for im-proved classification. The proposed method is evaluated on two widely used face spoofing datasets, NUAA Photograph Imposter and LCC-FASD, achieving 100% accuracy on NUAA and 99% on LCC-FASD. Ablation studies reveal that data augmentation does not always enhance performance, particularly on high-complexity datasets such as LCC-FASD, where augmentation increases the False Rejection Rate (FRR). Conversely, DenseNet201 benefits more from augmentation, while the proposed method performs best without augmentation. Comparative analysis with previous studies further confirms the superiority of the proposed approach in reducing error rates, particularly Half Total Error Rate (HTER), False Acceptance Rate (FAR), and FRR. These findings indicate that combining identity-based embeddings and texture-based feature extraction significantly improves spoofing detection and enhances model robustness across different attack scenarios. This study advances biometric security by introducing an efficient feature fusion strategy that strengthens deep learning-based spoof detection. Future research may explore further optimization strategies and evaluate the approach on more diverse datasets to enhance generalization.