Greenwashing poses significant challenges to sustainability initiatives in Indonesia, where weak regulatory frameworks and inconsistent enforcement exacerbate the issue. This study explores the interplay between greenwashing practices and legal regulations, aiming to identify gaps and propose solutions to address the problem. Employing a qualitative case study design, the research utilized document analysis, semi-structured interviews, and content observation to gather data from multiple stakeholders, including legal experts, industry practitioners, and consumer organizations. The findings reveal that 55% of sustainability claims in marketing materials lack verifiable evidence, highlighting transparency as a critical concern. Weak regulatory oversight and the absence of standardized definitions for terms like "sustainable" or "eco-friendly" further complicate the issue. The study identifies transparency, monitoring, and legal accountability as key themes requiring immediate attention. The research contributes to the existing literature by integrating legal perspectives with sustainable branding strategies, particularly in the context of developing countries. It also underscores the need for regulatory reforms that emphasize clear standards and stringent sanctions. Practical implications include recommendations for regulators to enhance oversight mechanisms and for companies to prioritize transparency to gain consumer trust. Future research should explore the effectiveness of standardized sustainability metrics and evaluate cross-country regulatory frameworks. This study offers a comprehensive foundation for addressing greenwashing and fostering authentic sustainability practices.

The supervision and enforcement of the advocate code of ethics in Indonesia continue to face various challenges, impacting the effectiveness of the existing regulatory framework. The number of ethical violations committed by advocates has been increasing, from 120 cases in 2018 to 345 cases in 2023, with the most prevalent infractions including conflicts of interest, abuse of authority, and gratification. One of the primary factors contributing to the weak enforcement of the code of ethics is the limited oversight system, which remains confined to advocate organizations without the involvement of independent institutions. This study aims to analyze the effectiveness of the advocate supervision system in Indonesia, compare it with best practices in other countries, and identify measures that can be implemented to enhance accountability and transparency within the profession. The research employs a normative legal method with a comparative law approach and empirical studies. The analysis is conducted on existing regulations, case reports on violations, and interviews with key stakeholders, including advocates, clients, and legal observers. The findings reveal that only 40% of reported violations result in strict sanctions, while merely 10% of advocates found guilty of ethical misconduct face license revocation. Additionally, a survey of 500 respondents indicates that only 35% of the public is aware of the complaint procedures against advocates who breach ethical standards, highlighting the low level of transparency in the supervision system. This study contributes to the discourse by recommending the establishment of an independent institution to oversee advocates' compliance with ethical standards, as well as the increased utilization of digital technology in the supervision system. With more transparent and accountable oversight reforms, it is expected that ethical violations within the legal profession can be minimized, thereby strengthening public trust in Indonesia’s legal system

The rapid digital transformation has significantly impacted public governance, particularly through the adoption of e-government systems. While numerous studies have highlighted the benefits of e-government at the national level, attention to its implementation at the local authority level remains limited. Local authorities face unique challenges, such as limited technological infrastructure, inadequate staff competencies, and varying levels of public trust. This study aims to identify the key factors influencing e-government adoption at the local level and evaluate its impact using a quantitative approach. Data were collected from 150 local authorities in urban and rural areas through structured surveys and analyzed using multiple linear regression. The study’s results indicate that digital infrastructure has the strongest influence on e-government adoption, with a regression coefficient of ? = 0.45 (p = 0.001). Staff competency (? = 0.35, p = 0.015) and public perception (? = 0.25, p = 0.045) also significantly contribute to adoption levels. Together, these three factors explain 65% of the variance in e-government adoption (R² = 0.65). These results align with the Technology Acceptance Model (TAM), which identifies perceived ease of use and perceived usefulness as key determinants of technology acceptance. This study contributes to the existing literature by focusing on the context of local authorities and providing empirical evidence on how infrastructure, staff competencies, and public perceptions influence e-government adoption. The results underscore the importance of improving technological infrastructure, investing in staff training, and promoting digital literacy among the public to accelerate digital transformation at the local level.

Scam threats on digital platforms continue to rise alongside the rapid adoption of technology in Indonesia. The unique characteristics of Indonesian digital users, such as low digital literacy and high social media usage, make them particularly vulnerable to various forms of scams, including phishing, impersonation, and emotional manipulation. This study aims to develop a machine learning-based model for predicting and detecting scams by identifying threat patterns within a local context. The methodology involves collecting a survey-based dataset from Indonesian digital users, capturing language patterns and user interaction behaviors. The dataset was processed through text-cleaning techniques, tokenization, normalization, and representation using TF-IDF and Word Embeddings. The machine learning models employed in this study are Random Forest and Support Vector Machine (SVM), evaluated using accuracy, precision, recall, and F1-score metrics. Hyperparameter tuning was conducted to optimize model performance, while k-fold cross-validation was utilized to minimize the risk of overfitting. The results indicate that the Random Forest model achieved the best performance, with an accuracy of 92.5%, precision of 90.7%, recall of 94.1%, and F1-score of 92.4%. The use of local datasets improved detection accuracy by 7.8% compared to global datasets, highlighting the critical importance of contextual representation in identifying scam patterns specific to Indonesia. The model was also effective in recognizing unique threat patterns, such as the use of informal language and manipulative phrases in scam messages. This study makes a significant contribution to the field of digital security by providing an effective machine learning-based approach to detecting scam threats in Indonesia. Moreover, the findings underscore the importance of developing local datasets and educating users as part of a holistic solution to enhance digital security. These insights emphasize the necessity of incorporating cultural and contextual factors into technology-driven approaches for combating scams in developing countries like Indonesia

Serial communication is a fundamental method for data transfer in electronic devices, particularly in Arduino-based systems. However, existing protocols, such as Universal Asynchronous Receiver/Transmitter (UART), often lack robust error detection mechanisms, leading to potential data integrity issues. This study aims to address the knowledge gap regarding error detection in UART communication by implementing Hamming Code, a well-established method for detecting and correcting single-bit errors. The research employs a systematic approach, including data encoding before transmission and decoding with error correction at the receiver end. The results demonstrate that the integration of the Hamming Code significantly enhances the reliability of data transmission, reducing error rates and improving overall system performance. The implications of this research extend to various applications requiring high data integrity, such as industrial control systems and Internet of Things (IoT) devices. By providing a practical solution to the challenges of error detection in serial communication, this study contributes to the advancement of reliable communication systems in modern technology.

The successful adoption of programmable machines for complex tasks opens up opportunities for productivity and more efficient communication, but also poses serious challenges in IT project management. This study aims to tackle the issue of high project failure rates caused by inadequate planning. It aims to assist project managers in enhancing their project planning by implementing real-time solutions through the utilization of machine learning (ML) algorithms and a user-friendly graphical interface. This research is divided into two key phases. The initial phase involves an examination of existing literature in the field of machine learning to identify relevant concepts applicable to project management. In the subsequent stage, two distinct types of ML algorithms, namely example-based learning and regression modeling, will be integrated into a user-friendly platform. This research develops a system that utilizes machine learning algorithms to assist project managers in real-time or near real-time through a user-friendly graphical interface, with a focus on improving project planning and risk mitigation. This research shows that machine learning algorithms provide positive results in overcoming human factors and preventing risks based on the experience of project managers.

In embedded systems, serial communication plays a crucial role in data transfer, particularly in Arduino-based projects. However, factors such as electromagnetic interference, noise, and signal degradation can compromise data integrity, leading to significant errors. Effective error detection systems are essential to ensure reliable data exchange. The Cyclic Redundancy Check (CRC) is one such method known for its ability to detect errors. Despite its potential, the practical application and impact of CRC on Arduino communication systems have not been extensively explored. This study implements CRC within Arduino serial communication by designing and developing software that integrates CRC for real-time error detection. The study rigorously tests this implementation in various scenarios to evaluate its performance, comparing data integrity with and without CRC. The results show that incorporating CRC significantly improves the reliability of data transmission in Arduino applications, enhancing error detection accuracy. This improvement strengthens existing systems and provides a solid foundation for developing more complex communication frameworks. The research advances reliable communication systems in embedded technologies. By demonstrating CRC's effectiveness in enhancing data integrity, the study offers valuable insights for developers and researchers seeking to improve serial communication across different applications.

Android is a complicated system that applications and component are usable and support for multiple work together, giving rise to highly complex interdependence relationships. Meanwhile, the Android environment is notable for being greatlty disparate and decentralized: different Operation System version is personalized and re-personalized by different parties about fast and used by whoever that can develop an application for that version. Android secure its explanation sources over an app sandbox and permissions model, where each application execution in this part can entrance only suspectible overall assets and another application component (value providers, services, activities, publication receivers) by the appropriate liscense.
This study uses Harehunter measurement to automatically detect Hare vulnerabilities in Android system applications. Harehunter and HareGuard performance evaluations were carried out in this study, both of which proved to be highly effective. The approach used here is divergent investigation, by searching all quoted, decompiled script, and obvious data for targeted attribute determination as an initial step, and running an XML parser. The outcome of this research show that the impact of Hares is very significant. The application of HareGuard in this study proved to be effective in detecting all attack applications that were made. Further evaluation of the performance impact on the minimum system host. For future research, to make Harehunter more effective, it is suggested to use a more qualified analyzer. So that this direction can be explored in more depth.

In the first quarter of 2017, retail in Indonesia recorded a growth of 2.5%, while in 2018 the growth was only in the range of 1% -1.5%. The cause of the slow growth is the change in the consumption pattern of the people and it will continue at the beginning of 2018. In addition, the decreasing productivity of the community at the lower middle level. As a retailer, Anterah store also faces the same thing, so anticipating a decline in sales requires an analysis of the best-selling products and how to find out the relationship between the products purchased by consumers. The association relationship between these products will be used as the basis for product arrangement, so that the frequency of products that consumers often buy can be arranged closely together so that consumers do not have to look for them longer. Market basket analysis to determine the relationship between products sold simultaneously is used to explore association rules (Association Rule Mining) which will produce products that are purchased simultaneously as a consideration for product arrangement in Anterah Retail storefront. Meanwhile, the best-selling products will be explored using the Frequent Pattern Growth method in order to obtain a ranking list of the most purchased products by consumers. This analysis is used as a basis for considering product promotion. The test results on the sales sample data obtained an average value of minimum support = 0.0025, minimum confidence = 0.610, LaPlace = 0.9985, Gain = -0.006, p-s = 0.003, Lift = 103.82, Convicting 2.5285 with a processing time of 41.456 seconds.