The Tweetology of New and Renewable Energy in Indonesia

 The amount of unstructured data is increasing annually, which is promising for gaining insights. Twitter, a platform producing unstructured data, is currently one of the most popular media platforms used for conducting research on a topic's trend. This study attempts to analyze the topic of New and Renewable Energy (NRE) in Indonesia. The purpose of this study is to gain insights into the NRE topic trend over the last ten years by modeling the topics discussed on Twitter and examining the location distribution of users who post tweets about the topic. Accordingly, this study employed descriptive analysis, geocoding analysis, and topic modeling. The results of descriptive analysis show that the development of NRE has accelerated in recent years, particularly in 2021. Geocoding analysis reveals that the distribution of people who engage in NRE posting activities is dominated by DKI Jakarta province. Topic modeling yielding two topics that were discussed the most by Indonesians over a 10-year period. The two topics are related to government policies that support the development of NRE and electricity, which is Indonesia's focus in NRE. This study highlights the importance of analyzing the Tweetology of NRE

Prototype Media Interaktif untuk Menanamkan Nilai Pancasila untuk Anak Usia Dini

The purpose of this study was to produce a valid interactive media prototype for instilling Pancasila values ​​in early childhood. This type of research is R&D with the Borg and Gall model with procedures for needs analysis, prototype design, development, and evaluation of implementation. Evaluation technique used with Tessmer evaluation. The data collection technique used a walkthrough which was assessed by the media validator and material validator. Based on the results of research from experts, namely material experts and media design experts with very valid categories with a feasibility percentage of 92.3% and 99.99% respectively, so that the product is valid in terms of visual media, audio media, aspects of typography, aspects of language, aspects of programming, aspects of presentation and feasibility of the content of Pancasila values. From the results of this study, interactive media for instilling Pancasila values ​​in early childhood is valid to be used and utilized by teachers and parent

MODEL BEHAVIORAL INTENTION DALAM MENGGUNAKAN KINERJA eWOM DI SOCIAL NETWORK SERVICES (SNS): Survei terhadap pengguna fintech di Indonesia

Pada tahun 2021-2023 ini fintech payment berkembang dengan inovasi seperti pembayaran menggunakan biometrik dan integrasi dengan e-commerce. Kepercayaan masyarakat pada pembayaran digital mendorong adopsi layanan fintech payment. Penelitian ini bertujuan untuk mengetahui bagaimana model peningkatan intention to use eWOM di SNS sebagai media sosial dalam konteks produk fintech payment (Gopay, OVO, dan LinkAJA) pada SNS di Indonesia dengan pembahasan variabel dan sub variabel lebih komprehensif menggunakan pendekatan theory acceptance model (TAM 3). Metode penelitian menggunakan metode penelitian kuantitatif dengan jenis penelitian deskriptif dan verifikatif. Populasi penelitian 1300 pengguna produk fintech payment (Gopay, OVO, dan LinkAJA) pada SNS di Indonesia, sehingga unit analisis menggunakan teori Hair 1:10 dari 59 item pertanyaan kuisioner sebagai instrument penelitian yaitu 590 responden. Teknik analisis data untuk mengetahui hubungan korelatif dalam penelitian ini menggunakan IBM SPSS AMOS versi 26. Berdasarkan hasil penelitian diketahui bahwa Behavioral Intention dengan sub variabel dominan yaitu penggunaan praktis dipengaruhi secara signifikan oleh Perceived Ease of Use dengan sub variabel dominan yaitu beralih pada fintech. Selanjutnya Perceived of Usefullness dengan sub variabel dominan yaitu kemudahan transaksi dipengaruhi secara signifikan oleh Perceived Ease of Use dengan sub variabel dominan yaitu beralih pada fintech dan Perceived Ease of Use dengan sub variabel dominan yaitu beralih pada fintech dipengaruhi secara signifikan oleh Perceived Enjoyment & Objective Usability dengan sub variabel dominan yaitu kemudahan fitur. Sehingga edukasi pengguna, penambahan fitur interaktif, peningkatan user interface (UI) dan user experience (UX), dorongan ulasan positif, program peningkatan keterampilan pengguna, kolaborasi dengan pihak ketiga, dan ketentuan privasi yang jelas menjadi implikasi penelitian ini. Between years 2021 and 2023, fintech payment has witnessed growth through innovations such as biometric-based payments and integration with e-commerce. The public's trust in digital payments has fueled the adoption of fintech payment services. This study aims to understand how to enhance the intention to use eWOM on SNS as a social media platform in the context of fintech payment products (Gopay, OVO, and LinkAJA) in Indonesia, with a more comprehensive discussion of variables and sub-variables using the Theory of Acceptance Model (TAM 3) approach. The research methodology employs a quantitative approach with descriptive and verification research. The study population consists of 1300 users of fintech payment products (Gopay, OVO, and LinkAJA) on SNS platforms in Indonesia, with the analysis unit applying the Hair's theory of 1:10, resulting in 590 respondents for the questionnaire's 59 items. Data analysis techniques to explore correlational relationships in this study employ IBM SPSS AMOS version 26. Based on the research findings, it is evident that Behavioral Intention, with the dominant sub-variable being practical usage, is significantly influenced by Perceived Ease of Use, with the dominant sub-variable being the switch to fintech. Furthermore, Perceived Usefulness, with the dominant sub-variable being transactional ease, is significantly influenced by Perceived Ease of Use, with the dominant sub-variable being the switch to fintech. Additionally, Perceived Ease of Use, with the dominant sub-variable being the switch to fintech, is significantly influenced by Perceived Enjoyment & Objective Usability, with the dominant sub-variable being feature ease. Consequently, the implications of this research encompass user education, the addition of interactive features, enhancement of user interface (UI) and user experience (UX), encouragement of positive reviews, user skill enhancement programs, collaborations with third parties, and clear privacy policies

Machine learning in weather prediction and climate analyses : applications and perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research - photovoltaic and wind energy, atmospheric physics and processes; in climate research - parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting

NusaCrowd: Open Source Initiative for Indonesian NLP Resources

We present NusaCrowd, a collaborative initiative to collect and unify existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have brought together 137 datasets and 118 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their value is demonstrated through multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and the local languages of Indonesia. Our work strives to advance natural language processing (NLP) research for languages that are under-represented despite being widely spoken

Big data analytics for demand response in smart grids

The transition to an intelligent, reliable and efficient smart grid with a high penetration of renewable energy drives the need to maximise the utilisation of customers’ demand response (DR) potential. More so, the increasing popularity of smart meters deployed at customers’ sites provides a vital resource where data driven strategies can be adopted in enhancing the performance of DR programs. This thesis focuses on the development of new methods for enhancing DR in smart grids using big data analtyics techniques on customers smart meter data. One of the main challenges to the effective and efficient roll out of DR programs particularly for peak load reduction is identifying customers with DR potential. This question is answered in this thesis through the proposal of a shape based clustering algorithm along with novel features to target customers. In addition to targeting customers for DR programs, estimating customer demand baseline is one of the key challenges to DR especially for incentive-based DR. Customer baseline estimation is important in that it ensures a fair knowledge of a customers DR contribution and hence enable a fair allocation of benefits between the utility and customers. A Long Short-Term Memory Recurrent Neural Network machine learning technique is proposed for baseline estimation with results showing improved accuracy compared to traditional estimation methods. Given the effect of demand rebound during a DR event day, a novel method is further proposed for baseline estimation that takes into consideration the demand rebound effect. Results show in addition to customers baseline accurately estimated, the functionality of estimating the amount of demand clipped compared to shifted demand is added

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

Indonesian and Malay are underrepresented in the development of natural language processing (NLP) technologies and available resources are difficult to find. A clear picture of existing work can invigorate and inform how researchers conceptualise worthwhile projects. Using an education sector project to motivate the study, we conducted a wide-ranging overview of Indonesian and Malay human language technologies and corpus work. We charted 657 included studies according to Hirschberg and Manning's 2015 description of NLP, concluding that the field was dominated by exploratory corpus work, machine reading of text gathered from the Internet, and sentiment analysis. In this paper, we identify most published authors and research hubs, and make a number of recommendations to encourage future collaboration and efficiency within NLP in Indonesian and Malay

Adapting by copying. Towards a sustainable machine learning

[eng] Despite the rapid growth of machine learning in the past decades, deploying automated decision making systems in practice remains a challenge for most companies. On an average day, data scientists face substantial barriers to serving models into production. Production environments are complex ecosystems, still largely based on on-premise technology, where modifications are timely and costly. Given the rapid pace with which the machine learning environment changes these days, companies struggle to stay up-to-date with the latest software releases, the changes in regulation and the newest market trends. As a result, machine learning often fails to deliver according to expectations. And more worryingly, this can result in unwanted risks for users, for the company itself and even for the society as a whole, insofar the negative impact of these risks is perpetuated in time. In this context, adaptation is an instrument that is both necessary and crucial for ensuring a sustainable deployment of industrial machine learning. This dissertation is devoted to developing theoretical and practical tools to enable adaptation of machine learning models in company production environments. More precisely, we focus on devising mechanisms to exploit the knowledge acquired by models to train future generations that are better fit to meet the stringent demands of a changing ecosystem. We introduce copying as a mechanism to replicate the decision behaviour of a model using another that presents differential characteristics, in cases where access to both the models and their training data are restricted. We discuss the theoretical implications of this methodology and show how it can be performed and evaluated in practice. Under the conceptual framework of actionable accountability we also explore how copying can be used to ensure risk mitigation in circumstances where deployment of a machine learning solution results in a negative impact to individuals or organizations.[spa] A pesar del rápido crecimiento del aprendizaje automático en últimas décadas, la implementación de sistemas automatizados para la toma de decisiones sigue siendo un reto para muchas empresas. Los científicos de datos se enfrentan a diario a numerosas barreras a la hora de desplegar los modelos en producción. Los entornos de producción son ecosistemas complejos, mayoritariamente basados en tecnologías on- premise, donde los cambios son costosos. Es por eso que las empresas tienen serias dificultades para mantenerse al día con las últimas versiones de software, los cambios en la regulación vigente o las nuevas tendencias del mercado. Como consecuencia, el rendimiento del aprendizaje automático está a menudo muy por debajo de las expectativas. Y lo que es más preocupante, esto puede derivar en riesgos para los usuarios, para las propias empresas e incluso para la sociedad en su conjunto, en la medida en que el impacto negativo de dichos riesgos se perpetúe en el tiempo. En este contexto, la adaptación se revela como un elemento necesario e imprescindible para asegurar la sostenibilidad del desarrollo industrial del aprendizaje automático. Este trabajo está dedicado a desarrollar las herramientas teóricas y prácticas necesarias para posibilitar la adaptación de los modelos de aprendizaje automático en entornos de producción. En concreto, nos centramos en concebir mecanismos que permitan reutilizar el conocimiento adquirido por los modelos para entrenar futuras generaciones que estén mejor preparadas para satisfacer las demandas de un entorno altamente cambiante. Introducimos la idea de copiar, como un mecanismo que permite replicar el comportamiento decisorio de un modelo utilizando un segundo que presenta características diferenciales, en escenarios donde el acceso tanto a los datos como al propio modelo está restringido. Es en este contexto donde discutimos las implicaciones teóricas de esta metodología y demostramos como las copias pueden ser entrenadas y evaluadas en la práctica. Bajo el marco de la responsabilidad accionable, exploramos también cómo las copias pueden explotarse como herramienta para la mitigación de riesgos en circunstancias en que el despliegue de una solución basada en el aprendizaje automático pueda tener un impacto negativo sobre las personas o las organizaciones

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy