A Big Data Analytics Model for Household Electricity Consumption Tracking and Monitoring

Dollah, Roimah; Aris, Hazleen

doi:10.1109/icbdaa.2018.8629769

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Dalam terminologi Layman, data analitik dideskripsikan sebagai kegiatan analisis data, baik besar atau kecil, untuk memahami dan melihat bagaimana menggunakan pengetahuan yang tersembunyi di dalamnya. Apabila dikaji berdasarkan modelnya dan diurutkan berdasarkan tingkat kesulitannya dari yang termudah sampai yang tersulit, terdapat empat jenis model analitik [9], yaitu : 1. Deskriptif Model ini menjawab pertanyaan "apa yang terjadi", analisis deskriptif mendeskripsikan dan merangkum fitur-fitur dasar yang telah tersirat pada seluruh populasi dari set data yang diberikan.…”

Section: Gambaran Umum Model Analitik Dan Big Data a Model Analitikunclassified

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A Survey on Big Data Analytics Model

Kurniawan

2019

ITEJ

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Topic about big data analytics have received a lot of attention and interest at this time. There are many topics can be discussed related to the analytical model, tools, and technology used. Big data analytics model involves many processes with various technologies used. Skills in handling big data, extracting mining, and developing insight are needed in applying big data analytics. Suitable analytical hardware and software also needed in decision making. Big data analytics is a key to a business strategy, but only a small portion of big data is currently used to support their business strategy. Big data analitycs can answer many questions about how to manage costs, time, and development or optimization strategies, and other decision making choices. However, there are many challenges in big data analytics technology. This survey paper addresses topics related to the analytical model, tools, and technology used. This paper also discusses the application of big data analytics in various fields.

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Section: Gambaran Umum Model Analitik Dan Big Data a Model Analitikunclassified

“…Peluang untuk implementasi model BDA yang berfokus pada sisi konsumen dari sektor energi cukup banyak. Dalam penelitian [9], pengembangan model BDA dilakukan untuk melacak dan memantau konsumsi listrik rumah tangga. Model analitik yang digunakan adalah analitik deskriptif dan preskriptif.…”

Section: Big Data Analytics Dalam Bidang Energi Dan Utilitasunclassified

A Survey on Big Data Analytics Model

Kurniawan

2019

ITEJ

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“…The collected historical data in massive amounts raises opportunities to perform analysis from many dimensions or known as multidimensional analysis (Agapito, Zucco, & Cannataro, 2020). Besides, data could be the driving aspect while making business decisions and understanding the current condition of the organization by extracting its value with data analysis (Dollah & Aris, 2018). The activity of data collection and analysis is not limited to profit organizations, but in the academic area as well (Yulianto, 2019).…”

Section: Introductionmentioning

confidence: 99%

Dimensional Data Design for Event Feedback Data Warehouse

Fattah

Ridwan

Sulistiyowati

2023

JISA

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Data is an important asset and a fundamental requirement for building valuable information for organizations. Association of Information Systems Students of Unsika (Himsika) as a university organization provides many events to develop student’s academic and professional skills. A post-event evaluation through a feedback survey was conducted and stored in Google Sheets spreadsheet format. However, the current analysis process using spreadsheets lacks standardization, making it difficult to compare satisfaction rates over time and between events. Additionally, the lack of standardization leads to semi-structured data on spreadsheets, with varying question formats and meanings. To address these limitations, implementing a centralized data warehouse is proposed as a solution. The data warehouse would provide a structured and standardized approach to analyzing event feedback, enabling better comparisons and evaluation of management quality within Himsika. The research aims to design a data warehouse that supports multidimensional analysis. As a way to simplify and optimize analytical queries, the data structure is standardized in the data warehouse. The Four-step Dimensional Design method is applied in designing dimensional modeling on the data warehouse, consisting of four stages including selecting the business process, declaring the grain, identifying the dimensions, and identifying the facts. The design process resulted in 4 dimensions of events, dim_instances, dim_degree_programs, and dim_professions, and a fact table called fact_rates_by_responses. Overall, the proposed data warehouse and dimensional modeling approach aim to enhance the analysis and evaluation of Himsika’s events.

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