Improving Data Quality and Data Governance Using Master Data Management: A Review

Master data management (MDM) is a method of maintaining, integrating, and harmonizing master data to ensure consistent system information. The primary function of MDM is to control master data to keep it consistent, accurate, current, relevant, and contextual to meet different business needs across applications and divisions. MDM also affects data governance, which is related to establishing organizational actors’ roles, functions, and responsibilities in maintaining data quality. Poor management of master data can lead to inaccurate and incomplete data, leading to lousy stakeholder decision-making. This article is a literature review that aims to determine how MDM improves the data quality and data governance and assess the success of MDM implementation. The review results show that MDM can overcome data quality problems through the MDM process caused by data originating from various scattered sources. MDM encourages organizations to improve data management by adjusting the roles and responsibilities of business actors and information technology (IT) staff documented through data governance. Assessment of the success of MDM implementation can be carried out by organizations to improve data quality and data governance by following the existing framework

Research Trend of Causal Machine Learning Method: A Literature Review

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system

Stemming Influence on Similarity Detection of Abstract Written in Indonesia

In this paper we would like to discuss about stemming effect by using Nazief and Adriani algorithm against similarity detection result of Indonesian written abstract. The contents of the publication abstract similarity detection can be used as an early indication of whether or not the act of plagiarism in a writing. Mostly in processing the text adding a pre-process, one of it which is called a stemming by changing the word into the root word in order to maximize the searching process. The result of stemming process will be changed as a certain word n-gram set then applied an analysis of similarity using Fingerprint Matching to perform similarity matching between text. Based on the F1-score which used to balance the precision and recall number, the detection that implements stemming and stopword removal has a better result in detecting similarity between the text with an average is 42%. It is higher comparing to the similarity detection by using only stemming process (31%) or the one that was done without involving the text pre-process (34%) while applying the bigram

Research Methodology for Analysis of E-Commerce User Activity Based on User Interest using Web Usage Mining

Visitor interaction with e-commerce websites generates large amounts of clickstream data stored in web access logs. From a business standpoint, clickstream data can be used as a means of finding information on user interest. In this paper, the authors propose a method to find user interest in products offered on e-commerce websites based on web usage mining of clickstream data. In this study, user interest was investigated using the PIE approach coupled with clustering and classification techniques. The experimental results showed that the method is able to assist in analyzing visitor behavior and user interest in e-commerce products by identifying those products that prompt visitor interest

The Social Engagement to Agricultural Issues using Social Network Analysis

Twitter is one of the micro-blogging social media which emphasizes the speed of communication. In the 4.0 era, the government also promotes the distribution of information through social media to reach the community from various lines.  In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identifying the application of technology adoption in decision making, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development.  This article also shows the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken limited to 3000 nodes. Then, the measurements of the SNA are represented on the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and has many followers among other accounts that spread the same information. While the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred information, and the highest Closeness Centrality is owned by the @baigmac account because of the fastest to re-tweet the first information

Comparison of Electrical Conductivity Prediction Models Using Gaussian Process

People living in coastal areas use clean water sourced from groundwater to support the household, agricultural, and industrial needs. However, human activities and natural factors can lead to a common problem in coastal areas, namely seawater intrusion. Seawater intrusion can be detected using water quality data. Today, one of the challenges in water resources management is the prediction of water quality parameters such as total dissolved solids (TDS), electrical conductivity (EC), and water turbidity. Incomplete EC data and limitations of direct measurements can affect the analysis. Machine learning models are known to provide the most accurate predictions. This research used EC parameter data to investigate the performance of algorithms, namely artificial neural networks (ANN), Gaussian processes (GP), and multiple regression (MLR). The prediction used seven hydrochemical parameters (K, Ca, Mg, Na, SO4, Cl, HCO3) and three physical parameters of groundwater (TDS, pH, EC). Performance measurement used R-squared (R2) and root mean squared error (RMSE). The testing showed the MLR model had R2 of 0.985 and RMSE of 0.030, which were slightly better than other models. Hence, it can be concluded that the MLR model can be a solution to difficult problems of EC prediction and incomplete data in the water resources management

Kesesuaian Minat Mahasiswa dengan Judul Tesis Mahasiswa Menggunakan Metode Fuzzy Mamdani

Intisari — Pemilihan minat tesis yang sesuai dengan minat mahasiswa dapat membantu mahasiswa dalam proses pengerjaan tesis. Selain minat, dibutuhkan juga motivasi sebagai dorongan dari dalam diri mahasiswa. Data dalam penelitian ini menggunakan kuesioner minat dan kuesioner motivasi. Data dari kuesioner tersebut diolah menggunakan fuzzy Mamdani. Dalam penelitian ini fuzzy mamdani digunakan untuk mengetahui kesesuaian minat tesis mahasiswa, dari 80 mahasiswa sebagai responden diketahui bahwa sebanyak  51,06 % mahasiswa memiliki minat yang sesuai dengan proposal tesis dan sekitar 48,94 % mahasiswa memiliki minat yang tidak sesuai dengan proposal tesis mahasiswa. Kata kunci — Minat dan motivasi, Logika Fuzzy, Metode Fuzzy Mamdani Abstract — Selection of interest in accordance with the thesis that the interest of students to help students in the process of thesis. In addition to interest, it needed a boost of motivation as the students themselves. The data in this study using questionnaires interest and motivation questionnaire. Data from the questionnaires were processed using fuzzy Mamdani. In this study, fuzzy mamdani used to determine the suitability of interest thesis students, 80 students as respondents note that as many as 51.06% of the students have an interest in accordance with the thesis proposal and approximately 48.94% of the students have an interest that is not in accordance with the student's thesis proposal. Keywords— Interest and motivation, Fuzzy Logic,  Fuzzy mamdani method

Analisis Kesuksesan Implementasi Sistem Informasi Skripsi Pada Program Studi Teknik Informatika Universitas Pembangunan Nasional “Veteran” YOGYAKARTA

—Sejak tahun 2007, program studi TeknikInformatika UPN “Veteran” Yogyakarta telahmengimplementasikan Sistem Informasi Skrispi untukmendukung kinerja organisasi yang berkaitan denganakomodasi kebutuhan layanan skripsi. Namun padakenyataannya Sistem Informasi Skripsi belum dimanfaatkansecara maksimal. Evaluasi terhadap Sistem Informasi Skripsiperlu dilakukan untuk mengetahui faktor-faktor apa saja yangmempengaruhi kepuasan pengguna Sistem Informasi Skripsi.Penelitian ini menggunakan modifikasi model penelitiankesuksesan sistem informasi Delon dan Mclean. Modifikasiyang dilakukan adalah dengan menghilangkan variabel usedari model penelitian. Variabel use dihilangkan karena SistemInformasi Skripsi bersifat mandatory. Terdapat lima variabeldalam penelitian ini yaitu, kualitas informasi, kualitas sistem,kualitas layanan, kepuasan pengguna dan net benefit.Penelitian dilakukan dengan menganalisis hasil kuesioner yangterkumpul dari 45 responden yaitu mahasiswa yang pernahmenggunakan Sistem Informasi Skripsi. Metode analisis yangdigunakan adalah Partial Least Square menggunakan softwareSmartPLS.Hasil analisis menunjukkan kepuasan pengguna SistemInformasi Skripsi dipengaruhi oleh kualitas informasi dankualitas sistem. Net benefit dipengaruhi oleh kepuasanpengguna. Dalam penelitian ini kualitas layanan tidakmempunyai pengaruh terhadap kepuasan pengguna. Secaraumum Sistem Informasi Skripsi telah memberikan manfaatkepada pengguna, namun dalam implementasi SistemInformasi Skripsi mahasiswa sebagai pengguna merasa perluadanya peningkatan kualitas layanan dari Sistem InformasiSkripsi.Keyword—kepuasan pengguna; sistem informasi skripsi;Model Delon dan Mclean; PL