Machine Learning Algorithms for Big Data Applications With Policy Implementation

This article examines the policy implementation literature using a text mining technique, known as a structural topic model (STM), to conduct a comprehensive analysis of 547 articles published by 11 major journals between 2000 and 2019. The subject analyzed was the policy implementation literature, and the search included titles, keywords, and abstracts. The application of the STM not only allowed us to provide snapshots of different research topics and variation across covariates but also let us track the evolution and influence of topics over time. Examining the policy implementation literature has contributed to the understanding of public policy areas; the authors also provided recommendations for future studies in policy implementation.

Segmentasi Perkebunan Kelapa Sawit dengan Data Mining Teknik K-Means Clustering Berdasarkan Luas Areal, Produksi dan Produktivitas

The oil palm is the most productive plantation product in Indonesia. Government strategies and policies related to oil palm plantations continue to be carried out considering that the plantation area is increasing every year. Segmentation of oil palm plantations based on area, production, and productivity aims to identify groups of potential oil palm plantations in the territory of Indonesia. This segmentation can provide consideration in formulating strategies and policies that will be made by the government. The segmentation method for grouping oil palm plantations uses the K-Means Clustering Data Mining technique with 3 clusters specified. Data mining stages start from data collection until representation is carried out, where 34 data sets are collected, only 25 data sets can be processed further. The results of this grouping obtained three plantation segments, namely 72% of the plantation group with low potential, 20% of the plantation group with medium potential, and 8% of the plantation group with high potential.

A Review of Predictive Systems for Patients at Risk of Developing Stroke

Stroke remains one of the leading causes of death worldwide. It is usually associated with a build-up of fatty deposits inside the arteries which increases the risk of blood clotting. The unannounced nature of the disease when it strikes has posed a major challenge in the health sector. Poor medical facilities, insufficient information on how to accurately diagnose stroke, late identification of the disease by the patients due to being ignorant of the disease are some of the reasons for the increasing mortality rate due it. The application of data mining technique in the field of medicine has brought about positive development in the area of diagnosing, prediction and deeply understanding of healthcare data. This study considers some of the Predictive Models developed using some data mining approaches to predict patients at risk of developing stroke in order for other researchers to build on.

Penerapan Data Mining Association Rule Menggunakan Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel

Many individuals are interested in starting a workshop. By responding to each customer's desires, the workshop company may continue to develop, and so the data mining technique can address this challenge. The FP-Growth algorithm is one of the methods that may be used to determine the stock availability of automotive spare components such as engine oil, spark plugs, oil filters, ac filters, batteries, tires, and so on. This research is divided into four stages: problem identification, data gathering, data processing, and data testing. Based on the results of the testing, AK (Battery), OM (Engine Oil), and BS (Spark plug) received support values of 33% and 80%, respectively. Furthermore, the BN (Ban) and KR (Kampas Bram) values were found with 33% support and 80% confidence. Furthermore, we obtain AK (Battery) and OM (Engine Oil) with 33% support and 80% confidence, and BN (Tires) and OM (Engine Oil) with 33% support and 80% confidence. OM (Engineering Oil), AK (Battery), and BS (Battery Storage) are the abbreviations for the terms OM (Engineering Oil), AK (Battery), and BS (Battery (Spark plug)).

Exploring Latent Topics and Research Trends in Mathematics Teachers’ Knowledge Using Topic Modeling: A Systematic Review

Mathematics teachers’ knowledge is considered one of the most critical factors in instruction and student achievement. As such, various studies have focused on mathematics teachers’ knowledge. Despite the expansion of the field, however, a systematic review was rarely implemented. Therefore, this study aimed to identify major research topics and trends on mathematics teachers’ knowledge by analyzing abstracts of 3485 scholarly articles published from 1987 to 2021. Using a text-mining technique, 11 underlying topics were found in the articles. The topics were classified based on their relationships and the following four groups were identified: “assessment”, “teachers’ knowledge for teaching”, “students’ knowledge and understanding”, and “teachers’ professional learning”. Over time, the analysis of research trends showed that professional development is the most popular topic, followed by pedagogical content knowledge and students’ mathematical understanding. Moreover, the popularity of these topics has not changed considerably over time. This study provides implications based on these results.