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All Journal Prosiding Seminar Nasional Teknologi Informasi dan Bisnis
Nurmalitasari
Universitas Duta Bangsa Surakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics

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Analisis Kemiskinan Menggunakan Metode Algoritma Clustering K-Means Dwiki Rasya Rahadian; Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Poverty has a broad and serious impact on the lives of individuals and society. When people live in poverty, they may face difficulties meeting basic needs, such as adequate food, adequate housing, and proper education. These limitations can negatively impact physical and mental health, education, employment opportunities, and overall quality of life. The purpose of this study is to find out the grouping of districts/cities that have similar characteristics based on the 2019 poverty indicators. This research uses data obtained from the BPS (Central Bureau of Statistics). The method used is the k-means clustering method which is a clustering partition method for grouping objects into k clusters. Based on the research results, the characteristics of each cluster were grouped based on the poverty indicator values in several districts/cities in 2019 as many as 2 clusters. Formed from 20 districts/cities in cluster 1 and 29 districts/cities in cluster 2. Cluster 1 has the characteristics of Low Work Challenges, with Low Per Capita Expenditure Rates and Low Unemployment Rates while Cluster 2 has the characteristics of High Job Challenges, with Per Capita Expenditure Levels High and High Non Working Rate.
Analisis Faktor-Faktor yang Mempengaruhi Produksi Padi di Sumatera Menggunakan Metode Regresi Linier Mohammad Yusuf Nugroho; Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

In research data must go through a processing process so that it can be used in the research. The data used must be valid to be able to produce an appropriate solution. This study aims to analyze the factors that influence rice production in paddy fields. The island of Sumatra has more than 50 percent of agricultural land in each province with the most dominant main food commodity being rice, while the remainder is corn, peanuts and sweet potatoes. Agricultural products in Sumatra are very vulnerable to climate change which can affect cropping patterns, planting time, production and yield quality. Climate change can have a negative impact on the production of these basic commodities. Moreover, an increase in the earth's temperature due to the impact of global warming which will affect the pattern of precipitation, evaporation, water runoff, soil moisture, and climate variations which are very fluctuating as a whole can threaten the success of agricultural production. Predictions of agricultural yields for food commodities are heavily influenced by climate change. The method used for analysis is Linear Regression and also uses the python library.
Analisis Faktor Utama Penentu Harga Rumah di Surakarta Menggunakan Principal Component Analysis Muhammad Rais Ramadhani; Nurmalitasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2023
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Residence is often referred to as one of the primary needs. Therefore, it is important to formulate a well-planned series of actions to ensure that every family has a dwelling of their own. In this planning process, an analysis of the factors determining house prices is necessary to serve as a basis for finding suitabel housing. The objective of this research is to conduct a Principal Component Analysis (PCA) on the main factors determining house prices using the dataset from Surakarta. The identified factors that contribute to house prices include the number of bedrooms, the number of bathrooms, the size of the house, the distance from the house to the city center, and the distance from the house to the nearest hospital. The analysis is performed using the PCA library in Python, resulting in two main factors with a variance above 90%. The first component represents accessibility factors, specifically the distance from the house to the city center and the distance from the house to the nearest hospital. On the other hand, the second component represents spatial and accommodation factors, including the number of bedrooms, the number of bathrooms, and the size of the house.
Co-Authors Dwiki Rasya Rahadian Mohammad Yusuf Nugroho Muhammad Rais Ramadhani