Implementasi Data Mining Estimasi Ketersediaan Lahan Pembuangan Sampah menggunakan Algoritma Simple Linear Regression

2018 ◽  
Vol 2 (1) ◽  
pp. 361-366
Author(s):  
Robi Yanto
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Simple Linear Regression

Tingginya aktivitas konsumsi yang dilakukan masyarakat berbanding lurus dengan meningkatnnya produksi sampah. Salah satu permasalahan tingginya produksi sampah yaitu rendahnya kesadaran masyarakat terhadap pengelolaan sampah. Hal ini merupakan masalah yang dihadapi di kota-kota besar. Sampah memberikan dampak negatif terhadap perubahan kondisi alam yang ada yaitu terjadinya polusi udara, air dan tanah yang mengakibatkan lingkungan menjadi tidak sehat. Kegiatan pengelolaan sampah melalui sosialisasi program 3R (Reduce, Reuse, Recycle) tentang sampah, telah memberikan dampak yang maksimal terhadap kesadaran masyarakat tentang pentingnya lingkungan yang sehat. seiring dengan peningkatan jumlah penduduk memberikan dampak pada peningkatan produksi sampah. Sehingga membutuhkan lahan pembuangan sampah yang mencukupi dalam jangka panjang. Untuk mengatasi permasalahan tersebut maka dilakukan analisa data terhadap estimasi ketersediaan lahan pembuangan sampah dalam jangka panjang dengan menggunakan teknik data mining. dari hasil analisa data mining menggunakan algoritma regresi linear sederhana dengan memperhatikan pertumbuhan penduduk tahun 2018 sampai dengan 2025 sebesar 201484 jiwa, maka diketahui bahwa peningkatan sampah dari tahun 2018 sampai dengan tahun 2025 adalah 36.052,326 ton. Sehingga dari luas lahan 30000 M2 hanya tersediaan lahan pembuangan sampah sampai tahun 2025 sebesar 5.965,1 M2.

IMPLEMENTASI ALGORITMA REGRESI LINEAR SEDERHANA DALAM MEMPREDIKSI BESARAN PENDAPATAN DAERAH (STUDI KASUS: DINAS PENDAPATAN KAB. DELI SERDANG)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Fransiskus Ginting ◽  
Efori Buulolo ◽  
Edward Robinson Siagian
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Visual Basic ◽  
Large Contribution ◽  
Simple Linear Regression ◽  
Information Discovery ◽  
Regional Income ◽  
Local Revenue ◽  
Local Taxes ◽  
The Future

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques do to collect records (Training set). Regional income is generally derived from local taxes and levies, local taxes are one source of funding for the region on the national average has not been able to make a large contribution to the formation of local revenue. By utilizing Regional Revenue data, it can produce forecasting and predictions of Regional Revenue income in the future to match the reality / reality so that the planned RAPBD can run smoothly. Simple Linear Regression or often abbreviated as SLR (Simple Linear Regression) is one of the statistical methods used in production to make predictions or predictions about the characteristics of quality and quantity to describe the processes associated with data processing for the acquisition of regional income. So that in the testing phase with visual basic net can help in processing valid Regional Revenue Amount data. Keywords: Data Mining, Local Revenue, Simple Linear Regression Algorithm, Visual Basic net 2008

scholarly journals Mining Educational Data to Analyze Students’ Performance: A Case Study of Mawuli School, Ho

2020 ◽  
Vol 5 (5) ◽  
pp. 1920-1948
Author(s):  
Cephas Lokpo
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Predictive Models ◽  
Quantitative Research ◽  
Educational Data Mining ◽  
Simple Linear Regression ◽  
Huge Data ◽  
Hidden Patterns ◽  
Insight Into

Data mining is the extraction of prospective valuable information from large chunk of data through the employment of many different data mining techniques. The usefulness of data mining coupled with the huge data generated in scholastic settings has made it an interesting field of research known as Educational Data Mining (EDM). The intent of EDM is to derive understanding from hidden patterns in data collected from institutions of learning to aid in identifying issues that influence students’ scholastic accomplishment, to solution of which will lead to improvement in accomplishment. Because scholastic achievement is dependent on several issues, it is essential to develop predictive models on students’ academic performance. This study’s objective, therefore, is to acquire an insight into performance through knowledge discovery by the use of simple linear regression in order to build a predictive model capable of predicting students’ grades to give a general overview of students’ performance in the WASSCE, and help improve students’ performance. In accomplishing the set objective for which the study was carried out, that is to predict the possible outcome of students in WASSCE, a widely sampled study was applied in undertaking the study (Quantitative research).

scholarly journals Analisis Pengaruh Faktor Kebutuhan Energi Listrik Tahun 2015 Terhadap Daya Yang Tersambung Dan Energi Yang Terjual Menggunakan Regresi Linear Sederhana (Studi Kasus Pada PT. PLN (Persero) Unit Area Pelayanan Dan Jaringan (APJ) Palembang)

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Nazori Suhandi ◽  
Irma Yuliawati ◽  
Indah Charista
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Electric Energy ◽  
Electrical Energy ◽  
Regression Method ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Level Of Significance

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>The availability of electrical energy is a very important aspect and even become a parameter to support the successful development of a region. Proper management of electrical energy resources and directed clearly will make the potential possessed of an area developed and utilized optimally. Population growth and economic development of a region can be influenced by the use of electrical energy. The supply of electricity must be taken into account so that the electrical energy can be available in an amount that suits your needs. Demand for the use of electricity in Indonesia will always increase with economic growth in addition to the development of electrical energy is also influenced by the development of the population in terms of quantity of customers to be electricity. Predicting methods such as using time series method (Gustriansyah, 2017) or data mining methods. The purpose of this research is to know how to overcome the influence of electricity usage (VA) connected with electric energy sold (KWh). Research done by simple linear regression method to facilitate writer in processing data. Based on the calculation result using simple linear regression method can be concluded 99.2% of the variation of electric power connected can be explained by the variable amount of electrical energy sold. While the rest (100% - 99.2% = 0.8%) is explained by other causes. And the level of significance &lt;0.05 so that the regression model can be used to predict the electrical energy sold.</em></p><p><strong><em>Keywords : </em></strong><em>Linear regression, analysis, electrical energy</em></p><p class="SammaryHeader" align="center"> </p><p class="SammaryHeader" align="center"><strong><em>Abstrak</em></strong></p><p><em>Ketersediaan energi listrik merupakan aspek yang sangat penting dan bahkan menjadi suatu parameter untuk mendukung keberhasilan pembangunan suatu daerah. Pengelolaan sumber daya energi listrik yang tepat dan terarah dengan jelas akan menjadikan potensi yang dimiliki suatu wilayah berkembang dan termanfaatkan secara optimal. Pertumbuhan populasi dan perkembangan ekonomi suatu wilayah dapat dipengaruhi penggunaan energi listrik. Penyediaan listrik harus diperhitungkan sehingga energi listrik dapat tersedia dalam jumlah yang sesuai dengan kebutuhan Anda. Permintaan untuk penggunaan energi listrik di Indonesia akan selalu meningkat dengan pertumbuhan ekonomi disamping pengembangan energi listrik juga dipengaruhi oleh perkembangan populasi dalam hal kuantitas pelanggan yang akan dialiri listrik. </em><em>Metode untuk memprediksi seperti menggunakan metode time series (Gustriansyah, 2017) atau metode data mining.</em><em> Adapun tujuan dari penelitian ini adalah untuk mengetahui bagaimana cara mengatasi pengaruh penggunaan tenaga listrik (VA) yang terhubung dengan energi listrik yang terjual (KWh). Penelitian dilakukan dengan metode regresi linier sederhana agar memudahkan penulis dalam mengolah data. Berdasarkan hasil perhitungan menggunakan metode regresi linier sederhana dapat disimpulkan sebesar 99,2% dari variasi daya listrik yang terhubung dapat dijelaskan oleh variabel jumlah energi listrik yang terjual. Sedangkan sisanya (100% - 99,2% = 0,8%) dijelaskan oleh penyebab lain. Dan tingkat signifikansi &lt;0,05 sehingga model regresi dapat digunakan untuk memprediksi energi listrik yang terjual.</em></p><p align="left"><strong><em>Kata kunc</em></strong><em>i: Regresi linier, analisis, energi listrik</em></p>

scholarly journals Data Mining Prediction Production of Water Needs using Simple Linear Regression Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 3575-3578
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Clean Water ◽  
Water Production ◽  
Simple Linear Regression ◽  
The Public ◽  
Human Needs ◽  
Regression Algorithms ◽  
Basic Human Needs ◽  
The City

Water is a natural resource that becomes the basic human needs. The city of Lubuklinggau every day provides the need for clean water to the community through PDAM Regional company Tirta Bukit Sulap. Clean water production is based on the analysis of water consumption by the community every month in advance. So, there is often a clean water delivery due to lack of clean water production. Also, the need for clean water will always increase with the increasing customer every month. Therefore, to be able to ensure the availability of clean water in the city of Lubuklinggau then need to be analyzed to predict the need for clean water in the city of each him is by applying data mining for prediction of needs Clean water by using simple linear regression algorithms. By applying data mining, clean water needs can be predicted for the next few years, making it easier for decisionmakers to determine the volume of clean water produced. So the public can utilize clean water according to the needs.

The Contribution of Sexual Identity Towards the Students’ Perception of Lesbian, Gay, Bisexual, and Transgender

10.32698/0642 ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 120
Author(s):  
Wiwi Delfita ◽  
Neviyarni S. ◽  
Riska Ahmad
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Sexual Identity ◽  
Student Perceptions ◽  
Undergraduate Students ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Simple Linear Regression ◽  
Simple Linear Regression Analysis ◽  
Bisexual And Transgender

Some students perceive lesbian, gay, bisexual, and transgender (LGBT) positively, even though LGBT is a sexual deviation that is not appropriate with values and norms. There are several factors that influence an individual's perception of LGBT, including sexual identity. This study aims at looking at the contribution of sexual identity to student perceptions about LGBT. This research used a quantitative approach with a descriptive method and a simple linear regression analysis. The sample of this research was 385 taken from 15.752 undergraduate students of Universitas Negeri Padang which the sample was drawn by using the Slovin formula and continued with a Proportional Random Sampling technique. The instrument used was the Guttman model's sexual identity scale and the scale of students' perceptions of the LGBT Likert model. After analyzing the data with the descriptive technique and the simple linear regression analysis, the results showed that sexual identity significantly contributed to the students' perceptions of LGBT. This research has implications as a basis for counselors to help students avoid sexual identity mismatches and prevent the emergence of positive perceptions of LGBT.

Pengaruh Penghargaan Terhadap Kinerja Karyawan pada CV. Dino Manunggal Karsa Bogor

2019 ◽  
Vol 4 (2) ◽  
pp. 17
Author(s):  
Dedy Mulyadi ◽  
Didik Purwanto
Keyword(s):  
Linear Regression ◽  
Performance Assessment ◽  
Correlation Coefficient ◽  
Linear Regression Analysis ◽  
Strong Relationship ◽  
Simple Linear Regression ◽  
New Employees ◽  
Company Policy ◽  
Effective Manner ◽  
Relationship Of

The question of compensation in addition to sensitive to be driving someone to worl due to an effect on morale and discipline employees. Therefore , any  agency or any organization should be able to provide compensation equal to the workload  to create a workforce that efficient and effective manner can be realized. Amaore than that, the company’s goal to improve performance. Performance assessment is a subjective process that involves human judgments. Thus, performance assessment is very likely wrong and very easily influonced by sources that are not actual, so it must be taken into account and considered reasinable. Frformance appraisals are considered  to meet the target if it has a good impact on new employees who rated their performance. Simple linear regression analysis using SPSS version 12:00 data processing obtained tegression equation Y = 0,487 X 74 + with an explanation of X = award, 74 = constant, 0.487 = coefficient awards, and Y = performance based on simple linear regression equation in case of increase of one unit of the  performance award will be increased 0.487 units. If company policy negates the performance award will remain at a constant rate (74) units . (A) Test results obtained thitung significant constants of (12.574) > t table for (1.960 then reject Ho constanta significant meaning. (B) significant Test award coefficient t count the results obtained by (2.164)> t table foe (1.96) then reject Ho the mean coeffent of appreciation affect the performance . (C) correlation coefficient analysis is done by calculating the product moment corration (pearson)  to test  whether or not a strong  relationship between the variables X  dan Y , based on the results of cakculations with SPSS  table valuse obtained by calculating the  correlation coefficient r (0.3100> r on the table for a = 0,05 (0.291) then reject Ho, which means there is a relationship of respect for performance. When we enter these valuse in the table shows the interpretation of the correlation coefficient between the interval from 0.20 to 0.399 which has a low relationship

Maintaining Customer Loyalty by Intensifying Service Quality and Customer Satisfaction in Commuter Line Jabodetabek

2019 ◽  
Vol 10 (9) ◽  
pp. 902-909
Author(s):  
Umbas Krisnanto ◽  
◽  
Conny Marpaung ◽  
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Hypothesis Testing ◽  
Customer Loyalty ◽  
T Test ◽  
Coefficient Of Determination ◽  
Simple Linear Regression ◽  
Significance Level

This study aims to determine and analyze the influence of Service Quality and Customer Satisfaction on Customer Loyalty in Jabodetabek Commuter Line. The sample of this study was 50 people. Methods of collecting data by distributing questionnaires. Data analysis using the analysis used is simple linear regression, t test and coefficient of determination. The results showed 1) Service Quality has a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0.048; and supported by the results of hypothesis testing with a t-count value of 4.433 > t-table value of 1.95, with a significance of 0.048 or < 0.05; 2) Customer Satisfaction positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a level significance of 0,000; and supported by the results of hypothesis testing with a t-count value of 4,969 > t-table value of 1.95, with a significance of 0,000 or < 0.05, 3) Service quality and Customer Satisfaction have a positive and significant effect on Customer Loyalty in Jabodetabek Commuter Line, with a significance level of 0,000. This means that the hypothesis H0 is rejected and Ha is accepted so that it can be concluded that service quality and customer satisfaction together have a positive and significant effect on customer loyalty in Jabodetabek Commuter Line.

PENGARUH INTELLECTUAL CAPITAL TERHADAP KINERJA KEUANGAN PADA PERUSAHAAN BARANG KONSUMSI YANG TERDAFTAR DI BEI TAHUN 2015-2017

2019 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
Niken Ayu Wulandari ◽  
Tegoeh Hari Abrianto ◽  
Edi Santoso
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Financial Performance ◽  
Intellectual Capital ◽  
Stock Exchange ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Simple Linear Regression ◽  
Return On Equity ◽  
Asset Turnover

This research to analyze and evaluate intellectual capital on financial performance obtained by return on equity, asset turnover and growth in revenue. The population in this study are consumer goods companies listed on the Stock Exchange in 2015-2017. The research sample was received by 21 companies obtained by using purposive sampling technique. The analytical method used is simple linear regression analysis with the SPSS version 20 application and uses the VAICTM method to measure intellectual capital. The results of this study indicate that intellectual capital has a significant effect on financial performance generated by return on equity, but intellectual capital does not have a significant effect on financial performance required by asset turnover and growth in revenue.

Pengaruh Corporate Social Responsibility Terhadap Nilai Dan Kinerja Perusahaan Manufaktur

2011 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Agustin Ekadjaja ◽  
Vony Vony
Keyword(s):  
Corporate Social Responsibility ◽  
Linear Regression ◽  
Confidence Level ◽  
Stock Exchange ◽  
Tobin’S Q ◽  
Manufacturing Companies ◽  
Simple Linear Regression ◽  
Tobin's Q ◽  
And Performance ◽  
Corporate Social

This study aims to determine the effect of CSR Index to the value and performance of manufacturing companies listed on the Indonesia Stock Exchange (BEI), and to find out how much the ability of the variable CSR Index in explaining the variable Tobin’s Q, ROA, and ROE manufacturing companies listed on Indonesia Stock Exchange (BEI). This study uses data sampled during the 75 years from 2007 to 2009. A statistical method used to test the research hypothesis is a simple linear regression model. Therefore, before performing hypothesis testing carried out tests of classical assumptions. The results of this study prove that, CSR Index has a significant effect on Tobin’s Q and ROE with 95% confidence level. However, CSR Index has no significant influence on ROA with 95% confidence level. Key words : CSR Index, Variabel Tobin’s Q, ROA, ROE

PENGARUH KUALITAS PRODUK DAN KUALITAS LAYANAN TERHADAP KEPUASAN PELANGGAN

2017 ◽  
Vol 5 (3) ◽  
Author(s):  
Ismail Razak, SE., MS. ◽  
Nirmala Putri Sari
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Customer Satisfaction ◽  
Service Quality ◽  
Product Quality ◽  
Sampling Method ◽  
Primary Data ◽  
Analysis Method ◽  
Simple Linear Regression ◽  
The City

The aim of this study was to analize the effects of product quality and service quality on the customer satisfaction. This study took place in the City of Bekasi and objects in this study are all customer who use the Matrass GM-9000. Primary data was obtained from customers of Matrass GM-9000 in Bekasi City through admission filling of questionnaire by using scale of Likert. The respondent of this study was chosen through accidental sampling method, and data analysis method was simple linear regression and multiple linear regression. The results of this study indicated that product quality and service quality positively and significant influenced the customer satisfaction

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