Analisis Regresi Logistik Biner Untuk Memprediksi Kepuasan Pengunjung Pada Rumah Sakit Umum Daerah Majene

Alwi, Wahidah; Ermawati, Ermawati; Husain, S. Abid

doi:10.24252/msa.v6i1.4783

Cited by 7 publications

(6 citation statements)

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“…Seperti yang telah dijelaskan sebelumnya, cangkang memiliki kekerasan dan kepadatan yang tinggi seperti pada material beton [16].…”

Section: Persamaan Dan Perbedaan Properti Mekanik Cangkang Dengan Betonunclassified

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

Mukhlis,

Munawir,

Yeni

et al. 2024

tameh

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The background of this research is that there is still a lot of oyster shell material that has not been utilized, meanwhile the shell has good mechanical properties as well as concrete. Up to this moment, the shell is not used properly so it needs to studied the use as concrete mixture. By the utilization of this waste is expected to reduce the environmental impact. This study was aims to obtain a regression equation of the compressive strength of concrete using shells, the shells used in this study were oyster shells (ostreidae), tested at 28 days of age with 3 variations in the percentage of the use of shell ash material and with 1 variation of normal concrete. The percentage variations used are 0% variation, 5% variation, 10% variation, and 15% variation. In addition, based on the regression equation, the strong relationship between variables will be obtained which is indicated by the value of r square. The implementation of this research was carried out by carrying out a compressive test of concrete cylinder samples with 3 variations of the water cement rasio (W/C), is W/C 0.4, W/C 0.5, and W/C 0.6. The activity was continued by recording and analyzing data by conducting a regression analysis between the relationship between the percentage of shell use and the compressive strength of concrete. The regression equation used is the exponential regression equation, linear regression, polynomial regression order 2. From the test data, it is found that the regression equation is the best for W/C variation 0.4, obtained polynomial regression equation order 2, y = -0.0351x2 + 0, 3279x + 25.075 with r² = 0.9999, for the W/C variation of 0.5, the second order polynomial regression equation is obtained, y = -0.0702x2 + 0.8062x + 23.406 with r² = 0.8983, and for the W/C variation of 0.6 , the second order polynomial regression equation is obtained, y = -0.0404x2 + 0.4008x + 19.074 with a value of r² = 0.9167..

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“…Seperti yang telah dijelaskan sebelumnya, cangkang memiliki kekerasan dan kepadatan yang tinggi seperti pada material beton [16].…”

Section: Persamaan Dan Perbedaan Properti Mekanik Cangkang Dengan Betonunclassified

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

Mukhlis,

Munawir,

Yeni

et al. 2024

tameh

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“…Logistic regression is a method for determining the model of the relationship between a predictor variable (numerical/ categorical) and a response variable (categorical) [11]. A logistic regression model whose response variables consist of 2 categories ("success" and "failure") is called binary logistic regression [5]. The general form of the binary logistic regression opportunity model can be seen in Equation 4.…”

Section: Logistic Regressionmentioning

confidence: 99%

“…Indicator Formula Accuracy: Percentage of observations that can be correctly classified by the model (5) Sensitivity: Percentage of positive observations correctly classified as positive (6) Specificity: Percentage of negative observations correctly classified as negative (7) The Proposed Method…”

Section: Table 3: Equations Of Confusion Matrixmentioning

confidence: 99%

Comparison of K-Nearest Neighbor, Naive Bayes Classifier, Decision Tree, and Logistic Regression in Classification of Non-Performing Financing

Putra

Ratih

2023

ijasca

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The Non-Performing Financing (NPF) indicator of one of the Islamic Banks in Indonesia in the 1st to 3rd quarter of 2021 in a row of 9.69%; 9.97%; 9.46%. The NPF movement tends to improve slightly from time to time but still exceeds the maximum limit stipulated in Bank Indonesia’s Regulation Number 23/2/PBI/2021, which is no more than 5%. This shows that the Islamic bank has a financing performance that can be said to be less good. Preventive steps that can be taken to reduce the NPF ratio in order to improve the health of the bank is to classify prospective financing customers. This research was conducted using the K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree, and Logistics Regression classification methods to predict potential financing customers. The dataset is divided into 80% training and 20% testing. It was found that the best classification result was the Naive Bayes Classifier in the proportion of distribution of 80% training data and 20% testing data with an accuracy value of 84.69%, sensitivity of 58.25%, and specificity of 90.16%.

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“…The simultaneous is tools for the test of predictor variables simultaneously on the response variable. According to Hosmer David testing of these parameters is done by using the likelihood ratio test with the G test statistics [13]. Mathematically the G statistic formula is [13]:…”

Section: Simultaneous Testmentioning

confidence: 99%

Consumer Preferences To Use Digital Payment OVO as Study of Industry Development 4.0 in Indonesia (Case Study: Student of Walisongo State Islamic University)

Fathonah¹,

Huda²,

Kristin³

2021

Proceedings of the 1st International Conference on Sustainable Management and Innovation, ICoSMI 2020, 14-16 September 2020, Bo

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Financial technology (fintech) emerged as a form of innovation in digital payment systems. OVO is one of the platforms that has contributed to the growth of digital payment service providers in Indonesia. This platform is a subsidiary of the Lippo Company managed by PT. Visionet International. According to a report released by CB Insight titled "The Global Unicorn Club OVO" has been a unicorn status since March 14 th , 2019, and seat in the 2 nd ranks with a valuation of $ 2.9 billion (Rp. 40.6 trillion). This is can be the effect of the various facilities and friendly users so that OVO continues to make a better future of Fintech as comfort that always available in accessing support economic activities in the industrial era 4.0. This study aims to determine what factors influence consumer preferences in using digital payment OVO. The technique used is purposive random sampling by distributing questionnaires to specific respondents. The factors that are considered influential are gender, student level, average student financial per month, brand image, risk perception, price, and ease of use. Using binary logistic regression analysis (logit), this research concluded that the factors of influence consumer preferences in using digital payments OVO are risk perception and price.

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Analisis Regresi Logistik Biner Untuk Memprediksi Kepuasan Pengunjung Pada Rumah Sakit Umum Daerah Majene

Cited by 7 publications

References 0 publications

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

Pengaruh Penggunaan Abu Cangkang Terhadap Kuat Tekan Beton

Comparison of K-Nearest Neighbor, Naive Bayes Classifier, Decision Tree, and Logistic Regression in Classification of Non-Performing Financing

Consumer Preferences To Use Digital Payment OVO as Study of Industry Development 4.0 in Indonesia (Case Study: Student of Walisongo State Islamic University)

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