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All Journal Inferensi Journal of Management and Social Sciences
Mochammad Firmansyah
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics Social Sciences

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Comparison of Logistic Regression and Support Vector Machine in Predicting Stroke Risk Lensa Rosdiana Safitri; Nur Chamidah; Toha Saifudin; Mochammad Firmansyah; Gaos Tipki Alpandi
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20420

Abstract

The issue of health is the third goal of Indonesia's Sustainable Development Goals (SDGs) which is state to ensuring a healthy life and promoting prosperity for all people at all ages. One of the SDGs’s concerns is deaths caused by non-communicable diseases (NCDs) including strokes. One prevention that can be done is by making a prediction of stroke for early detection. There are various methods available which are statistical methods and machine learning methods. In this research work, we aim to compare the two methods based on statistical method and machine learning method on stroke risk prediction. The data used in this research is primary data from Universitas Airlangga Hospital (RSUA) from June until August 2023. In this research, we compare the statistical method that is Logistic Regression (LR), and the machine learning method which is Support Vector Machine(SVM). We use Phyton to analyze all methods in this research. The results show that SVM with Radial Basis Kernel is better than LR in predicting stroke risk based on three goodness criteria namely sensitivity, F-1 score and accuracy where these three goodness criteria values of SVM are greater than those of LR.
Pengintegrasian Aplikasi Shopee Untuk Mengoptimalkan Ekonomi Kreatif UMKM Desa Pakel Kabupaten Jombang Pada Revolusi Industri 4.0. Sarah Putri Madania; Maulana Muhammad Daffa; Mu’tasim Billah; Ilham Nuril Fitri Huda; Aulia Nida’ul Husna; Bella Mega Riswanti; Mochammad Firmansyah
Jurnal of Management and Social Sciences Vol. 3 No. 2 (2025): April : Journal of Management and Social Sciences
Publisher : CV. Aksara Global Akademia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59031/jmsc.v1i2.146

Abstract

Technology always experiences changes along with the times, changes in technology have a dynamic nature so that new innovations can emerge to create a technology that facilitates human activities in various sectors of activity such as industry, agriculture, education, and no less the economic sector which feels the impact of existing technological developments. The manifestation of these technological developments in the economic sector, namely e- commerce applications in the form of Shopee, the application of these applications during the industrial revolution 4.0 is expected to have an impact on society and especially UMKM actors to further develop a more creative economy so that they can open up employment opportunities as a whole for all levels and community elements, besides that the presence of the Shopee application can provide a new idea for the community so that they can be more independent and not depend on the government. Nowadays, everything is digital, both in cities and villages, in this case the presence of the Shopee application can be a new step for village governments to be able to take advantage of this application to take advantage of developing the economy in villages by developing the existing UMKM sector by utilizing technology. The existing one is the Shoppe application. In this case Pakel Village together with the East Java KKNT 09 UPN Veterans group created an account on the Shopee application to integrate UMKM products in Pakel Village, Jombang Regency. more widely known by the community both outside Pakel Village and outside the City of Jombang, so that UMKM in Pakel Village are ready to compete and develop in the industrial era 4.0.
Co-Authors Aulia Nida’ul Husna Bella Mega Riswanti Gaos Tipki Alpandi Ilham Nuril Fitri Huda Lensa Rosdiana Safitri Maulana Muhammad Daffa Mu’tasim Billah Nur Chamidah Sarah Putri Madania Toha Saifudin