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All Journal Jurnal Pelayanan dan Pengabdian Masyarakat Indonesia Journal of Intelligent Systems and Information Technology
Fibia Sentauri Cahyaningrum
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Ceramah Perundang-Undangan Transportasi Sebagai Upaya Mewujudkan Tertib Lalu Lintas di Jalan Raya Oky Dedy Wijaya; Fibia Sentauri Cahyaningrum; Abdun Nafi Kurniawan; Moch. Wildan Makhrus; Yudhistira Wahyu Pambudi
Jurnal Pelayanan dan Pengabdian Masyarakat Indonesia Vol. 2 No. 2 (2023): Juni : Jurnal Pelayanan dan Pengabdian Masyarakat Indonesia
Publisher : Sekolah Tinggi Ilmu Administrasi Yappi Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jppmi.v2i2.596

Abstract

Public awareness of traffic order is still very worrying. This can be seen from the many violations while driving and the existence of fines from the traffic police, as evidenced by the ticket certificates received by the public. In addition to traffic violations, there are also many traffic accidents that occur due to people's ignorance in complying with applicable traffic regulations. These violations include: disobeying traffic signs, such as running a red light, not wearing an Indonesian National Standard helmet, and not equipping a vehicle such as not using a rearview mirror and not paying attention to vehicle lights (for example brakes) and lights turn off. These violations are usually committed not only by people who are old enough to drive a vehicle, but most are also committed by underage students. Based on these problems, the community service team gave a lecture on transportation legislation at SMA Muhammadiyah 4 Lamongan in an effort to increase awareness of orderly traffic to reduce accidents for students and the general public.
Comparison of Binary Logistic Regression and SVM to Classify Diabetes Sufferers Fibia Sentauri Cahyaningrum
Journal of Intelligent Systems and Information Technology Vol. 1 No. 2 (2024): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v1i2.76

Abstract

Diabetes is a chronic metabolic disorder characterized by high levels of glucose in the blood due to disruption of the insulin hormone which functions as a regulator of the balance of blood sugar levels. This disease continues to increase in prevalence in various countries, making it a global health problem. Diabetes has trigger factors that contribute to the incidence of the disease, such as age, gender, smoking habits, healthy eating patterns, high blood pressure, and others. Diagnosis of diabetes can be done by carrying out a fasting blood sugar test, a 2-hour postprandial (PP) blood sugar test, and a random blood sugar test. However, it is very possible for diagnoses made by health workers to have errors due to subjectivity and different experiences, so a fast and precise classification method is needed to classify patients undergoing diabetes examination based on variables related to diabetes. The classification method used in this research is binary logistic regression and Support Vector Machine (SVM). A similar study carried out classification of diabetes sufferers using the Naive Bayes and KNN methods by comparing the results with SVM, so in this study the binary logistic regression method and SVM will be used to determine the performance of the classification method. The data used is secondary data. Next, the data is divided into training and testing data. The analysis results show that the SVM method is slightly superior in classification accuracy of testing data, namely 97.75%. With this research, it is hoped that decisions on patients undergoing diabetes examination will be faster, more precise and effective, and classification methods with better performance can be applied
Co-Authors Abdun Nafi Kurniawan Moch. Wildan Makhrus Oky Dedy Wijaya Yudhistira Wahyu Pambudi