Jurnal Ilmiah Informatika

Implementasi Metodologi Extreme Programming Pada Sistem Informasi Sekolah Dan Penerimaan Siswa Baru Berbasis Web

Sun, 31 Dec 2023 00:00:00 +0700

Madrasah Ibtidaiyah Miftahul Ulum III is a basic-level educational institution that requires a school information system and a new student admission system to be used in managing its school. In developing this system, a simple and fast system development methodology was chosen, namely extreme programming. The analysis used is functional and non-functional system analysis. For design, there are several types of design, namely database design using conceptual data models and physical data models, as well as system design using a unified modeling language. The research results at this stage are in the form of analysis documents and design documents that will be used for the implementation and testing stages

Analisis Perbandingan Prediksi Harapan Hidup Hepatitis Menggunakan Algoritma K-Nearest Neighbor dan C4.5

Karina, Herlina Hanum, Anita Desiani — Sun, 31 Dec 2023 00:00:00 +0700

Hepatitis is an inflammatory disease of the liver caused by a virus that causes damage to the cells and function of the liver. This study compares the accuracy, precision, and recall results of the K-Nearest Neighbor (K-NN) and C4.5 algorithms using the Percentage Split and K-fold Cross Validation methods. Of the two algorithms, the best level of accuracy is obtained using the K-fold Cross Validation method. Based on the accuracy and error rate, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-NN algorithm. Based on the special Precision and Recall values on the Recall value to predict class zero the best algorithm is obtained using the C4.5 algorithm. To assess Precision and Recall, the other best algorithm in predicting the fixed response variable is obtained by using the K-NN algorithm. Overall, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-Nearest Neighbor (K-NN) algorithm.

Sistem Pendukung Keputusan Kawasan Kumuh Kota Semarang Menggunakan Metode Copras Dengan Data Vitalitas Non Ekonomi Sebagai Data Kriteria

Saifur Rohman Cholil, Yudi Prayoga, Yudi Cahyono, Susanto Susanto — Sun, 31 Dec 2023 00:00:00 +0700

Semarang City is the capital of Central Java Province which has a location in the middle of the province. Its strategic location is on the route across the North Coast of Java Island which connects major cities on Java Island. The high population in the city center requires the fulfillment of the need for livable housing, especially to accommodate urbanists whose jobs are concentrated in the trade and service sectors in the commercial area in the city center. The availability of complete facilities and infrastructure in the city center is also an attraction for people to live in the area. With the income level and economy of the community less so high, without realizing it, the need for livable settlements is difficult to accommodate. This study aims to build a decision support system (DSS) for the determination of Slum Areas in Semarang City using the COPRAS method. The COPRAS method has a good degree of selectivity because it can determine the purpose of conflicting criteria. System implementation is done using PHP language and MySQL database. The SPK that was built was able to produce recommendations by providing the ranking of slum areas to users.

Monitoring Infus Dan Detak Jantung Berbasis Internet Of Things (IoT)

M Syafiih, Nadiyah Nadiyah, Sri Astutik Andayani, Nur Hatima Inda Arifin — Sun, 31 Dec 2023 00:00:00 +0700

An alternative medical device called an IV is used to replenish lost body fluids and maintain the body's electrolyte balance. Infusion fluid administration is a very useful way to help and speed up the recovery of the patient's condition during the treatment period. In hospitals, clinics and health centers, nurses generally still control and monitor the use of IV fluids manually. Nurses must periodically check the condition of each patient's infusion. Thus, delays in infusion replacement often occur. This has a negative impact on the patient, such as blood being sucked into the infusion hose and the possibility of clotting in the hose. So that patients will experience losses to these conditions, nurses are required to always be on time in replacing infusions. There are still many people who do not understand the normal human heart rate, because health workers do not socialize to the public about heart rate. Lack of understanding of the human heartbeat that is often found in our environment. Based on the above problems, the research to be carried out will provide several solutions, it is necessary to make a sensor to detect infusion filling for patients who are under treatment so that it will help nurses in controlling infusion filling. The existence of monitor results related to human heart rate as a tool so that it can measure human heart rate. The method used is Rapid Application Development (RAD), this research produces a sensor tool to detect infusion and human heart rate.

Klasifikasi Naïve Bayes dan Confusion Matrix pada Pengguna Aplikasi E-Commerce di Play Store

Mohamad Rizki Humaidi, Alif Maulani — Wed, 31 Jan 2024 00:00:00 +0700

Shopee is an e-commerce application that is very popular among Indonesians. Shopee is an online shopping service center that is in great demand by Indonesians and offers many types of products. In addition, the shopee application has features, one of which is shopee food and shopee pay, which distinguishes the shopee application from other e-commerce applications. Although there are many Shopee application users, of course not all reviews and ratings given by users are good as a reference for improving the Shopee application features. In conducting research, a method is needed that can classify review data into positive and negative reviews. One that provides a review and rating feature is the Play Store application. In this study, using 1528 review data, Positive and negative class labels are the categories used.. The machine learning classification method used is Naïve Bayes classification. The outcomes of the method's classification accuracy testing are measured using a confusion matrix. So that the accuracy result using Naïve Bayes classification is 0.87 or 87%. Based on these results, using the Naïve Bayes classification gets high accuracy results in the process of classifying review data on Shopee application user research in the Play Store.