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All Journal Dinamisia: Jurnal Pengabdian Kepada Masyarakat Infotekmesin Journal of Innovation Information Technology and Application (JINITA) TIERS Information Technology Journal Jurnal Administrasi Bisnis Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat E-Amal: Jurnal Pengabdian Kepada Masyarakat
Laura Sari
Department Of Informatics, Politeknik Negeri Cilacap
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : Infotekmesin

 1 
Implementasi Metode Research and Development Pada Pengembangan Pembelajaran Matematika Berbasis Multimedia Linda Perdana Wanti; Laura Sari
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.279

Abstract

Mathematics is a lesson that is still a frightening specter for some students. There are several methods used to make mathematics fun to learn. One of them is to package material in mathematics to be more attractive and interesting, especially for children this can make them become interested in learning mathematics. The developed using research and development methods. This method begins by exploring the problem, collecting data needed, designing the product to be developed, validating the product design, testing the use of the system to be developed, revising the product, testing the product, revising the product and product design if there are errors or deficiencies and the last is mass production of product. This research aims to develop an interactive multimedia-based mathematics learning which can later be optimized to increase student interest in learning mathematics and be used to improve the quality of education.
Sistem Pakar Deteksi Dini Penyakit Preeklamsia pada Ibu Hamil Menggunakan Metode Certainty Factor Nur Wachid Adi Prasetya; Linda Perdana Wanti; Laura Sari; Lina Puspitasari
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1050

Abstract

Preeclampsia is a disease in pregnant women characterized by high blood pressure and positive urine protein. The disease has a high risk of maternal and fetal death, so there is a need for early detection of mothers at risk of preeclampsia. Early online detection of preeclampsia is the best solution during the Covid-19 pandemic by analyzing the influencing factors. The purpose of this study is to build an expert system for early detection of preeclampsia in pregnant women using the Certainty Factor method and the waterfall system development model in order to provide the possibility of pregnant women suffering from preeclampsia. Testing the accuracy of 30 medical record data for pregnant women resulted in a system accuracy level of 90%, while usability testing resulted in a user satisfaction level of 55 with the System Usability Testing (SUS) score criteria being "Poor", therefore improvements are needed on expert system in the future.
Penerapan Data Mining dalam Analisis Prediksi Kanker Paru Menggunakan Algoritma Random Forest Laura Sari; Annisa Romadloni; Rostika Listyaningrum
Infotekmesin Vol 14 No 1 (2023): Infotekmesin: Januari, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i1.1751

Abstract

Cancer is the second highest cause of death in the world. In Indonesia, it is a disease with a high mortality rate. Most patients do not realize that they have lung cancer thus the treatment is sometimes too late. A prediction method with a high degree of accuracy is needed to detect lung cancer earlier. Previous research used data mining calcification methods with the Naïve Bayes algorithm to predict lung cancer. This research resulted in high recall values for the positive class (Yes class) but low for the negative class (No class). This research was made using the Random Forest algorithm which is known to have good performance. The modeling is optimized by applying the K-fold Cross Validation technique. The Random Forest algorithm produces a higher Accuracy value than the Naïve Bayes algorithm, which is 98.4%. This algorithm produces 100% Recall for the positive class, 80% for the negative class and provides a 100% correct prediction as can be seen from the AUC value of 1. Although a statistical test with a significance level of 5% shows the results of the two algorithms are not significantly different.
Metode Fuzzy Time Series Markov Chain Untuk Peramalan Curah Hujan Harian Laura Sari; Annisa Romadloni; Rostika Listyaningrum; Fadhilla Hazrina; Nur Wahyu Rahadi
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.2182

Abstract

Cilacap Regency has diverse topography and geographical conditions which cause this region to have rainfall that varies spatially and temporally; therefore, a forecasting method to overcome these uncertainties and fluctuations is needed. Fuzzy Time Series Markov Chain utilizes Fuzzy logic which provides flexibility in handling uncertain and unstructured data. Moreover, the addition of Markov chain elements that utilize Fuzzy logic concepts provides flexibility in handling data allowing the model to capture inter-time relationships and changes in system state that depend on previous states. Therefore, the research aims to see the suitability of the Fuzzy Time Series Markov Chain for predicting daily rainfall in Cilacap Regency. The method is suitable for predicting rainfall data for Cilacap Regency. The accuracy value in this study can be seen from the RMSE and SMAPE values ​​on the training data (in-sample), respectively, which are 58.76469 and 0.7227493. Meanwhile, the testing data (out sample) was 56.01818 and 0.7055117.
Co-Authors Abdul Fadlil Abdul Rohman Supriyono Annisa Romadloni Arif Wirawan Muhammad Ayu Pramita Davito Rasendriya Rizqullah Putra Dwi Novia Fadhilla Hazrina Fadhillah Hazrina Fadlilah, Ilma Hety Dwi Hastuti Imam Riadi Lina Puspitasari Linda Perdana Wanti Muhammad Nur Faiz Nur Wachid Adi Prasetya Nur Wahyu Rahadi Nurlinda Ayu Triwuri Oman Somantri Oto Prasadi Ratih Hafsarah Maharrani Rostika Listyaningrum Rostika Lityaningrum Santi Purwaningrum Sukma Aji