IMPLEMENTASI DEMPSTER SHAFER UNTUK MENDIAGNOSA GANGGUAN KEHAMILAN PADA IBU

Kehamilan merupakan suatu moment dimana seorang ibu mengandung buah hati selama sembilan bulan pada normal kehamilan ibu. Tingkat kematian pada ibu hamil sangat tinggi di Kota Metro antara lain, adanya penyakit yang diderita oleh ibu hamil yakni Ektopik, Hidatidosa, Postterm, Plasenta Previa,dan Solutio Plasenta. Kurangnya pengetahuan dan mahalnya biaya konsultasi kepada dokter spesialis kandungan, maka penelitian ini berusaha mengaplikasikan metode dempster shafer kedalam data gejala yang diberikan oleh dokter spesialis kandungan. Pengembangan sistem yang digunakan pada penelitian ini menggunakan RAD (Rapid Aplication Development). Selanjutnya menerapkan dua user kedalam sistem berbasis website, serta sistem pakar diagnosa gangguan ibu hamil dengan tingkat presentase sistem pakar sebesar 100% yang telah diberikan sebuah contoh kasus oleh Bapak Madya Desma Aliana Sp.Kep.An. selaku asisten dokter RSIA AMC dan dibuktikan oleh dr. Ellyzawati Shinta,SPoG selaku dokter praktik RSIA AMC.

SISTEM PENGAMBILAN KEPUTUSAN PENERIMA BANTUAN RENOVASI RUMAH DENGAN MENGGUNAKAN METODE WP DAN SAW

Rumah merupakan banguan yang memeiliki peran yang sangat krusial sebagai tempat tinggal hunian dan berkumpulnjya suatu keluarga[1][2].pemerintah memiliki program untuk membantu masyarakat, salah satunya adalah program renovasi rumah.data yang didapat di provinsi lampung angka kemiskinan mencapai 13.01 persen pada tahun 2018[4].program ini diadakan untuk menurunkan angka kemiskinan tersebut.dalam penelitian ini enggunakan dua metode yaitu metode SAW dan Wp, kedua metode ini digunakan untuk mengetahui siapa yang paling layak dalam mendapatkan bantuan renovasi rumah.Adapun kriteria yang dibutuhkan ada 11 kriteria, diantaranya adalah Pekerjaan,,Status lahan tempat tinggal, Dinding Rumah,Sumber air minum,Bahan bakar untuk masak,.KOndisi MCK, , Konsumsi pertahun,Pendidikan ,Penghasilan , atapdan lantai.. Dengan adanya penelitian ini penulis telah merancang, Hasil dari penggunan spk menggunakan metode SAW ini menunjukan nilai error mencapai 0.070137683

SISTEM PAKAR FORWARD CHAINING , FUZZY-MAX DAN CERTAINTY FACTOR AYAM PEDAGING

Chicken farming is one of the promising business potentials, but in management and care, it is very important to determine the success of chicken farming. Often in the care of negligent employees so that they are exposed to disease outbreaks. With various types of diseases that attack the symptoms are very similar and even the same as other diseases, therefore an expert system can be used to diagnose a disease by looking at the characteristics of the disease suffered, and how the solution is to treat or prevent the disease In the study, it discusses 8 types of broiler diseases, where the expert system method used is the Fuzzy Max method, Forward Chaining, and Certainty Factor. From the results of the Fuzzy Max method, the results showed an accuracy of 80% - 90% for all types of diseases, while the Certainty Factor method showed 96% - 99% for all types of diseases.Keywords— Expert System, Fuzzy Max, Certainty Factor, Forward Chaining.

AUGMENTED REALITY PENGENALAN OBJEK WISATA TAMAN MUMBUL SANGEH BERBASIS ANDROID

Mumbul Park Tourism Object is a tourist attraction located in Sangeh Village, Abiasemal District, Badung Regency. In Mumbul Park there are two temples named Pura Luhur Mumbul (Puru Ulun Siwi) and Pura Ulun Mumbul. In addition to the temple in the Mumbul Sangeh Park Tourism Object, there is a pengelukatan called Pancoran Solas and there is a pond (lake) with thousands of fish. The Mumbul Park Tourism Object has its own charm for tourists, but the lack of information regarding the Mumbul Sangeh Park Tourism Object makes not many people know about the tourist area. Therefore, we need an introduction media regarding the object of the Mumbul Sangeh park which is more modern with Android based Augmented Reality media. This application contains direct information about the history of the Mumbul Park Tourism Object, Pancoran Solas 3D Penglukatan Object, Ulun Danu Temple and the Queen Niang Sakti Statue, Videos and Photos of the introduction of Mumbul Park. The method used in this research is the MDLC (Multimedia Development Life Cycle) method and the C # programming language. The making of this application uses Unity3D, CorelDraw and Adobe Illustrator as the interface designer, Adobe Premiere to edit the introduction video. Based on the results of tests carried out using two testing methods, namely blackbox testing and questionnaires. This application is distributed via Google Drive.. Keywords—Mumbul Sangeh Park, Pancoran Solas, Ulun Danu Temple, Ratu Niang Sakti Statue, Augmented Reality, Android

ANALISA DAN PREDIKSI IKLAN LOWONGAN KERJA PALSU DENGAN METODE NATURAL LANGUAGE PROGRAMING DAN MACHINE LEARNING

This research was conducted using the data provided by Kaggle. This data contains features that describe job vacancies. This study used location-based data in the US, which covered 60% of all data. Job vacancies that are posted are categorized as real or fake. This research was conducted by following five stages, namely: defining the problem, collecting data, cleaning data (exploration and pre-processing) and modeling. The evaluation and validation models use Naïve Bayes as a baseline model and Small Group Discussion as end model. For the Naïve Bayes model, an accuracy value of 0.971 and an F1-score of 0.743 is obtained. While the Stochastic Gradient Descent obtained an accuracy value of 0.977 and an F1-score of 0.81. These final results indicate that SGD performs slightly better than Naïve Bayes.Keywords—NLP, Machine Learning, Naïve Bayes, SGD, Fake Jobs

PENERAPAN DATA MINING TERHADAP DATA COVID-19 MENGGUNAKAN ALGORITMA KLASIFIKASI

Coronavirus 2019 or more commonly referred to as COVID-19 is a type of virus that attacks the respiratory system. Until now the number of spread and the number of deaths caused by this virus continues to increase. As of April 21, 2020, based on data from the WHO, the total number of cases infected with this virus reached 2,397,217 with 162 deaths from all over the world. For South Korea itself, as of March 21, 2020, the total number of infected cases was 10,683 with a total of 237 deaths. In this study, researchers conducted data processing on the spread of COVID-19 in South Korea with Rapidminer using a classification algorithm, namely Naïve Bayes, C4.5, and K-Nearest Neighbor by performing the stages of selection, preprocessing, transfotmating, data mining and interpretation or evaluating the quality of the best accuracy of 80.79% with AUC of 0.881 achieved by the Naïve Bayes algorithm. The distribution of the data found that the influential attribute of the isolated class factor from the patient contained in the sex attribute where more women experienced isolation. Keywords— COVID-19, data mining, classification, C4.5, Naïve Bayes, K-NN

ANALISIS SENTIMEN TERHADAP KINERJA MENTERI KESEHATAN INDONESIA SELAMA PANDEMI COVID-19

The pandemic that occurred in Indonesia has not yet subsided and far from under control. Indonesian Ministry of Health is most appropriate person to responsible for providing an explanation of actual situation and extent to which state has handled it. However, he has rarely appeared in public lately to explain about handling of Covid-19 pandemic. In response, many people are pros and cons come to give their opinions and feedback. The increasing use of internet during pandemic, especially on social media, where one of them is Twitter, which is a means of expressing opinions. Posting tweets is a community habit to assess or respond to events, as well as represent public's response to an event, especially Ministry of Health steps and policies in handling and breaking chain of Covid-19 pandemic.The tweet posts were taken only in Indonesian-language and also related to performance of Government, especially Ministry of Health. After that, a label is given so that sentiment of tweets is known. To test results of these sentiments, an algorithm is used by comparing two methods of Support Vector Machine (SVM) and Naïve Bayes (NB). Validation was carried out using k-Fold Cross Validation to obtain an accuracy value. The results show that accuracy value for NB algorithm is 66.45% and SVM algorithm has a greater accuracy value of 72.57%. So it can be seen that SVM algorithm managed to get the best accuracy value in classifying positive comments and negative comments related to sentiment analysis towards Ministry of Health. Keywords—Support Vector Machine, Naïve Bayes, Analisis sentimen, K-Fold Cross Validation