SISTEM PAKAR DIAGNOSA DINI PENYAKIT GIGI DAN MULUT

Gigi dan mulut adalah organ organ tubuh yang ada pada wajah .keduanya sangatlah vital keberadaannya oleh karenanya kesehatan kedua organ ini sangatlah penting . pada umumnya orang sangat menyepelekan masalah kesehatan sekitar mulut , karena mungkin mereka lebih mementingkan kesehatan organ-organ tubuh lainnya, yang di anggap lebih penting. Padahal penyakit yang menyerang gigi dan mulut dapat menimbulkan masalah yang berarti bagi kesehatan lainnya . contoh : masalah penampilan atau masalah di organ tubuh lainnya Bahkan berawal dari penyakit inilah akan timbul penyakit penyakit yang membahayakan dan menyerang anggota tubuh lainnya. Sistem pakar merupakan teori untuk mengatasi dalam ketidakpastian. Sejumlah teori telah ditemukan untuk menyelesaikan ketidakpastian, termasuk diantaranya probabilitas klasik (classicalprobability), probabilitas Bayes (Bayesianprobability), teori Hartley berdasarkan himpunan klasik (Hartleytheorybasedon classicalsets), teori Shannon berdasarkan pada probabilitas (Shannontheorybasedon probability), teori Dempster-Shafer (Dempster- Shafer theory), teori fuzzyZadeh (Zadehísfuzzy theory) dan faktor kepastian (certainty factor). Berdasarkan uraian diatas penulis tertarik untuk menyusun penelitian dengan judul ―Diagnosa Dini Penyakit Gigi dan Mulut Dengan Menggunakan Metode Dempster Shafer”. Penelitian ini berisi tentang deteksi awal penyakit Gigi dan Mulut yang dapat digunakan oleh dokter maupun masyarakat umum dalam mendiagnosa dini penyakit Gigi dan Mulut dimana saja dan kapan saja. Hasil penelitian ini memperlihatkan nilai Demster Shafer berada pada kisaran 0 sampai dengan 1, jika keluaran Demster Shafer mendekati 1, maka kepastiannya mendekati benar. Keyword : Gigi dan Mulut, Demster Shafer, sistem paka

Penggunaan Datamining Untuk Memprediksi Masa Studi Mahasiswa di Universitas Muhammadiyah Sidoarjo Dengan Algoritma Naive Bayes

Dalam dunia pendidikan tinggi, peningkatan kinerja mahasiswa dan peningkatan kualitas Pendidikan merupakan salah satuhal yang penting.Sistem pendidikan membutuhkan cara-cara inovatif demi meningkatkan kualitas pendidikan dan mencapai hasil yang terbaik serta meminimalisir tingkat kegagalan mahasiswa. Salah satu cara inovatif itu adalah dengan menerapkan data mining untuk memprediksi masa studi mahasiswa. dari hasil prediksi tersebut nantinya akan membantu mahasiswa atau dosen wali untuk memberikan peringatan dini atau memberi arahan yang lebih tepat kepada setiap mahasiswa, sehingga dapat melakukan hal-hal yang terbaik untuk meningkatkan peluang lulus tepat waktu.Datasetyang digunakan merupakandataalumniprogram studi Informatika angkatan 2017-2018 Universitas Muhammadiyah Sidoarjo. Data ini didapatkan dari Direktorat Sistem & Teknologi Informasi (DSTI) Universitas Muhammadiyah Sidoarjo sebanyak200 data. Permodelan menggunakan algoritma naïve bayes menggunakan software Anaconda Navigator dengan IDLE Jupyter Notebook dan Bahasa pemrograman python. Hasil yang didapatkan dari evaluasi menggunakan confusion matrix dan accuracy score didapatkan hasilaccuracy 68%, nilai precision 0.67, recall 0.77 dan f1-score 0.72. sedangkan nilai evaluasi accuracy score mendapatkan 67.35%

Unplag Demographic Attribute Selection Model For Prediction Of Election Participation Using Decision Tree

Implementing a democratic general election is expected to produce people's representatives who can channel the people's aspirations. Demographic data is information that discusses a group of people with several related attributes and involves many factors. In this study, we will relate the relationship between the implementation of elections and the condition of demographic data with a benchmark for the form of public participation in the election. By utilizing 2019 election data and Bangkalan Regency demographic data from the Central Statistics Agency (BPS), it is expected to determine the relationship between the two conditions of the dataset on the form of public participation at the polling station (TPS) level. By starting with the Preprocessing step, it implement a classification method with the Decision Tree (DT) algorithm to predict community presence at the polling station level. There are three versions of the dataset that will be used in modeling, namely initial data that has not been selected for attributes (version 1), data that has been chosen using correlation�based attribute selection (version 2), and data that has been selected using chi�square attributes ( version 3). The results show version 1 with a prediction of 81%, followed by version 2 with a prediction of 81%, and the last is version 3 with a prediction of 70%. The detachment model's formation with the selection attribute has a different impact, and the relationship between the election dataset and demographics has a significant effect, as indicated by the prediction results of version 2

UNPLAG INFLUENCE OF DEMOGRAPHICS FOR PREDICTION OF ELECTION PARTICIPATION USING LOGISTIC REGRESSION ALGORITHM

Indonesia is a democratic country for General Elections (Election) which are carried out directly, freely, confidentially, honestly, and fairly. Several stages of the election, among others, begin with compiling a permanent voter list (DPT), determination of polling stations (TPS), and recapitulating election results. Various factors, including the demographic factor, can affect citizen participation in the general election. Demographic data covers Energy, Geographic, Education, Health, Population, Economy, Communication, and Transportation factors. This study tries to combine election data with demographic data taken from the official website of the Central Statistics Agency (BPS) of Mojokerto Regency and data on the results of the 2019 Election calculations taken from the official website of the General Election Commission (KPU) of Mojokerto Regency. Preprocessing steps are data cleaning, data integration, and correlation attributes for a more optimal presentation of the dataset and the distribution of four split datasets (training data and testing data) to find the best results. Implementation of classification method with Logistic Regression (LR) algorithm to predict community participation at the TPS level. From the test results of four split datasets, the highest predictive value was 64.80% in composition 3 with a ratio of 80:20, where 127 data were labeled low, and 291 data were labeled high

peer review jurnal tasikmalaya

peer review jurnal tasikmalay

SISTEM INFORMASI PERMINTAAN PEMBELIAN BARANG DENGAN KONTROL BUDGET BERBASIS WEB (STUDI KASUS PT AGEL LANGGENG PASURUAN)

Work efficiency is now a top priority in every company. One form to support efficiency is by changing the old way of working to become more modern. To apply this, a program was created that could help speed up work and also minimize data errors and decision making. The system used is through a web-based program that can be accessed by all parts quickly because it is connected to the network. From the results of research conducted, it is known that using this program is able to accelerate the process of submitting goods requests. And also can quickly find out the arrival date of the goods and the budget used. &nbsp

SISTEM INFORMASI PERMINTAAN PEMBELIAN BARANG DENGAN KONTROL BUDGET BERBASIS WEB (STUDI KASUS PT AGEL LANGGENG PASURUAN)

Work efficiency is now a top priority in every company. One form to support efficiency is by changing the old way of working to become more modern. To apply this, a program was created that could help speed up work and also minimize data errors and decision making. The system used is through a web-based program that can be accessed by all parts quickly because it is connected to the network. From the results of research conducted, it is known that using this program is able to accelerate the process of submitting goods requests. And also can quickly find out the arrival date of the goods and the budget used. &nbsp