Analisis Regresi dan Analisis Diskriminan untuk Mengukur Tingkat Akurasi Feature Citra Termogram

Intisari — Analisis diskriminan adalah analisis multivariat yang diterapkan untuk memodelkan hubungan antara satu variabel respon yang bersifat kategori dengan satu atau lebih variabel prediktor yang bersifat kuantitatif. Sedangkan analisis regresi bertujuan untuk membentuk sebuah fungsi yang dapat menjelaskan hubungan dua variabel, yaitu variabel penjelas/prediktor (x) dan variabel respon (y). Banyak aplikasi pada bidang kedokteran atau industri yang berhubungan dengan data mining salah satunya untuk pengenalan pola pada citra termogram. Tujuan dari penelitian ini adalah membandingkan teknik analisis diskriminan atau linear discriminant analysis (LDA) dan analisis regresi pada tingkat akurasi pengenalan pola citra termogram. Penelitian ini menggunakan sampel citra digital termogram payudara yang diambil dari kamera Fluke Ti20. Jumlah sampel yang digunakan adalah 60 citra termogram yang di bagi masing-masing ke dalam tiga kelas yaitu kelas normal, kelas kanker payudara dini, dan kelas payudara lanjut. Dari penelitian yang telah dilakukan dapat dibuktikan bahwa analisis diskriminan dengan 2 feature (ciri), 3 ciri, dan 5 ciri pada citra termogram memberikan tingkat akurasi 81,7 %. Sedangkan analisis diskriminan dengan 4 ciri pada citra termogram memberikan tingkat akurasi yang paling tinggi yaitu 83,33 %. Kata kunci — termogram, multivariat, kovarian, ciri, analisis diskriminan Abstract — Discriminant analysis is a multivariate analysis applied to model the relationship between the response variable is the category with one or more predictor variables that are quantitative. While regression analysis aims to establish a function that can explain the relationship between two variables, namely the explanatory variables / predictors (x) and the response variable (y). Many applications in the medical field or industry related to one of data mining for pattern recognition in the thermogram image. The aim of this study is to prove the technique of linear discriminant analysis  (LDA) and regression analysis to distinguish the types of thermogram. This study used 60 samples of breast thermograms captured from camera Fluke Ti20. The samples used are images in the thermograms which each classify into three classes, namely breast normal thermogram, early breast cancer thermogram, and advanced breast cancer thermogram. The result of research, discriminant analysis with  two features, three features, and five features give 81.7% accuracy rate. While discriminant analysis with four features have the highest accuracy rate is 83.33%. Final results of the regression analysis is able to significantly separate the three types of normal, early, and advanced thermogram. Keywords —  thermogram, multivariate, covarian, feature, discriminat analysis, regression analysi

Analisis Citra Digital CT Scan Dengan Metode Ekualisasi Histogram dan Statistik Orde Pertama

this research aims to develop the science and technology of medical image processing CT Scan, which yield an identification system software. Histogram equalization method is the first step in image pre-processing that is applied to the digital images of CT Scan. The next step is statistical texture analysis method of first order to extract the parameters such as mean, variance, standard deviation.the result showed that the histogram equalization method and the statistical texture analysis can be used to distinguish normal CT Scan and abnormal that detected stroke

Expert system for campus environment indexing in wireless sensor network

Wireless sensor network can deliver environment data in campus area as CO, NO2, HC, particulate matter, temperature, humidity, and luminous intensity to provide accurate realtime data. This realtime environment data is used for environment indexing accurately, and then can be developed in an expert system. This expert system collects input data from the sensor. This expert system will help giving the accurate input for campus authority to state and evaluate campus developing policy continuously. This expert system uses forward chaining method, PHP programming language and MySQL database

WSN infrastructure for green campus development

A system providing accurate environmental data for campus stakeholders to formulate and evaluate policies of the sustainable campus development is needed. This paper presents the design of WSN infrastructure capable of providing accurate, real-time and reliable environment data, namely PM2.5, SO2, CO, O3, NO2, temperature, humidity, soil moisture and light intensity to be analyzed and presented by servers. This infrastructure is composed of fixed sensor nodes, mobile sensor nodes, display nodes and server nodes. The sensor node provides environment raw data to the server using an RF transceiver. The server processes, stores and presents environment information to public users through Internet and mobile network. This infrastructure can be used as a platform to provide environmental data to decision support system for campus stakeholders, so that a recommendation can be made

SISTEM PERAMALAN DAN PENGENDALIAN PERSEDIAAN MAKANAN PADA RUMAH SAKIT DENGAN METODE EXPONENTIAL SMOOTHING

Kepuasan pelayanan pasien merupakan indikator kinerja yang baik pada Rumah Sakit, salah satu yang berperan penting adalah pelayanan logistik makanan. Dengan jumlah pasien yang fluktuatif, Rumah Sakit harus mampu memenuhi permintaan jumlah pasien setiap hari. Penelitian ini bertujuan untuk membangun sistem peramalan dan pengendalian persediaan makanan untuk menentukan jumlah porsi makanan yang harus tersedia pada hari berikutnya. Jumlah bahan baku makanan dikendalikan dengan menggunakan model re-order point, yang bertujuan untuk mengantisipasi terjadinya kekurangan persediaan. Data diperoleh dari jumlah permintaan makanan selama 212 hari untuk tiga waktu, pagi, siang, dan malam hari. Nilai pemulusan dan peramalan menggunakan parameter alpha 0,3 dan 0,7 dengan perhitungan kesalahan peramalan minimal menggunakan MAPE untuk alpha 0,7 sebesar 12,81% untuk waktu pagi, 11,59% waktu siang, dan 10,96% waktu malam. Hasil peramalan tidak hanya dapat digunakan untuk mengalokasikan porsi makanan namun juga dapat mengendalikan persediaan bahan baku. Kata kunci : Forecasting Method, Exponential Smoothing, Re-Order Point The satisfaction of patient care is an indicator of good performance in hospitals, one of which plays a critical role is a logistic serving of food. With the fluctuating number of patients, the hospital should be able to meet the demand for the number of patients each day. This study aims to build the system of forecasting and controlling the food supplies to determine the number of servings of food supplies in the next period. The implementation of Exponential Smoothing method is used to predict the number of servings should be available for the next period. Amount of food raw material is controlled using re-orders point model, it aims to anticipate the occurrence of stockout with the minimum amount of food provides should be available. The data were obtain ed from the requested amount of food during 212 days for three times, morning, noon, and night. Forecasting values using alpha parameters 0.3 and 0.7 with a minimum forecasting error calculation using MAPE for alpha 0.7 with a value 12.81% for morning time, 11.59% during the day, and 10.96% night time. Forecasting result not only can be used to allocate food supplies but also to control stock of raw material food. Keywords : Forecasting Method, Exponential Smoothing, Re-Order Poin