Klasifikasi Bakteri Tuberkulosis pada Sampel Dahak Menggunakan K-Nearest Neighbor (K-NN) dan Backpropagation

Penelitian ini bertujuan untuk melakukan klasifikasi bakteri tuberkulosis berdasarkan fitur bentuk bakteri. Citra preparat digital dikonversi dari kanal warna Red, Green, Blue (RGB) ke Hue, Saturation, Value (HSV), kemudian dilakukan operasi morfologi untuk memperbaiki bentuk bakteri. Bakteri dipotong secara otomatis dari gambar digital preparat menggunakan Region of Interest (ROI), potongan bakteri hasil ROI kemudian di skeletonizing untuk mendapatkan bentuk bakteri dengan lebar satu piksel. Langkah selanjutnya dilokalisasi untuk memisahkan bagian yang bukan termasuk bakteri tuberkulosis. Fitur yang digunakan antara lain panjang bakteri, endpoint dengan menerapkan ridge ending dari minutiae, dan percabangan bakteri dengan menerapkan bifurcation dari minutiae. Fitur-fitur tersebut menjadi masukan pada proses klasifikasi. K-NN mampu mengklasifikasi bakteri tunggal dengan akurasi 88.16% dan bakteri rangkap sebesar 88.16%. Backpropagation mampu mengklasifikasi antara bakteri tunggal dengan akurasi 87.28% dan bakteri rangkap dengan akurasi 87.28%. K-Nearest Neighbor (K-NN) mampu mengklasifikasi kelompok preparat kuning dengan akurasi 93.22%, kelompok preparat hijau dengan akurasi 92%, kelompok preparat biru dengan akurasi 90.63% dan kelompok preparat gelap dengan akurasi 75%. Sementara backpropagation mampu mengklasifikasi kelompok preparat kuning dengan akurasi 91.53%, kelompok preparat hijau dengan akurasi 92%, kelompok preparat biru dengan akurasi 92.71%, kelompok gelap dengan akurasi 68.75%. Metode K-NN lebih unggul dalam akurasi klasifikasi pada kelompok preparat kuning, dan kelompok preparat gelap. Dan metode backpropagation lebih unggul pada kelompok preparat biru. Sedangkan dalam kelompok preparat hijau K-NN dan backpropagation memiliki akurasi klasifikasi sama sebesar 92%.Metode K-NN lebih unggul dalam mengklasifikasi jenis bakteri tunggal dan rangkap dari pada metode backpropagation. Sistem ini mampu digunakan sebagai alat bantu bagi dokter dan analis medis untuk mempercepat proses penghitungan bakteri tuberkulosis dan diagnosa pasien tuberkulosis pada bidang kesehatan. ======================================================================================================== This study aims to classify tuberculosis bacteria based on the features of bacterial forms. Digital image preparations are converted from RGB color channels to Hue, Saturation, Value (HSV), then morphologic surgery to repair bacterial forms. The bacteria is automatically cut from the digital image of the preparat using Region of Interest (ROI), piece of bacteria resulting from ROI then skeletonizing to obtain bacterial form with a width of one pixel. The next step is localized to separate parts that do not belong to tuberculosis bacteria. Features used include bacterial length, endpoints by applying ridge ending of minutiae, and branch of bacteria by applying bifurcation of minutiae. These features become input to the classification process. K-Nearest Neighbor (K-NN) is able to classify single bacteria with 88.16% and multiple bacteria with 88.16% accuracy. Backpropagation is able to classify between single bacteria with 87.28% and multiple bacteria with 87.28% accuracy. K-NN was able to classify yellow preparat groups with 93.22% accuracy, green preparat group with 92% accuracy, blue preparat group with 90.63% accuracy, and dark preparat group with 75% accuracy. Backpropagation was able to classify yellow preparat groups with 91.53% accuracy, green preparat group with 92% accuracy, blue preparat group with 92.71% accuracy, dark preparat group with 68.75% accuracy. The K-NN method is better in the classification accuracy of the yellow preparat group, and the dark preparat group. And the backpropagation method is better to the blue preparat group. While in the group of green preparat K-NN and backpropagation have the same classification accuracy of 92%. K-NN method is better in classifying single and multiple bacteria types than the backpropagation method.This system can be used as a tool for doctors and medical analysts to speed up the process of calculating tuberculosis bacteria and diagnosis of tuberculosis patients in the health field

Application of Feature Selection for Identification of Cucumber Leaf Diseases (Cucumis sativa L.)

According to data from BPS Kabupaten Jember, the amount of cucumber production fluctuated from 2013 to 2017. Some literature also mentions that one of the causes of the amount of cucumber production is disease attacks on these plants. Most of the cucumber plant diseases found in the leaf area such as downy mildew and powdery mildew which are both caused by fungi (fungal diseases). So far, farmers check cucumber plant diseases manually, so there is a lack of accuracy in determining cucumber plant diseases. To help farmers, a computer vision system that is able to identify cucumber diseases automatically will have an impact on the speed and accuracy of handling cucumber plant diseases. This research used 90 training data consisting of 30 healthy leaf data, 30 powdery mildew leaf data and 30 downy mildew leaf data. while for the test data as many as 30 data consisting of 10 data in each class. To get suitable parameters, a feature selection process is carried out on color features and texture features so that suitable parameters are obtained, namely: red color features, texture features consisting of contrast, Inverse Different Moment (IDM) and correlation. The K-Nearest Neighbor classification method is able to classify diseases on cucumber leaves (Cucumis sativa L.) with a training accuracy of 90% and a test accuracy of 76.67% using a variation of the value of K = 7.

Hubungan antara Manajemen Kelas dan Kepemimpinan Guru dengan Kepuasan Belajar Program Keahlian TKJ di SMK se Kabupaten Jember

ABSTRAK   Sahenda, Lalitya Nindita. 2013. Hubungan antara Manajemen Kelas dan Kepemimpinan Guru dengan Kepuasan Belajar Program Keahlian TKJ di SMK se Kabupaten Jember. Skripsi. Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Malang. Pembimbing: (I) Drs. Setiadi Cahyono Putro, M.T., M.Pd., (II) Dyah Lestari, S.T., M.Eng.   Kata Kunci:Manajemen Kelas, Kepemimpinan Guru, Kepuasan Belajar   Kepuasan pengguna pendidikan di Indonesia adalah salah satu cerminan kualitas pendidikan itu sendiri.Kualitas pendidikan di Indonesia juga dipengaruhi oleh peran guru yang merupakan ujung tombak pendidikan. Dengan adanya kemampuan manajemen kelas dan kepemimpinan yang dimiliki oleh guru diharapkan terjadi peningkatan kepuasan siswa dalam belajar di kelas.Penelitian ini bertujuan untukmengungkap signifikansi hubungan antara (1) manajemen kelas (X1) dengan kepuasan belajar (Y), (2) kepemimpinan guru (X2) dengan kepuasan belajar (Y), (3) manajemen kelas (X1) dan kepemimpinan guru (X2) dengan kepuasan belajar siswa jurusan TKJ di SMK se Kabupaten Jember (Y).    Rancangan penelitian yang digunakan adalah penelitian korelasional . Penelitian ini dilakukan di Kabupaten Jember, tanggal 26 Sempember sampai 4 Oktober 2013. Populasi penelitian adalah siswa SMK program keahlian TKJ se Kabupaten Jember sebanyak 1041 siswa. Sampel penelitian diambil menggunakan Purposive Random Sampling sehingga di peroleh sampel sebanyak 236 siswa dari lima SMK di Kabupaten Jember. Pengumpulan data variabel Y, X1 dan X2diperoleh dengan menggunakan angket tertutup. Uji prasyarat analis data menggunakan uji normalitas, linearitas, multikolinearitas, autokorelasi dan heteroskedasitas dan dari kelima pengujian semuanya memenuhi syarat. Data penelitian dianalisa menggunakan analisis regresi ganda dengan bantuan SPSS 16.0    Hasil penelitian ini menunjukkan bahwa (1) terdapat hubungan yang positif dan signifikan antara X1 dengan Y yang memiliki korelasi parsial (rx1y) sebesar 0,233, (2) terdapat hubungan yang positif dan signifikan antara X2denganY yang memiliki korelasi parsial (rx2y) sebesar 0,312, (3) terdapat hubungan yang positif dan signifikan secara simultan antara manajemen kelas dan kepemimpinan guru dengan kepuasan belajar dengan signifikansi 0,000, nilai F sebesar 33,132 dan nilai R2 sebesar 22,1%. Sumbangan relatif X1 dengan Y adalah sebesar 38% dan sumbangan relatif X2 dengan Y adalah sebesar 62%.                         ABSTRACT   Sahenda , Lalitya Nindita . 2013. Correlation between Classroom Management and Teacher Leadership with Student Satisfaction on Vocational Skills Computer Network Engineeringin Jember. Thesis. Department of Electrical Engineering, Faculty of Engineering, State University of Malang. Supervisor : (I) Drs. Setiadi Cahyono Putro, M.T., M.Pd., (II) Dyah Lestari, S.T., M.Eng.   Keywords : Classroom Management, Teacher Leadership, Student Satisfaction   Education satisfaction in Indonesia is one reflection of the quality of education itself . The quality of education in Indonesia is also influenced by the role of the teacher. With good management and leadership skills of teacher, is is expected to increase student satisfaction learning in the classroom.This study aims to reveal the significance of the correlation between (1) the classroom management (X1)with learning satisfaction (Y), (2) teacher leadership (X2) with student learning satisfaction (Y), (3) classroom management (X1) and teacher leadership (X2) with learning satisfaction in Computer Network Engineering on Vocational High School. The research design wes correlational research. This research was conducted in Jember, on September 26th until October 4th, 2013 . The population was 1041 vocational high school students on Computer Network Engineeringin Jember. Samples were taken using purposive random sampling so this research used 236 students from five vocational schools in Jember as sample. The data collection variables Y , X1 and X2 are obtained by using closed questionnaires. Prerequisite test data analysts using normality test, linearity, multicollinearity, autocorrelation and heteroskedasitas andall of the fifth test were qualified. Data were analyzed using multiple regression analysis using SPSS 16.0 The results of this study indicate that (1)there is a positive and significant relationship between X1 and Y which has partial correlation (rx1y) 0,233, (2) there is a positive and significant correlation between X2 and Y which has partial correlation (rx2y) 0,312, (3) there is a positive and significant relationship between classroom management and teacher leadership simultaneously with learning satisfaction, with a significance of 0.000, F value of 33.132 and R2 value of 22.1 % . The relative contribution of X1 to Y is 38% and the relative contribution of X2 and Y is 62%