Performance of the K-Nearest Neighbors method on identification of maize plant nutrients

Maize is one kind of commodity consumption in domestic as well as export that has high economic value. However, the low productivity is caused by the main factor, namely the decreased level of soil fertility, so that it has the same effect on crop yields. These problems require the application of technology with the K-Nearest Neighbor (KNN) method. The method of study is based on 17 signs of nutrient deficiencies with Minkowski distance calculation process, calculation of deficiency of soil nutrients based on the value of K determined. The test results of the research use K = 75 to get an accuracy of 92.40. Comparative analysis of the K-nearest neighbor (K-NN) and NB methods by looking for the closeness between the criteria for new cases and old case criteria based on the criteria for the closest cases. The results showed that the K-Nearest Neighbor (K-NN) Algorithm had a better accuracy value than NB.Jagung merupakan salah satu jenis komoditas konsumsi dalam negeri maupun ekspor yang memiliki nilai ekonomi tinggi. Namun rendahnya produktivitas tersebut disebabkan oleh factor utama yaitu tingkat kesuburan tanah yang menurun, sehingga berpengaruh sama hasil panen. Permasalahan tersebut memerlukan penerapan teknologi dengan metode K-Nearest Neighbor (KNN). Metode penelitian didasarkan pada 17 tanda kekurangan unsur hara dengan proses perhitungan jarak minkowski, perhitungan kekurangan unsur hara tanah berdasarkan nilai K yang ditentukan. Hasil pengujian penelitian menggunakan K = 75 untuk mendapatkan akurasi sebesar 92,40. Analisis komparatif metode K-nearest neighbor (K-NN) dan NB dengan mencari kedekatan antara kriteria kasus baru dan kriteria kasus lama berdasarkan kriteria kasus terdekat. Hasil penelitian menunjukkan bahwa Algoritma K-Nearest Neighbor (K-NN) memiliki nilai akurasi yang lebih baik daripada NB

A Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data

The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search.  In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques

Optimasi Bobot K-Means Clustering untuk Mengatasi Missing Value dengan Menggunakan Algoritma Genetica

Nilai yang hilang membutuhkan preprosesing dengan teknik imputasi untuk menghasilkan data yang lengkap. Proses imputasi membutuhkan initial bobot yang sesuai, karena data yang dihasilkan adalah data pengganti. Pemilihan nilai bobot yang optimal dan kesesuaian nilai K pada metode K-Means Imputation (KMI) merupakan masalah besar, sehingga menimbulkan error semakin meningkat. Model gabungan algoritma genetika (GA) dan KMI atau yang dikenal GAKMI digunakan untuk menentukan bobot optimal pada setiap cluster data yang mengandung nilai yang hilang. Algoritma genetika digunakan untuk memilih bobot dengan menggunakan pengkodean bilangan riel pada kromosom. Model hybrid GA dan KMI dengan pengelompokan menggunakan jumlah jarak Euclidian setiap titik data dari pusat clusternya. Pengukuran kinerja algoritma menggunakan fungsi kebugaran optimal dengan nilai MSE terkecil. Hasil percobaan data hepatitis menunjukkan bahwa GA efisien dalam menemukan nilai bobot awal optimal dari ruang pencarian yang besar. Hasil perhitungan menggunakan nilai MSE =0.044 pada K=3 dan replika ke-5 menunjukkan kinerja GAKMI menghasilkan tingkat kesalahan yang rendah untuk data hepatitis dengan atribut campuran. Hasil penelitian dengan menggunakan pengujian tingkat imputasi menunjukkan algoritma GAKMI menghasilkan nilai r = 0.526 lebih tinggi dibandingkan dengan metode lainnya. Penelitian ini menunjukkan GAKMI menghasilkan nilai r yang lebih tinggi dibandingkan metode imputasi lainnya sehingga dianggap paling baik dibandingkan teknik imputasi secara umum.  AbstractMissing values require preprocessing techniques as imputation to produce complete data. Complete data imputation results require the appropriate initial weights, because the resulting data is replacement data. The choice of the optimal weighting value and the suitability of the network nodes in the K-Means Imputation (KMI) method are big problems, causing increasing errors. The combined model of Genetic Algorithm (GA) and KMI is used to determine the optimal weights for each data cluster containing missing values. Genetic algorithm is used to select weights by using real number coding on chromosomes. GA is applied to the KMI using clustering calculated using the sum of the Euclidean distances of each data point from the center of the cluster. Performance measurement algorithms using the fitness function optimally with the smallest MSE value. The results of the hepatitis data experiment show that GA is efficient in finding the optimal initial weight value from a large search space. The results of calculations using the MSE value = 0.04 for K = 3 and the 5th replication. So, GAKMI resulted in a low error rate for mixed data. The results of research using imputation level testing performed GAKMI  produced r = 0.526 higher than the other methods. Thus, the higher the r value, the best for the imputation technique

Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation

Corn is a commodity in agriculture and essential to human food and animal feed. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where specific sources have physiological properties to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through direct visual observations. This research tries to find a solution for classifying corn kernels with high accuracy using a convolutional neural network. It is because in-depth training is used in deep learning. The problem with convolutional neural networks is that the training process takes a long time, depending on the number of layers in the architecture. The research contribution is adding Convex Hull. This method looks for edge points on an object and forms a polygon that encloses that point. It helps increase focus on the convolution multiplication process by removing images on the background. The 34-layer architecture maintains feature maps and uses dropout layers to save computation time. The dataset used is primary data. There are six classes, AR21, Pioner_P35, BISI_18, NK212, Pertiwi, and Betras1—data augmentation techniques to overcome data limitations so that overfitting does not occur. The results of the classification of corn kernels obtained a model with an average accuracy of 99.33%, 99.33% precision, 99.33% recall, and 99.36% F-1 score. The computational training time to obtain the model was 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using the proposal area can improve accuracy by about 0.3% because the focused object helps the convolution process

Implementasi Metode Naïve Bayes dan Information Gain Untuk Klasifikasi Penyakit dan Hama Tanaman Jagung

Jagung (Zea mays ssp. mays) adalah tanaman pangan ketiga terbesar setelah gandum dan beras, dan di Indonesia menempati posisi kedua setelah padi. Jagung dapat ditanam di daerah dengan suhu tinggi dan rendah serta curah hujan dan irigasi yang cukup. Namun jagung sangat rentan terhadap penyakit selama siklus hidupnya, yang dapat menurunkan kualitas dan kuantitasnya. Di Sumenep, Jagung dapat dikatakan sebagai bahan pangan pokok untuk sebagian masyarakat pedesaan atau pelosok. Penyebab terjadinya serangan pada tanaman jagung adalah ketidaktahuan petani dalam pencegahan dan penanganannya sehingga menyebabkan produksi jagung mengalami penurunan. Dinas pertanian kabupaten Sumenep juga belum mempunyai sistem untuk klasifikasi hama dan penyakit jagung. Tujuan penelitian ini adalah klasifikasi penyakit dan hama tanaman jagung menggunakan metode naïve bayes dengan information gain. Naïve Bayes digunakan untuk mengolah nilai-nilai probabilitas setiap gejala, dan nilai persentase dari setiap hama dan penyakit. Information Gain untuk menyeleksi bobot gejala yang paling berpengaruh dalam menentukan hama dan penyakit jagung. Hasil uji coba, akurasi naïve bayes dengan information gain dapat meningkatkan akurasi rata-rata sebesar 3,17 % dibanding klasifikasi tanpa seleksi fitur. Akurasi terbaik diperoleh dengan metode information gain dan naïve bayes sebanyak 15 fitur dari 47 fitur dengan akurasi sebesar 98,47 %. Penelitian ini merekomendasikan 15 fitur, dengan 3 fitur terbesar adalah tidak berbuah, daun berklorosis sebagian atau seluruh daun dan adanya bekas gigitan pada batang

The Erythemato-Squamous Dermatology Diseases Severity Determination using Self-Organizing Map

A new approach based on the implementation of Self Organizing Map is presented for automated detection of erythemato-squamous diseases. The purpose of clustering techniques is in order to determinate the severity of erythemato-squamous dermatology diseases. The studied domain contained records of patients with known diagnosis. Self-Organizing  Map algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the six clusters (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, dan pityriasis rubra pilaris). The algorithm was used to detect the six erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to determine an optimum classification scheme for this problem. The present research demonstrated that the features well represent the erythemato-squamous diseases and SOM algorithm's task achieved high classification accuracies. The best accuration for  psoriasis 85,94%, seboreic dermatitis 40,48%, lichen planus 56,25%, and pityriasis rosea 82,61%, with learning rate value were 0,1, 0,2, 0,9, and 0,

PEMBERDAYAAN MASYARAKAT KEPULAUAN TALANGO KABUPATEN SUMENEP MELALUI USAHA PERCETAKAN DAN SABLON DIGITAL PRINTING

The role of youth in development is very important because it is considered to be in the productive age to support various development activities in various sectors. Most youth can be absorbed in the labor market, and partly eliminated from the competition and become a static group. Not a few who engage in the business world ranging from the small to large, one form of business that is highly demanded by the youth is the Small and Medium Enterprises and Silk Screen Printing. However, the form of efforts among youth most still use manual design and printing due to lack of capital and expertise. With this activity, try to solve them with IBM is working with partners Silk Screen Printing Industry centers in villages Talango, Talango islands, Sumenep, Disperindag and local cooperative activities such as application of digital printing techniques, the design theme oflocal wisdom Madura images with coloror multi color mono color and entrepreneurship training and business management. IBM activity was done in the form of training, coaching and mentoring the youth group field of screen printing and printing for souvenirs and handicrafts which aims to: 1) increase the motivation of entrepreneurial partners; 2) improve the understanding of partner business planning and business management; 3) improve human resource capabilities in the production and marketing techniques; 4) develop a network to support youth entrepreneurship development of the creative economy. Youth empowerment group is expected to produce a model that can be used as a model youth entrepreneurial development youth empowerment-based society.Keywords: training, coaching, mentoring, printingandscreen printing, digital printing</p