Student grouping, particularly in high school, is a necessary process to divide and classify students into classes based on their abilities and interests. Each school may have different approaches to decide the grouping, but most schools use academic grades. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input. However, SOM is also known as one of clustering techniques, since dimensionality reduction may also be seen as reducing (or clustering) input data to lower dimensions (or clusters). This research aims to group new enrolled students to a high school based on their academic grades using a SOM learning algorithm. The grades came from their rapport books and national examination results from their previous study. The resulting groups are three distinct clusters which represents Life Sciences, Social Sciences, and Linguistics study areas.

Agricultural sector is the main sector that plays an important role in the national economy, from absorbing labours to playing as a contributor for foreign exchange. Indonesia is an agrarian country whose livelihood of the majority population is farming. Rice is one of the cultivation plants that becomes the staple food of most population. Thus, rice availability and its quality are factors that must be put in high consideration, either for national consumption or for export quality. This study aims to detect diseases in rice plants (by observing the leaves) that may cause a decrease in rice production or may result in bad quality rice using an artificial intelligent approach. The method used in this research is Convolutional Neural Network (CNN). This CNN is the result of the development of multilayer perception (MLP) which is used to manage two-dimensional data. The input from CNN is in the form of 2-dimensional data which is then propagated on a network with parameters at different weights and linear operations. CNN is one method of deep learning. The CNN method has many types of layers, namely the convolution layer, the subsampling / pooling layer, and the fully connected layer. This study uses different CNN architectures to find the best accuracy value. This study used four types of leaf diseases in rice plants with each type of disease consisting of 2,239 training image data and 168 image data. This research has succeeded in detecting diseases in leaf images automatically with the best training accuracy obtained at 91%.

This study conducted to see the effect of the company's firm size, leverage, growth opportunity and profitability on the decision of hedging with derivative instruments on manufacturing companies listed on the Indonesian Stock Exchange period 2011-2016. Sample in this study uses 34 companies by using purposive sampling method. This study uses a quantitative approach and data used secondary data. The analytical method used is logistic regression. The results shower that the variable firm size and growth opportunity have a significant positive effect on hedging decision making. The variable leverage and profitability did not influence hedging decision in Indonesian manufacturing companies. This study show that the higher value of firm size will increase the probabilitiy of using hedging activity in manufacturing companies in Indonesia.

Perawatan kulit atau skincare merupakan salah satu upaya untuk mendukung kesehatan dan kebersihan kulit, memelihara, merawat dan mempertahankan kondisi kulit, dengan melakukan perawatan kulit maka kebersihan dan kesehatan kulit dapat terjaga. Saat ini masih banyak wanita yang belum mengenali jenis kulitnya sendiri sehingga memiliki resiko kesalahan dalam pemilihan perawatan wajah yang sesuai untuk jenis kulit wajah. Maka dari itu diperlukan adanya sistem pendukung keputusan untuk mengenali jenis kulit yang dimiliki sehingga peluang kesalahan dalam memilih produk perawatan kulit bisa diminimalisir. Metode yang diterapkan pada penelitian ini adalah decision tree dengan menggunakan algoritma C4.5. Metode tersebut diterapkan untuk memperoleh keputusan akhir yang didapatkan dari pohon keputusan. yang digunakan sebagai acuan untuk mengetahui dan menentukan jenis kulit wajah yang dimiliki. Dari penelitian yang dilakukan dapat diketahui bahwa terdapat 5 output jenis kulit wajah dan 8 jenis produk perawatan yang mana hasil pada setiap orang akan berbeda sesuai dengan ciri-ciri yang dimiliki. Sistem ini memiliki nilai akurasi 100% dan evaluasi nilai presisi dan recall pada algoritma C4.5 yang diterapkan bernilai 1.

Glucose needs each year has increased significantly while glucose production has decreased, this is because supplies of the raw materials limited, where bamboo is one of the raw material alternatives to glucose. The selection of bamboo plants based on levels of cellulose which ranges from 42.4%-53.6%, bamboo plants are plants that can grow quickly and easily grown in various regions in Indonesia. The production of glucose from bamboo using hydrolysis and pretreatment process, in the hydrolysis process of the microcontroller equipped Proportional Integral Derivative (PID) type Arduino UNO, the application of microcontrollers PID using the Fuzzy method and simulation language Delphi programming. Research results in the form of the temperature profile, levels of cellulose hydrolysis time function, and the function time of the hydrolysis of glucose levels. At the time of the detailed set point temperature profile 97 0 C, at an early stage shows the result of the PID process control with fault temperature below ten 0 C, the temperature as measured 94,696 0 C, hydrolysis time 20 seconds. As time went on the hydrolysis process control, PID shows the temperature measured is 96.59 0 C, at 137 seconds, measured temperature shows 97 0 C following the temperature set point, used as a basis to design tools in the process of hydrolysis. Optimization of cellulose levels function hydrolysis time is 18.7% and the optimization of the hydrolysis time function of glucose levels is 23,6%. Process design with the production of glucose from bamboo with hydrolysis equipped microcontroller control PID temperature obtained the optimum levels of glucose.