Sistem Penjadwalan Kuliah Berbasis Click and Drag (Studi Kasus Di Fakultas Sains & Teknologi Universitas Teknologi YOGYAKARTA)

Utilization of technology in education is very important, especially at the University of Technology in Yogyakarta, which is one of the private universities in Yogyakarta with several objectives, one which is utilizing the maximum potential of technology to improve the effectiveness and efficiency of learning and dissemination of science and technology. One factor that can improve academic services are scheduling a lecture. Making a schedule of lectures is not an activity that is easy to do, because in doing scheduling not only arrange the schedule between subjects, time, lecturers and rooms. College scheduling systems currently running is less effective, because it requires a long time, and often clashes at student schedules. In this research, system development, scheduling lectures at the Faculty of Science and Technology, University of Technology Yogyakarta, where the scheduling system, click and drag more easily and to minimize clashes class schedules

Fuzzy-AHP approach using Normalized Decision Matrix on Tourism Trend Ranking based-on Social Media

This research discusses multi-criteria decision making (MCDM) using Fuzzy-AHP methods of tourism. The fuzzy-AHP process will rank tourism trends based on data from social media. Social media is one of the channels with the largest source of data input in determining tourism development. The development uses social media interactions based on the facilities visited, including reviews, stories, likes, forums, blogs, and feedback. This experiment aims to prioritize facilities that are the trend of tourism. The priority ranking uses weight criteria and the ranking process.  The highest rank is in the attractions of the Park/Picnic Area, with the final weight calculation value of 0.6361. Fuzzy-AHP can rank optimally with an MSE value of ≈0.0002

K-MEANS CLUSTERING FOR EGG EMBRYO'S DETECTION BASED-ON STATISTICAL FEATURE EXTRACTION APPROACH OF CANDLING EGGS IMAGE

This research discusses the detection of embryonic eggs using the k-means clustering method based on statistical feature extraction. The processes that occur in detection are image acquisition, image enhancement, feature extraction, and identification/detection. The data used consisted of 200 egg image data, consisting of 100 test data and 100 new test data. The acquisition process uses a smartphone camera by capturing candled egg objects. The results of image acquisition become a reference in the process of image enhancement and feature extraction using Statistical Feature Extraction. The statistical feature extraction applied is the Gray Level Co-occurrence Matrix (GLCM) method, which consists of 6 features, namely Energy, Contrast, Entropy, Variance, Correlation, and Homogeneity. The results of feature extraction (6 features) are grouped by the K-means Clustering method. The clustering process uses Euclidean distance calculations to determine the proximity of features. The results of grouping and testing give the best average results with an accuracy of ≈ 74% from several test samples

Identity Analysis of Egg Based on Digital and Thermal Imaging: Image Processing and Counting Object Concept

This Research was conducted to analyze the identification of eggs. The research processes use two tools, namely thermal imaging camera and smartphone camera. The identification process was done by using Matlab prototype tools. The image has been acquired by means of proficiency level, then analyzed and applied several methods. Image acquisition results of thermal imaging camera are processed using morphological dilation and do the complement in black and white (BW). While the digital image uses the merger method of morphological dilation and opening, and it doesn't need to be complemented. Labeling process is done, and the process of determining centroid and bounding box. The process has been done and it can be applied for identifying of chicken eggs with the accuracy rate of 100%. There are different methods of both images is obtained area (pixels) which is equivalent to the difference is very small as 6 x 10-3

Fuzzy Inference System Mamdani dalam Prediksi Produksi Kain Tenun Menggunakan Rule Berdasarkan Random Tree

Kain tenun merupakan salah satu produk yang diminati oleh banyak orang. Hal ini menjadi pemicu produsen untuk meningkatkan pengelolahannya. Salah satu usaha yang dilakukan adalah memprediksi produksi yang dapat dilakukan untuk mendapatkan jumlah optimal yang diperoleh, sehingga mendapatkan keuntungan yang besar. Dalam penelitian ini, untuk mendapatkan prediksi jumlah produksi kain tenun dilakukan dengan perhitungan komputerisasi menggunakan metode logika fuzzy Mamdani. Metode ini menggunakan konsep pohon keputusan random tree dalam membentuk rule. Rule yang dibuat berdasarkan pada kriteria dalam penentuan jumlah produksi kain tenun, diantaranya yaitu biaya produksi, permintaan, dan stok. Konsep pohon keputusan random tree dalam penelitian ini digunakan untuk membuat rule secara otomatis berdasarkan data yang tersedia. Pembentukan rule ini berdasarkan data-data kain tenun dan diimplementasikan dalam random tree, sehingga tidak perlu menggunakan pakar. Penelitian ini membuktikan bahwa prediksi yang dilakukan dapat membangun rule dengan nilai akurasi sebesar 100%. Hasil perbandingan prediksi dengan produksi sesungguhnya memiliki persentase error sebesar 3% dengan nilai kebenaran sebesar 97% (berdasarkan perhitungan Average Forecasting Error Rate (AFER)). Oleh karena itu ketika diimplementasikan dalam fuzzy Mamdani dapat menghasilkan prediksi produksi kain tenun yang optimal. AbstractWoven fabric is a product that is in demand by many people. It triggers producers to improve their management. One of the efforts made is to predict the production that can be done to get the optimal amount obtained, to get a significant profit. In this study, to obtain a prediction of the amount of woven fabric production is done by computerized calculations using the Mamdani fuzzy logic method. This method uses the concept of a random tree decision tree in forming rules. The rules are made based on the criteria in determining the amount of woven fabric production, including production costs, demand, and stock. The concept of a random tree decision tree in this study automatically generates rules based on available data. This rule's formation is based on woven fabric data and is implemented in a random tree, so there is no need to use experts. This study shows that the predictions made can build rules with an accuracy value of 100%. The comparison of predictions with actual production has an error percentage of 3% with a truth value of 97% (based on the calculation of the Average Forecasting Error Rate (AFER)). When implemented in Fuzzy Mamdani, it can produce optimal woven fabric production predictions with predicted results less than the actual production