Prediksi Target Sistem Productivity Services menggunakan Penerapan Algoritma C4.5 pada PT. Sanggar Sarana Baja Jakarta

There are many ways that each service provider company does, especially services to win the competition, among others, by increasing service productivity targets. One service provider company that is committed to increasing service productivity targets is PT. Sanggar Sarana Baja. This study aims to predict service productivity system targets using the application of Algortima C4.5 at PT. Sanggar Sarana Baja. The attributes of working time input in this study include area, performance, efficiency, and productivity. In this study, it was found that the results obtained came from several input attributes which resulted in a causal relationship in classifying the results of service productivity targets at PT. Sanggar Sarana Baja. This research is expected to help PT. Sanggar Sarana Baja in increasing customer satisfaction to retain customers and increase profits of PT. Sanggar Sarana Baja. Based on the classification results using the C4.5 Algorithm, it shows that the accuracy reaches 95.00%, which indicates that the C4.5 algorithm is suitable for measuring the target level at PT. Sanggar Sarana Baja. Keywords: Accuracy, Validation, Decision Tree, Data mining, KDD, C4.5 Algorithm, Services Companies, Target Services Productivity Systems Banyak cara yang dilakukan oleh masing - masing perusahaan penyedia jasa, khususnya servis untuk memenangkan persaingan, antara lain dengan meningkatkan target produktivitas servis. Salah satu perusahaan penyedia jasa servis yang berkomitmen dalam meningkatkan target produktivitas servis adalah PT. Sanggar Sarana Baja. Penelitian ini bertujuan untuk memperdiksi target sistem produktivitas servis menggunakan penerapan Algoritma C4.5 pada PT. Sanggar Sarana Baja. Atribut masukan waktu kerja dalam penelitian ini mencangkup daerah, kinerja, efisiensi, dan produktivitas. Dalam penelitian ini, didapatkan bahwa hasil yang didapatkan berasal dari beberapa atribut masukan menghasilkan hubungan sebab -akibat dalam mengklasifikasikan hasil dari target produktivitas servis pada PT. Sanggar Sarana Baja. Penelitian ini diharapkan dapat membantu pihak PT. Sanggar Sarana Baja dalam meningkatkan kepuasan konsumen untuk mempertahankan pelanggan dan meningkatkan laba PT. Sanggar Sarana Baja tersebut. Berdasarkan hasil klasifikasi menggunakan Algoritma C4.5 menunjukkan bahwa diperoleh akurasi mencapai 95,00%, yang menunjukkan bahwa algoritma C4.5 cocok digunakan untuk mengukur tingkat target pada PT. Sanggar Sarana Baja. Kata kunci: Akurasi, Validasi, Decision Tree, Data mining, KDD, Algoritma C4.5, Perusahaan Jasa, Target Sistem Productivity ServicesReferensi[1] Yulia and N. Azwanti, “Data Mining Prediksi Besarnya Penggunaan Listrik Rumah Tangga di Kota Batam Dengan Menggunakan Algoritma C4.5,” Semin. Nas. Ilmu Sos. dan Teknol., vol. 1, no. 1, pp. 175–180, 2018.[2] R. Novita, “Teknik Data Mining?: Algoritma C 4 . 5,” pp. 1–12, 2016.

Face Images Classification using VGG-CNN

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image. The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%).

ACCURACY VALIDATION OF TILTED CAMERA SETUPS IN OPEN SKIES PROJECT AND MAPPING APPLICATIONS

Signed March 24, 1992, the Open Skies Treaty permits each state-party to conduct short-notice, unarmed, reconnaissance flights over the others' entire territories to collect data on military forces and activities. Observation aircraft equipped with sensors shall enable the observing party to identify significant military equipment. The Open Skies Treaty agreed on an observation of 30cm GSD. Based on 8 mid-format cameras for 3 flight levels, a system was adjusted to comply with the regulations. However, the overall aim was to also use the system for mapping purposes in Romania, specifically the medium altitude configuration. From this medium altitude configuration, one specific combination raised our interest. The designed wide corridor mapping system, using two RGB tilted cameras and one RGB nadir camera generates a certain range of resolution of the sideward looking tilted cameras and a non-homogenous distribution of the GSD in the overlapping areas. While a reduction of the GSD in the remote parts of the tilted cameras is a well-known and accepted fact, the effect in the overlap of the tilted cameras with the nadir one is the opposite mathematically. In some cases, such an effect can cause a better GSD in these areas than expected.

Improving the Recognition Performance of Lip Reading Using the Concatenated Three Sequence Keyframe Image Technique

This paper proposes a lip reading method based on convolutional neural networks applied to Concatenated Three Sequence Keyframe Image (C3-SKI), consisting of (a) the Start-Lip Image (SLI), (b) the Middle-Lip Image (MLI), and (c) the End-Lip Image (ELI) which is the end of the pronunciation of that syllable. The lip area’s image dimensions were reduced to 32×32 pixels per image frame and three keyframes concatenate together were used to represent one syllable with a dimension of 96×32 pixels for visual speech recognition. Every three concatenated keyframes representing any syllable are selected based on the relative maximum and relative minimum related to the open lip’s width and height. The evaluation results of the model’s effectiveness, showed accuracy, validation accuracy, loss, and validation loss values at 95.06%, 86.03%, 4.61%, and 9.04% respectively, for the THDigits dataset. The C3-SKI technique was also applied to the AVDigits dataset, showing 85.62% accuracy. In conclusion, the C3-SKI technique could be applied to perform lip reading recognition.

Shooting-Bouncing-Rays Technique to Model Mine Tunnels: Theory and Accuracy Validation

We present a shooting-bouncing rays technique for electromagnetic modeling of wireless propagation in long tunnels focusing on the accuracy of ray-tracing computation. The examples demonstrate excellent agreement with the traditionally more accurate but less efficient alternative ray-tracing approach using path corrections based on image theory and with a commercial solver.