Telkom Institute of Technology

About: Telkom Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Network packet. The organization has 570 authors who have published 470 publications receiving 1390 citations.

Detection and reconstruction of Geometric Object based on line detection using Radon Transform

Abstract: Geometric Object has various forms. Generally, Geometric Object is formed by several straight lines that the end point connects to the other end point of line, make certain angle, and make covered area. Therefore, to detect geometric object, first to do is detected line in an image. When line is detected on an image, the system will determine relation between lines and determine the type of object geometric.

Refarming of frequency 700 MHz analysis for long term evolution (LTE) in Indonesia using link budget calculation

Abstract: Long Term Evolution (LTE) is known as broadband technology with bandwidth varying from 1.4 to 20 MHz which uses different modulation techniques at different distances. However, the availability of frequency allocations for LTE is exhausted. Now, the opportunity of frequency allocation for LTE emerges along with the government plan to change analog television (478-806 MHz) with 8 MHz bandwidth into digital television because digital television needs less bandwidth than analog television. The contribution of this paper is to know how many eNodeBs needed for LTE FDD or LTE TDD implementation for frequency of 700 MHz and to know the best scenarios for slot allocation of LTE providers.

Prediction of malaria incidence in Banggai Regency using Evolving Neural Network

Abstract: Malaria is an endemic disease in most of area in Indonesia, especially in rural and remote areas. Banggai, one of regencies in Central Sulawesi province, is a high endemic area of malaria with Annual Parasite Incidence (API) in 2010 reached 7.880/00. The incidence and spreading of malaria were influenced by environmental and weather factors, particularly rainfall and temperature. Therefore this study would like to develop a malaria incidence prediction system based on environmental and weather factors, so that it may assist Indonesian Ministry of Health to control malaria. The method used to solve the problem was Evolving Neural Network (ENN). This method was a mixture between Artificial Neural Network (ANN) and Genetic Algorithm (GA). The result of this study shows that the prediction system has acceptable performance for predicting malaria incidence based on weather factors. The best performance in predicting malaria incidence in 2008 was 21.3% MAPE, 75% accuracy, and 84.21% F-value. While in predicting malaria incidence in 2009 was resulted 15.29% MAPE, 75% accuracy, and 40% F-value. These findings proved that there was a sufficient correlation between weather and malaria incidence. ENN also improved the performance of ANN up to 14.84% in MAPE, 25% in accuracy and 40% in F-value.

Dirac four-potential tunings-based quantum transistor utilizing the Lorentz force

Abstract: We propose a mathematical model of quantum transistor in which bandgap engineering corresponds to the tuning of Dirac potential in the complex four-vector form. The transistor consists of n-relativistic spin qubits moving in classical external electromagnetic fields. It is shown that the tuning of the direction of the external electromagnetic fields generates perturbation on the potential temporally and spatially, determining the type of quantum logic gates. The theory underlying of this scheme is on the proposal of the intertwining operator for Darboux transfomations on one-dimensional Dirac equation amalgamating the vector-quantum gates duality of Pauli matrices. Simultaneous transformation of qubit and energy can be accomplished by setting the {control, cyclic}- operators attached on the coupling between one-qubit quantum gate: the chose of cyclic-operator swaps the qubit and energy simultaneously, while control-operator ensures the energy conservation.

A deep learning using Densenet201 to detect masked or non-masked face

Abstract: The use of masks on the face in public places is an obligation for everyone because of the Covid-19 pandemic, which claims victims. Indonesia made 3M policies, one of which is to use masks to prevent coronavirus transmission. Currently, several researchers have developed a masked or non-masked face detection system. One of them is using deep learning techniques to classify a masked or non-masked face. Previous research used the MobileNetV2 transfer learning model, which resulted in an F-Measure value below 0.9. Of course, this result made the detection system not accurate enough. In this research, we propose a model with more parameters, namely the DenseNet201 model. The number of parameters of the DenseNet201 model is five times more than that of the MobileNetV2 model. The results obtained from several up to 30 epochs show that the DenseNet201 model produces 99% accuracy when training data. Then, we tested the matching feature on video data, the DenseNet201 model produced an F-Measure value of 0.98, while the MobileNetV2 model only produced an F-measure value of 0.67. These results prove the masked or non-masked face detection system is more accurate using the DenseNet201 model.