Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)
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EVALUASI SISTEM INFORMASI PENGGUNAAN E-LEARNING SEBAGAI SISTEM PERKULIAHAN PERGURUAN TINGGI
No full textThis study aims to evaluate the use of technology to support teaching and learning activities. Lecturers and students have applied e-learning to teach subjects. The purpose of this evaluation is to measure the success of the use of STMIK Bumigora e-learning by using the Technology Acceptance Model (TAM) approach, which is an approach that can explain user behavior towards the use of technology. Evaluation of the use of e-learning is formulated into a model based on the TAM model, while SEM (Structural Equation Modelling) is used for data analysis. Based on the measurement analysis in this study, several factors most influenced the effectiveness of e-learning, namely the usage tutorial for users, ICT facilities related to the Ease of accessing the internet network. Meanwhile, in structural analysis, it was found that attitudes toward the use and perceived usefulness were strongly correlated with real use factors. The actual use is a real condition of the use of e-learning measured by the frequency and duration of time in using the technology, which is influenced by the user's belief in accepting the existence of e-learning in STMIK Bumigora and user beliefs related to the benefits when using it. Therefore, attitudes toward the use and perception of usefulness are the main determining factors in measuring the frequency and duration of e-learning use.This study aims to evaluate the use of technology to support teaching and learning activities. Lecturers and students have applied e-learning to teach subjects. The purpose of this evaluation is to measure the success of the use of STMIK Bumigora e-learning by using the Technology Acceptance Model (TAM) approach, which is an approach that can explain user behavior towards the use of technology. Evaluation of the use of e-learning is formulated into a model based on the TAM model, while SEM (Structural Equation Modelling) is used for data analysis. Based on the measurement analysis in this study, several factors most influenced the effectiveness of e-learning, namely the usage tutorial for users, ICT facilities related to the Ease of accessing the internet network. Meanwhile, in structural analysis, it was found that attitudes toward the use and perceived usefulness were strongly correlated with real use factors. The actual use is a real condition of the use of e-learning measured by the frequency and duration of time in using the technology, which is influenced by the user's belief in accepting the existence of e-learning in STMIK Bumigora and user beliefs related to the benefits when using it. Therefore, attitudes toward the use and perception of usefulness are the main determining factors in measuring the frequency and duration of e-learning use
Convolutional Neural Networks Based on Raspberry Pi for a Prototype of Vocal Cord Abnormalities Identification
Full text linkThis study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% accuracy.This study aims to make a device prototype for identifying vocal cord abnormalities based on Raspberry Pi. This prototype could classify the abnormalities into seven classes, i.e., cysts, granulomas, nodules, normal, papilloma, paralysis, and no vocal cords. The applied method to classify is a deep learning algorithm, mainly using Convolutional Neural Network (CNN). In building the CNN model, we used a statistical method to form a model training scenario, also modified the AlexNet architecture model by optimizing the parameters. The optimized parameters in the test scenario obtained 95.35% accuracy. The CNN model implemented on the Raspberry Pi, and the test results obtained 79.75% accurac
Design and Implementation of Boost Voltage Doubler for Maximum Power Point Tracker Application Using STM32F1038CT
Full text linkPhotovoltaic is an absolute device in the solar power plant system. A DC-DC converter with a maximum power point tracker (MPPT) algorithm is required to obtain the maximum power of photovoltaic. In general, solar power plant applications used a two-stage converter: the first stage is boosting DC-DC converter, and the second stage is the multilevel Inverter. Boost DC-DC converter is usually implemented singly, which causes many boost DC-DC converters to be implemented in a solar power plant application. The voltage doubler type boost DC-DC converter proposed in this paper is to simplify the circuit so that there is only one converter in a solar power plant application. This converter principle combines two conventional boost converters, which are integrated into one so that the power circuit and control circuit form become simpler. This proposal is verified through computation simulation and hardware design using the STM32F1038CT microcontroller for the final verification. The efficiency algorithm of the simulation is 99.7%, and the hardware experimental is 85.65%Photovoltaic is an absolute device in the solar power plant system. A DC-DC converter with a maximum power point tracker (MPPT) algorithm is required to obtain the maximum power of photovoltaic. In general, solar power plant applications used a two-stage converter: the first stage is boosting DC-DC converter, and the second stage is the multilevel Inverter. Boost DC-DC converter is usually implemented singly, which causes many boost DC-DC converters to be implemented in a solar power plant application. The voltage doubler type boost DC-DC converter proposed in this paper is to simplify the circuit so that there is only one converter in a solar power plant application. This converter principle combines two conventional boost converters, which are integrated into one so that the power circuit and control circuit form become simpler. This proposal is verified through computation simulation and hardware design using the STM32F1038CT microcontroller for the final verification. The efficiency algorithm of the simulation is 99.7%, and the hardware experimental is 85.65
Analisis Teks Pelamar Untuk Klasifikasi Kepribadian Menggunakan Multinomial Naïve Bayes dan Decision Tree
No full textEmployees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance