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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Information Systems Engineering and Business Intelligence Jurnal Informatika Universitas Pamulang JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat)
Maulidiya, Erika
Universitas Lambung Mangkurat
Computer Science & IT Dentistry Education Electrical & Electronics Engineering Engineering Library & Information Science

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Analisis Perbandingan Tingkat Stress Mahasiswa Saintek dan Soshum dalam Pembelajaran Daring pada Masa Pandemi Covid-19 Berbasis Internet of Things Erika Maulidiya; Iftihatul Aulia Rahmah; Putri Ridha Amalia; Ryan Ramel; siti sheilawati; Muhammad Alkaff
Jurnal Informatika Universitas Pamulang Vol 6, No 4 (2021): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v6i4.14470

Abstract

The spread of COVID-19 has occurred in 2019, which has had an enormous impact on the world's population. The continuous spread of COVID-19 has caused several countries to reduce the transmission of COVID-19, one of which is implementing online learning at schools and universities. The impact that occurs on students due to home study policies makes some students feel anxious and depressed. There are two study groups at the University of Lambung Mangkurat, namely the Social Humanities (Soshum) and Science and Technology (Saintek). The student who majored in science, technology, and social science has a different way of finding the information needed and understanding every material available online. This problem is due to cultural differences in the applied learning system. These differences certainly cause different stress levels for each student majoring in science, technology, and social sciences. Therefore, this study was conducted to determine the difference in stress levels experienced by Lambung Mangkurat University students in science, technology, and social media while online. This study uses two stages to compare the results of student stress levels, including filling out the DASS42 questionnaire and direct testing with 3 IoT sensors, namely GSR, body temperature (GY-906 MLX90614 Infrared Temperature Sensor), and pulse rate (MAX30102 Pulse Oximeter & Heart-Rate Sensor). The application of the Fuzzy Logic method is used as a parameter measurement when measuring IoT-based stress levels.
DESIGN OF AN INVENTORY INFORMATION SYSTEM FOR LABORATORY SUPPLIES Noor Razikin; Yuslena Sari; Erika Maulidiya
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 8 No. 1 (2023)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v8i1.159

Abstract

Data is inaccurate because it does not have a relevant data repository, data can be lost or damaged, inefficient data search and technicians cannot know for sure the amount of stock available. Based on some of the research above, the inventory information system for Laboratory goods in the Information Technology Study Program will be designed and built based on a website using the Laravel framework. The system development method used is the Incremental model. Incremental models are the result of a combination of elements from the waterfall model that are applied repeatedly, or it can be called a combination of the waterfall model and the Prototype Model. During testing, many errors were found in the system. Testing was carried out 4 times with a total of 164 test cases. In the first test, 98 bugs were found which were then reported to the programmer to be fixed. In the second test, 40 errors were found, in the third test, 19 errors were found, and in the last test conducted by the examiner, 0 bugs were found. The design of the Laboratory Goods Inventory Information System (SIMBA) begins with analyzing the weaknesses of the old system using the PIECES method. Then proceed with conducting a system requirements analysis and system feasibility analysis. After the analysis phase, it is continued with the design stage which begins with the UML design method.
UAV LAND COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK FEATURE MAP WITH A COMBINATION OF MACHINE LEARNING Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Baskoro, Fajar
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 22, No. 1, January 2024
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v22i1.a1214

Abstract

In geographic analysis, land cover plays an important role in everything from environmental analysis to sustainable planning methods and physical geography studies. The Indonesian National Standard (SNI 7645:2014) classifies vegetation analysis based on density. There are four categories of vegetation density index: non-vegetation, bare, medium, and high. Technically, vegetation data can be obtained through remote sensing. Satellite and UAV data are two types of data used in remote sensing to collect information. This research will analyze land cover based on vegetation density information that can be collected through remote sensing. Based on vegetation density information from remote sensing, the information can help in land processing, Land Cover Classification is carried out based on vegetation density. Convolutional neural networks (CNN) have been trained extensively to apply their properties to land cover classification. This research will evaluate features extracted from Convolutional Neural Networks (ResNet 50, Inception-V3, DenseNet 121) which have previously been trained and continued with Decision Tree algorithms, Random Forest, Support Vector Machine and eXtreme Gradient Boosting to perform classification. From the comparison results of classification tests between machine learning methods, Support Vector Machine is superior to other machine learning methods. This is proven by the accuracy results obtained at 85% with feature extraction using ResNet-50 where the processing time is 8 minutes. Followed by the second-best model, namely ResNet-50 with XGBoost which obtained accuracy results of 82% with a processing time of 55 minutes. Meanwhile, the use of feature extraction using the DenseNet-121 method was obtained using a combination of the Support Vector Machine method and the XGBoost method with the accuracy obtained being 81%.
Ground Coverage Classification in UAV Image Using a Convolutional Neural Network Feature Map Maulidiya, Erika; Fatichah, Chastine; Suciati, Nanik; Sari, Yuslena
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 2 (2024): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.2.206-216

Abstract

Background: To understand land transformation at the local level, there is a need to develop new strategies appropriate for land management policies and practices. In various geographical research, ground coverage plays an important role particularly in planning, physical geography explorations, environmental analysis, and sustainable planning. Objective: The research aimed to analyze land cover using vegetation density data collected through remote sensing. Specifically, the data assisted in land processing and land cover classification based on vegetation density. Methods: Before classification, image was preprocessed using Convolutional Neural Network (CNN) architecture's ResNet 50 and DenseNet 121 feature extraction methods. Furthermore, several algorithm were used, namely Decision Tree, Naí¯ve Bayes, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). Results: Classification comparison between methods showed that using CNN method obtained better results than machine learning. By using CNN architecture for feature extraction, SVM method, which adopted ResNet-50 for feature extraction, achieved an impressive accuracy of 85%. Similarly using SVM method with DenseNet121 feature extraction led to a performance of 81%. Conclusion: Based on results comparing CNN and machine learning, ResNet 50 architecture performed the best, achieving a result of 92%. Meanwhile, SVM performed better than other machine learning method, achieving an 84% accuracy rate with ResNet-50 feature extraction. XGBoost came next, with an 82% accuracy rate using the same ResNet-50 feature extraction. Finally, SVM and XGBoost produced the best results for feature extraction using DenseNet-121, with an accuracy rate of 81%.   Keywords: Classification, CNN Architecture, Feature Extraction, Ground Coverage, Vegetation Density.
Co-Authors Chastine Fatichah Fajar Baskoro Iftihatul Aulia Rahmah Muhammad Alkaff Nanik Suciati Noor Razikin Putri Ridha Amalia Ryan Ramel siti sheilawati Yuslena Sari, Yuslena