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All Journal IPTEK The Journal for Technology and Science Jurnal Saintek Lahan Kering Journal Of Information And Technology Unimor (JITU)
Patricia Gertrudis Manek
Fakultas Pertanian, Universitas Timor
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Public Health

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A Semantic Comparison of Feature Requirements Extraction Methods Patricia Gertrudis Manek; Abdullah Faqih Septiyanto; Adi Setyo Nugroho
IPTEK The Journal for Technology and Science Vol 32, No 3 (2021)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v32i3.13003

Abstract

Requirement engineering is an essential part of software development. The initial process in software development is to determine the needs of the stakeholders. To convert stakeholder needs into features of the system to be developed takes a long time, so it is a challenge for researchers to be able to extract features automatically based on the description of the needs of stakeholders. Previous research has also implemented feature extraction using user reviews on applications that public users have used. The feature extraction results will be used for feature development in future updated versions. The extraction process can use several proven methods to provide results that match the needs of the stakeholders in the system. This study compared the automatic feature extraction method using Natural Language Processing (NLP) with Hierarchical Pattern Recognition (HPR) on the dataset requirements and user reviews. Performance evaluation was conducted to test feature extraction results using Accuracy, precision, recall, and F-measure. The study results show that each method has advantages when implemented on both datasets. The NLP method excels in classifying the NL Requirement dataset. The HPR method has its advantages in extracting user review data.
Pengembangan Sistem Informasi Pengolahahan Data Guru dan Pegawai pada SMA Negeri 1 Tasifeto Barat Berbasis Website Stefanus Lau Manek; Yoseph Pius Kurniawan Kelen; Krisantus Jumarto Tey Seran; Patricia Gertrudis Manek
Saintek Lahan Kering Vol 5 No 2 (2022): JSLK DESEMBER 2022
Publisher : Fakultas Pertanian, Universitas Timor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/slk.v5i2.1999

Abstract

Being a formal education institution, a school must have information to be shared to public. Such information could be new school year students enrollment, general situation of the school, school activities, students, teachers and administration staff's personal data. Up to this moment, school information is still conventionally publicly announced, both from mouth to mouth and by writing it on an announcement board. Such condition will positively impact in the delay of the information being shared. This situation still exists at West Tasifeto State Senior High School One which is considered one of the best schools located on the border of Indonesia – Timor Leste. Apart from the information sharing mentioned above, teachers and administration staff data processing is still manually handled by means of typing it on paper which mostly have bad effects on finding whenever needed. In this research, we developed an information system to share information and the data processing of teachers and administration staff in the form of website, to ease school data management. We believe that the existence of such media is able to process teachers and administration staff data, teachers attendance list management, school scheduling, and other school information. A Prototype Model is utilized in developing this website. The website-based information system at West Tasifeto State Senior High School One is believed to assist with the school information spreading to public, especially to the remote community, residing the border of Indonesia and Timor Leste. Anybody seeking the school information just easily logging on to the school Website and they can straightly find it without having to come to school.
Segmentasi Daun Cendana Berbasis Citra Menggunakan Otsu Thresholding Patricia Gertrudis Manek; Budiman Baso; Kristoforus Fallo; Risald Risald; Hevi Herlina Ullu
Journal of Information and Technology Vol 3 No 1 (2023): Journal of Information and Technology Unimor (JITU)
Publisher : Department of Information Technology, Universitas Timor, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jitu.v3i1.3868

Abstract

The segmentation process is the separation of parts of the object area from the background in an image, so that segmented objects can be processed for other purposes such as pattern recognition. The results of segmentation must be accurate, if it is not accurate in separating objects in the image it will affect the results of further processing. The segmentation process is carried out using the Otsu Thresholding method on sandalwood leaf images by first applying the Median filter to reduce noise. After obtaining the segmented image, then performing performance measurements. The segmentation results from each test are evaluated using the RAE (relative foreground area error) and ME (misclassification error). The segmentation results of 8 sandalwood leaf images from 2 existing conditions show that, sandalwood leaf image segmentation with good leaf conditions obtains the best segmentation results with smaller errors of 5 image data. While the images of sandalwood leaves affected by the disease as many as 3 image data have more diverse areas so that the segmentation results are not good without any morphological process
Identifikasi Tingkat Kematangan Buah Pinang Menggunakan K-Nearest Neighbor Berdasarkan Fitur Tekstur dan Warna Patricia Gertrudis Manek; Budiman Baso; Biandina Meidyani
Journal of Information and Technology Vol 2 No 2 (2022): Journal of Information and Technology Unimor (JITU)
Publisher : Department of Information Technology, Universitas Timor, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jitu.v2i2.4205

Abstract

This research builds a system for identifying the maturity level of areca fruit based on digital image processing using texture and color features through the Gray Level Co-Occurrence Matrix (GLCM) and Color moments. The initial stage of the research is image pre-processing so that it can be processed to the next stage, namely feature extraction. Texture feature extraction was performed using the Gray Level Co-Occurrence Matrix (GLCM), namely the correlation value and color feature extraction using Color moments, the mean value used in this study. Classification is done based on the features that have been extracted before. This study uses the K-Nearest Neighbor (KNN) classification method. Tests were carried out to determine the parameters that cause changes in the classification results with scenarios including determining the number of Neighbors in KNN. By using 1 Neighbors in the KNN classifier, the best accuracy is 86.36% in the process of identifying the maturity level of areca fruit.
Co-Authors Abdullah Faqih Septiyanto Adi Setyo Nugroho Biandina Meidyani Budiman Baso Hevi Herlina Ullu Kristoforus Fallo Risald risald Stefanus Lau Manek Tey Seran, Krisantus Jumarto Yoseph Pius Kurniawan Kelen