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All Journal Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Soshum: Jurnal Sosial dan Humaniora Holistic Nursing PLUS
rohmawati
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Health Professions Languange, Linguistic, Communication & Media Nursing Social Sciences

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KLASIFIKASI TUMOR OTAK JINAK (BENIGNA) DAN GANAS (MALIGNA) MENGGUNAKAN EKSTRAKSI FITUR GLCM DAN SVM rohmawati

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v9i2.1774

Abstract

Brain tumors are a deadly disease that can attack anyone without knowing age. Technology that continues to grow makes the world of health inseparable from technology. One of the technologies used to identify a disease is CT Scan and MRI. Clinically it is difficult to distinguish between benign and malignant brain tumors because like normal brain tissue, doctors can diagnose the disease without having to do surgery. This study aims to detect benign and malignant brain tumors by using extraction of GLCM and SVM features. GLCM is one method for obtaining statistical characteristics by calculating the probability of the neighboring relationship between two pixels at a certain distance and angle orientation. While the SVM method is due to the best class and classification separation and is able to work on high-dimensional datasets. The ct-scan image that is entered will be segmented which will later be extracted using GLCM features, the features used include mean, contrast, correlation, homogeneity, IDM, variance and entropy. After testing, it can be concluded that the accuracy rate is 94.5%. While using the WEKA application is 91.6666% and an error of 8.3334%. Keywords : Classification, Ct- Scan and MRI, brain tumor, GLCM, SVM
Implementing the "SMART" Framework for Effective Management of Inactive Archives: A Case Study of the Faculty of Economics, Universitas Negeri Semarang Rohmawati; Saeroji, Ahmad; Sari, Maya; Rachmadi, Moch Faizal
Soshum: Jurnal Sosial dan Humaniora Vol. 15 No. 1 (2025): March 2025
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/soshum.v15i1.38-43

Abstract

Effective archive management is essential for organizations, companies, and agencies to support administrative processes, decision-making, and policy development. This study aims to enhance the structuring and management of inactive records at the Faculty of Economics and Business (FEB), Universitas Negeri Semarang (UNNES) through the implementation of the SMART (Selective, Comprehensive, Active, Responsive, and Trusted) method. The research follows the ADDIE (Analyze, Design, Development, Implementation, and Evaluation) model, which provides a structured and systematic approach suitable for archival research. The findings indicate a significant transformation in archival management, making it more organized, systematic, and integrated. FEB UNNES has adopted a barcode-based classification system, facilitating more efficient retrieval of records. Identified inactive records have been systematically arranged in the FEB Record Center, located in the L3 Building, 2nd Floor. Additionally, 76 types of records have been identified for potential destruction, with consultations conducted with the UNNES Archives Technical Management Unit to ensure proper procedures. Five sets of basic data files from 2017–2021 have been submitted for disposal. The implementation of the SMART model enhances accessibility, systematization, and digitalization in archive management at FEB UNNES, providing a more efficient and reliable archival system.
Factor Analysis Of Characteristics Of Respondents With Pregnancy Depression At The Bojong Rawalumbu Community Health Center, Bekasi City Pinem, Lina Herida; Bin Sansuwito, Tukimin; Nambyar, Nisha; Rohmawati
Holistic Nursing Plus Vol. 1 No. 2 (2023): Holistic Nursing Plus
Publisher : Sahabat Publikasi Kuu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58439/hnp.v1i2.184

Abstract

Background: Pregnancy is one of the things expected by women in the process of life. However, many women experience difficulties due to physical, social, and psychological changes during pregnancy, there are even some mothers who suffer from psychological disorders such as depression. Perinatal depression occurs in 10-15% of women. One of the causes is a lack of knowledge about the process of pregnancy until the postpartum period which results in birth complications and disrupts the baby's growth and development. Purpose: This study aimed to identify the characteristic factors of respondents associated with prenatal depression. Methods: This research is a quantitative research that uses a cross-sectional design. The sample in this study was 85 respondents of pregnant women using the Edinburgh Postnatal Depression Scale (EPDS) screening tool. Results: The results of this study showed that the factors associated with the incidence of depression in pregnant women were education (p = 0.011), income (P = 0.046), history of domestic violence (p = 0.028), and pregnancy complications (p = 0.043) while the unrelated factors were age (P = 0.098), occupation (P = 0.829) and parity (p = 0.139) with α = 0.05. Conclusion: Education, income, history of domestic violence and history of pregnancy complications affect the incidence of depression in pregnant women. Recommendations: It is expected that the team of health workers, especially midwives and nurses, will not only focus on physical health but mental health also needs attention, and is expected to provide screening services and pregnancy psychology services to identify patients at risk of depression. Keywords: Depression, pregnancy, domestic violence, prenatal.
Co-Authors Ahmad Saeroji Bin Sansuwito, Tukimin Lina Herida Pinem Maya Sari Nambyar, Nisha Rachmadi, Moch Faizal