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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi Brilliance: Research of Artificial Intelligence J-Icon : Jurnal Komputer dan Informatika
Sri Azizah Nazhifah
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Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Other

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Fully Homomorphic Encryption for Cloud Based E-Government Data Rini Deviani; Sri Azizah Nazhifah; Aulia Syarif Aziz
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 6, No 2 (2022)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v6i2.14861

Abstract

Cloud computing is a way of providing services, networks, hardware, storage, and interfaces for the construction of E-Government infrastructure that enables the delivery of services efficiently and achieves cost savings. Cloud services enable individuals and organizations to utilize cloud providers’ software and hardware resources stored remotely. The span between the client and the actual location of his data provides a barrier because this data can be obtained by a third party, risking the citizen’s data privacy. We examine a method based on the Fully Homomorphic Encryption (FHE) scheme in order to facilitate the processing of sensitive information pertaining to the E-Government that does not involve the disclosure of the original data. In this paper, we consider some general data operations to evaluate the feasibility of the FHE method and show that the accuracy are similar when data operations are applied to homomorphically encrypted data. The results of the experiment highlight the potential of the various privacy-preserving data operations that can be performed under FHE approach. These methods provide results that are equivalent to those achieved by unencrypted data and models within a decent amount of time. 
SISTEM PAKAR DIAGNOSA PENYAKIT GINJAL BERBASIS WEB MENGGUNAKAN METODE CERTAINTY FACTOR Kikye Martiwi Sukiakhy; Sri Azizah Nazhifah; Junidar Junidar
J-ICON : Jurnal Komputer dan Informatika Vol 11 No 1 (2023): Maret 2023
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jicon.v11i1.9931

Abstract

There are still limited health facilities in Indonesia, especially those dealing with kidney disease and the lack of knowledge and information in the community regarding the early symptoms of kidney disease, causing the death rate caused by this disease to continue to increase every year. An expert system that can diagnose kidney disease is urgently needed to provide a solution to this problem with the hope that people can easily obtain information regarding the symptoms of kidney disease they are experiencing, and that people can make a diagnosis independently. A method called the certainty factor (CF) is used in the expert system in this study. Diagnosis of kidney disease based on various symptoms experienced by the user is the output generated by this expert system. Based on the symptoms entered by the user, the system will then look for the highest CF value and then the results will be displayed to the user. The displayed results include 17 symptoms, disease name, diagnosis results and solutions. The functional tests carried out went as expected for the three access rights, namely admin, expert and user can access all functions and no errors were found in the system, using both the Windows 8 and Windows 10 operating systems for admin, expert and user access.
Sentiment Analysis of Mental Health Using Support Vector Machine (SVM) with FastAPI Implementation Maulyanda, Maulyanda; Sri Azizah Nazhifah
Brilliance: Research of Artificial Intelligence Vol. 5 No. 1 (2025): Brilliance: Research of Artificial Intelligence, Article Research May 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i1.6580

Abstract

Mental health is a vital aspect that contributes significantly to an individual’s productivity, daily activity, and overall quality of life. With the increasing prevalence of mental health issues, early detection and analysis are essential. This study aims to identify mental health conditions using a machine learning approach, specifically the Support Vector Machine (SVM) algorithm. The dataset used consists of 53,043 text-based statements that are classified into seven distinct categories of mental conditions: normal, depression, suicide, anxiety, bipolar, stress, and personality disorders. The preprocessing steps applied to the dataset include text cleaning, tokenization, stopword removal, and lemmatization to standardize the textual input. Following this, 80% of the data is allocated for training the model, while the remaining 20% is reserved for testing purposes. The SVM algorithm is utilized to build a predictive model capable of classifying mental conditions based on text input. Furthermore, this model is deployed through an application interface using the FastAPI framework, enabling integration with digital platforms. The results indicate that the model achieves an accuracy of 79%, a recall of 77%, and an F1-score of 73%. These findings suggest that SVM is a capable and efficient method for analyzing and detecting various mental health conditions. This approach supports early intervention and offers practical implications for digital mental health screening tools.
Co-Authors Aulia Syarif Aziz Junidar Junidar Kikye Martiwi Sukiakhy Maulyanda, Maulyanda Rini Deviani