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All Journal Jurnal Informatika Jurnal INKOM CommIT (Communication & Information Technology) Teknik dan Ilmu Komputer JOIV : International Journal on Informatics Visualization ComTech: Computer, Mathematics and Engineering Applications SERVIRISMA: Jurnal Pengabdian kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika
Nina Sevani
Universitas Kristen Krida Wacana – Jakarta Barat
Humanities Computer Science & IT Control & Systems Engineering Engineering Mathematics Other

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USING CERTAINTY FACTOR METHOD TO HANDLE UNCERTAIN CONDITION IN HEPATITIS DIAGNOSIS Saputri, Aprilia Eka; Sevani, Nina; Saputra, Fajar; Sali, Richardo Kusuma
ComTech: Computer, Mathematics and Engineering Applications Vol 11, No 1 (2020): ComTech (Inpress)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v11i1.5903

Abstract

The research aimed to develop a web-based application using the certainty factor. The use of this certainty factor method allowed processing the data based on the degree of confidence from the experts and the users. The users inputted their symptoms each with the level of confidence. The inference engine drew some conclusions based on the matching process between the input and the rules in the knowledge-based. For every matching pair, the system will calculate the certainty factor. The knowledge-based was developed through discussion with three specialist physicians and literature in some previous studies. The evaluation of the system involved three specialists for validation testing and 51 respondents for BlackBox testing. The final result is displayed in the form of a percentage for each hepatitis type, explanation of first aid for hepatitis, and referral hospital for hepatitis patients. The result shows that the error rate in the diagnosis process is under 36%. Most of the respondents think that the quality of the system is good overall.
DESAIN APLIKASI DIGITAL PADA PELAYANAN PERLAWATAN JEMAAT GKI DELIMA Yudhi Windarto; Benisius; Wiryasaputra, Rita; Sevani, Nina; Putro, Endi
Servirisma Vol. 3 No. 2 (2023): Servirisma : Jurnal Pengabdian kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/servirisma.2023.32.57

Abstract

Digital transformation in church congregational visiting services is a challenge for GKI Delima where there are problems with data accuracy, coordination and reporting processes. The use of appropriate technology can be the best solution to overcome existing problems. The purpose of this community service program is to develop an Android-based application to improve the process of church congregational visiting services at GKI Delima. The stages of implementing this activity start from the planning, design-development and implementation stages. Application development refers to the RAD (Rapid Application Development) and using Android Studio. The results of the development of mobile apps for the GKI Delima congregation as part of the digital transformation process were able to improve the quality of the congregation's treatment process to become more effective and efficient. The scheduling and coordination process becomes faster and easier. Data is more accessible and faster in the process of updating data. The process of reporting and documenting the results of congregation visits has become easier, faster, safer, and paperless. The process of digital transformation in the congregation's visiting service at GKI Delima has had a positive impact on improving the quality of the congregation's visiting process to make it easier, faster and safer.
Enhanced Image Classification by Eliminating Outliers with the Combination of Feature Selection and K-means Techniques Sevani, Nina; Cuvianto, Lukas; Octaviany, Jessica
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 10 No. 1 (2024): April 2024
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v10i1.4834

Abstract

Accurate image classification will yield valuable information to support decision-making. Support Vector Machine (SVM) is a widely used technique to achieve high classification accuracy. However, data outliers can reduce the SVM’s accuracy. To resolve this problem, the K-Means clustering method is used to eliminate the outliers by checking the proximity between data and clustering the data. Nevertheless, one of the challenges of using K-Means is the sensitivity of the initial centroid selection which is done randomly. Therefore, this study combines the use of K-Means, feature extraction with VGG-16 deep learning architecture, and feature selection using the Chi2 technique to get better classification accuracy. The combination of these methods is empirically proven to increase the accuracy of three image dataset about 20%. The results demonstrate that using these methods in conjunction can also reduce the amount of time needed for image classification. Nevertheless, label information is not taken into consideration in this study. Therefore, in the future, this research can still be developed by applying other standards and adding information labels in the feature selection process.
Denoising Ambulatory Electrocardiogram Signal Using Interval Dedependent Thresholds based Stationary Wavelet Transform Hermawan, Indra; Sevani, Nina; F. Abka, Achmad; Jatmiko, Wisnu
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2428

Abstract

Noise contamination in electrocardiogram (ECG) monitoring systems can lead to errors in analysis and diagnosis, resulting in a high false alarm rate (FAR). Various studies have been conducted to reduce or eliminate noise in ECG signals. However, some noise characteristics overlap with the frequency range of ECG signals, which occur randomly and are transient. This results in shape alteration and amplitude reduction in P and R waves. The author proposed a framework for eliminating noise in ECG signals using the stationary wavelet transform method and interval-dependent thresholds (IDT) based on the change point detection method to address these challenges. The proposed framework decomposes the input electrocardiogram (ECG) signal at a specific level using the Stationary Wavelet Transform method, resulting in detail and approximation coefficients. Interval detection focuses on the initial detailed coefficient, d1, chosen due to its significant content of noise coefficients, especially high-frequency noise. Subsequently, threshold values are computed for each interval. Hard and soft thresholding processes are then applied individually to each interval. Finally, reconstruction occurs using the inverse stationary wavelet transform method on the threshold coefficient outcomes. Two measurement matrices, root mean square error (RMSE) and percentage root mean squared difference (PRD), were used to measure the performance of the proposed framework. In addition, the proposed framework was compared to stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The test results showed that the proposed method outperforms DWT and SWT. The proposed framework obtained an average increase in RMSE scores of 18% and 45% compared to the SWT and DWT methods, respectively, and PRD values of 17% and 37% compared to the SWT and DWT methods, respectively. So, using IDT in the stationary wavelet transform method can improve the denoising performance. With the development of this new framework for denoising ECG signals, we hope it can become an alternative method for other researchers to utilize in denoising ECG signals.
Co-Authors Agus Setiawan Ali Djamhuri Benisius Chandra, Yosua Jaya Christina Amanda Savitri Christine Leonita Cuvianto, Lukas Endi Putro F. Abka, Achmad Foeng Hartono Fredicia Fredicia INDRA HERMAWAN Laksono Trisnantoro Maria Ariesta Marimin Marimin Octaviany, Jessica Persulessy, Grensya Bella Vega Pratama, Nova S Rita Wiryasaputra Sali, Richardo Kusuma saputra, fajar Saputri, Aprilia Eka Setiawan, Novianti Tadhan Emmanuel Wisnu Jatmiko Yan Gunawan Yudhi Windarto