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Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Published by Universitas Muhammadiyah Riau
ISSN : 20893353     EISSN : 28089162     DOI : https://doi.org/10.37859/jf.v11i3.2781
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) is expected to be a media of scientific study of research result, a thought and a study criticial analysis to a System engineering research, Informatics Engineering, Information Technology, Computer Engineering, Informatics Management, and Information System. We accept research papers which focused to these following topics: System Engineering Expert System Decision Support System Data Mining Artificial Intelligent Computer engineering Digital Image Processing Computer Graphic Computer Vision Genetic Algorithm Machine Learning Deep Learning Information System Design Business Intelligence and Knowledge Management Database System Big Data IOT Enterprise Computing ICT and Islam Technology Management and other relevant topics to field of Information Technology
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 423 Documents
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Analisis Sentimen Terhadap Pinjaman Online Kredivo Menggunakan Algoritma Naïve Bayes dan SVM Susanti, Sari; Nuryawan, Azril Tazidan Octa
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11224

Abstract

The growth of digital lending services in Indonesia has contributed to a substantial increase in user reviews and complaints distributed across various online platforms, with Google Play Store being one of the most prominent. This condition poses a considerable challenge in the automatic detection of sentiment polarity, in line with the continuously growing volume of text data generated. This study aims to analyze user sentiment toward the Kredivo online lending application. The research methodology follows the SEMMA framework, which consists of five stages: Sample, Explore, Modify, Model, and Assess. Two classification algorithms were employed, namely Naïve Bayes and SVM, under two data splitting configurations of 80:20 and 70:30 for training and testing, respectively. Experimental results indicate that under the 80:20 configuration, Naïve Bayes achieved an accuracy of 92.01%, while SVM reached 97.05%. Under the 70:30 configuration, Naïve Bayes recorded an accuracy of 91.48% and SVM reached 96.76%. Evaluation using accuracy, precision, recall, and F1-score metrics confirmed that SVM consistently produced better classification performance compared to Naïve Bayes in categorizing user sentiment of the Kredivo online lending application. Based on the research results, it can be concluded that positive sentiment is more dominant than negative sentiment, with 6,120 reviews classified as positive and 2,012 reviews as negative.
Implementasi UI/UX Pada Perancangan Sistem Informasi Konsultasi Klinik Menggunakan Metode Design Thinking Lufiani, Tias; Suratno, Tri; Fadhila Putri, Mutia
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11296

Abstract

Access to healthcare services at Klinik Pratama Dokter Yanti is still constrained by distance, time, and long waiting queues. In addition, the clinic has not yet implemented telemedicine services, highlighting the need for an information system to improve accessibility and efficiency. This study aims to design a user interface and user experience (UI/UX) for a clinical information system that supports telemedicine features. The approach used is Design Thinking, with its five key stages: empathize, define, ideate, prototype, and test, as it emphasizes problem-solving based on user needs. Data were collected through interviews and observations involving patients or individuals who have interacted with the clinic to identify user needs and issues. The outcome is a web-based UI/UX prototype that includes an online consultation feature. Usability testing was conducted using the Maze platform with six respondents, resulting in Maze Usability Scores of 96, 90, and 94, indicating a high level of usability. In conclusion, the Design Thinking approach produces a system that is user-friendly, efficient, and aligns with user needs. Therefore, the proposed system is expected to address service accessibility challenges and enhance the quality of healthcare services at the clinic.
Transfer Learning dengan CLAHE dan Sharpening filter untuk Deteksi Pneumonia pada Citra X-Ray Karan; Firdaus, Rahmad; Mukhtar, Harun
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11374

Abstract

Pneumonia is a respiratory infection that remains a leading cause of death, especially in children, requiring an automatic detection system based on chest X-ray images. The main challenge in automatic classification is low image quality, such as suboptimal contrast and unclear lung details, which can affect the feature extraction process by deep learning models. To address these issues, this study applies Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image contrast and a sharpening filter to clarify lung edge details. The study aims to analyze the effect of preprocessing on classification performance using EfficientNet-B0 based on Transfer Learning with a full fine-tuning strategy. The dataset used is Chest X-Ray Pneumonia from Kaggle with 5,856 images consisting of Normal and Pneumonia classes. Experiments compare the Baseline model, CLAHE, and a combination of CLAHE and sharpening in three data sharing scenarios. Evaluation is carried out using accuracy, precision, recall, and image quality metrics PSNR, SSIM, and CII. The results of the study showed that the combination of CLAHE and sharpening in the 80:10:10 scenario produced the best performance with an accuracy of 97.61%, precision of 0.97, recall of 0.99, and an increase in image quality based on a CII value of 1.157.

Page 43 of 43 | Total Record : 423

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