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Ahmad Ilham
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Journal of Intelligent Computing and Health Informatics (JICHI)
Published by Universitas Muhammadiyah Semarang
ISSN : 27156923     EISSN : 27219186     DOI : https://doi.org/10.26714/jichi
Core Subject : Health, Science, Engineering,
Computer Science & IT Dentistry Electrical & Electronics Engineering Medicine & Pharmacology Public Health
Journal of Intelligent Computing & Health Informatics (JICHI) was printed in March 2020. JICHI is a scientific review journal publishing that focus on exchanging information relating to intelligent computing and health informatics applied in industry, hospitals, government, and universities. All articles should include a validation of the idea presented, e.g. through case studies, experiments, or systematic comparisons with other approaches already in practice. Two types of papers are accepted: (1) A short paper that discusses a single contribution to a specific new trend or a new idea, and; (2) A long paper that provides a survey of a specific research trend using a systematic literature review (SLR) method, as well as a traditional review method. Topics of interest include, but are not limited to: Intelligent Computing Include Machine Learning; Reinforcement Learning; Computer Vision; Image Processing; Scheduling and Optimization; Bio-inspired Algorithms; Business Intelligence; Chaos theory and intelligent control systems; Robotic Intelligent; Multimedia & Application; Web and mobile Intelligence and Big Data, etc.) Health Informatics Include Electronic health record; E-Health Information; Medical Image Processing & Techniques; Data Mining in Healthcare; Bioinformatics & Biostatistics; Mobile applications for patient care; Medical Image Processing & Techniques; Hospital information systems; Document handling systems; Electronic medical record systems; standardization, and systems integration; ICT in health promotion programmes e-health Guidelines and protocols; E-learning & education in healthcare; Telemedicine Software- Portals-Devices & Telehealth; Public health & consumer informatics; Data Mining & Knowledge Discovery in Medicine; ICT for Patient empowerment; ICT for Patient safety; Medical Databanks-Databases & Knowledge Bases; Healthcare Quality assurance; Nursing Informatics; Evaluation & Technology Assessment; Home-based eHealth; Health Management Issues; Health Research; Health Economics Issues; Statistical Method for Computer Medical Decision Support Systems; Medical Informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol 3, No 2 (2022): September" : 5 Documents clear
Comparison of Deep Neural Network Architectural Models for Predicting Tourist Visits to Bali during the Pandemic Period Purnama, Nyoman
Journal of Intelligent Computing & Health Informatics Vol 3, No 2 (2022): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i2.11168

Abstract

Tourism plays a critical role in any economy as it provides a source of income for communities. Bali, one of Indonesia's provinces, holds significant potential in the tourism industry, with a majority of its population employed in this sector. However, fluctuations in tourist visits can pose challenges when creating policies to address issues in the field. Therefore, forecasting is necessary to anticipate post-pandemic tourist arrival patterns to ensure a smooth tourism recovery process. Forecasting is a vital tool that assists in making sound decisions. In this study, we utilized three forecasting methods: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). We took a comparative approach, using these three deep neural network architectures to predict tourist visits to Bali during the pandemic. We tested the architectural models using datasets from Badan Pusat Statistik (BPS) and evaluated the model's performance using RMSE and MAE. The results showed that the LSTM model outperformed the CNN and GRU models, with an RMSE value of 0,329036 and MAE value of 0,285874. Based on the study, we can conclude that the LSTM model performed better and can predict tourist arrivals in Bali with reasonable accuracy
Can Genomics of Gut Microbiota in Stool Samples be Analysed by MERLIN? Kamaruddin, Mudyawati
Journal of Intelligent Computing & Health Informatics Vol 3, No 2 (2022): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i2.11289

Abstract

Metagenomics is important for studying microorganisms that live in microbial communities, particularly those inhabiting the human body. For instance, the gut microbiota is a community of microorganisms that reside in the human gut and interact with humans through secondary metabolites. These metabolites produced by the gut microbiota are extremely important and serve as precursors for various human needs, such as short chain fatty acids (SCFA). While there have been reports of functional secondary metabolites produced by different gut microbiota, none have been utilized on the Merlin platform. In this article, we will examine how the Merlin platform can analyze the gut microbiota community. Metabolic Models Reconstruction using Genome-Scale Information (MERLIN) is a bioinformatics tool that can analyze the functional microbial community as well as the taxonomy of these bacteria.
Islamophobia Sentiment Classification Using Support Vector Machine Lubis, Aidil Halim
Journal of Intelligent Computing & Health Informatics Vol 3, No 2 (2022): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i2.11179

Abstract

Sentiment analysis is the process of understanding and classifying words into several categories. It is also known as opinion mining, which involves exploring opinions and emotions from text data. Sentiments can be classified into positive, negative, and neutral categories. Islam is a religion that has been in existence for centuries. Its teachings aim to foster peace and surrender to its creator, namely Allah SWT. The constructivist view of Islam has given rise to Islamophobia, which is the result of a long-standing construct that presents a negative image of Islam. Currently, Islamophobia is a growing issue that generates diverse views, especially on social media platforms. The analysis was conducted using the SVM algorithm and a dataset comprising 1000 tweets sourced from Twitter. The algorithm achieved an accuracy rate of 99.99% after testing, indicating its suitability for sentiment analysis. The error rate generated using MSE was 0.010, while the RMSE was 0.099.
New Fuzzy ServQual Build with Three Types of Fuzzy Numbers Wulandari, Ratri; sulistijanti, wellie
Journal of Intelligent Computing & Health Informatics Vol 3, No 2 (2022): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i2.11637

Abstract

Every community in the world desires optimal service quality that meets their expectations. To achieve this, it is essential to measure the level of public satisfaction with the quality of services provided by service providers. Fuzzy servqual is a method used to determine the level of service quality through survey data. The process involves calculating the value of fuzzification, defuzzification, and the GAP value between perception and expectation. This paper introduces a new approach to processing data by using three types of fuzzy numbers: shoulder fuzzy, triangle fuzzy, and trapezoidal fuzzy. The use of three types of fuzzy numbers yields different GAP calculation results compared to using only a triangular fuzzy. The results of this research show that the use of fuzzy number improvement during the fuzzyfication step leads to better GAP results with greater accuracy.
Topic Modelling using Latent Dirichlet Allocation (LDA) to Investigate the Latent Topics of Mathematical Creative Thinking Research in Indonesia Maulidiya, Della
Journal of Intelligent Computing & Health Informatics Vol 3, No 2 (2022): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i2.11428

Abstract

In mathematics education, there is increasing interest in Mathematical Creative Thinking (MCT), and numerous scientific documents on this topic have been published in Indonesia. The availability of publication databases has made it easier to access these documents and collect large amounts of data related to MCT. This data has the potential to uncover latent topics within MCT research articles published in Indonesian journals. This study analyzed a dataset of 102 articles obtained from Garuda (Digital Reference Garda) published between 2010 and 2022 in six proceedings and 49 journals. The study applied text processing techniques and used topic modeling with Latent Dirichlet Allocation (LDA) and variational expectation maximization algorithm (VEM) to produce 23 topics. Each topic consisted of general and special words from the beta probability value. The study found 30 unique words from topic modeling, including learning, abilities, problems, skills, mathematics, tests, levels, answers, approaches, assessments, basics, classes, developing, values, ideas, instruments, materials, mathematics, moderate, models, motivation, open-ended, processes, questions, reasons, solving, styles, subjects, teaching, and worksheets. The study also used LDA to classify documents into discovered themes and found that the five MCT research focuses were learning approaches, student competencies, teacher competencies, assessments, and learning resources. The study's findings revealed a research gap, specifically, the need for more MCT studies that concentrate on enhancing teacher competency.

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