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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 57 Documents
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Website-Based System Prototype Development for Classify Student Characteristics Kencana, Lisdi Inu; Rafrastara, Fauzi Adi; Paramita, Cinantya
Journal of Intelligent Computing & Health Informatics Vol 3, No 1 (2022): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i1.10364

Abstract

Student characteristics are important attributes in understanding their academic abilities and ways of thinking. In the teaching and learning process, the right learning strategy is very important to implement. According to the Hippocrates-Galenus Typology, personality types are categorized into four categories, including sanguinis, choleric, melancholics, and phlegmatics. The Classification of student characteristics using experience and intuition methods often gives inaccurate results and takes a long time to understand their behavior and way of thinking. In our research, we developed a prototype cognitive system website to classify student characteristics at SD Wijaya Kusuma 02 Semarang. There are several stages of the proposed method, including, communication, rapid planning, rapid design modeling, prototype construction, and delivery & feedback deployment. The C4.5 algorithm is applied as the modeling of student characteristics classification. The results showed a fairly good accuracy of 90.08%. It can be concluded that the C4.5 algorithm can classify student characteristics well.
Implementation of Named Entity Recognition with a Developing Question Answering System: A Case Study in the Merapi Volcano Museum Khusna, Arfiani Nur; Putri, Okhy Kharisma; Saputra, Dimas Chaerul Ekty
Journal of Intelligent Computing & Health Informatics Vol 3, No 1 (2022): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v3i1.9205

Abstract

Merapi volcano museum is a place to get some information about active mountain activities, the general public can access the website page at mgm.slemankab.go.id. Indeed, visitors are given easy access, but the information provided by the website is not fully complete, causing visitors to feel dissatisfied. Based on the results of a questionnaire from 40 respondents, it was found that 50.55% of website visitors did not get the information they wanted. Therefore, in this research, we built a Question Answering System (QAS) using the Named Entity Recognition (NER) method that has been implemented into Telegram. To improve the performance of the QAS system, testing and analysis has been carried out with a "white box" approach. The results show that the QAS system has 3 regions and 3 independent paths, with path 1 being 1-2-3-4-11, path 2 being 1-2-3-4-5-6-7-8-11, and path 3 being 1-2-3-4-5-6-7-9-10-11. Based on the results of this study, all three paths can produce the correct answer.
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.
A Linear Regression Model for Deploying a Cognitive Web for an Inventory Prediction System Fatman, Yenni; Majapahit, Sali Alas; Ramadhan, Muammar
Journal of Intelligent Computing & Health Informatics Vol 4, No 1 (2023): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v4i1.11258

Abstract

Inventory management plays a crucial role in the sales system as it indirectly impacts customer satisfaction. Inaccurate determination of the quantity of goods to be purchased often leads to unstable stock circulation in the warehouse. The numerous factors influencing procurement decisions pose challenges for managers. Several initiatives have been undertaken to maintain optimal stock levels to ensure availability when required. In this study, we developed a linear regression model to estimate the inventory for the upcoming one-month period. The selection of linear regression was motivated by its ability to forecast future trends. The research involved creating a web application that utilized sales data from the previous six months, focusing on examples of products sold in a store. The objective of the application is to assist store owners in making informed decisions regarding stock replenishment for the next period. By doing so, they can fulfill customer demands without excessive inventory accumulation, while considering the limitations of storage capacity. 
Part of Incomplete Medical Record Documents: Literature Review Indrayadi, Indrayadi; Asih, Hastin Atas
Journal of Intelligent Computing & Health Informatics Vol 4, No 1 (2023): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v4i1.11443

Abstract

The study aimed to examine the extent of incompleteness in various sections of medical histories within Indonesian hospitals by conducting a literature review based on inclusion and exclusion criteria. The data were analyzed, and the findings were presented. The databases Google Scholar, Garuda, Emerald, and DOAJ were searched using keywords such as 'medical records' and 'hospitals' between 2018 and 2022, yielding 461 papers. Fourteen papers were selected in the final stage. The most frequently incomplete components of medical record documents include essential reports, authentication, data entry, documentation completion, and record-keeping. To prevent incomplete medical records, comprehensive interventions that provide adequate support and training to all staff members involved in completing patient medical records are necessary.
Mapping Religious Harmony in the Special Capital Region Jakarta using K-Means Algorithm Istiawan, Deden; Sulistijanti, Wellie; Santoso, Arif Gunawan; Ustyannie, Windyaning
Journal of Intelligent Computing & Health Informatics Vol 4, No 1 (2023): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v4i1.11715

Abstract

D.K.I Jakarta is often called the window of Indonesia. As one of the largest cities in Indonesia, D.K.I Jakarta has various kinds of complex social problems. This research tries to identify and explore conditions of religious harmony in DKI Jakarta. In previous studies of religious harmony, the use of the index in assessing religious harmony could only describe the condition of religious harmony in general without indicating which factor was in measuring the level of religious harmony. This Research uses a clustering approach to analyse religious harmony in DKI Jakarta. The study found that cluster 0 has major problems that affect religious harmony compared to other clusters. Therefore, local government policies related to increasing religious harmony can be focused more on cluster 0, especially on variables that are shown to be low, namely empathy, non-violence, national commitment, and adaptability to local culture.
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.
Non Contact Temperature Monitoring System to Optimize Health Working Environment Based on IoT Ardiantoro, Luki Ardi; Muslimin, Moh; Rosita, Yesy Diah
Journal of Intelligent Computing & Health Informatics Vol 4, No 1 (2023): March
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v4i1.11163

Abstract

During the ongoing COVID-19 pandemic, monitoring body temperature is a crucial concern for the community, and it serves as an essential step in understanding the health condition of an individual's body. The workers in environments that involve physical contact and are prone to dirt are at a higher risk of contracting COVID-19. In this study, we created a body temperature detector to optimize the working environment in the waste processing area of Mojokerto Regency. Several modules and devices have been utilized, including the Arduino Uno R3 module, an ultrasonic sensor for object detection and temperature sensing, MLX90614 for detecting body temperature, and a buzzer system. The SDLC (Build & Fix design) method was employed to simulate the actual conditions at the Randegan Landfill in Mojokerto. Consequently, the designed IoT device can automatically send information and provide follow-up through a network of various functions. It can be concluded that the developed body temperature detection device has the potential to minimize the risk of contracting COVID-19 and enhance healthier and more productive working conditions.

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