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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 4, No 1 (2023): March" : 5 Documents clear
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.
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.
The Role of EEG Signals: SVM Classification of Cognitive Load as a Support for UX Evaluation Iksan, Ennu Intan; Mardhia, Murein Miksa
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.v4i1.11198

Abstract

Cognitive load is the mental effort that needs to be applied to working memory to process information received over a period of time. Cognitive load can be viewed as the level of mental energy required to process a given amount of information. In user experience design, cognitive load is considered as the mental processing power required to use a product. If the amount of information processed exceeds the user's ability to process it, the overall performance will be disrupted. An EEG device is needed that is used to record electrical activity that occurs in the brain by channeling brain electrical waves to cables and modulators that are sensitive to electrical waves. The object of this research is the EEG Beta signal with the attention wave type from UX testing activities on students aged 21-24 years with a frequency level of 13-30 Hz. The EEG tool records the activity of the respondent's wave signal by collecting data on the activity of working on a questionnaire about evaluating the WhatsApp application using the Google Form application. The classification of cognitive load studied is unencumbered and burdened. Unencumbered represents the ease that is felt when interacting with the application, while burdened represents the difficulty or confusion that is felt when interacting with the application. Testing is done with the Confusion matrix. The best accuracy results among the kernel types in the SVM method are linear kernel types with an accuracy result of 89% consisting of 1 data that is categorized as an unencumbered label and 8 data labels that are loaded

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