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All Journal Informatics Management, Engineering and Information System Journal Majalah Bisnis & IPTEK
Rizal Rachman
Universitas Adhirajasa Reswara Sanjaya, Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science Social Sciences Other

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Web-Based Expert System For Respiratory Disorders In Human Using Backward Chaining Method Rizal Rachman; Sandra Armayanti; Toni Kusnandar
Informatics Management, Engineering and Information System Journal Vol. 1 No. 1 (2023): Infotmatics Management, Engineering, and Information System Journal
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/imeisj.v1i1.214

Abstract

Respiratory disorders are various types of diseases or disorders that inhibit lung function. This disease can affect the ability to breathe. Respiratory disorders can be transmitted anytime and anywhere. Respiratory disorders are more common than other organ system disorders or infections. They start from the common cold with relatively mild to severe pneumonia, cough, fever, sore throat, and shortness of breath. Inadequate medical equipment also triggers the difficulty of treating and diagnosing a disorder. This study aims to help the community diagnose respiratory disorders and determine how to deal with them effectively and efficiently. This website-based expert system for diagnosing human respiratory disorders uses the Backward Chaining inference method. The results of this study will produce a website for diagnosing respiratory disorders in humans.
Analysis of Public Service Satisfaction using Artificial Intelligence K-Means Cluster Fadli Emsa Zamani; Toni Kusnandar; Fikri Emsa Silmi; Rizal Rachman
Majalah Bisnis & IPTEK Vol. 16 No. 1 (2023): Majalah Bisnis & IPTEK
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat (P3M) STIE Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55208/fmwvf958

Abstract

Public service refers to the provision of goods, services, and support by the government to meet the community's desires and needs. In order to assess the efficacy of this service, a metric for gauging service quality, referred to as the Community Satisfaction Index, has been devised. This data offers insights into the level of satisfaction within the community regarding a particular service. This study utilizes the K-Means Cluster algorithm, a form of unsupervised machine learning, to categorize data based on similarities and dissimilarities into distinct clusters. The objective of this study is to gain insight and conduct an analysis of the level of satisfaction within the community regarding the information services offered by the Communication and Information Department of West Java Province. Furthermore, the objective of this study is to ascertain the categorization of the public satisfaction index by using the K-Means Cluster technique, employing an artificial intelligence methodology. This approach will enable the identification of the public satisfaction index as well as the identification of specific indicators that necessitate enhancement. The initial step in examining the public satisfaction index through the utilization of Artificial Intelligence involves the application of the K-Means Cluster algorithm, which will generate multiple clusters based on their shared characteristics. The values utilized by each group consist of the integers 1, 2, 3, and 4. Subsequently, an assessment is conducted on each formed group in order to ascertain the most favorable outcomes. The study yielded clusters that were deemed optimal, with smaller values indicating areas in which the services could be enhanced. The present study aims to investigate the impact of Artificial Intelligence (AI) on public service quality, as measured by the Community Satisfaction Index (CSI). Specifically, we employ the K-Means clustering algorithm to analyze the data collected from a representative sample of community members. By utilizing AI techniques, we seek to gain insights into.
Co-Authors Fadli Emsa Zamani Fikri Emsa Silmi Sandra Armayanti Toni Kusnandar