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Journal : Jurnal Media Computer Science

Fuzzy Tsukamoto Method in Service Satisfaction Assessment at the Village Office Tanjung Aur 1 Pransiska, Yance; Yulianti, Liza; Jumadi, Juju
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8150

Abstract

IKM analysis must always be carried out periodically so that it can be seen that public satisfaction with the services provided, especially at the Tanjung Aur 1 Village Office. But in reality, so far the Tanjung Aur 1 Village Office has not had a benchmark that is used as a forum for knowing public satisfaction with these services.The application of the Fuzzy Tsukamoto Method in assessing service satisfaction at the Tanjung Aur 1 Village Office can assist in managing the results of the Community Satisfaction assessment of Services at the Tanjung Aur 1 Village Office, and can provide information on the results of measuring the level of community satisfaction with services at the Tanjung Aur 1 Village Office as an evaluation material to improve service quality. The application of the Fuzzy Tsukamoto Method in assessing service satisfaction at the Tanjung Aur 1 Village Office is built web-based using the PHP programming language and MySQL database which can be accessed online via the link https://kepuasandesatanjungaur1.site/, where there are 2 interface access rights in the application, namely Administrators and Villagers of Tanjung Aur 1. Based on the 2024 service satisfaction assessment data with 9 respondents who have been inputted into the web-based application, the results of the service satisfaction assessment that have been analysed through the Fuzzy Tsukamoto Method are Very Satisfied 44.44%, Satisfied 22.22%, Quite Satisfied 33.33%, Dissatisfied 0% and Very Dissatisfied 0%. Based on the results of the demo programme that has been carried out, 9 respondents have successfully provided service satisfaction assessments on the application online and also the operator can see the output of the satisfaction assessment results which will be given to the Village Head for evaluation.
Expert System For Diagnosing Eardrum (Tympanic Membrane) Diseases Using Certainty Factor Method Jayanda, Prengky; Yulianti, Liza; Suryana, Eko
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8413

Abstract

Dr. M. Yunus Regional General Hospital Bengkulu is one of the hospitals in Bengkulu City that provides health service facilities. The current problem at the M. Yunus Regional General Hospital is the limited time available at the ENT Polyclinic and the delay in examining eardrum disease. So patients must go directly to the ENT Specialist Doctor who treats eardrum disease. The limited time of the Specialist Doctor who treats eardrum disease at the Hospital is also one of the causes of the delay in handling patients with eardrum disease. The expert system for diagnosing eardrum disease (tympanic membrane) using the certainty factor method at the M Yunus Regional General Hospital, Bengkulu City can be used as a container to help determine the diagnosis of eardrum disease based on the symptoms felt, and can make it easier for users to consult online via the link https://spakarmembrantimpanicf.site/. The expert system for diagnosing eardrum disease (tympanic membrane) using the certainty factor method at the M Yunus Regional General Hospital, Bengkulu City was created using the PHP programming language and MySQL database, which can be accessed online. Based on the black box method testing that has been carried out, it can be concluded that the functionality of the application has run well and this expert system can provide consultation results for the diagnosis of eardrum disease (tympanic membrane) through the Certainty Factor method stages based on the symptoms selected by the user during the consultation. Based on the alpha testing method testing that has been carried out, it can be concluded that this expert system is quite helpful for users in consultations and provides early detection information for the diagnosis of eardrum disease from the symptoms felt.
Application Of Data Mining In Grouping Data On The Need For Social Welfare Services (Ppks) At The Dharma Guna Center In Bengkulu Wahyuni, Mera; Yulianti, Liza; Alinse, Rizka Tri
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8814

Abstract

Sentra Dharma Guna Bengkulu is an institution under the auspices of the Ministry of Social Affairs of the Republic of Indonesia that provides social rehabilitation services for people with disabilities, including therapy (physical therapy and occupational therapy) and training. At Sentra Dharma Guna Bengkulu, data collection is carried out on PPKS (Social Welfare Service Recipients) every month to determine the development of the PPKS based on 5 (five) assessment aspects, namely physical aspects, spiritual aspects, psychological aspects, social aspects, and vocational aspects. Every month the development of PPKS Mentally Disabled (PDM) is assessed against 5 assessment aspects to determine whether PPKS is in the severe, moderate or mild group. The application of data mining in grouping data on Social Welfare Service Recipients (PPKS) at the Dharma Guna Bengkulu Center can help collect data and assess the development of PPKS, especially People with Mental Disabilities (PDM), can help analyze and group PPKS data, especially People with Mental Disabilities (PDM), and can provide information on the results of grouping PPKS data, especially People with Mental Disabilities (PDM) every month. From the test data used, namely PPKS data for People with Mental Disabilities (PDM) in October 2024 as many as 49 PPKS, the results of data grouping were obtained using the K-Means Clustering Method which has been divided into 3 groups. The number of Cluster C1 data (Severe Group) consists of 9 PPKS data, Cluster C2 (Moderate Group) consists of 26 PPKS data, and Cluster C3 (Light Group) consists of 14 PPKS data.