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All Journal Jurnal Ilmiah Teknologi dan Rekayasa CCIT (Creative Communication and Innovative Technology) Journal ICIT (Innovative Creative and Information Technology) Journal Journal Sensi: Strategic of Education in Information System CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) MAVIB Journal : Jurnal Multimedia Audio Visual and Broadcasting
Wiranti Sri Utami
Universitas Cendekia Abditama
Arts Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Other

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Journal : CCIT (Creative Communication and Innovative Technology) Journal

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Application of Data Mining Using the K-Medoids Algorithm for Poverty Index Clustering Muhammad Faisal; Wiranti Sri Utami
CCIT Journal Vol 15 No 2 (2022): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (607.466 KB) | DOI: 10.33050/ccit.v15i2.2311

Abstract

Poverty index is a term for measuring poverty, this is done by a government agency or commonly referred to as the Central Statistics Agency (BPS). The poverty index or poverty rate is the percentage of the population in a province who is below the poverty line, which is the minimum in obtaining an adequate standard of living. In the government's efforts to reduce the level of poverty in a province, the government often provides special assistance programs for people belonging to the poverty line. Based on the explanations that have been discussed, a conclusion can be drawn. This research can be done using the Data Mining technique to group the total Poverty Index by Province in Indonesia using the K-Medoids Algorithm, then by determining the Clusters randomly. The results of this study are expected to assist the government in providing assistance to the affected population below the poverty line.
Comparison of the K-Means Method with the K-Medoids Method for Recipient Infant Clustering Measles Immunization Muhammad Faisal; Wiranti Sri Utami; Vera Nurlia; Nila Pratiwi
CCIT (Creative Communication and Innovative Technology) Journal Vol 17 No 1 (2024): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v17i1.2775

Abstract

Measles or meales is a disease that can be transmitted, if someone who is affected by measles is in the same room with other people who have never been exposed to or received the measles vaccine, it is estimated that they will be infected with measles. The measles vaccine is a very effective vaccine for preventing measles. This type of vaccine is included in the routine immunization program recommended by the Ministry of Health of the Republic of Indonesia. So far, there are two types of measles vaccine, namely the MR vaccine and the MMR vaccine. The MR vaccine is used to prevent measles and rubella, while the MMR vaccine prevents measles, rubella and mumps. The target of vaccine administration can be observed by applying the data mining method, while the data mining method that can be used in this study is by using K-Means and K-Medoids. The purpose of this study was to compare the K-Means and K-Medoids methods in analyzing the achievement of vaccine administration to infants aged 9 months to 18 months. The results of the study obtained optimal k values of 21 provinces with the highest cluster of 3 using the K-Means algorithm, while the optimal K values of 10 provinces with the highest cluster of 3 were obtained using the K-Medoids algorithm.
Co-Authors Ahmad Doni Saputra Hilwah Mauludiah Muhamad Misbach Razabi Muhammad Faisal Muhammad Faisal Muhammad Faisal Muhammad Faisal Nila Pratiwi Nina Rahayu Ruli Supriati, Ruli Sarah Parmica Suharmanto . Vera Nurlia Wahyu Aji Putra Prakoso