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All Journal Pelita : Jurnal Penelitian dan Karya Ilmiah
Rizaldy, Farhan
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KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5159

Abstract

The K-Means method is one of the Data Mining methods that is widely used in clustering research. Based on the results of research that has been conducted to build a process model for Poverty Data Clustering Analysis Using the K-Means Method Approach in Teluk Agung Village, Indramayu District, Indramayu Regency, can use RapidMiner tools by creating operators and processing parameters used for clustering the P3KE class category. The operators used in this study are Read Excel, Set Role, Select Attributes, Replace Missing Values, Nominal to Numerical, Multiply, Clustering (K-Means) and Performance operators. The operators used are 8 operators by applying the stages of Knowledge Discovery in Database (KDD). This research will apply the Davies Bouldin Index (DBI) as a way of optimising the number of clusters to group data, from the best cluster value experiment, the closest to 0 is K9 with a DBI value of -2.257, from this we can conclude that approximately 43 items from clusters 2 - 10 are included in the P3KE category, and other than the 43 items can be interpreted as still not included in the P3KE category.
Jurnal Klasifikasi KLASIFIKASI KELAYAKAN PENERIMA BANTUAN SEMBAKO MENGGUNAKAN METODE DECISION TREE Rizaldy, Farhan; Suprapti, Tati; Dwilestari, Gifthera
PELITA JURNAL PENELITIAN DAN KARYA ILMIAH Vol 25 No 2 (2025): Pelita : Jurnal Penelitian dan Karya Ilmiah [Juli - Desember]
Publisher : UNIVERSITAS ISLAM SYEKH - YUSUF TANGERANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/pelita.v25i2.5162

Abstract

Poverty is one of the fundamental problems that is of concern to governments in all countries.  An important aspect to support poverty alleviation strategies is the availability of accurate and targeted poverty data. Staple food is the nine basic needs of Indonesian people, including food or drinks used in daily life. On this basis, the government often organizes basic food assistance programs for those in need. classification is one of the most commonly used prediction techniques to predict new labels or categories based on experience gained from known data. The main purpose of classification is to understand patterns or relationships between input and output variables, so that you can take appropriate decisions or actions based on the available information. Based on the results of the analysis and implementation of the Decision Tree Algorithm for classification of eligibility for basic food aid recipients in the Teluk Agung Village area, Indramayu District, Indramayu Regency, it can be concluded that the model developed has a very high level of accuracy, namely 94.83%. This model has proven effective in classifying various categories that are worthy of receiving assistance, starting from class 1, 2, 3 and not worthy of receiving assistance. The factors used in this model, such as monthly income, have been processed well through stages in the Knowledge Discovery in Databases (KDD) framework, resulting in a reliable classification. With high accuracy and performance, it is hoped that this model can be implemented practically to support decision making in mitigating the risk of non-delivery of basic food aid in the Teluk Agung Village area, Indramayu District, Indramayu Regency.
Co-Authors Gifthera Dwilestari Tati Suprapti