PENERAPAN ALGORITMA DECISION TREE UNTUK MEMPREDIKSI TINGKAT KEMISKINAN PADA KELURAHAN BAKARAN BATU, KECAMATAN RANTAU SELATAN, KABUPATEN LABUHANBATU, PROVINSI SUMATERA UTARA

HABI SARONI, NPM 2209100051 (2026) PENERAPAN ALGORITMA DECISION TREE UNTUK MEMPREDIKSI TINGKAT KEMISKINAN PADA KELURAHAN BAKARAN BATU, KECAMATAN RANTAU SELATAN, KABUPATEN LABUHANBATU, PROVINSI SUMATERA UTARA. Skripsi thesis, Universitas Labuhanbatu.

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Abstract

Kemiskinan merupakan permasalahan sosial yang memerlukan identifikasi objektif agar penyaluran bantuan sosial lebih tepat sasaran. Penelitian ini bertujuan menerapkan algoritma Decision Tree untuk memprediksi tingkat kemiskinan masyarakat di Kelurahan Bakaran Batu serta mengetahui atribut yang paling berpengaruh dan performa model. Penelitian menggunakan pendekatan kuantitatif berbasis data mining dengan 100 data simulasi Kepala Keluarga. Atribut yang digunakan meliputi penghasilan, pekerjaan, pendidikan, kepemilikan rumah, dan jenis lantai, dengan klasifikasi Miskin dan Tidak Miskin. Pemodelan dilakukan menggunakan Orange Data Mining, sedangkan pengujian menggunakan 10-Fold Cross Validation dan Confusion Matrix. Hasil penelitian menunjukkan bahwa penghasilan menjadi atribut paling berpengaruh dan berperan sebagai root node. Model menghasilkan accuracy, precision, recall, F1-score, dan AUC sebesar 0,98, serta MCC sebesar 0,96. Hasil tersebut menunjukkan bahwa algoritma Decision Tree memiliki performa sangat baik dalam mengklasifikasikan tingkat kemiskinan. Namun, penggunaan data simulasi membuat model perlu diuji dengan data nyata untuk memastikan kemampuan generalisasinya. Kata Kunci: Data Mining, Decision Tree, Tingkat Kemiskinan, Klasifikasi, Orange Data Mining ====================================== Poverty is a social problem that requires objective identification to ensure that the distribution of social assistance is more accurately targeted. This study aims to apply the Decision Tree algorithm to predict the poverty level of the community in Bakaran Batu Village and to identify the most influential attributes and the performance of the model. This research employs a quantitative approach based on data mining using 100 simulated household head data records. The attributes used include income, occupation, education, home ownership, and floor type, with the classification consisting of Poor and Not Poor. The modeling process was conducted using Orange Data Mining, while model evaluation was performed using 10-Fold Cross-Validation and a Confusion Matrix. The results show that income is the most influential attribute and serves as the root node of the decision tree. The model achieved an accuracy, precision, recall, F1-score, and AUC of 0.98, with an MCC of 0.96. These results indicate that the Decision Tree algorithm demonstrates excellent performance in classifying poverty levels. However, the use of simulated data means that the model needs to be further tested using real-world data to ensure its generalization capability. Keywords: Data Mining, Decision Tree, Poverty Level, Classification, Orange Data Mining

Item Type:	Thesis (Skripsi)
Uncontrolled Keywords:	Data Mining, Decision Tree, Tingkat Kemiskinan, Klasifikasi, Orange Data Mining=================Data Mining, Decision Tree, Poverty Level, Classification, Orange Data Mining
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases
Divisions:	Fakultas Sains Dan Teknologi > Sistem Informasi
Depositing User:	Unnamed user with email repository@ulb.ac.id
Date Deposited:	04 Sep 2026 03:57
Last Modified:	04 Sep 2026 03:57
URI:	http://repository.ulb.ac.id/id/eprint/2752

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