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Jurnal SINTA 2

Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial

Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 7 No. 6 (2020) · pp. 1231–1236

DOI
10.25126/jtiik.0813087
ISSN
2528-6579, 2355-7699
Published
2020-12-02
Publisher
Fakultas Ilmu Komputer, Universitas Brawijaya
Language
id

Abstract

Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. Gradient Boosted Trees dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. Gradient Boosted Trees menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan Gradient Boosted Trees untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, Gradient Boosted Trees, K-NN-Gradient Boosted Treesdilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau Gradient Boosted Trees yaitu 98.17%.AbstractPoverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.

Collaboration network of the authors

20 authors · 37 co-authorship links · 8 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Indonesian Institute of Sciences
  • Telkom University
  • Universitas Bina Sarana Informatika
  • Universitas Nusa Mandiri
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Windu GataUniversitas Nusa Mandiri1
Basri BasriAffiliation unknown1
Iwan Muhammad ErwinIndonesian Institute of Sciences1
Esa PrakarsaIndonesian Institute of Sciences1
Bambang SugiartoIndonesian Institute of Sciences1
Eko Arif RiyantoAffiliation unknown1
Tri JuninisviantyAffiliation unknown1
Doddy Ferdian NasutionAffiliation unknown1
Ridwansyah RidwansyahUniversitas Bina Sarana Informatika1
Jajang Jaya PurnamaUniversitas Bina Sarana Informatika1
Hendri Mahmud NawawiUniversitas Bina Sarana Informatika1
Susy RosyidaAffiliation unknown1
Ema RachmawatiTelkom University1
Ahmad Habib FitriansyahTelkom University1
Arya PangestuTelkom University1
Bedy PurnamaTelkom University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://jtiik.ub.ac.id/index.php/jtiik/en/oai, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="id">Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial</dc:title>
	<dc:creator xml:lang="id">Firasari, Elly</dc:creator>
	<dc:creator xml:lang="id">Khultsum, Umi</dc:creator>
	<dc:creator xml:lang="id">Winnarto, Monikka Nur</dc:creator>
	<dc:creator xml:lang="id">Risnandar, Risnandar</dc:creator>
	<dc:description xml:lang="id">Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan  program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. Gradient Boosted Trees dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. Gradient Boosted Trees menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan Gradient Boosted Trees untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, Gradient Boosted Trees, K-NN-Gradient Boosted Treesdilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau Gradient Boosted Trees yaitu 98.17%.AbstractPoverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.</dc:description>
	<dc:publisher xml:lang="en">Fakultas Ilmu Komputer, Universitas Brawijaya</dc:publisher>
	<dc:date>2020-12-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://jtiik.ub.ac.id/index.php/jtiik/article/view/3087</dc:identifier>
	<dc:identifier>10.25126/jtiik.0813087</dc:identifier>
	<dc:source xml:lang="id">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol 7 No 6: Desember 2020; 1231-1236</dc:source>
	<dc:source xml:lang="en">Jurnal Teknologi Informasi dan Ilmu Komputer; Vol. 7 No. 6: Desember 2020; 1231-1236</dc:source>
	<dc:source>2528-6579</dc:source>
	<dc:source>2355-7699</dc:source>
	<dc:source>10.25126/jtiik.202076</dc:source>
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	<dc:rights xml:lang="en">Copyright (c) 2020 Jurnal Teknologi Informasi dan Ilmu Komputer</dc:rights>
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