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IMPLEMENTATION OF LONG SHORT-TERM MEMORY ALGORITHM IN THE CLASSIFICATION SYSTEM OF POTENTIAL DROP OUT STUDENTS

IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY PADA SISTEM KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT

AnoaTIK: Jurnal Teknologi Informasi dan Komputer · Vol. 3 No. 1 (2025) · pp. 50–57

DOI
10.33772/anoatik.v3i1.109
ISSN
2987-7652
Published
2025-06-28
Publisher
Program Studi Ilmu Komputer FMIPA-UHO
Language
ind
License
creativecommons.org/licenses/by-sa/4.0

Abstract

English

This research aims to produce a classification system for students who have the potential to drop out. This classification system is expected to help identify students who have the potential to drop out early on in prevention efforts. This research uses academic data in the form of Semester Grade Point Average (IPS) 1-7, Cumulative Grade Point Average Semester 7 (IPKS7), and Cumulative SKS 7, as well as non-academic data including Study Program and Entry Path as classification parameters. The method used is the Long Short-Term Memory (LSTM) algorithm with system development using the CRISP-DM approach. System testing is done using black box testing method and performance evaluation using confusion matrix. The results showed that the classification system developed achieved an accuracy rate of 93% based on confusion matrix evaluation, and all system functionality runs as expected based on the results of black box testing.

Bahasa Indonesia

Penelitian ini bertujuan untuk menghasilkan sebuah sistem klasifikasi mahasiswa yang berpotensi drop out. Sistem klasifikasi ini diharapkan dapat membantu mengidentifikasi mahasiswa yang berpotensi drop out sejak dini dalam upaya pencegahan. Penelitian ini menggunakan data akademik berupa nilai Indeks Prestasi Semester (IPS) 1-7, Indeks Prestasi Kumulatif Semester 7 (IPKS7), dan SKS7, serta data non akademik yang meliputi Program Studi dan Jalur Masuk sebagai parameter klasifikasi. Metode yang digunakan adalah algoritma Long Short-Term Memory (LSTM) dengan pengembangan sistem menggunakan pendekatan CRISP-DM. Pengujian sistem dilakukan menggunakan metode black box testing dan evaluasi performa menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa sistem klasifikasi yang dikembangkan mencapai tingkat akurasi 93% berdasarkan evaluasi confusion matrix, dan seluruh fungsionalitas sistem berjalan sesuai yang diharapkan berdasarkan hasil black box testing.

Collaboration network of the authors

11 authors · 21 co-authorship links

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

  • Universitas Halu Oleo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Pasrun AdamAffiliation unknown2
JejenAffiliation unknown1
Muhammad Riansyah TohambaAffiliation unknown1
Wali Aya RumbiaAffiliation unknown1
Rosnawintang RosnawintangAffiliation unknown1
Irma YahyaUniversitas Halu Oleo1
Arman ArmanUniversitas Halu Oleo1
Jufra JufraAffiliation unknown1

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://anoatik.uho.ac.id/index.php/atik/oai, first harvested 26 Okt 2026, last seen 1 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">IMPLEMENTATION OF LONG SHORT-TERM MEMORY ALGORITHM IN  THE CLASSIFICATION SYSTEM OF POTENTIAL DROP OUT STUDENTS  </dc:title>
	<dc:title xml:lang="id-ID">IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY PADA SISTEM KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT </dc:title>
	<dc:creator>Mutiara, Niken</dc:creator>
	<dc:creator>Saidi, La Ode</dc:creator>
	<dc:creator>Wijaya Rauf, Budi</dc:creator>
	<dc:description xml:lang="en-US">This research aims to produce a classification system for students who have the potential to drop out. This classification system is expected to help identify students who have the potential to drop out early on in prevention efforts. This research uses academic data in the form of Semester Grade Point Average (IPS) 1-7, Cumulative Grade Point Average Semester 7 (IPKS7), and Cumulative SKS 7, as well as non-academic data including Study Program and Entry Path as classification parameters. The method used is the Long Short-Term Memory (LSTM) algorithm with system development using the CRISP-DM approach. System testing is done using black box testing method and performance evaluation using confusion matrix. The results showed that the classification system developed achieved an accuracy rate of 93% based on confusion matrix evaluation, and all system functionality runs as expected based on the results of black box testing.</dc:description>
	<dc:description xml:lang="id-ID">Penelitian ini bertujuan untuk menghasilkan sebuah sistem klasifikasi mahasiswa yang berpotensi drop out. Sistem klasifikasi ini diharapkan dapat membantu mengidentifikasi mahasiswa yang berpotensi drop out sejak dini dalam upaya pencegahan. Penelitian ini menggunakan data akademik berupa nilai Indeks Prestasi Semester (IPS) 1-7, Indeks Prestasi Kumulatif Semester 7 (IPKS7), dan SKS7, serta data non akademik yang meliputi Program Studi dan Jalur Masuk sebagai parameter klasifikasi. Metode yang digunakan adalah algoritma Long Short-Term Memory (LSTM) dengan pengembangan sistem menggunakan pendekatan CRISP-DM. Pengujian sistem dilakukan menggunakan metode black box testing dan evaluasi performa menggunakan confusion matrix. Hasil penelitian menunjukkan bahwa sistem klasifikasi yang dikembangkan mencapai tingkat akurasi 93% berdasarkan evaluasi confusion matrix, dan seluruh fungsionalitas sistem berjalan sesuai yang diharapkan berdasarkan hasil black box testing.</dc:description>
	<dc:publisher xml:lang="id-ID">Program Studi Ilmu Komputer FMIPA-UHO</dc:publisher>
	<dc:date>2025-06-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://anoatik.uho.ac.id/index.php/atik/article/view/109</dc:identifier>
	<dc:identifier>10.33772/anoatik.v3i1.109</dc:identifier>
	<dc:source xml:lang="en-US">AnoaTIK: Jurnal Teknologi Informasi &amp; Komputer; Vol. 3 No. 1 (2025): Juni 2025; 50-57</dc:source>
	<dc:source xml:lang="id-ID">AnoaTIK: Jurnal Teknologi Informasi dan Komputer; Vol 3 No 1 (2025): Juni 2025; 50-57</dc:source>
	<dc:source>2987-7652</dc:source>
	<dc:language>ind</dc:language>
	<dc:relation>https://anoatik.uho.ac.id/index.php/atik/article/view/109/38</dc:relation>
	<dc:rights xml:lang="id-ID">Hak Cipta (c) 2025 Niken Mutiara, La Ode Saidi, Budi Wijaya Rauf</dc:rights>
	<dc:rights xml:lang="id-ID">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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