Analisis Prediksi Kelulusan Mahasiswa Menggunakan Decission Tree Berbasis Particle Swarm Optimization

Hendra Hendra(1*), Mochammad Abdul Azis(2), Suhardjono Suhardjono(3)

(1) STMIK Nusa Mandiri
(2) Universitas Bina Sarana Informatika
(3) Universitas Bina Sarana Informatika
(*) Corresponding Author

Abstract


Good accreditation results are the goal of the college. With good accreditation, prospective students can glance at and enter the tertiary institution. To achieve this, there are several aspects that affect good accreditation results, one of which is graduate students who play an important role in determining accreditation. Timely graduate students can benefit the college or a student. Graduates can be predicted before the final semester using a method one of which is the decision tree. Decision tree is a method that is simple and easy to understand by producing rules in the form of a decision tree, but using a decision tree model alone is not enough to produce optimal results. So we need a method for optimization that is particle swarm optimization with advantages can improve accuracy by eliminating unused features. From the results of research with primary data of 2000-2003 graduate students in Amik PPMI Tangerang explained that the particle swarm optimization method can increase accuracy by 87.56% and increase by 01.01% from the decision tree method with a value of 86.55%. From the particle swarm optimization method can also find out which unused attributes have no weight, so that way can improve accuracy. From the results of the increase, it can be used by the Amik University of Tangerang to prevent students from graduating on time.

Keywords


Decision Tree; Student Graduation; Particle Swarm Optimization

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References


V. Riyanto, A. Hamid, and Ridwansyah, “Prediction of Student Graduation Time Using the Best Algorithm,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, pp. 1–9, 2019.

F. A. Freitas and L. J. Leonard, “Maslow’s hierarchy of needs and student academic success,” Teach. Learn. Nurs., vol. 6, no. 1, pp. 9–13, 2011.

M. V. Ashok and A. Apoorva, “Data mining approach for predicting student and institution’s placement percentage,” 2016 Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS 2016, pp. 336–340, 2016.

M. T. Devasia, M. V. T. P, and M. V. Hegde, “Prediction of Students Performance using Educational Data Mining,” Int. J. Cogn. Ther., vol. 1, no. 3, pp. 266–279, 2008.

Suhardjono, G. Wijaya, and A. Hamid, “PREDIKSI WAKTU KELULUSAN MAHASISWA MENGGUNAKAN SVM BERBASIS PSO,” Bianglala Inform., vol. 7, no. 2, pp. 97–101, 2019.

Ridwansyah and E. Purwaningsih, “Particle Swarm Optimization Untuk Meningkatkan Akurasi Prediksi Pemasaran Bank,” J. PILAR Nusa Mandiri, vol. 14, no. 1, pp. 83–88, 2018.

H. M. Nawawi, S. Rahayu, J. J. Purnama, and S. I. Komputer, “Algoritma c4.5 untuk memprediksi pengambilan keputusan memilih deposito berjangka,” J. Techno Nuasa Mandiri, vol. 16, no. 1, pp. 65–72, 2019.

J. Han and M. Kamber, Data Mining Concept and Techniques. India: New Age International Limited, 2006.

Wu and Kumar, The Top Ten Algorithms in Data Mining. USA: CRC Press, 2009.

C. A. Sugianto, “Penerapan Teknik Data Mining Untuk Menentukan Hasil Seleksi Masuk Sman 1 Cibeber Untuk Siswa Baru Menggunakan Decision Tree,” Tedc, vol. 9, pp. 39–43, 2015.

F. Gorunescu, Data Mining: Concepts, Models and Technique. 2011.

D. Setiawati, I. Taufik, J. Jumadi, and W. B. Zulfikar, “Klasifikasi Terjemahan Ayat Al-Quran Tentang Ilmu Sains Menggunakan Algoritma Decision Tree Berbasis Mobile,” J. Online Inform., vol. 1, no. 1, p. 24, 2016.

H. Liu, H. Q. Tian, C. Chen, and Y. F. Li, “An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization,” Int. J. Electr. Power Energy Syst., vol. 52, no. 1, pp. 161–173, 2013.

T. Xu, Q. Peng, and Y. Cheng, “Identifying the semantic orientation of terms using S-HAL for sentiment analysis,” Knowledge-Based Syst., vol. 35, pp. 279–289, 2012.

J. Cao, H. Cui, H. Shi, and L. Jiao, “Big data: A parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce,” PLoS One, vol. 11, no. 6, pp. 1–17, 2016.

W. Dai and J. W, “A mapreduce implementation of C4.5 decision tree algorithm,” in nternational Journal ofDatabase Theory and Application, 2014, pp. 49–60.

L. Bin and Y. Min, “Analysis model




DOI: https://doi.org/10.32736/sisfokom.v9i1.756

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