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Abstract
The human development index (HDI) is one of the measuring tools for achieving the quality of life of a region or even a country, including Indonesia. There are 3 basic components of the HDI, namely the dimensions of health, knowledge, and decent living. Development in Indonesia is uneven as indicated by the Human Development Index (HDI) of districts/cities in 2021 which varies greatly. The purpose of this study is to compare several machine learning algorithms to classify districts/cities in Indonesia according to the Human Development Index (HDI) in 2021. There are six machine learning algorithms used in this study, namely Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearset Neighbor (K-NN), Random Forest, Decision Tree, and Naive Bayes. The k-Fold Cross Validation method is applied to form the training set and testing set, with 10 folds and 1 repetition. The results of the study showed that the classification results of the SVM algorithm using the Radial Basis Function (RBF) kernel parameters with sigma = 0.4864648 and C = 1 were the best among the other five algorithms with an average accuracy of 76.08% and a maximum accuracy of 88.24%.
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Copyright (c) 2025 Ni Kadek Ayu Purnami Sari Dewi, Arie Wahyu Wijayanto, Joko Ade Nursiyono
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References
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- Bramer, M, “Introduction to classification: na¨ive bayes and nearest neighbour”, Principles of Data Mining, 23-39, 2007.
- Rahmad, F., Suryanto, Y., & Ramli, K, “Performance comparison of anti-spam technology using confusion matrix classification”. IOP Conference Series: Materials Science And Engineering, 879(1), https://doi.org/10.1088/1757- 899X/879/1/012076, 2020.
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References
United Nations Development Programme, “Human development report 2019: beyond income, beyond averages, beyond today”, In United Nations Development Program, 2019.
Fauzi, Fatkhurokhman, “K-nearset neighbor (k-nn) dan support vector machine (svm) untuk klasifikasi indeks pembangunan manusia provinsi jawa tengah”, Jurnal Mipa, 40(2), 118–124, 2019.
Gunadi, G., & Sensuse, D. I, “Penerapan metode data mining market basket analysis terhadap data penjualan produk buku dengan menggunakan algoritma apriori dan frequent pattern growth (fp-growth): studi kasus percetakan pt. Gramedia”, Telematika MKOM, 4(1), 118-132, 2016.
Irmawati, Irmawati, Zainudin, Zahir, & Yuyun, Yuyun, “Data mining untuk penentuan model kelulusan murid sma pada perguruan tinggi negeri; studi kasus di iain bone”, JIKO (Jurnal Informatika Dan Komputer), 3(2), 113– 118. https://doi.org/10.33387/jiko.v3i2.1800, 2020.
Kranjčić, Nikola, Medak, Damir, Župan, Robert, & Rezo, Milan, “Support vector machine accuracy assessment for extracting green urban areas in towns”, Remote Sensing, 11(6), https://doi.org/10.3390/rs11060655, 2019.
Fauzi, Fatkhurokhman, Yamin, Moh, & Wahyu, Tiani, “Klasifikasi indeks pembangunan manusia kabupaten / kota se-indonesia dengan pendekatan smooth support vector machine ( ssvm ) kernel radial basis function ( rbf )”, Seminar Nasional Pendidikan, Sains Dan Teknologi Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Muhammadiyah Semarang, 88–97, Retrieved from https://jurnal.unimus.ac.id/index.php/psn 12012010/article/view/2986, 2017.
Fathurrahman, M., & Qisthi, N, “ Klasifikasi indeks pembangunan manusia (ipm) di pulau sumatera pada dataset multi-class dengan metode artificial neural network (ann)”, In Seminar Nasional Fisika (Vol. 1, No. 1, pp. 377-384, 2021.
Darsyah, M. Y, “Klasifikasi indeks pembangunan manusia (ipm) dengan pendekatan k-nearset neighbor (k-nn)”. In Prosiding Seminar Nasional & Internasional, 2017.
Kemala, I., & Wijayanto, A. W, “Perbandingan kinerja metode bagging dan non-ensemble machine learning pada klasifikasi wilayah di indonesia menurut indeks pembangunan manusia”, JUSTIN (Jurnal Sistem dan Teknologi Informasi), 9(2), 269-275, 2021.
Kusumodestoni, R. Hadapiningradja, & Sarwido, Sarwido, “Komparasi model support vector machines (svm) dan neural network untuk mengetahui tingkat akurasi prediksi tertinggi harga saham”, Jurnal Informatika Upgris, 3(1). https://doi.org/10.26877/jiu.v3i1.1536, 2017.
Tomasevic, Nikola, Gvozdenovic, Nikola, & Vranes, Sanja, “An overview and comparison of supervised data mining techniques for student exam performance prediction”, Computers and Education, 143, 103676. https://doi.org/10.1016/j.compedu.2019. 103676, 2020.
Wibisono, A. B., & Fahrurozi, A, “Perbandingan algoritma klasifikasi dalam pengklasifikasian data penyakit jantung coroner”, Jurnal Ilmiah Teknologi Dan Rekayasa, 24(3), 161–170, https://doi.org/10.35760/tr.2019.v24i3.239, 2019.
Sihombing, Pardomuan Robinson, “Perbandingan metode artificial neural network (ann) dan support vector machine (svm) untuk klasifikasi kinerja perusahaan daerah air minum (pdam) di indonesia”, Jurnal Ilmu Komputer, 13(1), 9. https://doi.org/10.24843/jik.2020.v13.i01. p02, 2020.
Wu, X., & Kumar, V, “The top ten algorithms in data mining”, CRC press, 2009.
Bramer, M, “Introduction to classification: na¨ive bayes and nearest neighbour”, Principles of Data Mining, 23-39, 2007.
Rahmad, F., Suryanto, Y., & Ramli, K, “Performance comparison of anti-spam technology using confusion matrix classification”. IOP Conference Series: Materials Science And Engineering, 879(1), https://doi.org/10.1088/1757- 899X/879/1/012076, 2020.
Purwaningsih, E, ”Seleksi mobil berdasarkan fitur dengan komparasi metode klasifikasi neural network, support vector machine, dan algoritma c4.5”, Jurnal Pilar Nusa Mandiri, 12(2), 153-160, 2016.