MODELLING THE NUMBER OF CRIMES IN EAST JAVA USING A TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION APPROACH

  • Yahya Vigo Tri Saputra Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia https://orcid.org/0009-0003-3174-3034
  • Moh. Hafiyusholeh Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia https://orcid.org/0000-0003-0183-574X
  • Hani Khaulasari Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia https://orcid.org/0000-0002-1360-6744
  • Yuniar Farida Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia https://orcid.org/0000-0001-8666-4980
  • Putroue Keumala Intan Department of Mathematics, Faculty of Science and Technology, Universitas Islam Negeri Sunan Ampel Surabaya, Indonesia https://orcid.org/0009-0003-9334-0820
DOI: https://doi.org/10.30598/barekengvol20iss2pp1627–1642
Keywords: Crime rate, Generalized Cross Validation, Ramsey’s Reset Test, Semiparametric Regression, Spline Truncated

Abstract

High crime rates can lead to unrest and financial losses for the community. East Java is one of the provinces with high crime rates, with a total of 21,046 reported crimes in 2023. This study aims to identify the factors that influence crime rates in East Java and evaluate the goodness of the model through truncated spline semiparametric regression. Truncated spline semiparametric regression is a combination of parametric and nonparametric methods that can adjust changes in data patterns through the presence of knot points. The data used in this study were sourced from the Central Statistics Agency, including variables such as the number of people living in poverty, average years of schooling, gross regional domestic product, population, Gini ratio, per capita expenditure, and open unemployment rate. The results of the analysis indicate that the predictor variables have a significant influence on the number of crimes simultaneously. Partially, the variables that influence the number of crimes in East Java Province are average years of schooling, population, Gini ratio, per capita expenditure, and open unemployment rate. The best regression model is obtained using the combination knot point (4,2,4,3) with a minimum GCV value of 49636.60. The coefficient of determination obtained is 93.60%, indicating that the predictor variables can explain 93.60% of the variation in the crime rate, while the remaining 6.40% is attributed to variables outside the scope of the study.

Downloads

Download data is not yet available.

References

D. R. Ningsih, P. K. Intan, and D. Yuliati, “PEMODELAN TINDAK PIDANA KRIMINALITAS DI KOTA TANGERANG MENGGUNAKAN METODE REGRESI LASSO,” Estimasi J. Stat. Its Appl., vol. 4, no. 1, pp. 64–77, 2023, doi: https://doi.org/10.20956/ejsa.vi.24853.

Badan Pusat Statistik, PROVINSI JAWA TIMUR DALAM ANGKA 2024. Surabaya: Badan Pusat Statistik Provinsi Jawa Timur, 2024.

B. C. Boeky, “FAKTOR PENYEBAB DAN UPAYA PENANGGULANGAN PENGULANGAN DALAM TINDAK PIDANA PENCURIAN DENGAN KEKERASAN (RESIDIV) DI WILAYAH HUKUM KEPOLISIAN RESOR KUPANG KOTA,” COMSERVA J. Penelit. dan Pengabdi. Masy., vol. 3, no. 8, pp. 3147–3158, 2023, doi: https://doi.org/ 10.59141/comserva.v3i08.1090.

W. Pratama, “TIGA PENCURI SEPEDA DI PERUMAHAN ELITE SURABAYA AKHIRNYA DIBEKUK POLISI,” 2022. https://www.suarasurabaya.net/kelanakota/2022/tiga-pencuri-sepeda-di-perumahan-elite-surabaya-akhirnya-dibekuk-polisi/ (accessed Mar. 09, 2025).

K. Rosidin, “PEMBUNUHAN SADIS DI MENGANTI, BEGINI KRONOLOGI KEJADIAN DAN PENANGKAPAN TERSANGKA,” 2023. https://infogresik.id/pembunuhan-sadis-di-menganti-begini-kronologi-kejadian-dan-penangkapan-tersangka/ (accessed Mar. 09, 2025).

M. A. F. Hakim and A. G. AE, “LIMA KEJAHATAN DI KEDIRI YANG CURI PERHATIAN PUBLIK SEPANJANG FEBRUARI 2024,” Kompas.com, 2024. https://surabaya.kompas.com/read/2024/03/08/111812278/lima-kejahatan-di-kediri-yang-curi-perhatian-publik-sepanjang-februari-2024?page=all (accessed May 08, 2025).

R. Septaria and Siti Mutmainnah Zulfaridatulyaqin, “TINGKAT KRIMINALITAS DI KOTA BANJARMASIN DENGAN PENDEKATAN EKONOMI,” JIEP J. Ilmu Ekon. dan Pembang., vol. 4, no. 1, pp. 50–64, 2021, doi: https://doi.org/ jiep.ulm.ac.id/index.php/jiep/article/view/2188.

A. I. Effendi and A. Julia, “FAKTOR EKONOMI YANG MEMPENGARUHI KEJAHATAN PROPERTI DI PULAU JAWA TAHUN 2014-2019,” J. Ris. Ilmu Ekon. dan Bisnis, vol. 1, no. 1, pp. 41–47, 2021.

A. A. C. Cindy and M. Ratna, “PEMODELAN KEJAHATAN DI INDONESIA DENGAN METODE REGRESI NONPARAMETRIK SPLINE TRUNCATED,” J. Sains dan Seni ITS, vol. 12, no. 2, pp. D167–D174, 2023, doi: https://doi.org/10.12962/j23373520.v12i2.112958.

Y. Febriani, “PENGARUH ASPEK SUMBER DAYA MANUSIA TERHADAP JUMLAH KRIMINALITAS DI SUMATERA SELATAN TAHUN 2019,” J. Media Wahana Ekon., vol. 18, no. 1, pp. 146–156, 2021, doi: https://doi.org/ 10.31851/jmwe.v18i1.5601.

D. A. Sulistiani, Y. Farida, S. K. Sari, and D. C. R. Novitasari, “MODELING CRIMINALITY IN SOUTH SULAWESI USING GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) METHOD,” AIP Conf. Proc., vol. 3142, no. 1, p. 20068, Mar. 2025, doi: https://doi.org/10.1063/5.0262022.

D. Kuciswara, F. Muslihatinningsih, and E. Santoso, “PENGARUH URBANISASI, TINGKAT KEMISKINAN, DAN KETIMPANGAN PENDAPATAN TERHADAP KRIMINALITAS DI PROVINSI JAWA TIMUR,” JAE J. Akunt. dan Ekon., vol. 6, no. 3, pp. 1–9, 2021, doi: https://doi.org/10.29407/jae.v6i3.16307.

M. Arynta, I. N. Budiantara, and M. Ratna, “PEMODELAN FAKTOR-FAKTOR YANG MEMPENGARUHI (CURANMOR) DI JAWA TIMUR MENGGUNAKAN REGRESI NONPARAMETRIK SPLINE TRUNCATED,” J. Sains dan Seni ITS, vol. 8, no. 2, pp. D246–D251, 2020, doi: https://doi.org/10.12962/j23373520.v8i2.44436.

L. Mahfiroh and Y. Farida, “SPLINE NONPARAMETRIC REGRESSION TO IDENTIFY FACTORS AFFECTING GENDER EMPOWERMENT MEASURE (GEM) IN EAST JAVA,” CAUCHY J. Mat. Murni dan Apl., vol. 7, no. 1, pp. 105–117, 2021, doi: https://doi.org/10.18860/ca.v7i1.12993.

F. Ubaidillah, A. A. R. Fernandes, A. Iriany, N. W. S. Wardhani, and S. Solimun, “TRUNCATED SPLINE PATH ANALYSIS MODELING ON IN COMPANY X WITH THE GOVERNMENT ’S ROLE AS A MEDIATION VARIABLE,” J. Stat. Appl. Probab., vol. 794, no. 3, pp. 781–794, 2022, doi: https://doi.org/10.18576/jsap/110303.

W. B. Altukhaes, M. Roozbeh, and N. A. Mohamed, “FEASIBLE ROBUST LIU ESTIMATOR TO COMBAT OUTLIERS AND MULTICOLLINEARITY EFFECTS IN RESTRICTED SEMIPARAMETRIC REGRESSION MODEL,” AIMS Math., vol. 9, no. 11, pp. 31581–31606, 2024, doi: https://doi.org/10.3934/math.20241519.

A. T. R. Dani and N. Y. Adrianingsih, “PEMODELAN REGRESI NONPARAMETRIK DENGAN ESTIMATOR SPLINE TRUNCATED DAN DERET FOURIER,” Jambura J. Math., vol. 3, no. 1, pp. 26–36, 2021, doi: https://doi.org/10.34312/jjom.v3i1.7713.

C. Christodoulou-Volos and D. Tserkezos, “SENSITIVITY OF THE RAMSEY’S REGRESSION SPECIFICATION ERROR TERM TEST ON THE DEGREE OF NONLINEARITY OF THE FUNCTIONAL FORM,” J. Appl. Econ. Sci., vol. XVIII, no. 1, pp. 2–7, 2023, doi: https://doi.org/10.57017/jaes.v18.1(79).01.

M. R. H. Nurdin, A. A. R. Fernandes, E. Sumarminingsih, and M. O. Ullah, “DEVELOPMENT OF RAMSEY RESET TO IDENTIFY THE POLYNOMIALS ORDER OF SMOOTHING SPLINE WITH SIMULATION STUDY,” JTAM (Jurnal Teor. dan Apl. Mat., vol. 9, no. 1, pp. 175–189, 2025, doi: https://doi.org/10.31764/jtam.v9i1.26785.

S. A. Mudawamah, G. T. Swastika, R. Narendra, and M. Qomarudin, “PEMODELAN REGRESI SEMIPARAMETRIK DENGAN PENDEKATAN SPLINE TRUNCATED PADA INDEKS PEMBANGUNAN MANUSIA (IPM) DI JAWA TIMUR,” J. Stat., vol. 22, no. 2, pp. 183–194, 2022, doi: https://doi.org/10.29313/statistika.v22i2.1433.

R. D. Fadlirhohim, Sifriyani, and A. T. R. Dani, “MODELING STUNTING PREVALENCE IN INDONESIA USING SPLINE TRUNCATED SEMIPARAMETRIC REGRESSION,” BAREKENG J. Math. Its Appl., vol. 18, no. 3, pp. 2015–2028, 2024, doi: https://doi.org/10.30598/barekengvol18iss3pp2015-2028.

Y. Wulandari and Yusnida, “PENGARUH PENDIDIKAN, JUMLAH PENDUDUK, DAN PENGANGGURAN TERHADAP KRIMINALITAS DI PROVINSI SUMATRA BARAT PERIODE 2020-2022,” Ranah Res. J. Multidiscip. Res. Dev., vol. 7, no. 3, pp. 2015–2024, 2025, doi: https://doi.org/10.38035/rrj.v7i3.1417.

M. A. Juniar, A. Fania, D. Ulya, R. Ramadhan, and N. Chamidah, “MODELLING CRIME RATES IN INDONESIA USING TRUNCATED SPLINE ESTIMATOR,” BAREKENG J. Math. Its Appl., vol. 18, no. 2, pp. 1201–1216, 2024, doi: https://doi.org/10.30598/barekengvol18iss2pp1201-1216.

C. B. Harahap and I. Sulhin, “PENGENDALIAN KEJAHATAN PADA SUB-KEBUDAYAAN GENG KLITIH (DALAM PARADIGMA KRIMINOLOGI BUDAYA),” Deviance J. Kriminologi, vol. 6, no. 1, pp. 86–102, 2022, doi: https://doi.org/10.36080/djk.1569.

H. Nurcahyani, I. N. Budiantara, and I. Zain, “THE SEMIPARAMETRIC REGRESSION CURVE ESTIMATION BY USING MIXED TRUNCATED SPLINE AND FOURIER SERIES MODEL,” AIP Conf. Proc., vol. 2329, 2021, doi: https://doi.org/10.1063/5.0042870.

A. T. Ampa, L. Laome, M. Ridwan, Baharuddin, and Makkulau, “MODELING GENDER DEVELOPMENT INDEX IN SOUTHEAST SULAWESI PROVINCE USING SEMIPARAMETRIC KERNEL REGRESSION,” BAREKENG J. Math. Its Appl., vol. 19, no. 3, pp. 1525–1536, 2025, doi: https://doi.org/10.30598/barekengvol19iss3pp1525-1536.

A. S. Suriaslan, I. N. Budiantara, and V. Ratnasari, “TRUNCATED SPLINE REGRESSION FOR BINARY RESPONSE: A COMPARATIVE DTUDY OF NONPARAMETRIC AND SEMIPARAMETRIC APPROACHES,” Commun. Math. Biol. Neurosci., vol. 2025, no. 51, 2025, doi: https://doi.org/10.28919/cmbn/9209.

R. Putra, M. G. Fadhlurrahman, and Gunardi, “DETERMINATION OF THE BEST KNOT AND BANDWIDTH IN GEOGRAPHICALLY WEIGHTED TRUNCATED SPLINE NONPARAMETRIC REGRESSION USING,” MethodsX, vol. 10, p. 101994, 2023, doi: https://doi.org/10.1016/j.mex.2022.101994.

N. Chamidah et al., “CONSISTENCY AND ASYMPTOTIC NORMALITY OF ESTIMATOR FOR PARAMETERS IN MULTIRESPONSE MULTIPREDICTOR SEMIPARAMETRIC,” Symmetry (Basel)., vol. 14, no. 2, p. 336, 2022, doi: https://doi.org/10.3390/sym14020336.

A. S. Suriaslan, I. N. Budiantara, and V. Ratnasari, “NONPARAMETRIC REGRESSION ESTIMATION USING MULTIVARIABLE TRUNCATED SPLINES FOR BINARY RESPONSE DATA,” MethodsX, vol. 14, p. 103084, 2025, doi: https://doi.org/10.1016/j.mex.2024.103084.

B. Lestari, N. Chamidah, I. N. Budiantara, and D. Aydin, “DETERMINING CONFIDENCE INTERVAL AND ASYMPTOTIC DISTRIBUTION FOR PARAMETERS OF MULTIRESPONSE SEMIPARAMETRIC REGRESSION MODEL USING SMOOTHING SPLINE ESTIMATOR,” J. King Saud Univ. - Sci., vol. 35, no. 5, p. 102664, 2023, doi: https://doi.org/10.1016/j.jksus.2023.102664.

A. M. Sadek and L. A. Mohammed, “EVALUATION OF THE PERFORMANCE OF KERNEL NON-PARAMETRIC REGRESSION AND ORDINARY LEAST SQUARES REGRESSION,” Int. J. Informatrics Vis., vol. 8, no. 3, pp. 1352–1360, 2024, doi: https://doi.org/10.62527/joiv.8.3.2430.

H. Prabowo, Suhartono, and D. D. Prastyo, “THE PERFORMANCE OF RAMSEY TEST, WHITE TEST AND TERASVIRTA TEST IN DETECTING NONLINEARITY,” INFERENSI, vol. 3, no. 1, pp. 1–12, 2020, doi: https://doi.org/10.12962%2Fj27213862.v3i1.6876.

F. H. Junianto, A. A. R. Fernandes, and Solimun, “STRUCTURAL EQUATION MODELING SEMIPARAMETRIC IN MODELING THE ACCURACY OF PAYMENT TIME FOR CUSTOMERS OF CREDIT BANK IN INDONESIa,” JTAM (Jurnal Teor. dan Apl. Mat., vol. 8, no. 4, pp. 1082–1095, 2024, doi: https://doi.org/10.31764/jtam.v8i4.23668.

A. T. R. Dani, V. Ratnasari, and I. N. Budiantara, “OPTIMAL KNOTS POINT AND BANDWIDTH SELECTION IN MODELING MIXED ESTIMATOR NONPARAMETRIC REGRESSION,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1115, p. 012020, 2021, doi: https://doi.org/10.1088/1757-899X/1115/1/012020.

H. Luepsen, “ANOVA WITH BINARY VARIABLES: THE F-TEST AND SOME ALTERNATIVES,” Commun. Stat. - Simul. Comput., vol. 52, no. 3, pp. 745–769, 2023, doi: https://doi.org/10.1080/03610918.2020.1869983.

G. F. Mumtaz, Suyitno, and Sifriyani, “GEOGRAPHICALLY WEIGHTED PANEL REGRESSION MODELLING OF POVERTY RATES IN TROPICAL RAINFOREST AREAS OF KALIMANTAN,” BAREKENG J. Math. Its Appl., vol. 19, no. 2, pp. 903–916, 2025, doi: https://doi.org/10.30598/barekengvol19iss2pp903-916.

D. Chicco, M. J. Warrens, and G. Jurman, “THE COEFFICIENT OF DETERMINATION R-SQUARED IS MORE INFORMATIVE THAN SMAPE , MAE , MAPE , MSE AND RMSE IN REGRESSION ANALYSIS EVALUATION,” PeerJ Comput. Sci., pp. 1–24, 2021, doi: https://doi.org/https://peerj.com/articles/cs-623/.

I. Sulistiana, M. Al Hafiz, E. R. Supangadi, J. I. Situmorang, and M. Ikhsan, “PEMODELAN REGRESI NONPARAMETRIK MENGGUNAKAN PENDEKATAN POLINOMIAL LOKAL PADA INFLASI DI INDONESIA,” MATHunesa J. Ilm. Mat., vol. 13, no. 2, pp. 545–551, 2025, [Online]. Available: https://ejournal.unesa.ac.id/index.php/mathunesa/article/view/67350

Y. Farida, M. Farmita, P. K. Intan, H. Khaulasari, and A. T. Wibowo, “MODELLING CRIME IN EAST JAVA USING SPATIAL DURBIN MODEL REGRESSION,” BAREKENG J. Math. Its Appl., vol. 18, no. 3, pp. 1497–1508, 2024, doi: https://doi.org/10.30598/barekengvol18iss3pp1497-1508.

Nurwahyuni, Z. W. Baskara, and N. A. Purnamasari, “MODEL REGRESI DATA PANEL PADA TINGKAT KRIMINALITAS DI INDONESIA NUSA TENGGARA BARAT DENGAN MENGGUNAKAN FIXED EFFECT MODEL,” Var. J. Stat. Its Appl., vol. 5, no. 2, pp. 169–184, 2023, doi: https://doi.org/10.30598/variancevol5iss2page169-184.

Published
2026-01-26
How to Cite
[1]
Y. V. T. Saputra, M. Hafiyusholeh, H. Khaulasari, Y. Farida, and P. Intan, “MODELLING THE NUMBER OF CRIMES IN EAST JAVA USING A TRUNCATED SPLINE SEMIPARAMETRIC REGRESSION APPROACH”, BAREKENG: J. Math. & App., vol. 20, no. 2, pp. 1627–1642, Jan. 2026.
Issue
Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2026 Yahya Vigo Tri Saputra, Moh. Hafiyusholeh, Hani Khaulasari, Yuniar Farida, Putroue Keumala Intan

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Authors who publish with this Journal agree to the following terms:

  1. Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
  2. Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
  3. Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.