Implementasi Hybrid ANP-TOPSIS untuk Evaluasi Pembinaan Narapidana (Studi Kasus: Lapas XYZ)
DOI:
https://doi.org/10.35889/jutisi.v15i4.3864Abstract
The evaluation of inmate rehabilitation at XYZ Prison is still conducted manually, which has the potential to introduce subjectivity and complicates the management of historical data needed for decision-making regarding remission recommendations. This study aims to implement a web-based decision support system to improve the objectivity and consistency of inmate rehabilitation evaluations. The system was developed by combining the Analytic Network Process method to determine subcriteria weights and the Technique for Order Preference by Similarity to Ideal Solution to rank alternatives based on rehabilitation outcomes. The system was implemented using the PHP programming language and a MySQL database. The results show that the system successfully generated a ranking of eight inmate alternatives based on preference scores, as well as parole recommendations in accordance with predetermined thresholds. Performance testing of the method using the Accuracy, Precision, and Recall metrics showed a good level of alignment between the system’s recommendations and the decisions of the Correctional Monitoring Team. The resulting system is capable of supporting the rehabilitation evaluation process in a more objective, structured, documented, and transparent manner to aid decision-making.
Keywords: Decision Support System; Analytic Network Process; Technique for Order of Preference by Similarity to the Ideal Solution; Evaluation of Inmate Rehabilitation; Sentence Reduction
Abstrak
Evaluasi pembinaan narapidana di Lapas XYZ masih dilakukan secara manual sehingga berpotensi menimbulkan subjektivitas dan menyulitkan pengelolaan data historis yang diperlukan dalam pengambilan keputusan rekomendasi remisi. Penelitian ini bertujuan mengimplementasikan sistem pendukung keputusan berbasis web untuk meningkatkan objektivitas dan konsistensi evaluasi pembinaan narapidana. Sistem dikembangkan dengan mengombinasikan metode Analytic Network Process untuk menentukan bobot subkriteria dan Technique for Order Preference by Similarity to Ideal Solution untuk melakukan pemeringkatan alternatif berdasarkan hasil pembinaan. Implementasi sistem dilakukan menggunakan bahasa pemrograman PHP dan basis data MySQL. Hasil penelitian menunjukkan bahwa sistem berhasil menghasilkan pemeringkatan delapan alternatif narapidana berdasarkan nilai preferensi serta rekomendasi remisi sesuai ambang batas yang telah ditetapkan. Hasil pengujian kinerja metode menggunakan metrik Accuracy, Precision, dan Recall menunjukan tingkat kesesuaian yang baik antara rekomendasi sistem dan keputusan Tim Pengamat Pemasyarakatan. Sistem yang dihasilkan mampu mendukung proses evaluasi pembinaan secara lebih objektif, terstruktur, terdokumentasi, dan transparan dalam mendukung pengambilan keputusan.
Kata kunci: Sistem Pendukung Keputusan; Analytic Network Process; Technique for Order Preference by Similarity to Ideal Solution; Evaluasi Pembinaan Narapidana; Remisi
References
[1] P. J. Gultom and L. Indahdewi, “Evaluasi Pelaksanaan Kebijakan Sistem Penilaian Pembinaan Narapidana pada Lembaga Pemasyarakatan Pemuda Kelas IIA Tangerang,” J-CEKI J. Cendekia Ilm., vol. 4, no. 1, pp. 3201–3215, 2024.
[2] P. R. I. Dewan Perwakilan Rakyat Republik Indonesia, Undang-Undang Republik Indonesia Nomor 22 Tahun 2022 Tentang Pemasyarakatan, no. 143384. Indonesia, 2022.
[3] Y. Dewanti, Rina Rohayu Harun, and Firzhal Arzhi Jiwantara, “Optimalisasi Sistem Penilaian Pembinaan Narapidana Untuk Remisi Berbasis Keadilan Distributif Di Lapas Perempuan Mataram,” Commer. Law, vol. 5, no. 1, pp. 115–125, 2025, doi: 10.29303/commercelaw.v5i1.7328.
[4] R. Sharda, D. Delen, and E. Turban, Analytics, Data Science, & Artificial Intelligence: Systems for decision support, Eleventh E. Pearson, 2021. [Online]. Available: https://www.pearson.com/se/Nordics-Higher-Education/subject-catalogue/information-systems/Sharda-Analytics-Data-Science-Artificial-Intelligence-Systems-for-Decision-Support-Global-Edition-11th-Edition.html
[5] A. Y. E. Dodu, Y. Anshori, and D. T. Limbong, “Sistem Pendukung Keputusan Rekomendasi Pemberian Remisi Pada Narapidana Menggunakan Metode Topsis (Studi Kasus Pada Lembaga Pemasyarakatan Klas II-A Palu),” Sci. Comput. Sci. Informatics J., vol. 1, no. 1, p. 11, 2019, doi: 10.22487/j26204118.2018.v1.i1.11898.
[6] M. Kusumadewi, Elyza, Sistem cerdas dan pendukung keputusan : decision support and intelligent system. Sleman: UII Press Yogyakarta, 2021.
[7] E. Anggara, I. Setiawan, and Y. S. Galih, “Implementasi Program Pembinaan Bagi Warga Binaan Dihubungkan Dengan Pasal 11 Ayat ( 1 ) Huruf B Undang-Undang Republik Indonesia Nomor 22 Tahun 2022 Tentang Pemasyarakatan,” J. Pustaka Galuh Justisi Fak. Huk. Univ. Galuh, vol. 03, no. 01, pp. 156–179, 2024, [Online]. Available: https://doi.org/10.25157/pustaka.v3i1.4581
[8] T. S. M. Hutapea et al., “Sistem Penenilaian Pembinaan Narapidana (SPPN) Bagi petugas di Lapas Super Maximum, Maximum, Medium dan Minimum Security,” pp. 1–99, 2021.
[9] Septa Erikson Ginting and Meitisa Vanya Simanjuntak, “Analisis Peran Wali Pemasyarakatan dalam Pemberian Penilaian SPPN (Sistem Penilaian Pembinaan Narapidana) di Rumah Tahanan Negara Kelas I Medan,” J. Ilmu Hukum, Hum. dan Polit., vol. 5, no. 1, pp. 564–569, 2024, doi: 10.38035/jihhp.v5i1.3082.
[10] M. Situmeang and M. Fakhriza, “Employee Performance Evaluation Using ANP and TOPSIS,” J. Inf. Syst. Informatics, vol. 6, no. 4, pp. 3053–3069, 2024, doi: 10.51519/journalisi.v6i4.963.
[11] R. Alfiansyah and I. Zufria, “Decision Support Systems for Career Recommendations for Graduates in Islamic Broadcasting Communication Studies Using ANP and TOPSIS Methods,” Int. J. Softw. Eng. Comput. Sci., vol. 3, no. 3, pp. 474–486, 2023, doi: 10.35870/ijsecs.v3i3.1914.
[12] R. A. Purba, N. A. Hasibuan, and D. P. Utomo, “Decision Support System for admission of new employees by applying a combination of ANP-TOPSIS Methods,” Sinkron, vol. 7, no. 4, pp. 2437–2448, 2022, doi: 10.33395/sinkron.v7i4.11724.
[13] M. M. Dakwah, M. N. Roodhi, and D. Suprayetno, “Penentuan Strategi Pemasaran Digital Menggunakan Kombinasi Analytical Network Process ( ANP ) dan Profile Matching Determining a Digital Marketing Strategy Using a Combination of Analytical Network Process ( ANP ) and Profile Matching,” vol. 6, no. 1, pp. 47–58, 2024, doi: 10.30812/bite/v6i1.4125.
[14] J. J. Thakkar, “Analytic Network Process (ANP),” in Studies in Systems, Decision and Control, vol. 336, 2021, pp. 63–82. doi: 10.1007/978-981-33-4745-8_4.
[15] N. P. Jason Papathanasiou, Multiple Criteria Decision Aid, 1st ed. Springer Cham, 2018. doi: https://doi.org/10.1007/978-3-319-91648-4.
[16] C. Z. Michalis Doumpos, José Rui Figueira, Salvatore Greco, Ed., New Perspectives in Multiple Criteria Decision Making, 1st ed. Springer Cham, 2019. doi: https://doi.org/10.1007/978-3-030-11482-4.
[17] O. Bozorg-Haddad, B. Zolghadr-Asli, and H. A. Loáiciga, A Handbook on multi-attribute decision-making methods, no. March. 2021. doi: 10.1002/9781119563501.
[18] D. Shih, TOPSIS and its Extensions: A Distance-Based MCDM Approach. 2022. doi: https://doi.org/10.1007/978-3-031-09577-1.
[19] G. Yang, Y. Huang, and L. Zhu, “Evaluating Provincial Resilience to Climate Change in China Using a Hybrid ANP-ANN-PROMETHEE-GAIA Framework,” Geol. J., vol. 60, no. 11, pp. 2773–2789, 2025, doi: 10.1002/gj.5213.
[20] M. M. Ismail, H. R. Abdelhady, and M. Emad, “Multi-Criteria Decision-Making Techniques: A Comprehensive Review of Methodologies and Applications,” Int. J. Comput. Informatics (Zagazig Univ., vol. 2, pp. 27–38, 2024, [Online]. Available: https://www.ijci.zu.edu.eg/index.php/ijci/article/view/70
[21] H. Taherdoost and M. Madanchian, “Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations,” J. Data Sci. Intell. Syst., vol. 1, no. 1, pp. 12–18, 2023, doi: 10.47852/bonviewJDSIS3202885.
