Analysis of Social Assistance Donor Classification at the Muhammadiyah Medan Orphanage Using SVM
DOI:
10.33395/sinkron.v9i1.14299Keywords:
Donors; Putra Muhammadiyah Orphanage; Social assistance; Classification; Support Vector Machine;Abstract
The Putra Muhammadiyah Orphanage in Medan City is a social institution that relies on donor assistance to support various social programs. The problem that occurs at the Putra Muhammadiyah Orphanage in Medan is the difficulty in identifying potential and non-potential donors who have the potential to provide sustainable social assistance contributions. This study aims to conduct a comprehensive analysis and classification of donors using the Support Vector Machine method, an effective method in machine learning to handle classification problems with SVM with high accuracy. The research data consists of donor data with several main characteristics such as the amount of donation, the frequency of donations given, and the type of assistance. The data is processed through a preprocessing stage including data normalization and data division into training and testing data. Then, the SVM model is trained to classify donors into two categories, namely Potential Donors and Non-potential Donors. Based on the data obtained from the donation bookkeeping records of the Putra Muhammadiyah Orphanage in Medan City, it can be concluded that around 55 potential donors out of 90 donors and 35 non-potential donors out of 90 donor data. From the results of the analysis and testing of the model conducted, it can be concluded that the SVM method can classify "Potential Donors" and "Non-Potential Donors" with a fairly high level of accuracy. The level of accuracy obtained reached up to 89% with a precision value of 93%, a recall value of 89% and an f1-score of 90%. With these results, this study can provide significant benefits in the management of social assistance, especially helping orphanages to determine who are potential and non-potential donors. Therefore, this study is expected to have an impact on improving the sustainability of social programs at the Putra Muhammadiyah Orphanage in Medan City.
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References
Berutu, S.S., Budiati, H., Jatmika, J., & Gulo, F. (2023). Data preprocessing approach for machine learning-based sentiment classification. Infotel Journal, 15(4), 317-325. Retrieved from https://doi.org/10.20895/infotel.v15i4.1030
Dadi Riskiono, S., Hamidy, F., & Ulfia, T. (2020). Sistem informasi manajemen dana berbasis web pada panti asuhan yatim madani. In Journal of Social Sciences and Technology for Community Service (JSSTCS), 1(1), 21-26. Retrieved from https://doi.org/10.33365/jta.v1i1.670
Fitriyah, N., Warsito, B., Asih, D., & Maruddani, I. (2020). Analisis sentiment gojek pada media social twitter dengan klasifikasi support vector machine (SVM). Gaussian Journal, 9(3), 376-390. Retrieved from https://ejournal3.undip.ac.id/index.php/gaussian/
Furqan, M., Kurniawan, R., & HP, KI (2020). Evaluasi performa support vector machine classifer terhadap penyakit mental. Journal Of Business Information Systems, 10(2), 203-210. Retrieved from https://doi.org/10.21456/vol10iss2pp203-210
Haryanti, D., Pamela, E., & Susanti, Y. (2019). Mental emotional development of adolescents in orphanages. Journal of Mental Health Nursing, 4(2), 97-104. Retrieved from https://doi.org/10.26714/jkj.4.2.2016.97-104
Junaidi, S., Dewi, M., & Kurniawan, H. (2023). Pelatihan pengelohan dan visualisasi data penduduk menggunakan python. ADMA: Journal of Community Service and Empowerment, 4(1), 151–162. Retrieved from https://doi.org/10.30812/adma.v4i1.2963
Mujilahwati, S. (2021). Visualisasi Data Hasil Klasifikasi Naive Bayes Dengan Matplotlib Pada Python. Prosiding Seminar Nasional Sains dan Teknologi Ke-11 Tahun 2021, 1(1), 205-211. Retrieved from http://dx.doi.org/10.36499/psnst.v1i1.5164
Nalepa, J., & Kawulok, M. (2019). Selecting training sets for support vector machines: A review. Artificial Intelligence Review, 52(2), 857–900. Retrieved from https://doi.org/10.1007/s10462-017-9611-1
Pambudi, A., & Suprapto, S. (2021). Effect of sentence length in sentiment analysis using support vector machine and convolutional neural network method. Indonesian Journal of Computing and Cybernetics Systems, 15(1), 21-30. Retrieved from https://doi.org/10.22146/ijccs.61627
Sukmana, O., Agustino, H., & Hidayat, W. (2021). Pendampingan pengelolaan panti asuhan putri aisyiyah kota malang alam upaya persiapan akreditasi lembaga kesejahteraan sosial anak (LKSA). Community Development Journal, 5(1), 143-154. Retrieved from https://doi.org/10.35326/pkm.v5i1
Rahmawati, FM., & Safitri, TA. (2023). Analisis SWOT Panti Asuhan Aisyiyah Putri Yogyakarta. Community Development Journal, 4(2), 1590-1596. Retrieved from https://journal.universitaspahlawan.ac.id/index.php/cdj/article/ view/ 13534/ 18119
Rianti, DL., Umaidah, Y., & Voutama, A. (2021). Tren marketplace berdasarkan klasifikasi ulasan pelanggan menggunakan perbandingan kernel support vector machine. STRING (Technology Research and Innovation Writing Unit), 6(1), 98-105. Retrieved from http://dx.doi.org/10.30998/string.v6i1.9993
Rochim, AF., Widyaningrum, K., & Eridani, D. (2021). Performance comparison of support vector machine kernel functions in classifying covid-19 sentiment. 2021 4th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 2021, pp, 224–228. Retrieved from https://doi.org/10.1109/ISRITI54043.2021.9702845
Sholeh, M., Nurnawati, K. E., & Lestari, U. (2023). Application of data mining with linear regression method to predict exam result value data using Rapidminer. In Jurnal Informatika Sunan Kalijaga, 8(1), 10-21. Retrieved from https://doi.org/10.14421/jiska.2023.8.1.10-21
Pratiwi, TA., Irsyad, M., & Kurniawan, R. (2021). Classification of forest and land fires using the Naive Bayes algorithm in (Case Study: Riau Province). Jurnal Sistem dan Teknologi Informasi, 09(2), 101-107. Retrieved from http://dx.doi.org/10.26418/justin.v9i2.42823
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Copyright (c) 2025 Ahmad Helmy, Zulham Sitorus, Dwika Ardya, Abdul Chaidir Hrp, Siti Isna Syahri T, Sukrianto

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