Implementation Of Decision Tree Algorithm For Classification Of Eligibility In Social Assistance Fund Distribution

Raden Deasy Mandasari; Hartana Hartana

doi:10.38043/tiers.v5i1.5378

Authors

Raden Deasy Mandasari Universitas Bina Sarana Informatika
Hartana Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.38043/tiers.v5i1.5378

Keywords:

C4.5 Algorithm, Cross-Validation, Decision Tree, Eligibility Classification, Social Assistance Distribution

Abstract

This study examined the use of decision tree algorithms, specifically C4.5, in classifying the eligibility for social assistance fund distribution in Kelurahan Bangka Belitung Laut, Kecamatan Pontianak Tenggara. Inaccuracies in distribution were caused by recipient selection based on village officials' recommendations, which only considered the type of occupation. To address this issue, this study developed a system based on the C4.5 algorithm to process economic census data. This research used a quantitative method with a descriptive analytical approach, collecting data through literature studies and economic census, and processing data using the Rapid Miner application. The classification model generated was evaluated using 10-fold cross-validation to ensure high accuracy. The results showed that the C4.5 algorithm achieved 100% accuracy, precision, and recall. The decision tree model indicated that the main attributes determining eligibility were occupation and income. Some rules derived from this model, such as those who are unemployed with an income below Rp29,500 being eligible for assistance, provided clear guidelines for policymakers. The implementation of this algorithm is expected to improve fairness and effectiveness in the distribution of social assistance funds in Kelurahan Bangka Belitung Laut, reduce public dissatisfaction, and prevent potential social conflicts. This study recommended adopting the model in other areas with adjustments to local data to enhance broader fairness in aid distribution.

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References

A. Abdulhafedh, Comparison between Common Statistical Modeling Techniques Used in Research, Including: Discriminant Analysis vs Logistic Regression, Ridge Regression vs LASSO, and Decision Tree vs Random Forest, OALib, vol. 09, no. 02, pp. 119, 2022.

Sugiono, A. Taufik, and R. Faizal Amir, Penerapan Penerapan Teknik Pso Over Sampling Dan Adaboost J48 Untuk Memprediksi Cacat Software, J. Responsif Ris. Sains dan Inform., vol. 2, no. 2, pp. 198203, 2020.

Y. Suhanda, L. Nurlaela, I. Kurniati, A. Dharmalau, and I. Rosita, Predictive Analysis of Customer Retention Using the Random Forest Algorithm, TIERS Inf. Technol. J., vol. 3, no. 1, pp. 3547, 2022.

S. Annisa and Y. Rahkmawati, Resampling Techniques in Rainfall Classification of Banjarbaru using Decision Tree Method, TIERS Inf. Technol. J., vol. 4, no. 2, pp. 122128, 2023.

P. Ladianto, Implementasi Data Mining Untuk Bantuan Dana Bansos Dengan Menggunakan Algoritma C4. 5 Di Pemerintahan Kabupaten Empat Lawang, JUPITER (Jurnal Penelit. Ilmu dan Tek. , no. 13, pp. 287297, 2022.

N. Aulia, N. Suarna, and W. Prihartono, Klasifikasi Penentuan Penerima Program Indnesia Pintar Di Krwilbidikcam Greged Menggunakan Algoritma C4.5, JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 39133919, 2024.

A. Nur, A. Rohim, A. I. Purnamasari, and I. Ali, Komparasi Efektifitas Algoritma C4.5 Dan Nave Bayes Untuk Menentukan Kelayakan Penerima Manfaat Program Keluarga Harapan (Studi Kasus: Kecamatan Cicalengka Kabupaten Bandung), J. Mhs. Tek. Inform., vol. 8, no. 2, pp. 23552362, 2024.

V. H. Nhu et al., Shallow landslide susceptibility mapping: A comparison between logistic model tree, logistic regression, nave bayes tree, artificial neural network, and support vector machine algorithms, Int. J. Environ. Res. Public Health, vol. 17, no. 8, 2020.

P. Golpour et al., Comparison of support vector machine, nave bayes and logistic regression for assessing the necessity for coronary angiography, Int. J. Environ. Res. Public Health, vol. 17, no. 18, pp. 19, 2020.

F. Itoo, Meenakshi, and S. Singh, Comparison and analysis of logistic regression, Nave Bayes and KNN machine learning algorithms for credit card fraud detection, Int. J. Inf. Technol., vol. 13, no. 4, pp. 15031511, 2021.

V. H. Nhu et al., Comparison of support vector machine, bayesian logistic regression, and alternating decision tree algorithms for shallow landslide susceptibility mapping along a mountainous road in the west of Iran, Appl. Sci., vol. 10, no. 15, 2020.

A. Tariq et al., Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data, Heliyon, vol. 9, no. 2, p. e13212, 2023.

I. Baabbad, T. Althubiti, A. Alharbi, K. Alfarsi, and S. Rasheed, A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications, J. Data Anal. Inf. Process., vol. 09, no. 03, pp. 162174, 2021.

M. I. Saad, D. Bryan, Kusrini, and Supriatin, Decision Support System for Covid19 Affected Family Cash Aid Recipients Using the Nave Bayes Algorithm and the Weight Product Method, 2020 3rd Int. Conf. Inf. Commun. Technol. ICOIACT 2020, pp. 120125, 2020.

R. Rusdiansyah, H. Supendar, and T. Tuslaela, Data Mining using K-means method for feasibility selection of Non-cash food Assistance recipients in the Era of Covid-19, SinkrOn, vol. 6, no. 1, pp. 2533, 2021.

M. Mardison, S. Defit, and S. Alturky, Prediction of Scholarship Recipients Using Hybrid Data Mining Method with Combination of K-Means and C4.5 Algorithms, Int. J. Artif. Intell. Res., vol. 5, no. 2, pp. 168179, 2021.

P. Nabillah, I. Permana, M. Afdal, F. Muttakin, and A. Marsal, A Comparative Study of the Performance of KNN , NBC , C4 . 5 , and Random Forest Algorithms in Classifying Beneficiaries of the Kartu Indonesia Sehat Program, JUSIFO (Jurnal Sist. Informasi), vol. 10, no. 1, pp. 1726, 2024.

F. Riandari and S. Defit, The Application of C4.5 Algorithm for Selecting Scholarship Recipients, ComTech Comput. Math. Eng. Appl., vol. 13, no. 1, pp. 1121, 2022.

N. Azwanti, Algoritma C4.5 Untuk Memprediksi Mahasiswa Yang Mengulang Mata Kuliah (Studi Kasus Di Amik Labuhan Batu), Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 1, pp. 1122, 2018.

F. Pirmansyah and T. Wahyudi, Implementasi Data Mining Menggunakan Algoritma C4.5 Untuk Prediksi Evaluasi Anggota Satuan Pengamanan Studi Kasus Pt. Yimm Pulogadung, J. Indones. Manaj. Inform. dan Komun., vol. 4, no. 3, pp. 15661580, 2023.

Y. Septiani, E. Arribe, and R. Diansyah, MENGGUNAKAN METODE SEVQUAL ( Studi Kasus: Mahasiswa Universitas Abdurrab Pekanbaru ), J. Teknol. dan Open Source, vol. 3, no. 1, pp. 131143, 2020.

K. Purba and K. Sudibjo, The Effects Analysis of Transformational Leadership, Work Motivation and Compensation on Employee Performance in PT. Sago Nauli, Budapest Int. Res. Critics Inst. Humanit. Soc. Sci., vol. 3, no. 3, pp. 16061617, 2020.

M. Meilinda, L. Louis, J. Jason, and H. Nazmi, The Influence of Discipline, Selection, and Organizational Culture on Employee Performance, Almana J. Manaj. dan Bisnis, vol. 5, no. 1, pp. 2028, 2021.

T. D. Anugraheni, L. Izzah, and M. S. Hadi, Increasing the Students Speaking Ability through Role-Playing with Slovins Formula Sample Size, J. Stud. Guru dan Pembelajaran, vol. 6, no. 3, pp. 262272, 2023.

Maurizio Zanardi and Stephen Martin, THE EFFECT OF SOCIAL SECURITY ON EMPLOYEE PERFORMANCE WITH WORK MOTIVATION AS AN INTERVENING VARIABLE (Case Study on Employees of United Kongdom Medical Dcotor and Nurses), MEDALION J. Med. Res. Nursing, Heal. Midwife Particip., vol. 1, no. 3, pp. 9095, 2020.