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All Journal INTECOMS: Journal of Information Technology and Computer Science Equivalent: Jurnal Ilmiah Sosial Teknik
Suharjito Suharjito
Universitas Bina Nusantara
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Metode Pembelajaran Mesin Untuk Menilai Data Depresi Dan Kesehatan Mental Maritza Anastasia; Vivia Surya Maulivia; Suharjito Suharjito
INTECOMS: Journal of Information Technology and Computer Science Vol 7 No 3 (2024): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/intecoms.v7i3.9584

Abstract

Salah satu gangguan kesejahteraan mental yang sering terjadi pada manusia adalah depresi. Mengenali depresi secara dini penting bagi individu. Namun, pada kenyataannya, melakukan skrining depresi secara dini masih memiliki beberapa kelemahan. Jika terus diabaikan, hal ini dapat berdampak pada kesejahteraan individu. Oleh karena itu, diperlukan metode lain yang dapat menggambarkan tingkat depresi pada individu. Penelitian ini bertujuan untuk menentukan gambaran persepsi pekerja tentang komponen psikososial dari lingkungan dan isi kerja serta menentukan gejala stres kerja yang mereka keluhkan. Variabel dependen adalah gejala stres kerja (fisik, mental, kognitif, dan perilaku), sedangkan variabel independen adalah karakteristik individu (usia, jenis kelamin, jenis pekerjaan, dan lama bekerja). Metode klasifikasi menggunakan berbagai model klasifikasi, yaitu K-Nearest Neighbor (KNN), Decision Tree, Random Forest Tree, Naïve Bayes, Support Vector Machine (SVM), dan model penghitungan AdaBoost. Hasil penelitian berdasarkan pengujian berbagai model penghitungan dapat disimpulkan bahwa model penghitungan terbaik untuk mengukur gaji pekerja adalah menggunakan K-Nearest Neighbor (KNN). Analisis variabel berdasarkan faktor Pekerjaan dan Jenis Kelamin memprediksi tingkat depresi dan kesejahteraan mental yang dirasakan oleh anggota berusia 25-30 tahun. Kata Kunci: Depresi dan Kesehatan Mental, Pembelajaran Mesin, Perbandingan Metode Algoritma.
Integration of Stacking Ensemble and Explainable AI for Taxpayer Compliance Risk Profiling Heru Pratama Agung; Suharjito Suharjito
Equivalent: Jurnal Ilmiah Sosial Teknik Vol. 8 No. 2 (2026): Equivalent: Jurnal Ilmiah Sosial Teknik
Publisher : Politeknik Siber Cerdika Internasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59261/jequi.v8i2.301

Abstract

Background: Non-compliance of corporate taxpayers is one of the biggest challenges for the Tax Authorities especially because no tangible data is available on corporate tax avoidance making tax evasion micro data sets have the characteristics of extreme class imbalance that are well known from the real-world tax data. Objective: To develop an accurate and transparent tax non-compliance prediction using an Ensemble Learning based prediction model incorporating Hybrid Resampling methods, and Explainable Artificial Intelligence (XAI). Methods: The dataset, which consists of 49159 observations, is extracted from the administrative record of Directorate General of Taxes where the ratio of imbalance about 18.81:1. In the former strategy, three hybrid resampling techniques (SMOTE-Tomek, SMOTEENN, Borderline-SMOTE Tomek) were integrated with tree-based classifiers (Random Forest, XGBoost, LightGBM) to act as the base-learners. These were all combined using two ensemble architectures, Stacking Classifier and Voting Classifier to utilize their respective predictive capabilities. We used the SHAP and LIME methods to break the black-box nature of the algorithm to interpret the predictive decisions. Results: Experimental results revealed that the best classification was achieved with the Stacking Classifier, yielding an Accuracy of 97.03% along with the minority class F1-Score of 0.7309 In turn, the strongest discrimination in probability was found for the Voting Classifier with an ROC-AUC metric 0.9859 Consequently, the XAI analysis confirmed that pure financial ratio being utterly secondary are dominated in the prediction of the non-compliance risk and shows that absolute financial scale indicators (e.g. Tax Payment Amount, Total Assets) and administrative profile characteristics (e.g. MSME Taxpayer Status, Non-Effective Status) are overwhelmingly DC dominated. Conclusion: The choice of Ensemble Learning provides an analytically sound and interpretable early warning system of tax audits beneficial for real risk-based audits with its composure of hybrid resampling and interpretability (XAI).
Co-Authors Heru Pratama Agung Maritza Anastasia Vivia Surya Maulivia