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All Journal Jurnal Sistem Komputer Techno.Com: Jurnal Teknologi Informasi JUITA : Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Sains dan Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal Teknoinfo ILKOM Jurnal Ilmiah Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika Ekonomi Bisnis Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Didaktikum Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Infotech: Jurnal Informatika & Teknologi Journal of Engineering and Applied Technology Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Algoritme Jurnal Mahasiswa Teknik Informatika Explore JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Arief Hermawan
Universitas Tekonologi Yogyakarta
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Prediksi Promosi Pegawai dengan Stacking Ensemble dengan SMOTE-ENN dan SHAP Andri Yudha Pratama; Arief Hermawan; Donny Avianto
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9879

Abstract

The paradigm of human resource management in the digital era demands an objective and data-driven employee promotion process. However, the extreme class imbalance (ratio 10.74:1) has the potential to introduce bias against minority groups that deserve promotion. This study proposes a stacking ensemble classification framework consisting of Random Forest, XGBoost, and LightGBM as base learners and Logistic Regression as a meta-learner, with the integration of SMOTE-ENN and two-level SHAP interpretability. This study shows that the application of SMOTE-ENN before cross-validation can result in a biassed performance estimate of up to +110% on the F1-Score; thus, the use of imblearn.Pipeline is proposed, which restricts resampling only to the training fold. Based on the evaluation using 10-fold stratified cross-validation free from data leakage, the stacking ensemble model achieved an accuracy of 0.9022, precision of 0.4342, recall of 0.4889, F1-score of 0.4598, and AUC-ROC of 0.8053. Although it did not achieve the highest F1 score, this model attained the best recall value among competitive models, making it relevant for contexts sensitive to false negative errors. SHAP analysis identifies avg_training_score, age, and performance_index as the main determinants of promotion decisions. The proposed framework provides a methodological contribution to model evaluation on imbalanced data while offering a more transparent and accountable decision support system to support the implementation of meritocracy in both government and corporate organisations.
Analisis Pengaruh Preprocessing Data dan Hyperparameter Tuning pada Backpropagation Neural Network dalam Klasifikasi Stroke Asrul Gunawan; Arief Hermawan; Donny Avianto
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.956

Abstract

Data imbalance and scale differences between features are often the main factors that reduce the performance of neural network-based classification models. This study aims to analyze the effect of data preprocessing and hyperparameter tuning on the performance of Backpropagation Neural Network (BPNN) in stroke classification. This study used a stroke dataset from the Kaggle platform consisting of 5,110 patient data with 10 clinical features. The evaluation was conducted using five schemes and consisted of several data balancing techniques. These techniques include no balancing, SMOTE, and ADASYN. In addition, the evaluation also involved data normalization including no normalization, MinMaxScaler, and Z-Score. The BPNN model used has an architecture of 19 input neurons, 29 neurons in the hidden layer, and 1 output neuron. Hyperparameter tuning was performed by finding the best learning rate and number of epochs. The evaluation results showed that the model in scheme one has limitations. This limitation is most visible in identifying stroke classes. The application of SMOTE and MinMaxScaler in scheme two proved that the results were better and its performance increased significantly. On the other hand, the combination of ADASYN and Z-Score in scheme three showed more stable performance and was able to detect stroke cases more accurately. The hyperparameter tuning process in schemes four and five also proved to improve performance. The best results were obtained in scheme five, with an accuracy of 96.47%, a precision of 97.34%, a recall of 95.62%, and an F1-score of 96.47%. These findings indicate that the combination of adaptive balancing techniques, distribution-based normalization, and optimal parameter tuning is very effective in improving the accuracy and stability of BPNN for stroke classification.
Co-Authors Achmad Safruddin Aditya Dwi Putro Adityo Permana Adityo Permana Wibowo Akmal Setiawan Wijaya Aldi Tri Wijaya Andri Yudha Pratama Arrosyid, Muhammad Zydane Asrul Gunawan Bagus Wibowo Kusumo Baiq Nurul Azmi Bowo Hirwono Cahyo Prakoso Donny Avianto Edi Priyanto Enggar Novianto Ferdinandus Edwin Penalun Gunawan Wibisono Ilham Rafiedhia Pramutighna Imantoko Imantoko Intan Setiawati Khalifatur Rauf M. Royani Sopia Al Hasan Muh Arifandi Muhammad Aulia Muhammad Rizki Muhammad Rizki Muhammad Rousydi Hunafa Nur Hidayati Nur Widiastuti Nurcakhyadi, Fredianto Panji Al Muqsith Prasetyo Reski Noviana Rian Oktafiani Rianto Rianto Rizky Diar Panuntun Saifullah, Shoffan Shoffan Saifullah Sutarman . Tri Waluyo Tri Widodo Triyas Hevianto Saputro Wardaya, Imanuel Puspa