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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Applied Informatics
Mochammad Reza Habibi
Institut Teknologi Sepuluh Nopember
Computer Science & IT Electrical & Electronics Engineering

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Explainable Machine Learning dalam Analisis Risiko Akademis Mahasiswa Fakultas Vokasi Institut Teknologi Sepuluh Nopember Lovinki Fitra Ananda; Mukti Ratna Dewi; Mochammad Reza Habibi
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 1 (2024)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i1.94691

Abstract

Abstrak:Mahasiswa dengan performa akademis yang buruk dan tingkat  drop out yang relatif tinggi dapat memengaruhi akreditasi dan citra institusi pendidikan tinggi. Hal tersebut dapat diantisipasi dengan cara mengevaluasi kondisi akademik mahasiswa, khususnya pada mahasiswa yang menunjukkan penurunan performa akademis. Penelitian ini bertujuan memberikan informasi mengenai faktor-faktor yang memengaruhi risiko akademis mahasiswa menggunakan explainable machine learning. Persentase mahasiswa yang berisiko akademis hanya sebesar 7,3% sehingga kasus imbalance ini perlu ditangani menggunakan SMOTE untuk mengoptimalkan kinerja model klasifikasi. Model random forest pada data yang telah seimbang memiliki kemampuan prediksi dengan tingkat akurasi 96,4%, specificity mencapai 95%, dan nilai recall atau sensitivity sebesar 98%. Selanjutnya, SHAP diimplementasikan untuk mengetahui kontribusi masing-masing faktor terhadap potensi risiko akademik. Hasil dari SHAP menunjukkan bahwa skor TPKA kuantitatif, diikuti oleh jenis kelamin dan jalur masuk memiliki kontribusi paling tinggi terhadap risiko akademis mahasiswa===========================================Abstract:Students with poor academic performance and relatively high dropout rates can affect the accreditation and image of higher education institutions. This can be anticipated by evaluating the academic conditions of students, especially those who show a decline in academic performance. This study uses explainable machine learning to provide information on the factors that influence students’ academic risk. The percentage of students at academic risk is only 7.3%, so this imbalance case needs to be handled using SMOTE to optimize the performance of the classification model. The random forest model on balanced data has a predictive ability with an accuracy level of 96.4%, specificity reaching 95%, and a recall or sensitivity value of 98%. Furthermore, SHAP is implemented to determine the contribution of each factor to the potential academic risk. The results of SHAP show that the three most significant contributing factors to students’ academic risk are the quantitative TPKA score, followed by gender and type of student admission.
Prediction of carbon emissions from vehicles using interpretable neural networks Ridho Sholehurrohman; Mochammad Reza Habibi; Joko Triloka
Bulletin of Electrical Engineering and Informatics Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i4.12064

Abstract

Vehicle carbon dioxide emissions are an important environmental concern that requires prediction methods with both high accuracy and transparent explanations. This study proposes an interpretable neural network (INN) framework for estimating vehicle carbon dioxide emissions using a public Canadian fuel-consumption and emissions dataset. A feed-forward neural network is employed for regression, while SHapley additive exPlanations (SHAP) are used to interpret the contribution of each input feature. The proposed model is compared with linear regression, Bayesian regression, support vector regression (SVR), and extreme gradient boosting (XGBoost). Experimental results show that the neural-network model achieves a root mean squared error (RMSE) of 4.98, a mean absolute percentage error (MAPE) of 0.0128, and a coefficient of determination (R-squared) of 0.9926, outperforming all baseline models. SHAP analysis identifies combined fuel economy, city fuel consumption, and highway fuel consumption as the most influential predictors. Higher fuel economy is associated with lower predicted emissions, whereas higher city and highway fuel consumption increases predicted emissions. These findings demonstrate that the proposed framework provides accurate and interpretable vehicle carbon dioxide emission estimation.
Co-Authors Joko Triloka Lovinki Fitra Ananda Mukti Ratna Dewi Ridho Sholehurrohman