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Mukti Ratna Dewi
Insitut Teknologi Sepuluh Nopember
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A Hybrid Model to Enhance The Performance of Classifier in Financial Distress Prediction Mukti Ratna Dewi; Destri Susilaningrum
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.v9i1.94725

Abstract

Accurately predicting financial distress is a critical issue in financial decision-making. Financial distress must be detected as early as possible as an important determining factor in decision-making for internal companies and financial institutions related to financing or loan decisions. Various studies on financial distress prediction in Indonesia have been carried out, ranging from traditional statistical approaches to machine learning. However, the performance of the two methods is still not optimal. Therefore, this study tries to develop machine learning techniques by combining cluster analysis and classification in a hybrid model to improve the prediction model’s performance. The case study adopted in this study is the prediction of financial distress in non-financial companies listed on the IDX from 2018-2021 by combining k-means clustering and Support Vector Machine. The analysis results show that the hybrid classifier has an accuracy value of 92.7%, which is higher than the accuracy of the single classifier, which is 88.6%.
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.
Analisis Sentimen Pasar melalui Berita Finansial untuk Prediksi Harga Saham PT Bank Rakyat Indonesia Tbk Ferdyansyah Permana Putra; Mukti Ratna Dewi; Fausania Hibatullah
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.v9i1.94692

Abstract

Abstrak:Sentimen pasar merupakan salah satu faktor yang mempengaruhi fluktuasi harga saham yang dapat bersumber dari masyarakat umum maupun berita-berita yang terkait dengan saham. Dalam penelitian ini, pengaruh sentimen pasar melalui berita keuangan dianalisis terhadap harga saham PT Bank Rakyat Indonesia Tbk (BBRI). Penelitian ini menggunakan pendekatan machine learning dengan metode Support Vector Regression (SVR) untuk memprediksi harga penutupan saham BBRI berdasarkan sentimen berita. Model SVR dioptimalkan dengan algoritma Fruit Fly Optimization Algorithm (FOA). Sentimen pasar terlebih dahulu dievaluasi menggunakan metode IndoBERT yang menunjukkan tingkat akurasi sentimen keseluruhan di atas 90%. Setelah itu, empat skenario pemodelan diusulkan untuk menemukan model prediksi terbaik: (1) model tanpa sentimen, (2) model dengan sentimen pada periode , (3) model dengan sentimen pada periode , dan (4) model dengan sentimen pada periode  dan periode . Hasil akhir menunjukkan bahwa model pada skenario (1) memiliki kesalahan prediksi terendah dibandingkan dengan model lainnya==============================================Abstract:Market sentiment is one of the factors that influences the fluctuation of stock prices, which can originate from the general public or news related to stocks. In this study, we explore the effect of market sentiment through financial news on the stock price of PT Bank Rakyat Indonesia Tbk (BBRI). This research adopts a machine learning approach using the Support Vector Regression (SVR) method to predict the closing price of BBRI stock based on news sentiment, and the function is later optimized with the Fruit Fly Optimization Algorithm (FOA) algorithm. The market sentiment is first evaluated using the IndoBERT method, which shows an overall sentiment accuracy level above 90%. Afterward, four modeling scenarios are proposed to find the best prediction model: (1) a model without sentiment, (2) a model with sentiment at period , (3) a model with sentiment at period , and (4) a model with sentiment at both period  and period . The final results indicate that the model in scenario (1) has the lowest prediction error compared to other models
Co-Authors Destri Susilaningrum Fausania Hibatullah Ferdyansyah Permana Putra Lovinki Fitra Ananda Mochammad Reza Habibi