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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Penelitian Akuntansi
Leonardo Leonardo
Universitas Pelita Harapan
Computer Science & IT Economics, Econometrics & Finance Engineering

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ANALISIS FAKTOR DETERMINAN TERHADAP FINANCIAL DISTRESS DENGAN PROFITABILITAS SEBAGAI VARIABEL PEMODERASI Leonardo Leonardo; Mulyadi Noto Soetardjo
Jurnal Penelitian Akuntansi (JPA) Vol 4, No 2 (2023): Oktober
Publisher : Universitas Pelita Harapan

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Abstract

Penelitian yang dilakukan memiliki tujuan untuk mengetahui serta melakukan analisis terhadap operating capacity dan likuiditas dengan profitabilitas sebagai variabel moderasi terhadap financial distress perusahaan sektor pertambangan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2018 hingga 2022. Pada penelitian ini menggunakan sebanyak 212 data sampel penelitian. Pengukuran terhadap financial distress menggunakan pengukuran perhitungan Altman Z-Score dengan menggunakan metode analisis regresi logistik. Hasil penelitian menujukkan bahwa variabel likuiditas dapat memberikan pengaruh negatif secara signifikan terhadap financial distress, namun variabel lainnya belum dapat secara signifikan memberikan pengaruh terhadap financial distress, dan profitabilitas sebagai variabel moderasi tidak mampu memberikan pengaruh baik memperkuat atau memperlemah terhadap financial distress.
Early Detection of Cardiovascular Disease Risk Using the K-Nearest Neighbors Algorithm Leo Fernandy; Leonardo Leonardo; Stanley Lim; Vincent Liawis; Ade Maulana
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 5 No. 2 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v5i2.5430

Abstract

Health is a fundamental aspect in determining the quality of human life. Along with changes in lifestyle and environmental conditions, various new challenges have emerged in the healthcare sector. Technological advancements, particularly in artificial intelligence, have opened significant opportunities for the development of more accurate and efficient healthcare systems. One of the most rapidly growing applications of AI is machine learning for disease prediction. This study aims to develop a model for predicting the risk of cardiovascular disease using the K-Nearest Neighbors (KNN) algorithm. The “Cardiovascular Disease” dataset from Kaggle, consisting of 68,205 entries and 17 medical attributes, was used as the basis. The research stages included data preprocessing (cleaning, categorical transformation, and normalization), selection of key features, model training, and performance evaluation. The dataset was split into 80% training data and 20% testing data. The experiment showed that k = 41 achieved the highest accuracy of 73%. Evaluation using precision, recall, and f1-score indicated fairly good performance, particularly in identifying high-risk patients. This model has the potential to serve as a decision-support tool for early detection of cardiovascular disease, enabling more accurate and preventive medical actions..
Co-Authors Ade Maulana Leo Fernandy Mulyadi Noto Soetardjo Stanley Lim Vincent Liawis