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All Journal Tawazun: Journal of Sharia Economic Law METIK JURNAL
Dimas Aprilianto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Hifdz Al-Maal dalam Regulasi Rahasia Perbankan Luqman Nurhisam; Dimas Aprilianto
TAWAZUN : Journal of Sharia Economic Law Vol 3, No 2 (2020): Tawazun: Journal of Sharia Economic Law
Publisher : Sharia Faculty Islamic Economic Law Study Department

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/tawazun.v3i2.8269

Abstract

Bank secrecy refer to secrets in the relationship between a bank and a customer. In accordance with Article 40 paragraph (1) of Law Number 10 Year 1998 concerning Banking, it is stated that banks are required to keep confidential information regarding their depositing customers and their deposits. The research was conducted using the library research method, which looks for normative sources of law by reviewing the laws and regulations that apply or are applied to a particular legal problem. The approach used is the statutory approach, namely the approach taken by examining laws relating to bank secrecy. The purpose of this study is to further examine how Islamic law views the regulation of bank secrecy in Indonesia. The results of this study are related to the maintenance of one of the basic needs elements, namely assets that must be protected (hifdz al-maal), so if other parties ask for an explanation of the financial condition of a customer from a bank, this is not allowed.
Klasifikasi Penyakit Kanker Paru Menggunakan Algoritma Random Forest Berbasis Streamlit Dimas Aprilianto; Rizal, Erian
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/svz4r327

Abstract

Lung cancer is one of the leading causes of global mortality, often difficult to detect early due to its nonspecific initial symptoms. This study proposes a Machine Learning-based approach to classify lung cancer risks using the Random Forest algorithm optimized with GridSearchCV. The identified research gap is the lack of interactive web-based implementations that deliver real-time classification results with a user-friendly interface for general users. The objective of this study is to develop an accurate and efficient classification model and integrate it into a web application using Streamlit. The dataset was sourced from Kaggle, consisting of 5,000 patient records and 18 clinical and lifestyle-related features. The preprocessing steps included data cleaning, normalization, encoding, and feature recategorization. Model performance evaluation using Accuracy, Precision, Recall, and F1-Score metrics showed an accuracy of 90%. Feature importance analysis identified smoking habits, throat discomfort, and respiratory issues as dominant predictors of lung cancer. The model was then deployed into a Streamlit-based web application and tested via a User Acceptance Test (UAT) involving 50 respondents, resulting in a Mean Opinion Score (MOS) average above 84%. These findings indicate that the developed prediction system is not only technically accurate but also well-accepted by users, highlighting its potential as a practical and efficient tool for early lung cancer screening.
Co-Authors Luqman Nurhisam Rizal, Erian