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All Journal EKSAKTA: Journal of Sciences and Data Analysis International Journal of Humanities and Social Science Studies
Al Hafidz, Muhammad
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Religion Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology

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Exploring the Wisdom of the Sunnah or Hadith as a Guide to Islamic Law Soleh, Soleh; Apriantoro, Muhamad Subhi; Al Hafidz, Muhammad
Ethica: International Journal of Humanities and Social Science Studies Vol 2 No 3 (2024): September
Publisher : Global Research Network

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Abstract

Sunnah is a good or bad habit that is followed, both before and after the Prophet Muhammad SAW, including his words, actions and approval. The Hadith is a record of the words, actions or approval of the Prophet Muhammad SAW which includes information about his life and teachings. Then the role in Islamic law is that the Sunnah and hadith are important sources of law that provide guidance for everyday life. The Sunnah also provides strength, explanation and establishes laws that have not been regulated in the Qur'an. There are controversies and solutions. There is a group that doubts or rejects the validity of hadith as the basis of sharia. Reasons for rejection include the belief that the Qur'an already includes everything that is necessary and the validity of the hadith is questionable. There are also those who continue to defend the Hadith. The ulama have scientifically responded to the rejection arguments, strictly classified the hadith, and explained the need for further explanation of the Qur'an through hadith or sunnah.
A Comparative Evaluation of XGBoost and LightGBM for Diabetes Mellitus Risk Prediction Using a Public Dataset and Web-Based Dashboard Wahyuningtyas, Sischa; Al Hafidz, Muhammad
EKSAKTA: Journal of Sciences and Data Analysis VOLUME 7, ISSUE 1, April 2026
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/EKSAKTA.vol7.iss1.art11

Abstract

Diabetes mellitus is a health problem of global concern, considering that most cases are only identified when complications arise. Therefore, early detection is essential in controlling the health and financial consequences of the disease. The purpose of this study is to compare two machine learning models using gradient boosting techniques, namely Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). This study use a technique called RandomizedSearchCV to optimize the performance of the proposed machine learning models. In evaluating the machine learning models, the study used a variety of metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. The LightGBM is a more efficient machine learning model than XGBoost based on the result. The LightGBM model had a classification accuracy of 77.3%, a precision of 71.1%, and a recall of 59.3%, which is the same value obtained by the XGBoost model. However, the LightGBM model had a higher F1 score of 64.6% and a ROC-AUC of 83.0% which indicates that the model is more balanced and can accurately classify and distinguish between the two classes. The best-performing machine learning model was integrated with a web-based system using a framework called Streamlit to create a system that is responsive, interactive, and user-friendly. The system is useful for early detection of diabetes mellitus and can be used by non-experts to determine whether a patient is at risk of developing the disease using real-time prediction and user-friendly data input. The results of the study showed that gradient boosting machine learning models can be used to diagnose and detect early cases of diabetes mellitus.
Co-Authors Muhamad Subhi Apriantoro Soleh, Soleh Wahyuningtyas, Sischa