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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) HAYATI Journal of Biosciences PHARMACON Jurnal Bios Logos Animal Production : Indonesian Journal of Animal Production Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Makara Journal of Science Heca Journal of Applied Sciences Malacca Pharmaceutics Health & Medical Sciences Journal of Educational Management and Learning Infolitika Journal of Data Science Grimsa Journal of Science Engineering and Technology Jurnal Inovasi Global Borneo Journal of Pharmacy
TRINA EKAWATI TALLEI
Faculty of Mathematics and Natural Sciences, Sam Ratulangi University Manado
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Education Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Infolitika Journal of Data Science

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Machine Learning Approach for Diabetes Detection Using Fine-Tuned XGBoost Algorithm Maulana, Aga; Faisal, Farassa Rani; Noviandy, Teuku Rizky; Rizkia, Tatsa; Idroes, Ghazi Mauer; Tallei, Trina Ekawati; El-Shazly, Mohamed; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.72

Abstract

Diabetes is a chronic condition characterized by elevated blood glucose levels which leads to organ dysfunction and an increased risk of premature death. The global prevalence of diabetes has been rising, necessitating an accurate and timely diagnosis to achieve the most effective management. Recent advancements in the field of machine learning have opened new possibilities for improving diabetes detection and management. In this study, we propose a fine-tuned XGBoost model for diabetes detection. We use the Pima Indian Diabetes dataset and employ a random search for hyperparameter tuning. The fine-tuned XGBoost model is compared with six other popular machine learning models and achieves the highest performance in accuracy, precision, sensitivity, and F1-score. This study demonstrates the potential of the fine-tuned XGBoost model as a robust and efficient tool for diabetes detection. The insights of this study advance medical diagnostics for efficient and personalized management of diabetes.
Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review Noviandy, Teuku Rizky; Maulana, Aga; Idroes, Ghazi Mauer; Emran, Talha Bin; Tallei, Trina Ekawati; Helwani, Zuchra; Idroes, Rinaldi
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.91

Abstract

This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.
Co-Authors Abas, Abdul Hawil Adikila, Gregorius Giani Angelina Stevany Regina Masengi Antasionasti, Irma Any Aryani Arifin, Mulyani Asep Rusyana Azzania Fibriani Balansa, Endrile Golmen Barasarathi , Jayanthi BEIVY JONATHAN KOLONDAM Celik, Ismail Daniel Febrian Sengkey Dantje Tarore Diah - Kusumawaty Diah Puspitasari Dian Handayani Diana Setya Ningsih, Diana Didik Priyandoko Dolongtelide, Jeclin Inebel Dzikrina, Hanina El-Shazly, Mohamed Elly Suoth Emran, Talha Bin Erwin Wantasen Estevam, Ethiene Castellucci Faisal, Farassa Rani Fatimawali . Florencia N. Sompie Ghazi Mauer Idroes Halimatushadyah, Ernie Hariyanto, Yuanita Amalia Herny E.I. Simbala Hizir Sofyan Idroes, Ghifari Maulana Illah Sailah Irvanizam, Irvanizam Jein Rinny Leke, Jein Rinny Kalalo, Marko Jeremia Kemala, Pati Kepel, Billy Johnson Khairan Khairan Laksono Trisnantoro Lala, Andi Lydia E. N. Tendean, Lydia E. N. Mamahit, Juliet Merry Eva Martha Marie Kaseke Masengi, Kyoko Itsuko Etsuko Gabriela Maulana, Aga Maulydia, Nur Balqis Mirda, Erisna Moh. Yani Mohd Fauzi, Fazlin Monoarfa, Alexander James Muliadi Ramli Musdalifah, Annisa Nabila, Fiki Farah Niode, Nurdjannah Jane Nurul Faridah, Nurul Patwekar, Mohsina Paulina yamlean Pendong, Christa Hana Angle Purukan, Christy Purwanto, Diana Shintawati Rahman, Sunarti Abd Ratte, Titah Amelia Rinaldi Idroes Rizkia, Tatsa Roni Koneri Runtunuwu, Stefanus Vicky Bernhard Elisa Salaki, Christina Leta Salaswati, Salaswati Sambul, Alwin Melkie Sari, Nadia Warda Sekar Sasmita, Novi Reandy Siampa, Jainer Pasca Sri Sudewi, Sri Takawaian, Agrita Feisilia Tamala, Yulianida Tania, Adinda Dwi Tendean, Lydia Estelina Naomi Teuku Rizky Noviandy Tumilaar, Sefren Geiner Turalaki, Grace Lendawati Amelia Unsratdianto Sompie, Sherwin Reinaldo Utami, Wulandari Putri Wawo, Arsianita Ester Wijaya, Puspita Wungouw, Herlina Ineke Surjane Zuchra Helwani, Zuchra Zulkarnain Jalil