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All Journal Jurnal Informatika Global BIMASAKTI
EP, Amak Yunus
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Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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MONITORING KUALITAS PH DAN KEKERUHAN AIR LAUT DALAM MENJAGA EKOSISTEM TERUMBU KARANG MENGGUNAKAN METODE FUZZY LOGIC Asrori, Hazynatul; EP, Amak Yunus; Muhammad Priyono Tri Sulistyanto
Jurnal Fakultas Teknologi Informasi Vol 7 No 1 (2024): BIMASAKTI
Publisher : Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v7i1.10705

Abstract

 The condition of the sea and coastal areas is getting worse every day. Poor sea conditions will damage the marine ecosystem because the quality of sea water is a very important factor for the survival of coral reefs and other marine biota. To reduce the impacts that occur due to poor water quality, a monitoring and control system is needed so that water quality can be controlled properly. Therefore, the author makes a coral reef ecosystem monitoring system using the Fuzzy Logic method, the system will monitor the pH and turbidity of water, then use fuzzy as a decision support method to determine the quality of sea water.
PERBANDINGAN ALGORITMA XTREME GRADIEN BOOSTING DAN ALGORITMA DECISIEN TREE DALAM KLASIFIKASI HIV/AIDS Saru, Bonefasius Erifan; Nugaraha, Danang Aditya; EP, Amak Yunus
Jurnal Fakultas Teknologi Informasi Vol 8 No 1 (2025): BIMASAKTI
Publisher : Prodi Teknik Informatika, Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v8i1.13018

Abstract

This study compares the performance of the Decision Tree and Extreme Gradient Boosting (XGBoost) algorithms in classifying HIV/AIDS infection status. A quantitative experimental design was employed using a secondary dataset of 2,139 records and 23 attributes obtained from an open-source platform. Data preprocessing included checking for missing values, removing duplicates, detecting and handling outliers with the Interquartile Range (IQR) method, and applying feature scaling. The models were trained and tested with three data split ratios (70:30, 80:20, and 90:10). Evaluation metrics comprised accuracy, precision, recall, and F1-score derived from the confusion matrix.The results show that XGBoost achieved the highest performance, reaching 99.16 % accuracy, 98.17 % precision, 99.16 % recall, and 99.16 % F1-score with a 90:10 data split. In comparison, the Decision Tree achieved a maximum accuracy of 95 % with an F1-score of approximately 95 % under the same conditions. These findings confirm that XGBoost consistently outperforms the Decision Tree in accuracy and generalization across all data-split scenarios. This research concludes that XGBoost is more suitable for developing data-driven decision support systems for HIV/AIDS infection detection.
Co-Authors Anggri Sartika Wiguna Asrori, Hazynatul Jodianus Nugaraha, Danang Aditya Saru, Bonefasius Erifan Sulistyanto, Muhammad Priyono Tri