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All Journal VOX EDUKASI: Jurnal Ilmiah Ilmu Pendidikan JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Abdimas Bina Bangsa Kohesi: Jurnal Sains dan Teknologi Jurnal Informatika Polinema (JIP)
Octariadi, Barry Ceasar
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Classification Of Anxiety Levels Based On General Anxiety Disorder Data Using The XGBoost Method Rahman, Rovi Royyan; Octariadi, Barry Ceasar; Sucipto
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 11 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v11i1.495

Abstract

Anxiety is a common psychological disorder experienced by individuals and has the potential to reduce quality of life if not treated properly. This study aims to classify anxiety levels into four categories, namely normal, mild, moderate, and severe, using the Extreme Gradient Boosting (XGBoost) algorithm. The data used came from the Kaggle platform, consisting of 671 entries with 11 anxiety symptom features and one target label. The research process involved data exploration (EDA), handling missing values, data balancing using the Synthetic Minority Oversampling Technique (SMOTE), and feature selection based on multivariate correlation. Two models were built with training and test split ratios of 70:30 and 80:20. The evaluation results showed that the XGBoost model achieved good classification performance, with accuracy, precision, recall, and F1-score reaching 93% after optimization. The best model was then implemented as a Streamlit web application to facilitate interactive prediction of anxiety levels. This research is expected to be a tool for initial screening of anxiety disorders and a reference in the development of machine learning-based classification systems in the field of mental health.
Implementasi Fuzzy Mamdani untuk Rekomendasi Total Kalori Harian Bagi Penderita Diabetes Tipe 2 : Implementasi Fuzzy Mamdani untuk Rekomendasi Total Kalori Harian Bagi Penderita Diabetes Tipe 2 Widiasari, Putu Puja Diva; Siregar, Alda Cendekia; Octariadi, Barry Ceasar
Computer Science and Information Technology Vol 7 No 1 (2026): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v7i1.11027

Abstract

Type 2 Diabetes Mellitus is a metabolic disease that requires precise regulation of daily calorie intake to maintain stable blood sugar levels. Determining calorie requirements is not simple because it must take into account factors such as age, gender, body mass index (BMI), and physical activity level. This study aims to develop a Mamdani fuzzy logic-based expert system to provide recommendations for daily calorie requirements for people with type 2 diabetes. The system process is carried out through the stages of fuzzification, inference, aggregation, and defuzzification using the centroid method. Testing was conducted using 20 type 2 diabetes patient data with input variables of age, height, weight, gender, and physical activity. The testing methods used were accuracy and black-box. Accuracy testing was performed by comparing the system's results with manual calculations based on medical standards, while black-box testing ensured that the system functioned as designed. The results showed that the system had an accuracy rate of 80%, making it sufficiently valid and usable as a tool for recommending daily calorie intake to support diet management for type 2 diabetes patients.
Co-Authors Alinafiah, Andika Muhammad Asrul Abdullah azwar, agim aljanata Cahyo, Rahandya Chatarina Umbul Wahyuni Febrilian, Uray Menur Wahyu Pangestika, Menur Wahyu Muhammad Agus Muljanto Putri, Alya Amanda Rachmat Wahid Saleh Insani Rahman, Rovi Royyan Repan Sabima, Muhamad Angga Rizki Saputra, Deny Saputra Setyawan, Rizki Fajar Siregar, Alda Cendekia Sucipto Sucipto Sucipto Widiasari, Putu Puja Diva Yanti, Dewi