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All Journal Informatics and Computer Engineering Journal Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Findi Ayu Sariasih
Universitas Bina Sarana Informatika
Computer Science & IT

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Perancangan Sistem Prediksi Deteksi Alzheimer Berbasis Random Forest Menggunakan Metode Scrum Daira Syahfitri; Dian Rahayuningtyas; Raihano Garcia; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/gkep7058

Abstract

Alzheimer's disease is a neurodegenerative characterized by a gradual decline in memory and cognitive function, with a prevalence that continues to increase globally and in Indonesia. Constraints in early detection, such as limited healthcare facilities and the high cost of conventional diagnosis, drive the need for easily accessible technology-based solutions. This research aims to develop a web system named MindCare that integrates the Random Forest algorithm to predict the risk of Alzheimer's based on clinical and lifestyle data. The system development method uses the Agile Scrum approach with four sprint cycles, covering needs analysis, model training, web system integration, as well as testing and refinement. The model was trained using Alzheimer's and mental health datasets from Kaggle, with evaluation results showing perfect accuracy and AUC (100%). The features FamilyHistoryAlzheimers, Age, and PhysicalActivity proved to be the most influential in prediction. The resulting web system provides risk prediction features, result visualization, personalized prevention recommendations, and education about Alzheimer's. Black-box testing showed all functions worked as expected. The conclusion of this research is that the MindCare system is suitable for use as an easily accessible medium for early detection and education on Alzheimer's, with recommendations for further development through database expansion, exploration of other algorithms, and the addition of consultation and monitoring features
Pengembangan Sistem Prediksi Risiko Gangguan Mental Remaja Menggunakan Support Vector Machine (SVM) Anisya Septianur; Elsya Bani Aulia; Nugroho Fathul Aziz; Findi Ayu Sariasih; Syifa Nur Rakhmah; Imam Sutoyo
BETRIK Vol. 16 No. 03 (2025): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/zh47p731

Abstract

Adolescent mental health has become an increasingly critical issue due to the rising prevalence of emotional and behavioral disorders among young individuals. Social pressure, academic demands, and psychological changes often trigger stress, anxiety, and even depression, which affect learning activities and social interactions. This study aims to develop a web-based system to detect mental disorder risk in adolescents using a machine learning approach with the Support Vector Machine (SVM) algorithm. Three open datasets from the Kaggle platform—Big Five Personality Test Dataset, Symptom2Disease Dataset, and Mental Health in Tech Survey Dataset—were utilized to integrate personality traits, physical conditions, and mental health indicators. The data underwent preprocessing involving duplicate removal, missing value imputation, standardization, and categorical-to-numerical transformation before being split into 70% training and 30% testing sets. The system was developed using the Agile Scrum methodology in an iterative and adaptive manner based on user feedback. The experimental results show that the SVM model with an RBF kernel achieved 91.3% accuracy, 89.7% precision, and 91.9% F1-score. The resulting system, can classify mental disorder risk levels and provide prevention recommendations according to the assessment results. With an interactive interface, this system is expected to assist adolescents in recognizing their mental conditions early, increase awareness of psychological well-being, and serve as a technologybased educational tool for mental health prevention. 
Prediksi Risiko Kesehatan Bayi Berbasis Parameter Tumbuh Kembang dengan Menggunakan Gradient Boosting Astatia Hulu; Juan Sebastian Aimar; Firyal Aufa Nabilah; Syifa Nur Rakhmah; Findi Ayu Sariasih; Imam Sutoyo
Informatics and Computer Engineering Journal Vol 6 No 1 (2026): Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/icej.v6i1.11066

Abstract

Kesehatan bayi merupakan indikator penting kualitas generasi masa depan, namun deteksi dini risiko kesehatan sering terkendala keterbatasan tenaga medis dan sistem pemantauan efektif. Penelitian ini mengembangkan sistem prediksi risiko kesehatan bayi berusia 0-30 hari menggunakan algoritma Gradient Boosting berdasarkan parameter tumbuh kembang. Metode pengembangan sistem menggunakan Agile Scrum dengan dataset "Infant Wellness and Risk Evaluation" yang melalui tahap pra-pemrosesan data dan feature engineering. Hasil evaluasi menunjukkan model mencapai akurasi 94%, recall 84% untuk kelas berisiko, dan precision 71%. Analisis feature importance mengidentifikasi age_days, oxygen_saturation, dan heart_rate_zscore sebagai fitur paling berpengaruh. Sistem prediksi berbasis web yang dihasilkan ini nantinya diharapkan dapat menjadi alat bantu yang efektif bagi tenaga medis. Infant health is an important indicator of future generation quality, but early detection of health risks is often constrained by limitations of medical personnel and effective monitoring systems. This research develops a health risk prediction system for infants aged 0-30 days using Gradient Boosting algorithm based on growth and development parameters. The system development method uses Agile Scrum with "Infant Wellness and Risk Evaluation" dataset through data preprocessing and feature engineering stages. Evaluation results show the model achieves 94% accuracy, 84% recall for at-risk class, and 71% precision. Feature importance analysis identifies age_days, oxygen_saturation, and heart_rate_zscore as the most influential features. The resulting web-based system has potential as an effective assistance tool for medical personnel.  
Co-Authors Anisya Septianur Astatia Hulu Daira Syahfitri Dian Rahayuningtyas Elsya Bani Aulia Firyal Aufa Nabilah Imam Sutoyo Juan Sebastian Aimar Nugroho Fathul Aziz Raihano Garcia Syifa Nur Rakhmah