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All Journal Edumatsains JRIIN :Jurnal Riset Informatika dan Inovasi
Rewan Jayadi
Universitas Airlangga
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics Physics

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Application of the Analytical Hierarchy Process (AHP) Method in Supporting Decision-Making Regarding Drug Abuse Factors Rewan Jayadi; Herry Suprajitno; Miswanto
EduMatSains : Jurnal Pendidikan, Matematika dan Sains Vol 10 No 2 (2025): October
Publisher : Fakultas Keguruan dan Ilmu Pendidikan, Universitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/edumatsains.v10i2.7386

Abstract

Drugs are an abbreviation for narcotics and dangerous drugs, whether natural, synthetic, or semi-synthetic. The use and spread of illegal drugs worldwide shows a sharp increase, spreading and affecting all countries and people of all religions, and has caused many fatalities. The purpose of this study is to apply the Analytic Hierarchy Process (AHP) method in evaluating risk factors and analyzing the performance of each criterion against drug abuse areas. This data primarily comes from the Bengkulu Province National Narcotics Agency. The consistency test results for each criterion and alternative in the AHP method show consistent values of less than 0.1, indicating that the data used meet the consistency standards required by Saaty for the AHP method. The results of the analysis of preferences for risk factors show that family factors have the highest weight, meaning the role of family is very important, followed by peer group factors, personality, community environment, economic and psychosocial factors, drug availability, anxiety and depression, and school environment. Keywords: AHP, drug abuse, MCDM, risk factors, Bengkulu Province
Prediksi Risiko Penyakit Parkinson Menggunakan Seleksi Fitur Algoritma Genetika dan SMOTE-XGBoost Verina Tita Nabila; Rewan Jayadi
Jurnal Riset Informatika dan Inovasi Vol 3 No 11 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Parkinson’s disease is a progressive neurodegenerative disorder that affects the central nervous system and is characterized by reduced motor control and changes in voice quality due to impaired motor function. To date, the diagnosis of Parkinson’s disease largely depends on the expertise and clinical experience of specialists. The uneven distribution of clinicians across regions remains a major challenge in providing accurate diagnosis and appropriate treatment. Therefore, this study aims to develop a machine learning–based model for predicting the risk of Parkinson’s disease by incorporating feature selection using a genetic algorithm, handling data imbalance through the SMOTE approach, and performing prediction using the XGBoost method. The results indicate that the proposed method achieves excellent performance, with an accuracy of 95%, sensitivity of 93%, specificity of 100%, precision of 100%, an F1-score of 97%, and an AUC value of 97%. Several selected features include fundamental voice frequency, pitch stability, amplitude perturbation across voice cycles averaged over five periods, harmonic-to-noise ratio, and spectral spread measure 2.
Co-Authors Herry Suprajitno M. Miswanto Verina Tita Nabila