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Muhammad Fadli
Politeknik Negeri Lampung
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Hybrid XGBoost-SVM Model untuk Sistem Pendukung Keputusan dalam Prediksi Penyakit Diabetes Muhammad Surono; Muhammad Fadli; Dian Sri Purwanti; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 3 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i3.5410

Abstract

Diabetes is a chronic disease that continues to rise globally each year, requiring early detection for more effective prevention. This study develops an artificial intelligence-based decision support system for diabetes prediction using a Hybrid XGBoost-SVM model. The model combines the Support Vector Machine (SVM), known for its interpretability, with XGBoost (XGB), which enhances accuracy through boosting techniques. The study utilizes the Pima Indians Diabetes Dataset, undergoing preprocessing, normalization, data splitting, and model training. The evaluation compares accuracy, precision, recall, and F1-score across the three models. Experimental results indicate that XGBoost and SVM both achieve an accuracy of 75%. However, the Hybrid XGBoost-SVM model provides consistently improved performance, achieving the highest accuracy (77%), along with increased precision (70%) and F1-score (65%). Although the numerical improvement in accuracy appears relatively small, this enhancement is significant in the medical context, especially due to improved precision and balanced classification. This study concludes that the Hybrid XGBoost-SVM approach offers a more optimal and reliable alternative in decision support systems for diabetes prediction. Future research can explore other model combinations, such as Stacking or Weighted Voting, to enhance predictive performance further.
Deteksi Dini Stroke Menggunakan Machine Learning Kevinda Sari; Muhammad Fadli; Muhammad Fahmi Fudholi; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.5590

Abstract

Stroke is one of the leading causes of death and disability worldwide. Early detection of stroke risk is crucial to prevent more severe complications. This study aims to develop a stroke prediction model based on machine learning using an open dataset from Kaggle containing patients' medical and demographic information. Four machine learning algorithms were utilized and compared: AdaBoost, Gradient Boosting, LightGBM, and XGBoost. Data preprocessing steps included missing value imputation, categorical variable encoding, numerical feature normalization, and class balancing using the SMOTEENN method. Additionally, feature selection was performed using the Extra Trees algorithm to enhance model performance. The results showed that the XGBoost model delivered the best performance, achieving an accuracy of 97.16%, an F1-score of 97.49%, and an AUC of 99.75%. This model proved to be effective in detecting stroke cases and holds potential for integration into clinical decision support systems. The study concludes that a combination of modern boosting algorithms and optimal preprocessing techniques can yield a reliable stroke prediction system suitable for implementation in digital healthcare contexts.
Rancang Bangun Modul Kontrol Berbasis PID untuk Pengaturan Kecepatan dan Posisi Motor DC Menggunakan STM32 dan Rotary Encoder Doris Juarsa; Muhammad Fadli; Erliyan Redy Susanto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 4 No. 4 (2025): Agustus 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v4i4.6081

Abstract

Precision control of DC motors in industrial and robotics applications is often compromised by external loads and the limitations of open-loop systems, which cause instability in rotational speed and angular position. This study aims to design and build a PID-based intelligent control module for integrated DC motor speed and position control using an STM32F103C8T6 microcontroller and a rotary encoder as feedback. This system is designed as a closed-loop system to reduce the error between the setpoint and the actual value. Tests were conducted under no-load and with-load conditions at various speed setpoints (10–30 RPM) and angular changes (slow and fast). The results show that the system is able to stabilize motor performance with an average speed error of −0.3033 and 0.2766 RPM (no-load) for Motors A and B, and 0.2633 and 3.47 RPM (with-load). For angular position control, the average errors were 0.69° and 0.895° (without load), and 0.475° and 0.335° (with load). These findings demonstrate the effectiveness of the PID-based intelligent control module in improving system accuracy and stability. This system offers a compact and practical solution for industrial automation and robotics applications requiring precise motor control.
Co-Authors Amarudin Amarudin Amarudin Ani Novitasari Budi Rahman Dewi Suryanti Dian Sri Purwanti Dian Windriani Doris Juarsa Ega Budiman Erliyan Redy Susanto Gita Hilmi Prakoso Hadori Rosadi Helidatasa Utami Izza Maulida Santoso Kevinda Sari Mc. Fulvia. Hy Mc. Fulvya.Hy Muhammad Fahmi Fudholi Muhammad Surono Ratu Sinar Sari Tanjung Refdi Akmal, Refdi Retno Anggraini Rifka Simbolon Rio Eka Nugraha Riyan Maruly Siska Fitriani Sitti Suharni Syaifuddin Muhammad Mirza Tina Nurzachra Latifah Rizki Liana Valdi Mughni Budiman Windia Hanifah Yunita Fitri Yanti