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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Fountain of Informatics Journal Sistemasi: Jurnal Sistem Informasi Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Mantik Journal of Computer System and Informatics (JoSYC) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sains dan Teknologi STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer
Susanto, Erliyan Redy
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Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Journal : Jurnal Sains dan Teknologi

 1 
Deteksi Dini Stroke Menggunakan Machine Learning Kevinda Sari; Muhammad Fadli; Fudholi, Muhammad Fahmi; Susanto, Erliyan Redy
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; Susanto, Erliyan Redy
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 ., Rusliyawati Adela, Marsya Agus Wantoro Amarudin, Amarudin Amelya, Eka Andi Nurkholis Az Zahra, Renny Fatimah Budiman, Ega Cahyana, Agum David Kurniawan DAVID KURNIAWAN Doris Juarsa Eka Pranajaya, Akbar Estri Pratiwi, Kyky Fidya Fadli, Muhammad Fudholi, Muhammad Fahmi Hendratno, Hary Inzaghi, M. Rana Irawan, Made Kevinda Sari Lestari, Ninik Maylanda, Putri Oktaria Misdiantoro, Diky Muhammad Fadli Neneng Neneng Neneng, N Nirwana Hendrastuty Purwanti, Dian Sri Putri, Made Yuninda Rahmada, Alfin Ramadhani, Resvi Restu Anggoro S, Muhammad Ikhsan Setiawansyah Setiawansyah Simarmata, Yohanes Surono, Muhammad Tyas, Khoirunisa Cahyaning Ulum, Faruk Wijaya, Suhenda Yulian, Tri