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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri
Maulana, Wildan Arif
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Navigasi Pergerakan Robot Berdasarkan Rekam Data Sensor Odometry Maulana, Wildan Arif; Winarno, Totok; Siradjuddin, Indrazno
Jurnal Elektronika dan Otomasi Industri Vol. 10 No. 1 (2023): Jurnal Elkolind Vol. 10, No. 1, 2023
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v10i1.2751

Abstract

Berkembangnya teknologi robotika yang sangat cepat dan pesat mengakibatkan peran robotika sangat berpengaruh dalam kehidupan manusia. Pada bidang industri, robot difungsikan salah satunya untuk memindahkan barang ke tempat yang sudah ditentukan. Pada penelitian mobile robot beroda ini menggunakan perekaman data menggunakan sensor odometry. Sensor odometry digunakan untuk memperkirakan perubahan posisi robot dari pergerakan aktuator yang digunakan. Menggunakan sensor rotary encoder yang digunakan untuk menghitung pergerakan mobile robot pada koordinat X dan Y pada proses perhitungan odometry. Untuk dapat bergerak ke titik yang sudah ditentukan menggunakan metode kontrol kinematik, dengan mengetahui posisi awal, memungkinkan robot bergerak menuju titik tujuan dan arah hadap robot dengan koordinat pergerakan yang diajarkan sebelumnya. Pada penelitian ini menggunakan pengujian secara realtime untuk parameter pengujiannya meliputi ketepatan titik tujuan, arah hadap akhir dan kecepatan mencapai tujuan. Dengan menggunakan metode kontrol Forward dan Invers Kinematics, robot dapat bergerak dari posisi awal menuju ke titik tujuan dengan tepat menggunakan tracking trajectory yang telah dibuat berdasarkan rekam data sensor odometry dengan rata-rata error posisi sebesar 0,083 meter dan error arah hadap sebesar 4,6°
Enhancing CNN Performance for Alzheimer’s Disease Classification Using Genetic Algorithm Optimization Maulana, Wildan Arif; Abidin, Zainul; Rahmadwati, Rahmadwati
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2543

Abstract

The increasing global life expectancy has led to a rapidly growing elderly population, resulting in a higher prevalence of Alzheimer’s disease and a pressing need for effective diagnostic solutions. Despite advances in medical imaging, the early and accurate detection of Alzheimer’s disease remains a major challenge due to subtle differences in brain structures across disease stages. However, the interpretation of MRI images still depends heavily on the abilities of individual medical personnel, which risks introducing subjectivity and potential errors in the diagnostic process. In this context, particularly deep learning, emerges as an effective strategy to overcome these limitations by automating the analysis of medical images and reducing human bias. To address this issue, a custom Convolutional Neural Network (CNN) model was developed from scratch for Alzheimer’s disease classification using brain MRI images. To enhance data diversity and mitigate overfitting, a combination of Albumentations and CutMix data augmentation techniques was applied, yielding an initial classification accuracy of 90%. Model performance was further optimized using a Genetic Algorithm (GA), which efficiently explored the hyperparameter space and identified optimal configurations, boosting classification accuracy to 96%. The optimized model demonstrated robust generalization across all disease categories, confirming the effectiveness of the proposed approach. This research contributes to the development of a more reliable and adaptive deep learning framework for early-stage Alzheimer’s disease detection, offering potential support for clinical diagnostic systems
Co-Authors Rahmadwati Rahmadwati Siradjuddin, Indrazno Winarno, Totok Zainul Abidin