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All Journal Bulletin of Electrical Engineering and Informatics Jurnal Fortech J-ENSITEC (Journal of Engineering and Sustainable Technology) Jurnal FORTECH
Ahmad Ilham, Amil
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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DETEKSI KEPITING MOLTING MENGGUNAKAN TEKNIK KLASIFIKASI MACHINE LEARNING: Indonesia rezkiawan , runal; Niswar, Muhammad; Ahmad Ilham, Amil
J-ENSITEC (Journal of Engineering and Sustainable Technology) Vol. 8 No. 01 (2021): December 2021
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jensitec.v8i01.1909

Abstract

Soft crab is an export product where foreign demand is much higher than production. In the production of soft crabs, it is done by keeping the crabs individually in a crab box which is placed in the pond until they molt. Molting is a natural process of molting, i.e. removing the old tough skin for growth purposes. Shortly after molting, the new crab shells are still very soft and will harden again after water absorption occurs. Therefore it is important to monitor molting crabs to help farmers in the cultivation of soft shell crabs. The number of crab datasets is 1060 which consists of 1000 training data and 60 testing data. There are several popular image classification algorithms, namely K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest Classifier (RFC). KNN, SVM, and RFC are classification algorithms from Machine Learning. This study aims to compare the performance of the three algorithms so that the performance of the three algorithms is known. Several parameters are used to configure the KNN, SVM, and RFC algorithms. From the results of the trials conducted, KNN has the best performance with 98.33% accuracy, 98.33% precision, 98.38% recall, and 98.38% F1 Score.
Sistem Monitoring Kesehatan Pasien Dengan Multi Sensor Syam, Syahril; Ahmad Ilham, Amil; Palantei, Elyas; Ruswandi Djalal, Muhammad
Jurnal FORTECH Vol. 5 No. 2 (2024): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v5i2.5202

Abstract

Penelitian ini ditujukan untuk aktifitas mengukur dan memonitor berbagai indikator-indikator kesehatan secara rutin dan berkesinambungan yang mengeksploitasi keunggulan teknologi sensor memberikan dampak positif yang besar bagi kesehatan dan untuk memberikan solusi yang lebih efisien dan optimal bagi pihak tenaga medis (dokter, perawat, atau pihak berwenang lainnya) dalam memberikan pelayanan medik yang lebih mutakhir dan memuaskan kepada masyarakat luas. Melalui tindakan perawatan yang intensif, berkesinambungan dan efisien ini diharapkan dapat mengurangi resiko kematian pasien akibat kelalaian dalam mengambil tindakan medis yang cepat dan tepat. Penelitian dilakukan dengan membuat metode rancangan jangka panjang dengan menyesuaikan fungsi sensor-sensor dan dilakukan eksperiment, yaitu pembuatan sistem multi sensor sesuai dengan metode yang telah dibuat. Hasil penelitian ini menunjukkan bahwa alat sensor detak nadi dan sensor suhu LM35 dapat ditransmisikan dengan menggunakan media transmisi Bluetooth dengan jarak baca sensivitas mencapai 17 meter dengan nilai throughput 1-0.75 Kbps. Setelah dilakukan pengujian dan pengukuran validasi data maka ditarik kesimpulan bahwa untuk pengukuran detak nadi mendapatkan nilai error 3.88% sedangkan untuk pengukuran suhu tubuh mendapatkan nilai error 16.94%.
Sistem Monitoring Kesehatan Pasien Dengan Multi Sensor Syam, Syahril; Ahmad Ilham, Amil; Palantei, Elyas; Ruswandi Djalal, Muhammad
Jurnal FORTECH Vol. 5 No. 2 (2024): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v5i2.5202

Abstract

Penelitian ini ditujukan untuk aktifitas mengukur dan memonitor berbagai indikator-indikator kesehatan secara rutin dan berkesinambungan yang mengeksploitasi keunggulan teknologi sensor memberikan dampak positif yang besar bagi kesehatan dan untuk memberikan solusi yang lebih efisien dan optimal bagi pihak tenaga medis (dokter, perawat, atau pihak berwenang lainnya) dalam memberikan pelayanan medik yang lebih mutakhir dan memuaskan kepada masyarakat luas. Melalui tindakan perawatan yang intensif, berkesinambungan dan efisien ini diharapkan dapat mengurangi resiko kematian pasien akibat kelalaian dalam mengambil tindakan medis yang cepat dan tepat. Penelitian dilakukan dengan membuat metode rancangan jangka panjang dengan menyesuaikan fungsi sensor-sensor dan dilakukan eksperiment, yaitu pembuatan sistem multi sensor sesuai dengan metode yang telah dibuat. Hasil penelitian ini menunjukkan bahwa alat sensor detak nadi dan sensor suhu LM35 dapat ditransmisikan dengan menggunakan media transmisi Bluetooth dengan jarak baca sensivitas mencapai 17 meter dengan nilai throughput 1-0.75 Kbps. Setelah dilakukan pengujian dan pengukuran validasi data maka ditarik kesimpulan bahwa untuk pengukuran detak nadi mendapatkan nilai error 3.88% sedangkan untuk pengukuran suhu tubuh mendapatkan nilai error 16.94%.
Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment Wiria Nugraha, Deny; Ahmad Ilham, Amil; Achmad, Andani; Arief, Ardiaty
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8454

Abstract

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
Co-Authors Andani Achmad Arief, Ardiaty Deny Wiria Nugraha Muhammad Niswar Muhammad Ruswandi Djalal Palantei, Elyas rezkiawan , runal Syam, Syahril