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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengabdian Barelang Jurnal Quancom: Jurnal Quantum Komputer
Muhammad Abrar Masril
Institut Teknologi Batam
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences

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The Analysis of Dilation Morphology for Quality Improvement of the Edge Detection Imagery on Batik Patterns using Prewitt Operator and Laplacian of Gaussian Muhammad Abrar Masril; Refli Noviardi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 6 (2020): Desember 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (686.397 KB) | DOI: 10.29207/resti.v4i6.2601

Abstract

The results of the edge detection process using several operators are not yet optimal. Therefore we need a method to improve the quality of edge detection images, the method used in this study is morphology dilation. The results of testing the improvement of image quality using 10 batik patterns, resulting in an accuracy level on Laplacian of Gaussian operators is 80% and for Prewitt operators is 60%. In the process of improving the edge detection quality, Morphology Dilation can connect broken edges using structuring elements. therefore it can improve the quality of edge images.
Dunia Teknologi Informasi & Revolusi Industri 4.0 Muhammad Jufri; Alvendo Wahyu Aranski; Zainul Munir; Joni Eka Candra; Ririt Dwiputri Permatasari; Muhammad Abrar Masril; Hendri Kremer
Jurnal Pengabdian Barelang Vol 5 No 2 (2023): Jurnal Pengabdian Barelang
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jpb.v5i2.7343

Abstract

Perkembangan ilmu pengetahuan dan teknologi yang semakin pesat pada awal abad 20 telah melahirkan teknologi informasi dan proses produksi yang dikendalikan secara otomatis. Sebagaimana yang dihadapi Indonesia saat ini, revolusi industri 4.0 telah mendorong inovasi-inovasi teknologi yang memberikan dampak disrupsi atau perubahan fundamental terhadap kehidupan masyarakat. Siklus perputaran evolusi yang akan terus ditapaki oleh masyarakat diseluruh kalangan dapat menciptakan inovasi-inovasi baru yang berpengaruh terhadap perkembangan bisnis. Berbagai situs dalam pengaplikasian teknologi digital memberikan kemudahan untuk mengkoordinir setiap input dan output pelaku bisnis. Kehadiran tech industry diberbagai bidang dapat dimanfaatkan untuk menghidupkan kembali beberapa bisnis yang sudah exsist dimasa lampau. Keseluruhan aspek yang berkaitan dengan teknologi akan memonitoring cepat atau lambatnya dunia digital memasuki ranah laju ekonomi, serta meminimalisasikan fungsi fisik manusia
IMPLEMENTASI ARDUINO UNO UNTUK DETEKSI KEBAKARAN HUTAN fajar; Muhammad Abrar Masril
JURNAL QUANCOM: QUANTUM COMPUTER JURNAL Vol. 1 No. 1 (2023): JUNI 2023
Publisher : LPPM-ITEBA

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Abstract

Forest is a gift from the Almighty God that we have to preserve, forests are very useful in the survival of humans, but due to human activity itself can damage forests such as cutting down trees wildly and burning forests to make land. The most forest fires occur from year to year, namely Indonesia resulting from forest fires will have a lot of negative impacts that occur on all living things. Therefore a forest fire detection system is needed to minimize forest fires. In this study the authors made a forest fire detection system using an Arduino-based microcontroller. The tools used are in the form of Arduino uno, Arduino mega 2560, fire sensor, MQ-7 smoke sensor and sim 800l module this tool is made so that the fire does not spread and expand. the workings of this system are when the fire and smoke sensors detect fire and smoke in the forest location, the data will be processed and directly the forested sim800l module sends sms to the sim800l module located in the office according to the number specified and the data received will produce output in the form of sirens from the buzzer, marker lights, and LCD as a description of the location of the fire. From the results of all testing tools that are made to work as expected. It can be concluded that each sensor has the maximum distance to detect smoke and fire, the sim800l module sends a message in 10 seconds, and the sound of the siren on the buzzer will live according to the conditions specified.
Sistem Pencegahan Illegal Fishing di Laut Batam menggunakan YOLOv7 berbasis Notifikasi Telegram MUHAMMAD ABRAR MASRIL; DEOSA PUTRA CANIAGO
Jurnal Elkomika Vol 12, No 1 (2024): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v12i1.175

Abstract

ABSTRAKPulau Batam menjadi salah satu pulau indonesia terluar yang berbatasan langsung dengan negara tetangga. Penerapan YOLOv7 untuk mendeteksi kapal di laut Batam mampu mendeteksi objek kapal dengan hasil pengujian setelah melakukan training 100 epoch menghasilkan nilai precision sebesar 1.00 dan nilai confidence 0.882 menunjukkan tingkat kepercayaan hasil deteksi yang tinggi pada model YOLOv7. Hasil skor F1 sebesar 0.99 pada confidence 0.729 menunjukkan hasil bahwa model ini menghasilkan tingkat akurasi yang tinggi dalam menemukan objek. Berdasarkan hasil evaluasi menggunakan confusion matrix menunjukkan hasil akurasi yang tinggi dari setiap class pada model YOLOv7 yaitu Ferry 93%, KapalNelayanIndo 85%, KapalNelayanMalaysia 89%, KapalNelayanThailand 91%, KapalNelayanVietnam 82%, Speedboat 94%, dan Tanker 83%. Hasil pengujian aplikasi website yang terintegrasi dengan YOLOv7 dan bot Telegram menghasilkan website yang mampu mendeteksi objek dan mengirimkan notifikasi sehingga diharapkan mampu mencegah illegal fishing.Kata kunci: Deteksi, Deep Learning, Kapal, Telegram, YOLOv7 ABSTRACTBatam Island is one of Indonesia's outermost islands bordering neighboring countries. The application of YOLOv7 to detect ships in the Batam Sea was able to detect ship objects with test results after carrying out 100 epoch training resulting in a precision value of 1.00 and a confidence value of 0.882 indicating a high level of confidence in the detection results in the YOLOv7 model. The F1 score of 0.99 at confidence 0.729 shows that this model produces high accuracy in finding objects. Based on the evaluation results using the confusion matrix, it shows high accuracy results for each class in the YOLOv7 model, namely Ferry 93%, Indonesian Fisherman's Ship 85%, Malaysian Fisherman's Ship 89%, Thai Fisherman's Ship 91%, Vietnamese Fisherman's Ship 82%, Speedboat 94%, and Tanker 83%. The test results of the website application integrated with YOLOv7 and Telegram bot resulted in a website that is able to detect objects and send notifications so that it is expected to be able to prevent illegal fishing.Keywords: Detection, Deep Learning, Ship, Telegram, YOLOv7
Co-Authors Alvendo Wahyu Aranski Deosa Putra Caniago fajar Hendri Kremer Joni Eka Candra Muhammad Jufri Noviardi, Refli Ririt Dwiputri Permatasari Zainul Munir