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All Journal Sinergi Seminar Nasional Informatika (SEMNASIF) JASEE Journal of Application and Science on Electrical Engineering
MS Hendriyawan Achmad
Program Studi Teknik Elektro, Universitas Teknologi Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Disain Piranti Digital Perekam Paras Air Otomatis Menggunakan Teknologi Rendah Daya Achmad, M.S. Hendriyawan; Nuryadi, Satyo; Fadlun, Wira
JASEE Journal of Application and Science on Electrical Engineering Vol. 1 No. 02 (2020): JASEE
Publisher : Teknik Elektro Fakultas Teknik Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jasee.v1i02.25

Abstract

Parameter hidrologi adalah salah satu bahan pengamatan yang biasa digunakan untuk memetakan potensi bencana alam yang berhubungan dengan air, seperti banjir dan tanah longsor. Salah satu parameter hidrologi yang dipantau oleh peneliti adalah ketinggian permukaan air sungai. Pengamatan secara tradisional menggunakan metode bilah ukur yang memiliki kelemahan utama yaitu akurasi yang rendah dan membutuhkan pengamatan secara visual. Peneliti membutuhkan data pengamatan yang memiliki akurasi tinggi dalam periode panjang, sehingga peneliti memerlukan alat perekam yang mampu membaca tinggi permukaan air secara akurat dan terus-menerus dalam kurun waktu bulan atau tahun. Penelitian ini menawarkan rancangan piranti perekam digital rendah daya untuk pemantauan tinggi muka air dengan resolusi bacaan sebesar 10 mm dan rata-rata konsumsi energi hanya sebesar 20 μW. Teknologi rendah daya yang ditawarkan terdiri dari dua bagian, perangkat keras yang menawarkan model teknologi pico-power yang dikendalikan oleh pengendali mikro jenis 8-bit, sedangkan perangkat lunak menjalankan fungsi rendah daya dengan operasi dasar sleep – wake up. Hasil analisis menunjukkan bahwa piranti perekam digital dengan catu tegangan utama 3.6 V dan daya 2200 mAH mampu beroperasi selama 3.7 tahun dengan nilai cut-off sumber energi diasumsikan serendah 60% dan rata-rata konsumsi arus listrik sebesar 27 μA
Diabetic Retinopathy Severity Level Classification Based on Fundus Image Using Convolutional Neural Network (CNN) MS Hendriyawan Achmad; Wahyu Saputro RM
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2021): Inovasi Teknologi dan Pengolahan Informasi untuk Mendukung Transformasi Digital
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Diabetic retinopathy is an eye disease and is a complication of diabetes mellitus. The longer a person suffers from diabetes mellitus, the more likely they are to experience diabetic retinopathy. Diabetic retinopathy is divided into two types, namely Non-Proliferative Diabetic Retinopathy (NPDR) with 4 phases (normal, mild, moderate and severe) and Pre-proliferative Diabetic Retinopathy (PDR). To classify the severity of this disease requires an expert doctor and takes a long time. This study applies the Convolutional Neural Network (CNN) method to fundus image input to classify the severity of diabetic retinopathy, namely mild, moderate, severe, or regular. The fundus image dataset for training and testing was taken from the APTOS 2019 dataset. The pre-processing stage of the fundus image includes: resizing, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gaussian filtering. After that, classification is carried out using the CNN Model, consisting of a convolution layer, a pooling layer, a dropout layer, and a fully connected layer. The results of the CNN model implementation show a classification accuracy of 75% in the training process and 73% in the model validation process. Meanwhile, in the confusion matrix testing process, the accuracy is 68%, the precision is 69%, and the recall is 68%.
HUMAN FOLLOWING ON ROS FRAMEWORK A MOBILE ROBOT Gigih Priyandoko; Choi Kah Wei; Muhammad Sobirin Hendriyawan Achmad
SINERGI Vol 22, No 2 (2018)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (241.455 KB) | DOI: 10.22441/sinergi.2018.2.002

Abstract

Service mobile robot is playing a more critical role in today's society as more people such as a disabled person or the elderly are in need of mobile robot assistance. An autonomous person following ability shows great importance to the overall role of service mobile robot in assisting human. The objective of this paper focuses on developing a robot follow a person. The robot is equipped with the necessary sensors such as a Microsoft Kinect sensor and a Hokuyo laser sensor. Four suitable tracking methods are introduced in this project which is implemented and tested on the person following algorithm. The tracking methods implemented are face detection, leg detection, color detection and person blob detection. All of the algorithms implementations in this project is performed using Robot Operating System (ROS). The result showed that the mobile robot could track and follow the target person based on the person movement. 
Co-Authors Choi Kah Wei Fadlun, Wira Ming, Tan Yee Mohd Razali Daud Nuryadi, Satyo Priyandoko Gigih Wahyu Saputro RM Wei, Choi Kah