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All Journal Elkom: Jurnal Elektronika dan Komputer INOVTEK POLBENG International Journal of Research in Vocational Studies (IJRVOCAS)
benrad edwin simanjuntak
Politeknik Negeri Medan
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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SISTEM PENDETEKSI KEMIRINGAN TANAH LONGSOR DENGAN MENGGUNAKAN ARDUINO UNO benrad edwin simanjuntak
INOVTEK POLBENG Vol 9, No 2 (2019): INOVTEK VOL.9 NO 2 - 2019
Publisher : POLITEKNIK NEGERI BENGKALIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (265.301 KB) | DOI: 10.35314/ip.v9i2.1135

Abstract

Bencana tanah longsor sering terjadi tanpa terduga oleh siapapun sehingga dapat menyebabkan korban harta maupun jiwa. Hal ini memerlukan sistem pendeteksi tanah longsor sehingga korban harta maupun jiwa dapat berkurang bahkan sama sekali tidak ada. Sistem pendeteksi tanah longsor ini menggunakan arduino uno yang bekerja bila terjadi kemiringan tanah pada derajat atau sudut tertentu. Informasi tanah longsor yang diperoleh akan dikirim ke operator melalui SIM800L sehingga dapat diketahui status dari bahaya tanah longsor yaitu terdiri dari status normal, siaga I, siaga II dan awas. Pada status normal, kemiringan tanah longsor sebesar 00-50, SIM800L tidak mengirim ke operator sehingga operator hanya memantau situasi. Pada status siaga I, kemiringan tanah longsor sebesar 60-100, SIM800L akan mengirim informasi ke operator dan buzzer akan berbunyi hanya sekali selama 15 detik sehingga operator menganjurkan warga sekitar untuk segera evakuasi. Pada status siaga II, kemiringan tanah longsor sebesar 110-150 dan SIM800L akan mengirim informasi ke operator dan buzzer akan berbunyi setiap 15 detik dengan interval waktu berhenti selama 5 detik sehingga operator mengharuskan warga sekitar untuk  evakuasi secepat mungkin. Pada status awas, kemiringan tanah longsor lebih besar dari 150 dan SIM800L akan mengirim informasi ke operator dan dinyatakan dalam keadaan awas/warning dan buzzer akan berbunyi secara terus-menerus. Pada keempat status tersebut dapat dilihat juga besarnya tegangan dan arus listrik serta frekuensi getaran tanah longsornya.
Indentification of Beef in Beef and Chicken Experiments using Conducting Polymer Sensor Series and Kohonen Algorithm Method Benrad Edwin Simanjuntak; Marhaposan Situmorang; Syahrul Humaidi; Marzuki Sinambela
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 2 No. 4 (2023): IJRVOCAS - Special Issues - International Conference on Science, Technology and
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v2i4.162

Abstract

Chicken, and beef each have a distinctive aroma. Identification of Chicken and beef based on the aroma of the meat using an electronic nose. This electronic nose uses a series of sensors consisting of 6 (six) pieces and uses a Conducting Polymer. This polymer has a high resistance so it is widely used as an insulator. However, this resistance has a certain limit where the polymer surface will turn into carbon and conduct electric current if exposed to excessive electric charge. This research was conducted by taking samples of chicken and beef as test samples where these meats were placed in a closed container at room temperature. Data is taken alternately every day to find out the odor of each meat where on the first day data is taken from the odor of chicken, and on the second day data is taken from the odor of beef. This condition is done to ensure the freshness of each meat. This study uses a Neural Network (NN) as pattern recognition and ATMega16 microcontroller as data acquisition. Neural Network is trained using Kohonen. The sensor used is a Conducting Polymer sensor because of the nature of the Conducting Polymer where the output is a voltage generated due to changes in the polymer resistance resistance. A two-layer neural network consisting of six input nodes and three output neurons is trained using the Kohonen algorithm with the training process completed in 31 iterations. The test was carried out 30 times for each exposure to steam from the odor of chicken and beef which was carried out alternately. The percentage of success of the system is 100%.
Identifikasi Daging Segar terhadap Daging Busuk dengan Menggunakan Sensor Polimer Konduktif dan Jaring Saraf Tiruan (JST) Benrad Edwin Simanjuntak; Berman Pandapotan Panjaitan
Elkom : Jurnal Elektronika dan Komputer Vol 16 No 2 (2023): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v16i2.1706

Abstract

Fresh or rotten meat is a different matter. Damage to the meat will produce a distorted odor, mucus, discoloration in certain areas and an undesirable taste due to the formation of metabolism. The odor is described as fishy, ​​rotten, containing sulfur and like ammonia. In this research, the author discusses a system for identifying the condition of meat based on the odor that arises from meat in three states, namely odorless odor, fresh odor and rotten odor. Fresh odor is taken from meat odor that is within 1 (one) day after being cut and rotten odor is taken from meat odor that is on the 2nd day. In this study, the test sample meat was placed in a closed container at room temperature for 2 days. Data was taken for 2 days from meat odors of known type. The sensor array consists of eight sensors made of conducting polymer material. The polymer materials used are silicon DC-200, PEG-20M, 0V-101, 0V-17, DEGA, PEG-200, PEG-1540, and PEG-6000 mixed with Carbon black. A two-layer artificial neural net consisting of eight input nodes and three output neurons, was trained using the Kohonen algorithm with a training process that was completed in 4 iterations. From 20 tests, 10 times exposure to steam from fresh odor and 10 times exposure to steam from rotten odor, carried out alternately, it was found that the system failed twice. Thus, the system success percentage reached 95 percent.
Co-Authors Berman Panjaitan Marhaposan Situmorang Marzuki Sinambela Syahrul Humaidi