Article Per Year (5 Year)
p-Index From 2020 - 2025
0.659
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Computer Science and Information Technologies
Ramayanty Bulan
Syiah Kuala University
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Measurement push and pull forces on automatic liquid dispensers Agustami Sitorus; Eko K. Pramono; Yusnan H. Siregar; Ari Rahayuningtyas; Novita D. Susanti; Irwin S. Cebro; Ramayanty Bulan
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp4825-4832

Abstract

Since the COVID-19 pandemic, automated liquid dispensers have been increasingly developed to assist transmission prevention. However, data availability of automatic liquid dispenser mechanism's technical characteristics is not yet widely available. This causes frequent over or under design in its development. Therefore, we specifically measure push and pull forces engineering characteristics generated by the automatic liquid dispenser mechanism. A wire mechanism-based automatic liquid dispenser apparatus was used to experiment. A load-cell sensor was used to detect the force that occurs from a servo motor controlled by a microcontroller. The force data (push and pull) will be sent directly to the database server cloud with a recording frequency of every second. Three types of fluid treatment levels are used i.e. water, liquid soap, and hand sanitizer gel. Three types of fluid volume treatment levels used were 50 ml, 150 ml, and 250 ml. Each treatment level combination is carried out at the servo motors rotation steps 180°, 150°, 120°, 90°, 60°, and 30°. The results show that no significant differences were found in maximal forces required to release the water, liquid soap, and hand-sanitizer gel. It is also known that the volume of the fluid has a very significant effect on the amount of push and pull forces generated.
Non-invasive moisture content measurement system based on the ESP8266 microcontroller Agustami Sitorus; Novrinaldi Novrinaldi; Ramayanty Bulan
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (735.802 KB) | DOI: 10.11591/eei.v9i3.2178

Abstract

Moisture content in the process of drying is often unknown when carrying out the drying process, especially the fluidized dryer. A lot of experimental designs are needed when observing the drying phenomenon more deeply.  It is because to stop and repeat drying process from the beginning again when the sample is taken to test its moisture content needed more experiments. Therefore, this paper presents development of a non-intrusive moisture measurement system prepared for fluidization type dryers. The method used in to conduct this research consists of (i) structural design analysis and (ii) functional (mechanical and electrical systems) and (iii) simple testing of the water content measurement system of constructed material. Test parameters observed include errors in measuring and fluctuating sensor signals against vibration applied to the weighing system. The results showed that non-intrusive moisture content measurement system for fluidized dryers based on the ESP8266 microcontroller had been successfully developed and worked normally. The measurement system has been calibrated with a coefficient of determination (R2) close to one. Measurement error resulting from the effect of vibration on this system shows a very satisfactory value of 6.89%.
Prediction of mango firmness by near infrared spectroscopy tandem with machine learning Sri Agustina; Ramayanty Bulan; Agustami Sitorus
Computer Science and Information Technologies Vol 3, No 3: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i3.p148-156

Abstract

The firmness of the mango fruit is one of the internal physical properties that can show its quality. Unfortunately, non-destructive methods to measure this are not yet available. In the current study, we develop a calibration model using near infrared spectroscopy to predict the physical properties (firmness) of the mango cultivar Arumanis (Mangifera indica cv. Arumanis) via machine learning. Spectral data were acquired using the fourier transform near-infrared (FTNIR) benchtop with a wavelength range of 1000 to 2500 nm. Multivariate spectra analysis based on machine learning, including principal component regression (PCR), partial least squares regression (PLSR), and support vector machine regression (SVMR), was utilized and compared to estimate the firmness of fresh mangos. The results obtained show that the prediction of machine learning by PLSR is better than that of SVMR and PCR for the prediction of mango firmness. The coefficient correlation of calibration (rc) and validation (rcv), the root means square error of calibration (RMSE-C) and validation (RMSE-CV), and the ratio of prediction to deviation (RPD) were 0.941, 0.382 kgf, 0.920, 0.472 kgf, and 2.556, respectively. The general results satisfactorily indicate that near infrared spectroscopy technology integrated with an appropriate machine learning algorithm has optimistic results in determining the firmness of mango non-destructively.
Co-Authors Agustami Sitorus Agustami Sitorus, Agustami Ari Rahayuningtyas Eko K. Pramono Irwin S. Cebro Novita D. Susanti Novrinaldi Novrinaldi Sri Agustina Yusnan H. Siregar