Amrullah Kamaruddin
@amru001@brin.go.id
Research Centre for Sustainable Production Systems and Life Cycle Assessment
Research Center for Appropriate Technology, Research Organization for Agriculture and Food National Research and Innovation Agency (BRIN)
My name is Amrullah Kamaruddin, born in Sinjai, South Sulawesi on 12 November 1973.I have worked for 16 years at BRIN in the energy and manufacturing research organisation as an implementer of sustainable production system research activities and life cycle assessment, Kelris smart & sustainable manufacturing system with a functional position of associate expert engineer.
EDUCATION
I graduated with a bachelor's degree in Electrical Engineering at UMI Makassar in 1999 and a master's degree in Informatics Engineering at Bina Nusantara University Jakarta in 2015 and a doctoral degree in Physics in the second semester at IPB
RESEARCH, TEACHING, or OTHER INTERESTS
Ecology, General Agricultural and Biological Sciences, Management Information Systems, Artificial Intelligence
FUTURE PROJECTS
Sistem Pengukuran Cepat Unsur Hara Tanah dengan Integrasi Near Infrared Spectroscopy
Soil nutrient management is a crucial aspect of smart and sustainable agriculture. However, challenges in rapid nutrient measurement and accurate data management often hamper the effectiveness of agricultural practices. These limitations lead to the need for technological solutions that can improve the reliability and security of nutrient measurement data, which ultimately affects productivity and environmental sustainability. The main objective of this research is to develop an optimal system for soil nutrient data measurement and management. The research methods include: system design integrating near infrared spectroscopy (NIRS) and blockchain, prototype development of the system design, and testing and validation of the prototype to evaluate system performance both in experimental and field situations. The practical implications of this research are the contribution to the development of smart and sustainable agriculture, as well as increasing agricultural productivity through more
Applications Invited
Scopus Publications
Scopus Publications
Pratondo Busono, Riky Alam Ma’arif, Dede Sumantri, I. Putu Ananta Yogiswara, Rony Febryanto, Syaeful Karim, Riyanto Riyanto, Marlin A. Baidillah, I. Made Astawa, Amrullah Kamaruddin,et al.
AIP Publishing
Adnan Adnan, Yaya Suryana, Abdul Aziz, Taslim Rochmadi, Arie Rakhman Hakim, Andari Risliawati, Arifuddin Kasim, Fahrodji Fahrodji, Amrullah Kamaruddin, Wenny Oktaviani,et al.
International Information and Engineering Technology Association
ABSTRACT
T I Ramdhani, Adnan, Y Suryana, T Rochmadi, A Aziz, A Kamaruddin, N Ghazali, A Hadi, W Oktaviani, S V Budiwati,et al.
IOP Publishing
Abstract This study provides a detailed analysis of predicting soil nutrient content using spectral data and machine learning techniques in four Indonesian provinces: West Java, Central Java, Yogyakarta (DIY), and East Java. The research collected 145 soil samples to predict various key soil nutrients, such as N Total, NH4, NO3, P Total, P Available, K Total, K Available, C Organic, and pH. The study used linear regression (LR) and deep neural networks (DNN) with a deep cross-network (DCN) architecture to model the relationships between soil spectral data and nutrient content. LR was used as a baseline model to understand linear relationships between spectral features and soil properties and identify the most influential spectral frequencies in predicting soil nutrient levels. On the other hand, the DNN model captured complex, non-linear patterns within the data. Results showed that while the DNN model displayed advanced capabilities, the LR model generally outperformed it in predictive accuracy, particularly for nutrients like N-Total, P-Total, and K-Total. The findings highlight the potential of combining spectral data with advanced machine-learning techniques for precise soil nutrient estimation, which could significantly enhance agricultural productivity and soil management practices in Indonesia.
P Busono, R Febryarto, R A Ma’arif, I M Astawa, I P A Yogiswara, S Karim, F A Majid, A Amrullah, S Rahayu, and M Rahmah
IOP Publishing
Abstract Sweat contains numerous biomarkers, including glucose, which can provide valuable insights into an individual’s metabolic state. Noninvasive glucose monitoring eliminates the need for frequent finger pricks or blood samples, offering greater convenience and reducing discomfort for individuals with diabetes. These biosensors are designed to detect and quantify glucose levels through various sensing mechanisms, such as enzymatic reactions or electrochemical measurements. The integration of biosensors into wearable devices, such as smartwatches, patches, or flexible electronics, allows for mobile glucose monitoring in sweat. Accurate glucose measurements require calibration and validation against reference measurements, such as blood glucose levels. Research focuses on developing calibration algorithms and improving the accuracy and reliability of mobile glucose biosensors in sweat.
P Busono, S Karim, A Kamaruddin, and I P A Yogiswara
IOP Publishing
Cardiac auscultation is the examination of the heart by listening to the sound produced by the heart through a stethoscope. Heart sounds can provide information about the functioning of the heart valve condition as well as information about the structural abnormalities of the heart. However, it needs intensive training for mastering. The objective of the work was to develop an algorithm for heart sound signal classification applied to computer-assisted digital auscultation in order to identify pathological events. The method can be described as follow: data collection, pre-processing, segmentation, feature extraction, and classification. The data were collected from volunteers using an electronic stethoscope and from a database available in the internet. The heart sound data were then extracted and split into training, validation, and testing datasets. In the training process the dataset was labeled as normal and abnormal (aortic stenosis, mitral stenosis, aortic regurgitation, pulmonic regurgitation, tricuspid stenosis, flow murmur, and patent ductus arteriosus). The convolution neural network is used as a classifier in the learning process to obtain the learning model. The model was validated and tested using the available datasets. The experimental results show that the algorithm has the capability to classify the heart sound into normal and abnormal with a high detection rate.
F Febryanti, H Ahmad, Nurhasanah, and A Kamaruddin
IOP Publishing
Abstract To realize the mandate of law number 20 of 2003, the teacher can integrate Islam value in mathematics learning. This study is a quasi-experimental study that aims to determine the effectiveness of the application of cooperative learning models with numbered structure types that are integrated Islamic values with the ability to understand students’ mathematical concepts. The sample of this research is the class X students of SMK Mega Link Majene, amounting to 20 people. Indicators of effectiveness in this study include (1) the value of student learning attain Minimum Completion Criteria (KKM = 70) with 80% classical completeness, (2) observation sheet for student activities in each observed aspect reaching > 75%, and (3) the questionnaire response to the application of cooperative learning models with integrated structure numbered head type Islamic values reached > 75% who responded positively. The analysis results obtained a significant value of 0, 000 < α means that H0 is rejected and accepts H1. While the classical completeness value got 85% of students who achieved KKM, the observation sheet of student activities reached 85% of the eight observed aspects and calculated the students’ responses to the application of learning data 88% responded positively. Based on this analysis, the use of cooperative learning models with head types with structural numbers integrated with effective Islamic values on the ability to understand students’ mathematical concepts.