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Simarmata, Allwin M
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Computer Science & IT

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Decision Support System for Determining Land Priority for Housing Development Using Fuzzy Analytical Process (Fuzzy-AHP) Method Simarmata, Allwin M; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (941.739 KB) | DOI: 10.33395/sinkron.v4i1.10243

Abstract

The National Housing Development Public Corporation (Perumnas) was established as a government solution in providing adequate housing for the middle to lower classes, to realize this, Perumnas set a target of building 100,000 houses / year. The aspect of development is to ensure that all communities occupy decent homes in a healthy environment, an increase in the number of residents in the city, especially the city of Medan, causes settlement problems because the area of land is a fixed factor, while the population is always growing, thus requiring a system that can help decision in determining the priority of land for housing development. In this study an analysis of patterns related to applicable criteria or rules is implemented by applying the Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) method, to produce accurate and effective information for making priority decisions on the location of the best residential development land. The analysis framework uses a data collection approach sourced from Public Relations, then an analysis is carried out to determine the criteria, rules and standards used, then a system is built by implementing the Fuzzy-AHP method to produce optimal alternatives that can be used as information. Alternative results will be evaluated by quantitative and qualitative analysis compared to the existing system. The developed system is expected to be used as a tool in decision making in the Regional Sumatra Regional Public Corporation I company that is optimal according to the criteria, rules or standards used. Data collection using the method of literature study, observation, interviews, and sampling. This research is expected to be one of the references in the application of decision support systems in a real scope and contribute to building a constructive community culture based on logical and scientific values.
Drug Demand Prediction Model Using Adaptive Neuro Fuzzy Inference System (ANFIS) Husein, Amir Mahmud; Simarmata, Allwin M
Sinkron : jurnal dan penelitian teknik informatika Vol. 4 No. 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (534.989 KB) | DOI: 10.33395/sinkron.v4i1.10238

Abstract

Drug planning is the process of activities in the selection of types, quantities, and prices in accordance with the needs and budget for the coming procurement period in order to avoid the occurrence of excess or emptiness of drug supplies when needed by patients. Management of planning that is not optimal drug needs will have a negative impact on hospitals, both medically and economically, because 50-60% of the total budget used for treatment and medical equipment, uncertainty of drug needs due to disease population and the number of patients can change according to conditions the volume of patient diagnostic data, thus requiring an automatic way to select drug needs according to disease progression. This study aims to create a prediction model for drug needs with the ANFIS method, the data analysis framework used is sourced from drug usage / sales data at the Royal Prima Hospital 2016-2017 by building a software that implements the ANFIS method. Stages of application testing are carried out by applying the previous year's data to predict the current year, namely the 2016 data for 2017 predictions, while the 2017 data for 2018 predictions. The data source will be used to analyze the ANFIS membership function that generates parameters for the ANFIS method in training and testing data. The results of the analysis of the ANFIS parameters will be updated to produce a small error value (close to 0), based on the value of Root Mean Square Error (RMSE), then an evaluation is carried out with a quantitative and qualitative analysis of the predicted results with the existing system.
Breast Cancer Classification Through CT Scan Using Convolutional Neural Network (CNN) Loi, Anita; Panjaitan, Ruth N; Siregar, Saut Dohot; Simarmata, Allwin M
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13706

Abstract

A common disease suffered by Indonesian women is breast cancer. Early awareness of breast cancer is very important to minimize the negative impact and increase the chances of recovery for breast cancer patients. Breast cancer detection efforts using CT scan image technology. CT scan images provide a detailed picture of the internal structure of the breast, allowing the identification of pathological changes that may be early signs of breast cancer. The purpose of the study is to utilize CNN algorithm for breast cancer classification using CT scan images. The dataset used consists of three labels namely benign cancer, malignant cancer, normal. The three data sets consist of 1096 data. CNN is a type of algorithm in the field of artificial intelligence that has proven successful in pattern recognition on image data. The collected breast CT scan image dataset includes breast cancer and non-breast cancer cases. The data is used to train and test the CNN model. Furthermore, breast cancer classification through CT scans is carried out by applying the CNN method. The results of the research conducted obtained an accuracy of 97.26%. In Benign classification with precision 0.99 (99%), recall 0.96 (96%), f1-score 0.98 (98%), support 186, then Malignant classification with precision 93% or with points 0.93, recall 98% with points 0.98, and f1-score 96% with points 0.96, and support 202. The last is the normal classification with 99% precision with 0.99 points, 97% recall with 0.97 points, 98% f1-score with 0.93 points, and 269 support.
Co-Authors Amir Mahmud Husein, Amir Mahmud Loi, Anita Panjaitan, Ruth N Siregar, Saut Dohot Yennimar, Yennimar