Papers by Iis Afrianty
Studies in Computational Intelligence, 2014
Determination of gender is the foremost and important step of forensic anthropology in determinin... more Determination of gender is the foremost and important step of forensic anthropology in determining a positive identification from unidentified skeletal remains. Gender determination is the classification of an individual into one of two groups, male or female. The classification technique most used by anthropologists or researchers is traditional gender determination with applied linear approach, such as Discriminant Function Analysis (DFA). This paper proposed non-linear approach specific Back-Propagation Neural Network (BPNN) to determine gender from sacrum bone. Sacrum bone is one part of the body that is usually regarded as the most reliable indicator of sex. The data used in the experiment were taken from previous research, a total of 91 sacrum bones consisting of 34 females and 57 males. Method of measurement used is metric method which is measured based on six variables; real height, anterior length, anterior superior breadth, mid-ventral breadth, anterior posterior diameter of the base, and max-transverse diameter of the base. The objective of this paper is to examine and compare the degree of accuracy between previous research (DFA) and BPNN. There are two architectures of BPNN built for this case, namely [6; 6; 2] and [6; 12; 2]. The best average accuracy obtained by BPNN is model [6; 12; 2] with accuracy 99.030 % for training and 97.379 % for testing on experiment lr = 0.5 and mc = 0.9, then obtained Mean Squared Error (MSE) training is 0.01 and MSE testing is 1.660. Previous research using DFA only obtained accuracy as high as 87 %. Hence, it can be concluded that BPNN provide classification accuracy higher than DFA for gender determination in forensic anthropology.
Determination of gender is the foremost and important step of forensic anthropology in determinin... more Determination of gender is the foremost and important step of forensic anthropology in determining a positive identification from unidentified skeletal remains. Gender determination is the classification of an individual into one of two groups, male or female. The classification technique most used by anthropologists or researchers is traditional gender determination with applied linear approach, such as Discriminant Function Analysis (DFA). This paper proposed non-linear approach specific Back-Propagation Neural Network (BPNN) to determine gender from sacrum bone. Sacrum bone is one part of the body that is usually regarded as the most reliable indicator of sex. The data used in the experiment were taken from previous research, a total of 91 sacrum bones consisting of 34 females and 57 males. Method of measurement used is metric method which is measured based on six variables; real height, anterior length, anterior superior breadth, mid-ventral breadth, anterior posterior diameter of the base, and max-transverse diameter of the base. The objective of this paper is to examine and compare the degree of accuracy between previous research (DFA) and BPNN.
SECS 2013, VIETNAM
Forensic anthropology is a discipline that concerned on postmortem identification from skeletal re... more Forensic anthropology is a discipline that concerned on postmortem identification from skeletal remains in sex determination. In sex determination, besides empirical techniques such as Discriminant Function Analysis (DFA), Artificial Intelligence techniques such as Artificial Neural Network (ANN) should be considered to get more accurate result. This paper proposes back propagation ANN model for sex determination. By using data and DFA result from previous work, this paper compares the result with the result of ANN model obtained from the experiment. A total sample data of 113 patellae has been generated based on statistics values of previous study. The data is divided into three groups of ages (young, middle, and old) and is measured using three parameters (width, height, and thickness). The ANN model produces average accuracy until 96.1% compared to 92.9% result from DFA technique. This concludes that ANN produces more accurate result in sex determination compared to DFA.
SNATI 2011, INDONESIA, Jan 1, 2012
Selection of the best employee aimed to improve morale and employees performance in work. Selecti... more Selection of the best employee aimed to improve morale and employees performance in work. Selection of the best employee carried out according to criteria of the company. At PT. "X", the criteria applied best employee are SOP (Standard Operational Procedure), attitude and personality, consumer assessment, and assessment of work environment (team). On every criterion has each of the three sub-criteria. Reviewing the best employee conducted in each month by the assessment team (Area Manager, Service Centre Manager, Head Cashier, and Warehouse Supervisor). The problem faced is how to determine the best employee's decision to the criteria or sub-criteria that there are more subjective nature and the uncertainty of determining the value of data in quick time. For solving problem, will to build a Decision Support System (DSS) for selecting of the best employee using Fuzzy AHP (F-AHP) method. Objective of DSS is to aim the decision maker in make effective decisions and ensure that the chosen criterion is relevant. F-AHP is an amalgamation of Analytical Hierarchy Process (AHP) method with fuzzy approach. This paper using triangular fuzzy numbers produced by the employees and experts for each comparison were successfully used in the pairwise comparison matrices. F-AHP to complete AHP shortfalls in dealing with uncertain data or more is subjective. This system is built using desktop-based programming language VB.6 and Microsoft Access 2007. In results, F-AHP can solve multi-criteria decision making problem, especially it has more subjective criteria.
Book Reviews by Iis Afrianty
Identification is a key issue in forensic anthropology to recognize a biological profile like sex ... more Identification is a key issue in forensic anthropology to recognize a biological profile like sex determination. The sex determination is an important aspect as first step in the identification from unidentified skeletal remains. There are many parts of skeletal remains such as skull, mandible, clavicle, pelvis and long bones. Researches in forensic anthropology show that accurate and fast techniques in determining sex based on skeletal remains is still a problem, especially in the case of skeletal remains with some conditions like not intact, burned, partial bones or badly damaged. In general sex determination is not difficult problem when the complete skeleton is available. This paper addresses review on some techniques in gender determination of skeletal remains for forensic anthropology. There are five aspects in sex determination that will be discussed in this paper namely issues and problems, parts of body used and parameter in measurement, techniques used, methodology, and conclusion.
Conference Presentations by Iis Afrianty
SNTIKI5, INDONESIA, Oct 2, 2013
Forensic anthropology has objective to identify skeletal remains to determine the biological prof... more Forensic anthropology has objective to identify skeletal remains to determine the biological profile, such as gender determination. Besides empirical techniques such as Discriminant Function Analysis (DFA), Artificial Intelligence techniques such as Artificial Neural Network (ANN) should be considered to get more accurate result. This paper proposes a method of ANN, namely Backpropagation Neural Network (BPNN) model for gender determination. By using data of pelvic bones and DFA result from previous work, this paper will compares the accuracy of result obtained from the BPNN models. A total sample of 136 pelvic bones have been collected in the age range from 21 to >67 years old and measured using nine parameters. The BPNN model gave total of average classification accuracy up to 99.05% when compared with result of DFA gave 98.5%. The results of this study confirm that, BPNN gives high accuracy for pelvic bones in gender determination compared to DFA.
The determination of gender is an important part of forensic anthropology because as the first es... more The determination of gender is an important part of forensic anthropology because as the first essential step for positive identification process. Besides empirical methods for gender determination such as Discriminant Function Analysis (DFA), Artificial Intelligence methods such as Artificial Neural Network (ANN) should be considered to obtain more accurate determination result. This paper proposes Back propagation Neural Network (BPNN) model of ANN methods. By using data and DFA result of pelvic bones and patella from previous work, this paper compares accuracy of result obtained from the BPNN models. A total sample data of 136 pelvic bones and 133 patellae have been collected. For pelvic bones, BPNN gave average accuracy as much as 98.5% for training and 98.3 for testing. While on left pelvic bones, average accuracy that is obtained are 98.49% for training and 86.6% for testing. For patella bones, all average accuracy (males and females) are obtained by BPNN is 94.09%. If compared with previous study that using DFA obtained accuracy as much as 92.9%. It is concluded that in gender determination, BPNN gives high accuracy of classification for both bones compared with DFA
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Papers by Iis Afrianty
Book Reviews by Iis Afrianty
Conference Presentations by Iis Afrianty