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Knowledge Engineering and Data Science
Published by Universitas Negeri Malang
ISSN : -     EISSN : 25974637     DOI : http://dx.doi.org/2597-4637
Core Subject : Science, Engineering,
Computer Science & IT Engineering
The journal welcomes experimental and theoretical findings on data science and knowledge engineering along with their applications to real-life situations.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 117 Documents
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Change Vulnerability Forecasting for Southeast Asiausing Deep Learning Algorithm Ismail, Amelia Ritahani; Ali, Nur 'Atikah Binti Mohd; Sulaiman, Junaida
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Climate change is expected to change people’s livelihood in significant ways. Several vulnerability factors and readiness factors used for measuring the prediction index of that particular country on how vulnerable of a country towards global change. Primary data was collected from University of Notre Dame Global Adaptation Index (NDGAIN). The data has been trained for the forecasting purpose with support from the validated statistical analysis. The summary of the predicted index is visualized using machine learning tools. The results developed the correlation between vulnerability and readiness factors and shows the stability of the country towards climate change. The framework is applied to synthesize findings from Prediction index studies in South East Asia in dealing with vulnerability to climate change.
The Diffusion of ICT for Corruption Detectionin Open Government Data Darusalam, Darusalam; Said, Jamaliah; Omar, Normah; Janssen, Marijn; Sohag, Kazi
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Corruption occurs in many places within the government. To tackle the issue, open data can be used as one of the tools in creating more insight into the government. The premise of this paper is to support the notion that data opening can bring up new ways of fighting corruption. The current paper aimed at investigating how open data can be employed to detect corruption. This open data is trivial due to challenges like information asymmetry among stakeholders, data might only be opened partly, different sources of data need to be combined, and data might not be easy to use, might be biased or even manipulated. The study was conducted using a literature review approach. The reviews implied that corruption can be detected using Open Government Data, Thus, by conducting the open data technique within the government, the public could monitor the activities of the governments. The practical contribution of this paper is expected to assist the government in detecting corruption by using a data-driven approach. Furthermore, the scientific contribution will originate from the development of a framework reference architecture to uncover corruption cases.
Selection of Marine Security Policyusing Fuzzy-AHP TOPSIS Hybrid Approach Hozairi, Hozairi; Buhari, Buhari; Lumaksono, Heru; Tukan, Marcus
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The research was focused on the integration of Fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to choose the optimum maritime security policy to achieve Indonesia recognition as the world's maritime axis. The method used is AHP with fuzzy based enhancement. Here, the weight of each criterion is calculated to overcome the criticism of the scale of unbalanced rating, uncertainty, and inaccuracy in the pairwise of comparison process. The best recommendation for Indonesian maritime policies is multi task single agency which is greatly infuenced by several factors such as technology, regulations, infrastructure, economic, politic, and socio-culture. The finding shows that the hybrid approach is able to produce the best recommendation for Indonesian maritime security policy.
High Dimensional Data Clustering using Self-Organized Map Febrita, Ruth Ema; Mahmudy, Wayan Firdaus; Wibawa, Aji Prasetya
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution.
Crude Palm Oil Prediction Based on Back propagation Neutral Network Approach Aini, Hijratul; Haviluddin, Haviluddin
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.
Adam Optimization Algorithmfor Wide and Deep Neural Network Jais, Imran Khan Mohd; Ismail, Amelia Ritahani
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The objective of this research is to evaluate the effects of Adam when used together with a wide and deep neural network. The dataset used was a diagnostic breast cancer dataset taken from UCI Machine Learning. Then, the dataset was fed into a conventional neural network for a benchmark test. Afterwards, the dataset was fed into the wide and deep neural network with and without Adam. It was found that there were improvements in the result of the wide and deep network with Adam. In conclusion, Adam is able to improve the performance of a wide and deep neural network.
Neural Network Classification of Brainwave Alpha Signalsin Cognitive Activities Azhari, Ahmad; Susanto, Adhi; Pranolo, Andri; Mao, Yingchi
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The signal produced by human brain waves is one unique feature. Signals carry information and are represented in electrical signals generated from the brain in a typical waveform. Human brain wave activity will always be active even when sleeping. Brain waves will produce different characteristics in different individuals. Physical and behavioral characteristics can be identified from patterns of brain wave activity. This study aims to distinguish signals from each individual based on the characteristics of alpha signals from brain waves produced. Brain wave signals are generated by giving several mental perception tasks measured using an Electroencephalogram (EEG). To get different features, EEG signals are extracted using first-order extraction and are classified using the Neural Network method. The results of this study are typical of the five first-order features used, namely average, standard deviation, skewness, kurtosis, and entropy. The results of pattern recognition training show that 171 successful iterations are carried out with a period of execution of 6 seconds. Performance tests are performed using the Mean Squared Error (MSE) function. The results of the performance tests that were successfully obtained in the pattern test are in the number 0.000994.
Handwriting Character Recognition usingVector Quantization Technique Haviluddin, Haviluddin; Alfred, Rayner; Moham, Ni’mah; Pakpahan, Herman Santoso; Islamiyah, Islamiyah; Setyadi, Hario Jati
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Comparison of Naïve Bayes Algorithm and Decision Tree C4.5for Hospital Readmission Diabetes Patientsusing HbA1c Measurement Pujianto, Utomo; Setiawan, Asa Luki; Ar Rosyid, Harits; Salah, Ali M. Mohammad
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Diabetes is a metabolic disorder disease in which the pancreas does not produce enough insulin or the body cannot use insulin produced effectively. The HbA1c examination, which measures the average glucose level of patients during the last 2-3 months, has become an important step to determine the condition of diabetic patients. Knowledge of the patient's condition can help medical staff to predict the possibility of patient readmissions, namely the occurrence of a patient requiring hospitalization services back at the hospital. The ability to predict patient readmissions will ultimately help the hospital to calculate and manage the quality of patient care. This study compares the performance of the Naïve Bayes method and C4.5 Decision Tree in predicting readmissions of diabetic patients, especially patients who have undergone HbA1c examination. As part of this study we also compare the performance of the classification model from a number of scenarios involving a combination of preprocessing methods, namely Synthetic Minority Over-Sampling Technique (SMOTE) and Wrapper feature selection method, with both classification techniques. The scenario of C4.5 method combined with SMOTE and feature selection method produces the best performance in classifying readmissions of diabetic patients with an accuracy value of 82.74 %, precision value of 87.1 %, and recall value of 82.7 %.
Optimisation of Rice Fertiliser Composition using Genetic Algorithms Anissa, Retno Dewi; Mahmudy, Wayan Firdaus; Widodo, Agus Wahyu
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

There are so many problems with food scarcity. One of them is not too good rice quality. So, an enhancement in rice production through an optimal fertiliser composition. Genetic algorithm is used to optimise the composition for a more affordable price. The process of genetic algorithm is done by using a representation of a real code chromosome. The reproduction process using a one-cut point crossover and random mutation, while for the selection using binary tournament selection process for each chromosome. The test results showed the optimum results are obtained on the size of the population of 10, the crossover rate of 0.9 and the mutation rate of 0.1. The amount of generation is 10 with the best fitness value is generated is equal to 1,603.

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