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Knowledge Engineering and Data Science

keds
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Published by Universitas Negeri Malang

ISSN : - EISSN : 25974637 DOI : https://doi.org/10.17977

Core Subject : Science, Engineering,

Computer Science & IT Engineering

Knowledge Engineering and Data Science (2597-4637), KEDS, brings together researchers, industry practitioners, and potential users, to promote collaborations, exchange ideas and practices, discuss new opportunities, and investigate analytics frameworks on data-driven and knowledge base systems.

Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)

Articles 98 Documents

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High Dimensional Data Clustering using Self-Organized Map Ruth Ema Febrita; Wayan Firdaus Mahmudy; Aji Prasetya Wibawa
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

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.

Adam Optimization Algorithm for Wide and Deep Neural Network Imran Khan Mohd Jais; Amelia Ritahani Ismail; Syed Qamrun Nisa
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

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.

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach Hijratul Aini; Haviluddin Haviluddin
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

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.

The Diffusion of ICT for Corruption Detection in Open Government Data Darusalam Darusalam; Jamaliah Said; Normah Omar; Marijn Janssen; Kazi Sohag
Knowledge Engineering and Data Science Vol 2, No 1 (2019)
Publisher : Universitas Negeri Malang

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.

Profiling and Identifying Individual Users by Their Command Line Usage and Writing Style Darusalam Darusalam; Helen Ashman
Knowledge Engineering and Data Science Vol 1, No 2 (2018)
Publisher : Universitas Negeri Malang

Profiling and identifying individual users is an approach for intrusion detection in a computer system. User profiles are important in many applications since they record highly user-specific information - profiles are basically built to record information about users or for users to share experiences with each other. This research extends previous research on re-authenticating users with their user profiles. This research focuses on the potential to add psychometric user characteristics into the user model so as to be able to detect unauthorized users who may be masquerading as a genuine user. There are five participants involved in the investigation for formal language user identification. Additionally, we analyze the natural language of two famous writers, Jane Austen & William Shakespeare, in their written works to determine if the same principles can be applied to natural language use. This research used the n-gram analysis method for characterizing user’s style, and can potentially provide accurate user identification. As a result, n-gram analysis of a user's typed inputs offers another method for intrusion detection as it may be able to both positively and negatively identify users. The contribution of this research is to assess the use of a user’s writing styles in both formal language and natural language as a user profile characteristic that could enable intrusion detection where intruders masquerade as real users.

Handwriting Character Recognition using Vector Quantization Technique Haviluddin Haviluddin; Rayner Alfred; Ni’mah Moham; Herman Santoso Pakpahan; Islamiyah Islamiyah; Hario Jati Setyadi
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

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 Indonesian Imports Forecasting by Limited Period Using SARIMA Method Harits Ar Rosyid; Mutyara Whening Aniendya; Heru Wahyu Herwanto
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

The development of Indonesia's imports fluctuate over years. Inability to anticipate such rapid changes can cause economic slump due to inappropriate policy. For instance, recent years imports in rice led to the extermination of rice reserves. The reason is to maintain the market price of rice in Indonesia. To overcome these changes, forecasting the amount of imports should assist the Government in determining the optimum policy. This can be done by utilizing an algorithm to forecast time series data, in this case the amount of imports in the next few months with a high degree of accuracy. This study uses data obtained from the official website of the Indonesian Ministry of Trade. Then, Seasonal Autoregressive Integrated Moving Average (SARIMA) method is applied to forecast the imports. This method is suitable for the interconnected dependent variables, as well as in forecasting seasonal data patterns. The results of the experiment showed that 6-period forecast is the most accurate results compared to forecasting by 16 and 24 periods. The research resulted in the best model, that is ARIMA (0, 1, 3)(0, 1, 1)12 produces forecasting with a MAPE value of 7.210 % or an accuracy rate of 92.790 %. By applying this imports forecast model, the government can have a forward strategic plans such as selectively imports products and carefully decide the amount of the incoming products to Indonesia. Hence, it could maintain or improve the economic condition where local businesses can grow confidently.

Optimisation of Rice Fertiliser Composition using Genetic Algorithms Retno Dewi Anissa; Wayan Firdaus Mahmudy; Agus Wahyu Widodo
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

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.

Neural Network Classification of Brainwave Alpha Signals in Cognitive Activities Ahmad Azhari; Adhi Susanto; Andri Pranolo; Yingchi Mao
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

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.

Comparison of Naïve Bayes Algorithm and Decision Tree C4.5 for Hospital Readmission Diabetes Patients using HbA1c Measurement Utomo Pujianto; Asa Luki Setiawan; Harits Ar Rosyid; Ali M. Mohammad Salah
Knowledge Engineering and Data Science Vol 2, No 2 (2019)
Publisher : Universitas Negeri Malang

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 %.

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Contact Name
Aji Prasetya Wibawa

Contact Email
keds.journal@um.ac.id

Phone
+62818539333

Journal Mail Official
keds.journal@um.ac.id

Editorial Address
Semarang St. No 5, Malang, Indonesia

Location
Kota malang,

Jawa timur

INDONESIA

Abstract

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Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Aji Prasetya Wibawa

Contact Email keds.journal@um.ac.id

Phone +62818539333

Journal Mail Official keds.journal@um.ac.id

Editorial Address Semarang St. No 5, Malang, Indonesia

Location Kota malang, Jawa timur INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Aji Prasetya Wibawa

Contact Email
keds.journal@um.ac.id

Phone
+62818539333

Journal Mail Official
keds.journal@um.ac.id

Editorial Address
Semarang St. No 5, Malang, Indonesia

Location
Kota malang,

Jawa timur

INDONESIA