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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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Market Basket Analysis to Identify Customer Behaviorsby Way of Transaction Data Kurniawan, Fachrul; Umayah, Binti; Hammad, Jihad; Nugroho, Supeno Mardi Susiki; Hariadi, Mochammad
Knowledge Engineering and Data Science
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Abstract

Transaction data is a set of recording data result in connections with sales-purchase activities at a particular company. In these recent years, transaction data have been prevalently used as research objects in means of discovering new information. One of the possible attempts is to design an application that can be used to analyze the existing transaction data. That application has the quality of market basket analysis. In addition, the application is designed to be desktop-based whose components are able to process as well as re-log the existing transaction data. The used method in designing this application is by way of following the existing steps on data mining technique.The trial result showed that the development and the implementation of market basket analysis application through association rule method using apriori algorithm could work well. With the means of confidence value of 46.69% and support value of 1.78%, and the amount of the generated rule was 30 rules.
Decision Support System Determination of Main Work Unitin WPP-711 using Fuzzy TOPSIS Hozairi, Hozairi; Krisnafi, Yaser
Knowledge Engineering and Data Science
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Abstract

Decision-making to determine the working units for being prioritized to be developed in order to improve fishery monitoring in WPP-711 is imperative. The Ministry of Maritime Affairs and Fisheries should make no mismatch decision-making through long-term calculation and analysis. The problem of determining the priority of working units is a complex problem, thus it is required to find an appropriate method to avoid a mismatch decision. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a decision-making method capable of solving multi-criteria problems. TOPSIS working principle determines the alternative by considering the shortest distance from the positive ideal alternative and furthest from the ideal negative solution. To improve the performance of TOPSIS, this research is integrated with Fuzzy logic with the aim of giving the right numeric value preference. From the test of 11 alternatives of 6 criteria, the priority of development of fishery monitoring in FMA 711 is: Pontianak Working Unit= 0.883, Batam Working Unit = 0.767 Natuna Working Unit = 0.681 and Tanjung Pinang Working Unit = 0.423. Furthermore, the ranking result will be used as the basis for determining the strategy in increasing the monitoring of WPP-711 to minimize State losses due to the illegal fishing within Indonesia’s WPP-711 Regions.
Human Intestinal Condition Identification Based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier Athiyah, Ummi; Muhammad, Arif Wirawan; Azhari, Ahmad
Knowledge Engineering and Data Science
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Abstract

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure.
Feature Engineering and Anchor Optimization for Enhancing Faster R-CNN Detection of Low-Contrast Steel Surface Defects Darwis, Herdianti; Nurhalimah, Sitti; Azis, Huzain
Knowledge Engineering and Data Science
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Abstract

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of 43.74% for crazing and 53.04% for rolled-in-scale. Although these results fall short of the performance of state-of-the-art architectures that utilize feature fusion and attention mechanisms (80.2% mAP), our approach demonstrates that preprocessing improvements alone can yield competitive baseline performance without additional architectural complexity. This study confirms the effectiveness of Bilateral Filtering and CLAHE in removing defective signals, while highlighting the need for more advanced feature-extraction modules to achieve higher accuracy. Further research will examine hybrid approaches that combine preprocessing with attention-based architectures for steel inspection systems in industry.
SQL Logic Error Detection using Start End Mid Algorithm Ardiansyah, Jevri Tri; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Yasuhisa, Okazaki
Knowledge Engineering and Data Science
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Abstract

Database is an important part of a system and it stores data to be manipulated. SQL (Structured Query Language) is used for manipulating those data to extract information and make decision. There are two types of error which make SQL is challenging to learn, namely syntax error and logic error. Compiler can detect syntax error, but it does not show error warning while logical error occurred. It makes logic error more difficult to understand than syntax error. A web based SQL compiler with errors detection ability by using Start End Mid algorithm is then developed, To help database's user to learn SQL in practical implementation.
Digit Classification of Majapahit Relic Inscriptionusing GLCM-SVM Septianto, Tri; Setyati, Endang; Santoso, Joan
Knowledge Engineering and Data Science
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Abstract

A higher level of image processing usually contains some kind of classification or recognition. Digit classification is an important subfield in handwritten recognition.Handwritten digits are characterized by large variations so template matching, in general, is inefficient and low in accuracy. In this paper, we propose the classification of the digit of the year of a relic inscription in the Kingdom of Majapahit using Support Vector Machine (SVM). This method is able to cope with very large feature dimensions and without reducing existing features extraction. While the method used for feature extraction using the Gray-Level Co-Occurrence Matrix (GLCM), special for texture analysis. This experiment is divided into 10 classification class, namely: class 1, 2, 3, 4, 5, 6, 7, 8, 9, and class 0. Each class is tested with 10 data so that the whole data testing are 100 data number year. The use of GLCM and SVM methods have obtained an average of classification results about 77 %.
Signature Pattern Recognition using Kohonen Network Sari, Nadia Roosmalita; Sarwani, Mohammad Zoqi; Aulia, Yudha Alif; Mahmudy, Wayan Firdaus
Knowledge Engineering and Data Science
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Abstract

A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition.
Profiling and Identifying Individual Usersby Their Command Line Usage and Writing Style Darusalam, Darusalam; Ashman, Helen
Knowledge Engineering and Data Science
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Abstract

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.
Capital Letter Pattern Recognition in Text to Speechby Way of Perceptron Algorithm Wijaya, Novan
Knowledge Engineering and Data Science
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Abstract

Computer vision is a data transformation retrieved or generated from webcam into another form in means of determining decision. All kinds of transformations are carried through to attain specific aims. One of the supporting techniques in implementing computer vision on a system is digital image processing as the objective of digital image processing is to transform digital-formatted picture so that it can be processed in computer. Computer vision and digital image processing can be implemented in a system of capital letter introduction and real-time handwriting reading on a whiteboard supported by artificial neural network mode “perceptron algorithm” used as a learning technique for the system to learn and recognize the letters. The way it works is captured in letter pattern using a webcam and generates a continuous image that is transformed into digital image form and processed using several techniques such as grayscale image, thresholding, and cropping image.
Energy Efficiency Metrics of University Data Centers Hernandez, Leonel; Jimenez, Genett; Marchena, Piedad
Knowledge Engineering and Data Science
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Abstract

The data centers are fundamental pieces in the network and computing infrastructure,and evidently today more than ever they are relevant. Since they support the processing, analysis, assurance of the data generated in the network and by the applications in the cloud, which every day increases its volume thanks to technologies such as Internet of Things, Virtualization, and cloud computing, among others. Precisely the management of this large volume of information makes the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Centers offer a solution to this problem, reducing the impact produced by the data centers in the environment, through the monitoring and control of these. The metrics are the tools that allow us to measure in our case the energy efficiency of the data center and evaluate if it is friendly to the environment. These metrics will be applied to the data centers of the ITSA University Institution, Barranquilla and Soledad campus, and the analysis of these will be carried out. In previous research, the most common metric (PUE) was analyzed to measure the efficiency of the data centers, to verify if the University's data center is friendly to the environment. It is planned to extend this study by carrying out an analysis of several metrics to conclude which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment.

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