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Economic Performance: Leading Sector, Economic Structure and Competitiveness of Export Commodities
Adi WIJAYA,Zainal ILMI,Dio Caisar DARMA 한국유통과학회 2020 Asian Journal of Business Environment (AJBE) Vol.10 No.3
Purpose: The purpose of this study is for the leading sector, a pattern of shifting structure of the economic sector, and community export competitiveness on the economy Malinau Regency. Research design, data, and methodology: The type of data used is secondary data with a quantitative approach of 2009-2018. The study data used Location Quotient (LQ), Shift Share Analysis (SSA), and Revealed Comparative Advantage (RCA) analysis tools. Results: There are 6 leading sectors: agriculture; electricity, gas, and clean water; building and construction; trade, hotels, and restaurants. That has been classified has changed the economic structure of the Malinau Regency from the secondary sector to the tertiary and primary sectors in 10 years. While, community export competitiveness of the Malinau Regency through RCA Analysis, see if the export products of coal and excavation (types A, B, C) are shown to have a higher comparative advantage with comparative advantage. This shows that only a few commodities that can provide the good performance of export. Conclusions: Analysis of economic growth in the Malinau Regency after regional autonomy shows that there has been a shift in the economic structure of the economy which is dominated by the structure of the primary sector.
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A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest
Aydadenta, Husna,Adiwijaya, Adiwijaya Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.5
Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.
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A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest
( Husna Aydadenta ),( Adiwijaya ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.5
Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.
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Semantic Feature Analysis for Multi-Label Text Classification on Topics of the Al-Quran Verses
( Gugun Mediamer ),( Adiwijaya ) 한국정보처리학회 2024 Journal of information processing systems Vol.20 No.1
Nowadays, Islamic content is widely used in research, including Hadith and the Al-Quran. Both are mostly used in the field of natural language processing, especially in text classification research. One of the difficulties in learning the Al-Quran is ambiguity, while the Al-Quran is used as the main source of Islamic law and the life guidance of a Muslim in the world. This research was proposed to relieve people in learning the Al-Quran. We proposed a word embedding feature-based on Tensor Space Model as feature extraction, which is used to reduce the ambiguity. Based on the experiment results and the analysis, we prove that the proposed method yields the best performance with the Hamming loss 0.10317.
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