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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics
Larasati, Ukhti Ikhsani
Program Studi Informatika
Computer Science & IT Education

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Penyajian Data Komoditi Batik Kabupaten Sukoharjo Dengan Google Earth Larasati, Ukhti Ikhsani; Muslim, Much Aziz
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 2, No 2 (2016): Volume 2 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.789 KB) | DOI: 10.26418/jp.v2i2.17454

Abstract

Kabupaten Sukoharjo memiliki banyak potensi daerah yang merupakan komoditi unggulan kabupaten yaitu komoditi mebel kayu, mebel rotan, batik, tekstil dan produk tekstil, kaca grafir, kerajinan kulit/tatah sungging (wayang), kerajinan gitar, kerajinan gamelan, shuttlecock, jamu tradisional, emping mlinjo, sarung goyor, beras, dan alkohol. Dinas Perindustrian dan Perdagangan kabupaten Sukoharjo adalah salah satu pelaksana urusan Pemerintah Daerah kabupaten Sukoharjo di bidang perindustrian dan perdagangan. Metode pengumpulan data yang digunakan adalah metode observasi dan studi pustaka. Observasi dilakukan dengan mengamati langsung bagaimana data-data komoditi unggulan kabupaten Sukoharjo disajikan di Dinas Perindustrian dan Perdagangan kabupaten Sukoharjo. Setelah mengetahui sistem penyajian data yang diterapkan yaitu secara manual, kemudian muncul gagasan menggunakan aplikasi Google Earth yang digunakan untuk menyajikan data komoditi unggulan khususnya komoditi unggulan batik. Dengan adanya perubahan sistem penyajian data ini Dinas Perindustrian dan Perdagangan kabupaten Sukoharjo lebih terbantu dalam menemukan lokasi-lokasi produksi batik di kabupaten Sukoharjo. Sehingga Dinas Perindustrian dan Perdagangan kabupaten Sukoharjo dapat dengan mudah dalam memantau perkembangan produsen komoditi unggulan. Ada sebanyak 36 data komoditi batik yang berhasil disajikan ke dalam Google Earth dari 36 data komoditi batik kabupaten Sukoharjo.   Kata kunci— GIS, Google Earth, Komoditi Batik
Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis Larasati, Ukhti Ikhsani; Muslim, Much Aziz; Arifudin, Riza; Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 6, No 1 (2019): Mei 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.14244

Abstract

Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.
Co-Authors Much Aziz Muslim Riza Arifudin S.Pd. M Kes I Ketut Sudiana .