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All Journal International Journal of Electrical and Computer Engineering ILKOM Jurnal Ilmiah Buletin Sistem Informasi dan Teknologi Islam Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Mutiara: Multidiciplinary Scientifict Journal Journal Pharmacy and Application of Computer Sciences Jurnal Fisika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Pengabdian Masyarakat Konstruksi Jurnal Balireso: Jurnal Pengabdian pada Masyarakat Linier: Literatur Informatika dan Komputer
Lutfi Budi Ilmawan, Lutfi Budi
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Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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MEMBANGUN WEB CRAWLER BERBASIS WEB SERVICE UNTUK DATA CRAWLING PADA WEBSITE GOOGLE PLAY STORE Ilmawan, Lutfi Budi
ILKOM Jurnal Ilmiah Vol 10, No 2 (2018)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v10i2.282.215-224

Abstract

At this time, Google Play Store is not providing API that can be used for accessing datas from applications on it’s application store. With that plenty application’s data, it could be used to make it a good research object, specially on data mining field. In this research, the system that is built is the system that can retrieve that applications’ data. For multiplatform’s purpose, web services are used for being an interface between client and server. Finally, the built system is working as expected. The system can retrive data from Google Play Store and it is suitable from requirements of data analysis stage. It can also integrated with REST web service to provide multiplatform access.
Performance comparison of support vector machine (SVM) with linear kernel and polynomial kernel for multiclass sentiment analysis on twitter Mukarramah, Rifqatul; Atmajaya, Dedy; Ilmawan, Lutfi Budi
ILKOM Jurnal Ilmiah Vol 13, No 2 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i2.851.168-174

Abstract

Sentiment analysis is a technique to extract information of ones perception, called sentiment, on an issue or event. This study employs sentiment analysis to classify societys response on covid-19 virus posted at twitter into 4 polars, namely happy, sad, angry, and scared. Classification technique used is support vector machine (SVM) method which compares the classification performance figure of 2 linear kernel functions, linear and polynomial. There were 400 tweet data used where each sentiment class consists of 100 data. Using the testing method of k-fold cross validation, the result shows the accuracy value of linear kernel function is 0.28 for unigram feature and 0.36 for trigram feature. These figures are lower compared to accuracy value of kernel polynomial with 0.34 and 0.48 for unigram and trigram feature respectively. On the other hand, testing method of confusion matrix suggests the highest performance is obtained by using kernel polynomial with accuracy value of 0.51, precision of 0.43, recall of 0.45, and f-measure of 0.51.
Co-Authors Anizah, Nurul Arysespajayadi Asyrafi, Hilman Atmajaya, Dedy Didik Dwi Prasetya Dolly Indra Erick Irawadi Alwi, Erick Irawadi Fattah, Farniwati Firman Aziz Firstania Azis, Andi Adillah Fuadi Syam, Rahmat Handayani, Nita Indrawan, Alfad Irawati Irawati Ismaldyka, Ismaldyka Jamal, Ridwan Jeffry Junaid, Annisa Lokapitasari B, Poetri Lestari Lokapitasari Belluano, Poetri Lestari Miftahul Jannah Muh. Aliyazid Mude Muhammad Arfah Asis Muhammad Kamil Mukarramah, Rifqatul Muladi Nasser, Andi Eka Nurwana Pahendra, Muhammad Agung Maugi Patahillah, Muh Nur Ichsan Salim, Yulita Sesuaiapitasari B, Poetri Lestari L Shabir, Fadly Siska Anraeni Syahrul Usman Zulfiana H, Yuli