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All Journal JUITA : Jurnal Informatika Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informatika: Jurnal Pengembangan IT
Azhari Azhari
Universitas Gadjah Mada
Computer Science & IT

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Challenges of Sarcasm Detection for Social Network : A Literature Review Afiyati Afiyati; Azhari Azhari; Anny Kartika Sari; Abdul Karim
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 2, November 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.329 KB) | DOI: 10.30595/juita.v8i2.8709

Abstract

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.
Classification of the Mainstay Economic Region Using Decision Tree Method Heru Ismanto; Azhari Azhari; Suharto Suharto; Lincolin Arsyad
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1037-1044

Abstract

The development of the region cannot be separated from the concept of economic growth and the determination of the mainstay region as a regional center that is expected to have a positive impact on economic growth to the surrounding regions. In fact, the determination of the mainstay region is a difficult thing to do. Some cases of the determination of the mainstay region are mostly on the basis of the prerogative rights of the policy makers without carefully seeing the achievements of the development of a region. The objective of this study is to develop a classification model of the mainstay economic region using computational techniques. The decision tree methods of NBTree and J48 are used in this study and combined with Klassen typology. The results of this study show that J48 algorithm has better accuracy than NBTree in the formation process of decision tree. The accuracy of J48 is higher than NBTree i.e. 68.96%. The comparative result of the classification of the mainstay economic region between Klassen and J48 shows that there is a shift in the class position of the development quadrant. In Klassen classification, there are three regions that are categorized into the mainstay regions with advanced development and rapid growth (K1). Meanwhile, J48 results show that there is no region categorized into K1. However, the mainstay economic region on J48 is based on the level of development with the level below K1, i.e. K2. J48 classification results show that there are ten regencies that are categorized into the mainstay economic regions, namely Biak, Regency of Jayapura, Jayawijaya, Kerom, Merauke, Mimika, Nabire, Ndunga, Yapen, and the Municipality of Jayapura.
Implementasi Optical Character Recognition berbasis Deep Learning untuk Ekstraksi Data Sertifikat Tanah Dinar Nugroho Pratomo; Diyah Utami Kusumaning Putri; Azhari Azhari
Jurnal Informatika: Jurnal Pengembangan IT Vol 7, No 3 (2022)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v7i3.3657

Abstract

Data yang dimiliki BPN berupa salinan sertifikat tanah sudah terhimpun di brankas penyimpanan pada setiap BPN daerah bertahun-tahun lamanya. Banyak kasus yang dapat membuat pajak yang seharusnya ditanggung oleh pemilik tanah bisa jadi tidak terbayarkan karena kurangnya pembaharuan data. Permasalahan ini adalah belum adanya pendigitalisasian data dengan baik. Kegiatan penelitian diawali dengan pengumpulan contoh beberapa salinan sertifikat tanah dari beberapa BPN daerah Brebes, kemudian dilakukan pra-pemrosesan data (pre-processing) dengan 3 tahapan yaitu scalling, greyscalling, dan binarization.  Metode yang digunakan menggunakan OCR dan CNN untuk mengekstraksi data yang diperoleh. Pengujian dilakukan di setiap halaman penting pada sertifikat. Halaman tersebut adalah bagian cover, pendaftaran pertama, surat ukur, kutipan dan daftar buku c. setiap halaman memiliki hasil ekstraksi data yang berbeda sesuai dengan pelabelan model yang sudah dilakukan. Rata-rata prosentase hasil kesesuaian sebesar 97,05%. Hal ini membuktikan bahwa metode yang digunakan sudah sangat handal.
Co-Authors Abdul Karim Afiyati, Afiyati Anny Kartika Sari Dinar Nugroho Pratomo Diyah Utami Kusumaning Putri Heru Ismanto Lincolin Arsyad Suharto Suharto