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All Journal Buana Information Technology and Computer Sciences (BIT and CS) JATI (Jurnal Mahasiswa Teknik Informatika)
Pahlevi Putra, Rangga
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Computer Science & IT

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Identification of Socio Economic Registration Data Using OCR Based Tesseract and Google Cloud Vision Ursaputra Pratama, Lionardi; Yuniar Rahman, Aviv; Pahlevi Putra, Rangga
Buana Information Technology and Computer Sciences (BIT and CS) Vol 5 No 2 (2024): Buana Information Technology and Computer Sciences (BIT and CS)
Publisher : Information System; Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/bit-cs.v5i2.6258

Abstract

The Indonesian government program, called Socio-Economic Registration (Regsosek), aims to measure and monitor the socio-economic conditions of low-income people. One of the relevant data used for research is Regsosek. This method is used to analyze the influence of economic and social infrastructure on economic growth, analyze the socio-economic determinants of ownership of work accident insurance for informal workers, create a women's socio-economic vulnerability index (IKSEP), and study intercultural literacy from a social, economic and political perspective. The success of the government's Socio-Economic Registration program depends on the role of data collection officers or surveyors, who directly interact with the community to obtain information about Socio-Economic Registration (Regsosek) data collection. This method also has other obstacles that significantly affect the overall results of the survey, where the survey results must be entered manually by the surveyor from a form with handwritten data, after which it is entered into the website. This method is vulnerable to human error, where the handwriting is difficult to read, and mistakes are made during the data input. The technology that can be used to handle this problem is implementing the OCR method, where writing that was initially handwritten manually can be identified and converted into digital text that can be edited (editable text) and processed automatically. This research shows that the proposed method has good accuracy, with an Accuracy of 96.45%, CER 0.3%, and WER 4.30%.
KLASIFIKASI TEKS PADA ULASAN OBJEK WISATA FULAN FEHAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) Maria Alfanita Bhia, Meisya; Pahlevi Putra, Rangga; Yuniar Rahman, Aviv
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 6 (2024): JATI Vol. 8 No. 6
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i6.11865

Abstract

Nusa Tenggara Timur (NTT) memiliki banyak potensi wisata yang belum sepenuhnya tergali, salah satunya adalah kawasan wisata Fulan Fehan. Untuk mendukung pengembangan pariwisata di daerah ini, analisis sentiment terhadap ulasan wisatawan dapat memberikan informasi berharga bagi pengelola wisata. Penelitian ini bertujuan untuk melakukan Klasifikasi Tinjauan tentang objek wisata Fulan Fehan yang ada di Google Maps dianalisis menggunakan metode K-Nearest Neighbor (K-NN). Data yang digunakan dalam penelitian ini sebanyak 1.164 ulasan yang diklasifikasi menjadi ulasan positif dan negatif. Proses klasifikasi meliputi tahap pengumpulan data melalui web scraping, preprocessing data yang mengcakup cleansing, filtering, tokenizing, transformasi kasus, filter stopword, hingga tahap klasifikasi menggunakan algoritma K-NN. Hasil penelitian menunjukan bahwa nilai k=1 dengan rasio data training dan testing 70:30 memberikan akurasi sebesar 99%, sedangkan k=10 memberikan akurasi terendah sebesar 39%. Penelitian ini memberikan kontribusi bagi pengembangan wisata Fulan Fehan dengan menyediakan informasi sentiment pengunjung yang dapat digunakan untuk meningkatkan kualitas layanan dan fasilitas wisata.
Co-Authors Aviv Yuniar Rahman Maria Alfanita Bhia, Meisya Ursaputra Pratama, Lionardi