Local Adaptive Thresholding Pada Preprocessing Citra Lontar Aksara Bali

Sutramiani, Ni Putu; Darmaputra, IKetut Gede; Sudarma, Made

doi:10.24843/mite.2015.v14i01p06

Cited by 10 publications

(10 citation statements)

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“…Some areas are known for their manuscript collections, such as Yogyakarta (Nugraha & Laugu, 2021;Widodo & Purwantoro, 2019), Bali (Fakhriati et al, 2022;Wicaksana & Wicaksandita, 2023), Sumatra (Nalan et al, 2023;Ottoman & Rochmiatun, 2020), and East Java (Pradana & Setyadi, 2019;Sulaiman & Bastian, 2019). These manuscripts are written in various languages, including Javanese (Setyanto, 2016), Balinese (Sutramiani et al, 2015), Sanskrit, and others. The scripts used are also diverse, including Javanese, Balinese (Sutramiani et al, 2015), Pallava (RASYID, 2020), Kawi (Laras, 2021), Buginese (WAJO, n.d.), Batak (Hidayani & Pinem, 2021), Sundanese (Sopian et al, 2017), Lontara (Mahdi, 2018), Lampung (Indriyani, 2018), and Rejang (Yusuf, 2012).…”

Section: Condition Of Ancient Manuscripts In Indonesiamentioning

confidence: 99%

“…These manuscripts are written in various languages, including Javanese (Setyanto, 2016), Balinese (Sutramiani et al, 2015), Sanskrit, and others. The scripts used are also diverse, including Javanese, Balinese (Sutramiani et al, 2015), Pallava (RASYID, 2020), Kawi (Laras, 2021), Buginese (WAJO, n.d.), Batak (Hidayani & Pinem, 2021), Sundanese (Sopian et al, 2017), Lontara (Mahdi, 2018), Lampung (Indriyani, 2018), and Rejang (Yusuf, 2012).…”

Section: Condition Of Ancient Manuscripts In Indonesiamentioning

confidence: 99%

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The Urgency of Digitizing Ancient Indonesian Manuscripts in Libraries: A Literature Review

2024

ISVS

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Ancient Indonesian manuscripts are invaluable intellectual treasures that reflect a rich cultural and historical heritage. In libraries throughout Indonesia, collections of these ancient manuscripts serve as crucial sources of information for studies in history, linguistics, anthropology, and various other academic disciplines. Thus, researchers conduct an investigation into the digitization of ancient manuscripts in Indonesia through a literature review.The research method employed in this study is a systematic literature review (SLR). The SLR method is carried out systematically by following stages that allow the literature review process to be free from bias and the subjective understanding of the researcher. The techniques used in the systematic literature review process include planning, conducting, and reporting. Data are collected from scholarly publications available on Scopus and Google Scholar with the topic of digitizing ancient manuscripts in Indonesia.The study concludes that the digitization of Indonesian ancient manuscripts is crucial. First, digitization provides broader access to ancient manuscripts. Researchers worldwide can access and study Indonesian ancient manuscripts without having to visit a physical library. Second, digitization is used to preserve ancient manuscripts. Ancient manuscripts are prone to damage due to time and environmental factors. By producing secure digital copies, the risk of losing manuscripts is significantly reduced. Third, digitization facilitates deeper text analysis. With the assistance of natural language processing technology and data analysis, researchers can unveil hidden information in ancient manuscripts. This information may include language relationships, themes, or historical trends. Finally, digitization supports the development of digital libraries and institutional repositories.

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Section: Condition Of Ancient Manuscripts In Indonesiamentioning

confidence: 99%

Section: Condition Of Ancient Manuscripts In Indonesiamentioning

confidence: 99%

The Urgency of Digitizing Ancient Indonesian Manuscripts in Libraries: A Literature Review

2024

ISVS

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“…Citra tulisan tangan lebih optimal jika citra dikonversi ke dalam bentuk biner. Pada tahap ini, digunakan metode Local Adaptive Thresholding [14] dengan persamaan (2),…”

Section: Konversi Grayscale Ke Binerunclassified

Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

Wijaya

Chamidah

Santoni

2019

Indonesian J. Electron. Instrum. Syst.

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AbstrakKarakter pada tulisan tangan cukup sulit untuk dikenali dengan mesin karena setiap orang memiliki gaya penulisan yang berbeda-beda. Penelitian ini mengenali pola karakter angka dan huruf pada tulisan tangan dengan memanfaatkan algoritma klasifikasi K-Nearest Neighbor (KNN). Proses pengenalan tulisan tangan dilakukan dengan mempraproses citra tulisan tangan, melakukan segmentasi untuk mendapatkan karakter tunggal dari sekumpulan karakter pada citra, ekstraksi ciri untuk mendapatkan fitur, dan klasifikasi yang terdiri dari proses pelatihan dan pengujian. Ekstraksi ciri dilakukan dengan metode Zona yang akan dimanfaatkan untuk klasifikasi dengan membagi data kedalam data latih dan data uji. Data latih dari hasil ekstraksi ciri direduksi menggunakan metode K-Support Vector Nearest Neighbor (K-SVNN) dengan hanya mengambil support vector sebagai data latih. Proses terakhir, untuk mengenali pola tulisan tangan dari data uji digunakan K-Nearest Neighbor (KNN). Hasil pengujian menunjukkan bahwa proses reduksi data latih K-SVNN berhasil meningkatkan akurasi pengenalan karakter tulisan tangan. AbstractHandwritten characters are difficult to be recognized by machine because people had various own writing styles. This research recognizes handwritten character pattern of numbers and alphabet using K-Nearest Neighbour (KNN) algorithm. Handwritten recognition process is done by preprocessing handwritten image, segmentation to obtain separate single characters from a set of character in an image, feature extraction to get feature vector, and classification consisting of training and testing process . Features extraction is done by utilizing Zone method that will be used for classification by splitting features data into training data and testing data.

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“…Untuk mengolah dokumen yang didapat agar lebih mempermudah dalam proses clustering, dilakukanlah preprocessing dokumen. Preprocessing berfungsi untuk meningkatkan citra, menghilangkan noise, maupun menentukan bagian citra yang akan digunakan dalam tahapan selanjutnya [7]. Preprocessing dokumen terdiri dari case folding yaitu mengubah semua huruf dalam dokumen menjadi huruf kecil dan karakter selain huruf dihilangkan, selanjutnya tokenization atau pemisahan kata, kemudian filtering yaitu penghilangan token berdasarkan stopword, terakhir adalah stemming yaitu pencarian kata dasar dari setiap kata.…”

Section: B Preprocessing Dokumenunclassified

Penerapan Metode Clustering Text Mining Untuk Pengelompokan Berita Pada Unstructured Textual Data

Yudiarta¹,

Sudarma²,

Ariastina³

2018

JTE

Self Cite

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Good governance was a government whose programs were known and beneficial to the people. In Bali Provincial Government which has duty in disseminating information is Bureau of Public Relations Regional Secretariat Bali through media owned. Because at the time of news input to the media in this case Public Relations Bureau website was not included causing the emergence of problems in the form of difficulty knowing the news, which news that goes into certain categories. Clustering was a method to solve the problem. One of the algorithms used in the Clustering method is the K-Means algorithm. This study focused on designing to classify news data into a category using K-Means. To process the documents obtained to make it easier in the process of clustering, was done by preprocess documents first. Document preparation consists of case folding, tokenization, filtering and stemming. Tf-Idf was done to pass the weighting of the terms obtained on the preprocessed documents. The results of experiments conducted using different amounts of data that are 50, 100, 200, 300, 400, and 500 data obtained results that the K-Means algorithm applied to cluster news, able to work and provide a satisfactory accuracy, Precision average of 70.76% while Recall of 70.86% and Purity of 0.76 for all test data. Intisari-Pemerintahan yang baik adalah pemerintahan yang program-programnya diketahui dan bermanfaat bagi masyarakatnya. Pada Pemerintah Provinsi Bali yang memiliki tupoksi dalam melakukan penyebarluasan informasi adalah Biro Humas Setda Provinsi Bali melalui media yang dimiliki. Dikarenakan pada saat input berita ke media dalam hal ini website Biro Humas tidak disertakan kategori menyebabkan timbulnya permasalahan berupa sulitnya mengetahui beritaberita yang mana saja yang masuk ke kategori tertentu.Clustering merupakan metode untuk mengatasi permasalahan tersebut. Salah satu algoritma yang digunakan dalam metode Clustering adalah algoritma K-Means. Penelitian ini berfokus pada perancangan untuk mengelompokan data berita ke suatu kategori dengan menggunakan K-Means. Untuk mengolah dokumen yang didapat agar lebih mempermudah dalam proses clustering, dilakukanlah preprocessing dokumen terlebih dahulu. Preprocessing dokumen terdiri dari case folding, tokenization, filtering dan stemming. Tf-Idf dilakukan untuk melalukan pembobotan terhadap term yang didapatkan pada preprocessing dokumen. Hasil coba yang dilakukan dengan menggunakan jumlah data yang berbeda yaitu 50, 100, 200, 300, 400, dan 500 data didapatkan hasil bahwa algoritma K-Means yang diterapkan untuk meng cluster berita, mampu bekerja dan memberikan akurasi yang memuaskan, dengan rata-rata Precision sebesar 70,76% sedangkan Recall sebesar 70,86% serta Purity sebesar 0,76 untuk semua data uji.

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Local Adaptive Thresholding Pada Preprocessing Citra Lontar Aksara Bali

Cited by 10 publications

References 0 publications

The Urgency of Digitizing Ancient Indonesian Manuscripts in Libraries: A Literature Review

The Urgency of Digitizing Ancient Indonesian Manuscripts in Libraries: A Literature Review

Pengenalan Karakter Tulisan Tangan Dengan K-Support Vector Nearest Neighbor

Penerapan Metode Clustering Text Mining Untuk Pengelompokan Berita Pada Unstructured Textual Data

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