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Journal : JOIV : International Journal on Informatics Visualization

Arabic Character Recognition Using CNN LeNet-5 Satya Nugraha, Gibran; Suta Wijaya, I Gede Pasek; Bimantoro, Fitri; Yudo Husodo, Ario; Hamami, Faqih
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2422

Abstract

The human handwriting pattern is one of the research areas of pattern recognition; it is very complex. Therefore, research in this field has become quite popular. Moreover, human handwriting pattern recognition is needed for several things, one of them being character recognition. Recognition of Arabic handwriting is complex because everyone has different characteristics in writing and Arabic characters have quite abstract shapes and patterns. From previous research, Convolutional Neural Network (CNN), a deep learning-based algorithm, has a fairly high accuracy value when used for public datasets such as AHDB and private datasets. In this study, private datasets are used with a fairly high level of complexity because the respondents appointed to write Arabic letters come from different age categories. The CNN architecture used in this research is the architecture developed by Yan LeCun known as LeNet-5. The local dataset used was 8400 images, with details of 6720 for training data (each letter has 240 images) and 1680 for testing data (each letter has 60 images). The total respondents who wrote Arabic script were 30 people, and each person wrote each letter ten times. The accuracy obtained is 81% higher than in previous studies. The following study will test a number of additional CNN architectures to increase the accuracy of the results. In addition to accuracy, this study will also calculate the misclassification rate, root mean square error, and mean absolute error.
Comparison of Apache SparkSQL and Oracle Performance: Case Study of Data Cleansing Process Hidayati, Ilma Nur; Kusumasari, Tien Fabrianti; Hamami, Faqih
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.928

Abstract

A dataset with good quality is a valuable asset for a company. The data can be processed into information to help companies improve decision-making. However, the data increased more and more over time to decrease data quality. Thus, good data management is important to keep data quality meeting company standards. One of the efforts that can be done is conducting data cleansing to clean data from errors, inaccuracies, duplication, format discrepancies, etc. Apache Spark is an engine that can analyze large amounts of data. Oracle Database is a database management system used to manage databases. Both have their own reliability and can be used to analyze SQL-shaped data. This study compared Spark and Oracle performance based on query processing time. Both were tested on queries used to perform data cleansing of millions of rows of the dataset. The research focuses on finding out Spark and Oracle's performance through quantitative analysis. The results of this study showed that there were differences in query processing times on both tools. Apache Spark is rated better because it has a relatively faster query processing time than Oracle Database. It can be concluded that Oracle is more reliable in storing complex data models than in analyzing large data. For future research, it is suggested to add other comparison aspects such as memory and CPU usage. The researchers can also consider using query optimization techniques to enrich query experiments.
Co-Authors Agus Maolana Hidayat Ahmad, Mokhtarrudin Al amudi, Farhan Hasan Aldi Akbar Ambarita, Ruth Sesilya Anis Farihan Mat Raffei Arrahmani, Farras Hilmy Aziz, Abdurrahman Azzam Imaduddin, Muhammad Budi Rustandi Kartawinata Dahlan, Iqbal Ahmad Deandra, Valen Deden Witarsyah Dimas Raihan Zein Dina Meliana Saragi Fa'rifah, Riska Yanu Fabrianti Kusumasari, Tien Fadhil Hidayat Faishal Mufied Al Anshary Febrianti, Ferda Ayu Dwi Putri Ferda Ayu Dwi Putri Febrianti Ferda Ernawan Fetty Fitriyanti Lubis Firzania, Heidea Yulia Fitri Bimantoro Hadwirianto, Muhammad Raihan Helmayanti, Sheva Aditya Hidayati, Ilma Nur I Gede Pasek Suta Wijaya Iqbal Santosa Irfan Darmawan Ismail, Mohd Arfian Jauhari, M.Habib Joel Rayapoh Damanik Kardila, Yuni Kurniawan, Muhammad Rayhan Kuswandi, Brillian Adhiyaksa Lubis, Rizki Aulia Akbar Mangsor, Miza Mardika, Jody Mat Raffei, Anis Farihan Maulana, Fakhri Hassan Muhammad Bryan Gutomo Putra Muhammad Fahmi Hidayat, Muhammad Fahmi Muhammad Fauzan Nasrullah Muhammad Hafizh Murahartawaty Murahartawaty Nasrullah, Muhammad Fauzan Nicolaus Advendea Prakoso Indaryono Novanza, Alvin Renaldy Nuraliza, Hilda Nurul Hidayati Nuryatno, Edi Oktariani Nurul Pratiwi Orvalamarva Pratiwi, Oktaria Nurul Puruhita, Maretha Fitrie Puspitasari, Aprilia Mega Rachmadita Andreswari Raffei, Anis Farihan Mat Rahmah, Najma Syarifa Rahmat Fauzi Ramdani, Dwi Fickri Insan Ramli, Muhammad Ayyub Razali, Raja Razana Raja Rd. Rohmat Saedudin Salsabila Riswanti, Khairunnisa Satya Nugraha, Gibran Sheva Aditya Helmayanti Silmy Sephia Nurashila Sinung Suakanto Suhono Harso Supangkat Sujak, Aznul Fazrin bin Abu Syfani Alya Fauziyyah Tatang Mulyana Tien Fabrianti Kusumasari Vina Fadillah Widyadhari, Dinda Putri Yanu Fa'Rifah, Riska Yudo Husodo, Ario Yulizar, Iqbal Zahid, Azham