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All Journal Jurnal Algoritma JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research Jurnal Algoritma Jurnal Pembelajaran, Bimbingan, dan Pengelolaan Pendidikan Journal of Law, Poliitic and Humanities SENTRI: Jurnal Riset Ilmiah Coreid Journal Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Integrated : Journal Of Information Technology and Vocational Education
Aldy Rialdy Atmadja
UIN Sunan Gunung Djati Bandung
Aerospace Engineering Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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 1 
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Gerhana, Yana Aditia; Atmadja, Aldy Rialdy; Padilah, Muhamad Farid
JOIN (Jurnal Online Informatika) Vol 5, No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

Abstract

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Yana Aditia Gerhana; Aaz Muhammad Hafidz Azis; Diena Rauda Ramdania; Wildan Budiawan Dzulfikar; Aldy Rialdy Atmadja; Deden Suparman; Ayu Puji Rahayu
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Yana Aditia Gerhana; Aldy Rialdy Atmadja; Muhamad Farid Padilah
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

Abstract

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
Sistem Pendukung Keputusan Penentu Dosen Penguji Dan Pembimbing Tugas Akhir Menggunakan Fuzzy Multiple Attribute Decision Making dengan Simple Additive Weighting (Studi Kasus: Jurusan Teknik Informatika UIN SGD Bandung) Septiana, Ian; Irfan, Mohamad; Atmadja, Aldy Rialdy; Subaeki, Beki
JOIN (Jurnal Online Informatika) Vol 1 No 1 (2016)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v1i1.10

Abstract

Penentuan dosen penguji dan pembimbing skripsi adalah hal yang harus dilakukan disetiap universitas untuk membantu mahasiswa dalam menyelesaikan skripsinya. Dalam menentukan hal tersebut kadang terjadi keputusan yang kurang optimal dimana dosen yang ditunjuk kurang sesuai dengan topik skripsi mahasiswa akibatnya dapat mengurangi kualitas karya ilmiah mahasiswa. Untuk memecahkan masalah tersebut maka dibutuhkan sistem pendukung keputusan yang dapat memberikan rekomendasi dosen penguji dan pembimbing. Salah satu metode yang dapat digunakan adalah FMADM (Fuzzy Multiple Attribute Decission Making). Proses penentuan rekomendasi dosen penguji dan pembimbing dilakukan dengan mencari alternatif terbaik berdasarkan kriteria-kriteria yang telah ditentukan melalui metode SAW (Sample Additive Weighting). Adapun metode FMADM dipilih karena mampu menyeleksi alternatif terbaik dari sejumlah alternatif. Dengan mencari nilai bobot untuk setiap atribut, kemudian dilakukan proses perangkingan yang menghasilkan alternatif yang optimal, untuk menentukan dosen penguji dan pembimbing
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Gerhana, Yana Aditia; Atmadja, Aldy Rialdy; Padilah, Muhamad Farid
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

Abstract

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Co-Authors Aaz Muhammad Hafidz Azis Abdullah Azzam Adawiyah, Ayuni Adilla Febrina Agung Wahana Agustin, Ilham Rizky Akhmad Anwar Dani Akhmad Anwar Dani Akmaliyah Akmaliyah Aldy Rialdy Atmadja Alfi Dawa Mumtaazy Amiq Angelyna, Angelyna Arif Zainudin Arkaan, Shabiq Ghazi Asep Erlan Maulana Asep Erlan Maulana Asep Rohimat Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Busro, B. Cecep Nurul Alam, Cecep Nurul Dede Kurniadi Deden Suparman, Deden Dedi Rahman Nur Diena Rauda Ramdania Dini Destiani Siti Fatimah Dyka Afan Afthori Dzulfikar, Wildan Budiawan Eko Pramudya Laksana Eko Pramudya Laksana Fadlilah, Muhammad Furqon Firdaus, Muhammad Deden Fridayanti Fridayanti Hani Hamidah Hendar Riyadi Ian Septiana, Ian Ichsan Budiman Ichsan Taufik Ichsan Taufik, Ichsan Ikhwan Arief Iqbal, Arif Muhamad Irhamnillah, Sami Juliansyah, Roby Jumadi Jumadi Kartamanah, Fatih Fauzan Leni Fitriani Lucky Zamzami Lupi Krisrupianti Mochammad Tanzil Multazam Mochammad Tanzil Multazam Mohamad Irfan Mohamad Irfan, Mohamad Mohammad Fauziddin Much. Fuad Saifuddin Muh. Firyal Akbar Muh. Firyal Akbar Muhamad Farid Padilah Muhamad Fawaz Nurfauzan Muhamad Ratodi Muhamad Ratodi Muhammad Adam Dzulqarnain Muhammad Adi Nugraha Muhammad Dzulfi Muwaffaq Muhammad Fauzi Rachman Muhammad Luthfi Nadia Rohimah Nurfikri Habibulloh Padilah, Muhamad Farid Pamungkas, Arba Adhy Pasha, Muhammad Kemal Pebri Alkautsar Ratnasih, Teti Rifan Alamsyah Rifqi Syamsul Fuadi Roofiad, Ahmad Maulidi Sipa Almasik Sri Rahayu Sri Sulastri Utama Alan Deta Wildan Budiawan Zulfikar Wisnu Uriawan Wisnu Uriawan, Wisnu Yana Aditia Gerhana Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yeni Pariyatin Yeni Pariyatin Yusro, Andista Candra Zanuar Ekaputra Rus’an