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"Indonesia kaya akan warisan budaya, salah satunya adalah aksara Pegon. Aksara ini merupakan sistem penulisan yang berkembang selama penyebaran Islam di nusantara. Warisan budaya ini adalah adaptasi dari aksara Arab yang sering digunakan oleh para ulama Islam dalam penulisan manuskrip di masa lalu. Namun, seiring dominasi aksara Latin di Indonesia, penggunaan aksara Pegon saat ini semakin berkurang, hanya pada kalangan tertentu, sehingga menyebabkan sedikitnya individu yang mampu membaca aksara Pegon. Oleh karena itu, transliterasi antara aksara Pegon ke Latin diperlukan. Penelitian ini berfokus pada pengembangan transliterasi menggunakan pendekatan berbasis data dengan model sequence-to-sequence, mengikuti pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil penelitian menunjukkan bahwa pendekatan berbasis data menggunakan metode sequence-to-sequence mencapai akurasi 99.76% dan CER 0.010624 untuk dataset bahasa Sunda dengan model terbaik BiGRU-Att, akurasi 99.31% dan CER 0.029255 untuk dataset bahasa Jawa dengan model terbaik BiGRU-Att, dan akurasi 99.58% dan CER 0.020466 untuk dataset gabungan bahasa dengan model BiLSTM-Att. Dari hasil ini, dapat dikatakan bahwa hasil prediksi tergolong baik dengan nilai akurasi di atas 70%, nilai loss mendekati 0, dan nilai Character Error Rate (CER) mendekati 0 untuk semua dataset.

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Indonesia, rich in cultural heritage, includes Pegon script, a writing system that evolved during the spread of Islam in the archipelago. This cultural heritage is an adaptation of the Arabic script often used by Islamic scholars in manuscript writing in the past. However, the current use of Pegon script is less popular compared to the past due to the dominance of the Latin script in Indonesia, resulting in few individuals being able to read Pegon script. Therefore, transliteration between Pegon and Latin scripts is necessary. The research concentrates on developing transliteration using a data-driven approach with sequence-to-sequence models, following the Arabic-Latin transliteration guidelines from the Ministry of Religious Affairs in 1987, the results of the Pegon Script Congress 2022, and SNI 9047:2023. The results show that the data-driven approach using the sequence- to-sequence method achieves an accuracy of 99.76% and a CER of 0.010624 for the Sundanese dataset with the best model BiGRU-Att, an accuracy of 99.31% and a CER of 0.029255 for the Javanese dataset with the best model BiGRU-Att, and an accuracy of 99.58% and a CER of 0.020466 for the combined language dataset with the BiLSTM-Att model. From these results, it can be said that the prediction results are classified as good with accuracy values above 70%, loss values close to 0, and Character Error Rate (CER) values close to 0 for all datasets."

"Indonesia kaya akan warisan budaya, salah satunya adalah aksara Pegon. Aksara ini merupakan sistem penulisan yang berkembang selama penyebaran Islam di nusantara. Warisan budaya ini adalah adaptasi dari aksara Arab yang sering digunakan oleh para ulama Islam dalam penulisan manuskrip di masa lalu. Namun, seiring dominasi aksara Latin di Indonesia, penggunaan aksara Pegon saat ini semakin berkurang, hanya pada kalangan tertentu, sehingga menyebabkan sedikitnya individu yang mampu membaca aksara Pegon. Oleh karena itu, transliterasi antara aksara Pegon ke Latin diperlukan. Penelitian ini berfokus pada pengembangan transliterasi menggunakan pendekatan berbasis data dengan model sequence-to-sequence, mengikuti pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil penelitian menunjukkan bahwa pendekatan berbasis data menggunakan metode sequence-to-sequence mencapai akurasi 99.76% dan CER 0.010624 untuk dataset bahasa Sunda dengan model terbaik BiGRU-Att, akurasi 99.31% dan CER 0.029255 untuk dataset bahasa Jawa dengan model terbaik BiGRU-Att, dan akurasi 99.58% dan CER 0.020466 untuk dataset gabungan bahasa dengan model BiLSTM-Att. Dari hasil ini, dapat dikatakan bahwa hasil prediksi tergolong baik dengan nilai akurasi di atas 70%, nilai loss mendekati 0, dan nilai Character Error Rate (CER) mendekati 0 untuk semua dataset.

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Indonesia, rich in cultural heritage, includes Pegon script, a writing system that evolved during the spread of Islam in the archipelago. This cultural heritage is an adaptation of the Arabic script often used by Islamic scholars in manuscript writing in the past. However, the current use of Pegon script is less popular compared to the past due to the dominance of the Latin script in Indonesia, resulting in few individuals being able to read Pegon script. Therefore, transliteration between Pegon and Latin scripts is necessary. The research concentrates on developing transliteration using a data-driven approach with sequence-to-sequence models, following the Arabic-Latin transliteration guidelines from the Ministry of Religious Affairs in 1987, the results of the Pegon Script Congress 2022, and SNI 9047:2023. The results show that the data-driven approach using the sequence- to-sequence method achieves an accuracy of 99.76% and a CER of 0.010624 for the Sundanese dataset with the best model BiGRU-Att, an accuracy of 99.31% and a CER of 0.029255 for the Javanese dataset with the best model BiGRU-Att, and an accuracy of 99.58% and a CER of 0.020466 for the combined language dataset with the BiLSTM-Att model. From these results, it can be said that the prediction results are classified as good with accuracy values above 70%, loss values close to 0, and Character Error Rate (CER) values close to 0 for all datasets."

"Indonesia kaya akan warisan budaya, salah satunya adalah aksara Pegon. Aksara ini merupakan sistem penulisan yang berkembang selama penyebaran Islam di nusantara. Warisan budaya ini adalah adaptasi dari aksara Arab yang sering digunakan oleh para ulama Islam dalam penulisan manuskrip di masa lalu. Namun, seiring dominasi aksara Latin di Indonesia, penggunaan aksara Pegon saat ini semakin berkurang, hanya pada kalangan tertentu, sehingga menyebabkan sedikitnya individu yang mampu membaca aksara Pegon. Oleh karena itu, transliterasi antara aksara Pegon ke Latin diperlukan. Penelitian ini berfokus pada pengembangan transliterasi menggunakan pendekatan berbasis data dengan model sequence-to-sequence, mengikuti pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil penelitian menunjukkan bahwa pendekatan berbasis data menggunakan metode sequence-to-sequence mencapai akurasi 99.76% dan CER 0.010624 untuk dataset bahasa Sunda dengan model terbaik BiGRU-Att, akurasi 99.31% dan CER 0.029255 untuk dataset bahasa Jawa dengan model terbaik BiGRU-Att, dan akurasi 99.58% dan CER 0.020466 untuk dataset gabungan bahasa dengan model BiLSTM-Att. Dari hasil ini, dapat dikatakan bahwa hasil prediksi tergolong baik dengan nilai akurasi di atas 70%, nilai loss mendekati 0, dan nilai Character Error Rate (CER) mendekati 0 untuk semua dataset.

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Indonesia, rich in cultural heritage, includes Pegon script, a writing system that evolved during the spread of Islam in the archipelago. This cultural heritage is an adaptation of the Arabic script often used by Islamic scholars in manuscript writing in the past. However, the current use of Pegon script is less popular compared to the past due to the dominance of the Latin script in Indonesia, resulting in few individuals being able to read Pegon script. Therefore, transliteration between Pegon and Latin scripts is necessary. The research concentrates on developing transliteration using a data-driven approach with sequence-to-sequence models, following the Arabic-Latin transliteration guidelines from the Ministry of Religious Affairs in 1987, the results of the Pegon Script Congress 2022, and SNI 9047:2023. The results show that the data-driven approach using the sequence- to-sequence method achieves an accuracy of 99.76% and a CER of 0.010624 for the Sundanese dataset with the best model BiGRU-Att, an accuracy of 99.31% and a CER of 0.029255 for the Javanese dataset with the best model BiGRU-Att, and an accuracy of 99.58% and a CER of 0.020466 for the combined language dataset with the BiLSTM-Att model. From these results, it can be said that the prediction results are classified as good with accuracy values above 70%, loss values close to 0, and Character Error Rate (CER) values close to 0 for all datasets."

"Indonesia memiliki kekayaan warisan budaya yang berlimpah, salah satunya adalah aksara Pegon, sebuah modifikasi aksara Arab yang tumbuh dan berkembang pada zaman penyebaran agama Islam di Nusantara. Kekayaan tersebut tentu tak dapat dilepaskan pada keaktifan menulis para ulama sehingga terbentuklah kekayaan manuskrip Pegon yang kaya. Namun dikarenakan masyarakat Indonesia lebih paham aksara Latin, tidak banyak yang bisa membaca aksara Pegon, bahkan yang memahami aksara Arab pun belum tentu dapat mengerti Pegon. Oleh karena itu, dibutuhkan transliterasi antara aksara Pegon dan Latin. Penelitian ini berfokus pada peningkatan akurasi model transliterasi rule-based yang telah dikembangkan sebelumnya pada mata kuliah Proyek Perangkat Lunak (PPL) genap 2022/2023. Lalu, penelitian ini juga berfokus pada pengembangan transliterasi dengan model Naive Bayes berbasis tokenisasi bigram yang mengacu pada pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, Hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil uji coba model rule-based yang telah direvisi dengan perbaikan 15 rules dari total 87 rules menghasilkan akurasi 98.8%. Sedangkan model bigram yang telah dikembangkan menghasilkan akurasi 94%. Dua model tersebut terbukti memiliki hasil yang jauh lebih baik dibandingkan dengan model rule-based sebelumnya yang hanya memiliki akurasi 60% saja.

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Indonesia is rich in cultural heritage, one of which is the Pegon script, a modification of the Arabic script that grew and developed during the spread of Islam in the archipelago. This wealth is undoubtedly linked to the writing activities of religious scholars, leading to the formation of a wealth of Pegon manuscripts. However, due to the Indonesian public’s better understanding of the Latin script, not many can read the Pegon script, and even those who understand the Arabic script may not necessarily understand Pegon. Therefore, a transliteration between the Pegon and Latin scripts is needed. This study focuses on improving the accuracy of a previously developed rule-based transliteration model in the Software Project (PPL) course in the even semester of 2022/2023. Then, this study also focuses on the development of transliteration with a Naive Bayes model based on bigram tokenization, referring to the Arabic-Latin transliteration guidelines from the Ministry of Religion in 1987, Kongres Aksara Pegon 2022 results, and SNI 9047:2023. The revised rule-based model, with corrections to 15 rules out of a total of 87, achieved an accuracy of 98.8%. Meanwhile, the developed bigram model achieved an accuracy of 94%. These two models have proven to yield much better results than the previous rule-based model, which only had an accuracy of 60%.

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"Indonesia memiliki kekayaan warisan budaya yang berlimpah, salah satunya adalah aksara Pegon, sebuah modifikasi aksara Arab yang tumbuh dan berkembang pada zaman penyebaran agama Islam di Nusantara. Kekayaan tersebut tentu tak dapat dilepaskan pada keaktifan menulis para ulama sehingga terbentuklah kekayaan manuskrip Pegon yang kaya. Namun dikarenakan masyarakat Indonesia lebih paham aksara Latin, tidak banyak yang bisa membaca aksara Pegon, bahkan yang memahami aksara Arab pun belum tentu dapat mengerti Pegon. Oleh karena itu, dibutuhkan transliterasi antara aksara Pegon dan Latin. Penelitian ini berfokus pada peningkatan akurasi model transliterasi rule-based yang telah dikembangkan sebelumnya pada mata kuliah Proyek Perangkat Lunak (PPL) genap 2022/2023. Lalu, penelitian ini juga berfokus pada pengembangan transliterasi dengan model Naive Bayes berbasis tokenisasi bigram yang mengacu pada pedoman transliterasi Arab-Latin dari Kementerian Agama tahun 1987, Hasil Kongres Aksara Pegon 2022, dan SNI 9047:2023. Hasil uji coba model rule-based yang telah direvisi dengan perbaikan 15 rules dari total 87 rules menghasilkan akurasi 98.8%. Sedangkan model bigram yang telah dikembangkan menghasilkan akurasi 94%. Dua model tersebut terbukti memiliki hasil yang jauh lebih baik dibandingkan dengan model rule-based sebelumnya yang hanya memiliki akurasi 60% saja.

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Indonesia is rich in cultural heritage, one of which is the Pegon script, a modification of the Arabic script that grew and developed during the spread of Islam in the archipelago. This wealth is undoubtedly linked to the writing activities of religious scholars, leading to the formation of a wealth of Pegon manuscripts. However, due to the Indonesian public’s better understanding of the Latin script, not many can read the Pegon script, and even those who understand the Arabic script may not necessarily understand Pegon. Therefore, a transliteration between the Pegon and Latin scripts is needed. This study focuses on improving the accuracy of a previously developed rule-based transliteration model in the Software Project (PPL) course in the even semester of 2022/2023. Then, this study also focuses on the development of transliteration with a Naive Bayes model based on bigram tokenization, referring to the Arabic-Latin transliteration guidelines from the Ministry of Religion in 1987, Kongres Aksara Pegon 2022 results, and SNI 9047:2023. The revised rule-based model, with corrections to 15 rules out of a total of 87, achieved an accuracy of 98.8%. Meanwhile, the developed bigram model achieved an accuracy of 94%. These

two models have proven to yield much better results than the previous rule-based model, which only had an accuracy of 60%.

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