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Indonesian to Bengkulu Malay Statistical Machine Translation System Bella Okta Sari Miranda; Herman Yuliansyah; Muhammad Kunta Biddinika
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1323

Machine translation is an automatic tool that can process language translation from one language to another. This research focuses on developing Statistical Machine Translation (SMT) from Indonesian to Bengkulu Malay and evaluating the quality of the machine translation output. The training and testing data consist of parallel corpora taken from Bengkulu Malay dictionaries and online resources for Indonesian corpora, with a total of 5261 parallel sentence pairs. Several steps are performed in SMT. The initial step is preprocessing, aimed at preparing the parallel corpus. After that, a training phase is conducted, where the parallel corpus is processed to build language and translation models. Subsequently, a testing phase is carried out, followed by an evaluation phase. Based on the research results, various factors influence the quality of SMT translation output. The most important factor is the quantity and quality of the parallel corpus used as the foundation for developing translation and language models. The machine translation output is automatically evaluated using the Bilingual Evaluation Understudy (BLEU), indicating accuracy values observed when using 500 sentences, 1500 sentences, 2500 sentences, 4000 sentences, and 5261 sentences are 80.56%, 90.86%, 92.50%, 92.91%, and 94.48% respectively.

Pelatihan Edukasi Dampak Positif Dan Negatif Interaksi Media Sosial Terhadap Remaja Di SMK Muhammadiyah Bangunjiwo Bella Okta Sari Miranda; Khoirul Anam Dahlan; Moch. Nasheh Annafii; Anton Yudhana; Rusydi Umar
Jumat Informatika: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2024): Agustus
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasif.v5i2.4660

In this dynamic technological era, changes permeate various aspects of life. Technology accelerates the shift towards the digital realm, with social media becoming the primary platform facilitating rapid interaction. However, its impact on psychological well-being and communication patterns is not always positive. To address this, an educational training program has been developed to provide adolescents with a deep understanding of the positive use of social media and how to manage its negative effects. The training takes place over one day at SMK Muhammadiyah Bangunjiwo, involving 30 students from grades 11 and 12. The methods include presentations, discussions, and interactive sessions. Survey results show that most students spend considerable time on social media, but they also recognize the importance of understanding its positive and negative impacts. This training successfully increases students' awareness of responsible social media usage, and it is hoped that similar activities can be conducted sustainably. The results indicate that with better understanding, students can optimize the use of social media to support learning and other activities, as well as develop positive character traits. This program contributes to shaping wise and responsible attitudes toward technology use among the younger generation. Awareness of the importance of digital literacy is also increasing, as evidenced by the post-test literacy index increasing by 0.49 compared to the Indonesian average in 2022.

Machine Translation Indonesian Bengkulu Malay Using Neural Machine Translation-LSTM Miranda, Bella Okta Sari; Yuliansyah, Herman; Biddinika, Muhammad Kunta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 3 (2024): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.98384

The machine translator is an application in Natural Language Processing (NLP) that focuses on translating between languages. Several previous research have used Statistical Machine Translation (SMT) with a parallel corpus of Indonesian and Bengkulu Malay totaling 3000 data points. However, SMT performs poorly when confronted with limited data and infrequent language pairs. Therefore, this study aims to build a machine translation model from Indonesian to Bengkulu Malay using an NMT approach with Long Short-Term Memory (LSTM), and to create a parallel corpus of 5261 data pairs between Indonesian and Bengkulu Malay. The research was conducted in three stages: data collection, data preprocessing, training and modeling, and evaluation. The performance of the machine translator was evaluated using the Bilingual Evaluation Understudy (BLEU). The evaluation results show that this model achieved the highest average score of 0.6016332 on BLEU-1 and the lowest average score of 0.3680788 on BLEU-4. These results indicate that considering the natural linguistic structural differences between Indonesian and Bengkulu Malay can be suggested as the best solution for translating from Indonesian to Bengkulu Malay.

Indonesian to Bengkulu Malay Statistical Machine Translation System Sari Miranda, Bella Okta; Yuliansyah, Herman; Biddinika, Muhammad Kunta
International Journal of Advances in Data and Information Systems Vol. 5 No. 2 (2024): October 2024 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v5i2.1323

Machine translation is an automatic tool that can process language translation from one language to another. This research focuses on developing Statistical Machine Translation (SMT) from Indonesian to Bengkulu Malay and evaluating the quality of the machine translation output. The training and testing data consist of parallel corpora taken from Bengkulu Malay dictionaries and online resources for Indonesian corpora, with a total of 5261 parallel sentence pairs. Several steps are performed in SMT. The initial step is preprocessing, aimed at preparing the parallel corpus. After that, a training phase is conducted, where the parallel corpus is processed to build language and translation models. Subsequently, a testing phase is carried out, followed by an evaluation phase. Based on the research results, various factors influence the quality of SMT translation output. The most important factor is the quantity and quality of the parallel corpus used as the foundation for developing translation and language models. The machine translation output is automatically evaluated using the Bilingual Evaluation Understudy (BLEU), indicating accuracy values observed when using 500 sentences, 1500 sentences, 2500 sentences, 4000 sentences, and 5261 sentences are 80.56%, 90.86%, 92.50%, 92.91%, and 94.48% respectively.

Evaluasi Sentimen Pengguna ChatGPT Menggunakan Naive Bayes: Tinjauan dari Confusion Matrix dan Classification Report Dianda Rifaldi; Tri Stiyo Famuji; Bella Okta Sari Miranda; Fauzan Purma Ramadhan; Iriene Putri Mulyadi; Vanji Saputra6; Fanani, Galih Pramuja Inngam
Jurnal Riset Sistem dan Teknologi Informasi Vol. 3 No. 2 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v3i2.1990

The development of artificial intelligence (AI) technology, particularly in natural language processing (NLP), has led to various innovations, including ChatGPT. Its growing popularity highlights the need for user sentiment analysis. This study evaluates user sentiment toward ChatGPT using the Naive Bayes algorithm. The dataset, obtained from Kaggle, consists of 500 labeled English tweets categorized as positive, neutral, or negative. The process involved text preprocessing, TF-IDF feature extraction, data splitting (80% training, 20% testing), and model training. The results show an accuracy of 56%, with the highest f1-score in the negative class (0.67) and the lowest in the neutral class (0.38). The model exhibits classification imbalance, with high precision but low recall in the neutral class, and high recall but low precision in the positive class. The confusion matrix further confirms frequent misclassifications between classes. These findings reflect the limitations of Naive Bayes in handling contextual relationships in text data. Improvements can be achieved through data balancing, enhanced NLP-based feature representation, and the application of more complex classification algorithms.

Pelatihan Edukasi Dampak Positif Dan Negatif Interaksi Media Sosial Terhadap Remaja Di SMK Muhammadiyah Bangunjiwo Sari Miranda, Bella Okta; Dahlan, Khoirul Anam; Annafii, Moch. Nasheh; Yudhana, Anton; Umar, Rusydi
Jumat Informatika: Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2024): Agustus
Publisher : LPPM Universitas KH. A. Wahab Hasbullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/abdimasif.v5i2.4660

In this dynamic technological era, changes permeate various aspects of life. Technology accelerates the shift towards the digital realm, with social media becoming the primary platform facilitating rapid interaction. However, its impact on psychological well-being and communication patterns is not always positive. To address this, an educational training program has been developed to provide adolescents with a deep understanding of the positive use of social media and how to manage its negative effects. The training takes place over one day at SMK Muhammadiyah Bangunjiwo, involving 30 students from grades 11 and 12. The methods include presentations, discussions, and interactive sessions. Survey results show that most students spend considerable time on social media, but they also recognize the importance of understanding its positive and negative impacts. This training successfully increases students' awareness of responsible social media usage, and it is hoped that similar activities can be conducted sustainably. The results indicate that with better understanding, students can optimize the use of social media to support learning and other activities, as well as develop positive character traits. This program contributes to shaping wise and responsible attitudes toward technology use among the younger generation. Awareness of the importance of digital literacy is also increasing, as evidenced by the post-test literacy index increasing by 0.49 compared to the Indonesian average in 2022.

Pelatihan Unity dan AI untuk Pengembangan Animasi Edukatif di MAN 3 Banyumas Imam Adiyana; Purbaya, Muhammad Eka; Miranda, Bella Okta Sari; Damayanti, Novita
Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Vol. 4 No. 1 (2026): Februari 2026 - Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/2be6kj49

Revolusi Industri 4.0 dan Kurikulum Merdeka Belajar menuntut inovasi media pembelajaran interaktif. Namun, kendala biaya, kompleksitas teknis, dan kesenjangan literasi digital menghambat produksi animasi edukatif berkualitas, khususnya di daerah. Pengabdian masyarakat ini bertujuan membangun kapasitas civitas akademika MAN 3 Banyumas dalam mengembangkan animasi edukatif dengan memanfaatkan teknologi unity dan kecerdasan buatan (AI). Metode yang digunakan adalah pelatihan berbasis praktik langsung (hands on training) dan pendampingan berkelanjutan, meliputi empat tahap: persiapan, pelaksanaan pelatihan, penerapan proyek, serta evaluasi. Hasil kegiatan menunjukkan peningkatan kompetensi peserta sebesar 31%, dengan 80% proyek animasi yang dihasilkan berkategori baik hingga sangat baik. Tingkat kepuasan peserta terhadap pelatihan juga baik (skor rata-rata 4,76). Kegiatan ini berhasil mengubah peran peserta dari konsumen menjadi produsen konten digital, sekaligus menjadi langkah konkret dalam meningkatkan literasi digital menuju level kreasi. Pelatihan semacam ini merupakan model efektif untuk memperkenalkan teknologi emerging secara aplikatif dan mendukung terciptanya ekosistem pembelajaran inovatif di tingkat madrasah aliyah.

Understanding Generation Z’s Mental Health in Relation to Working Hours: A TikTok Sentiment Analysis Approach Putri, Isyiffah Falujjah Anugrah; Rahmatika, Alfilia Hilda; Miranda, Bella Okta Sari; Adiyana, Imam
MDP Student Conference Vol 5 No 1 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i1.15335

In the digital work environment, long working hours and blurred work-life boundaries have become increasingly common, raising concerns about employees’ mental health, particularly among Generation Z. This study examines how Generation Z perceives working hours and their impact on mental health using social media–based sentiment analysis. Data were collected from 848 TikTok videos posted between 2023 and 2025, consisting of captions and comments related to work-related stress. Sentiment classification was performed using machine learning models, namely Multinomial Naïve Bayes, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). Text representation employed Bag-of-Words and Term Frequency–Inverse Document Frequency (TF-IDF). The results indicate that positive and negative sentiments dominate discussions, reflecting strong emotional responses to working-hour pressure, while neutral sentiment remains difficult to identify. Among the evaluated models, SVM with a radial basis function kernel achieved the best overall performance. These findings highlight the potential of TikTok based sentiment analysis for understanding Generation Z’s mental health in digital work contexts.

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