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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) International Journal of Advances in Data and Information Systems Jurnal Pengabdian kepada Masyarakat Nusantara Jumat Informatika: Jurnal Pengabdian Masyarakat MDP Student Conference Jurnal Riset Sistem dan Teknologi Informasi Jurnal Informatika, Komputasi, Aplikasi & Bisnis (INFOKABIN)
Bella Okta Sari Miranda
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Understanding Generation Z’s Mental Health in Relation to Working Hours: A TikTok Sentiment Analysis Approach Isyiffah Falujjah Anugrah Putri; Alfilia Hilda Rahmatika; Bella Okta Sari Miranda; Imam Adiyana
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

Abstract

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.
Analisis Thermal dan Perubahan Fase Active Pharmaceutical Ingredients Menggunakan Hot-Stage Microscopy dan Pengolahan Citra Digital Muhammad Fahmi Mubarok Nahdli; Bella Okta Sari Miranda; Julia Mega Reski
INFOKABIN (Informatika Komputasi Aplikasi dan Bisnis) Vol 1 No 2 (2026): Juli 2026
Publisher : Universitas Al-Irsyad Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36760/ifkb.v1i2.771

Abstract

Karakterisasi termal dan analisis perubahan fase Active Pharmaceutical Ingredients (API) merupakan tahapan penting dalam pengembangan formulasi farmasi untuk menjamin stabilitas, kualitas, dan efektivitas produk obat. Penelitian ini bertujuan untuk menganalisis karakteristik termal dan perubahan fase API menggunakan metode Hot-Stage Microscopy (HSM) yang dipadukan dengan pengolahan citra digital guna meningkatkan objektivitas identifikasi perubahan morfologi selama proses pemanasan. Metode penelitian dilakukan secara eksperimental melalui tahapan persiapan sampel, pemanasan terkontrol menggunakan hot-stage microscope, observasi perubahan morfologi secara real-time, akuisisi citra digital, serta analisis data berdasarkan karakteristik visual dan temperatur pengujian. Sampel yang digunakan terdiri dari Nicotinamide, Ferulic Acid, dan Paracetamol. Hasil penelitian menunjukkan bahwa Nicotinamide mengalami pelelehan pada rentang suhu 128–131°C, Ferulic Acid mengalami pelelehan sempurna pada suhu 172°C, sedangkan Paracetamol mengalami perubahan fase bertahap pada rentang suhu 168–170°C. Pengolahan citra digital berhasil mengidentifikasi perubahan morfologi kristal, penurunan ketajaman tekstur, serta peningkatan area cair yang terbentuk selama proses pemanasan. Integrasi HSM dan pengolahan citra digital memberikan informasi visual dan kuantitatif yang lebih komprehensif dibandingkan observasi konvensional, sehingga berpotensi dikembangkan sebagai pendekatan berbasis computer vision untuk mendukung karakterisasi termal dan evaluasi perubahan fase pada bahan farmasi.
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

Abstract

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.
Pemberdayaan Anggota PKK Desa Kalisalak melalui Pelatihan Pembuatan Abon Lele untuk Meningkatkan Nilai Tambah hasil Perikanan Lokal Chusnul Hidayat; Bella Okta Sari Miranda; Faizah
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Potensi perikanan lokal di Desa Kalisalak berupa ikan lele masih dimanfaatkan sebagai produk segar sehingga nilai tambah ekonomi yang diperoleh masyarakat, khususnya anggota PKK, belum optimal. Kondisi tersebut disebabkan oleh terbatasnya pengetahuan dan keterampilan dalam diversifikasi produk olahan perikanan. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas anggota PKK melalui pelatihan pembuatan abon lele sebagai produk bernilai tambah. Metode yang diterapkan bersifat partisipatif melalui tahapan sosialisasi, penyuluhan, demonstrasi, praktik langsung, dan pendampingan. Kegiatan diikuti oleh 50 anggota PKK Desa Kalisalak dengan materi meliputi diversifikasi produk perikanan, teknik pembuatan abon lele, higiene dan sanitasi pangan, serta pengemasan produk. Hasil pelaksanaan menunjukkan bahwa peserta mampu mempraktikkan proses produksi abon lele secara mandiri dan menghasilkan produk yang memiliki nilai jual lebih tinggi dibandingkan ikan segar. Selain itu, kegiatan ini meningkatkan motivasi peserta untuk mengembangkan usaha rumah tangga berbasis potensi perikanan lokal sehingga berkontribusi terhadap pemberdayaan perempuan dan penguatan ekonomi keluarga secara berkelanjutan.
Co-Authors Alfilia Hilda Rahmatika Anton Yudhana Chusnul Hidayat Dianda Rifaldi Faizah Fanani, Galih Pramuja Inngam Fauzan Purma Ramadhan Herman Yuliansyah Herman Yuliansyah, Herman Imam Adiyana Iriene Putri Mulyadi Isyiffah Falujjah Anugrah Putri Julia Mega Reski Khoirul Anam Dahlan Moch. Nasheh Annafii Muhammad Fahmi Mubarok Nahdli Muhammad Kunta Biddinika Muhammad Kunta Biddinika Rusydi Umar Tri Stiyo Famuji Vanji Saputra6