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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Scan : Jurnal Teknologi Informasi dan Komunikasi Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIEET (Journal of Information Engineering and Educational Technology) INOVTEK Polbeng - Seri Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Linguistik Komputasional Indonesian Journal of Engagement, Community Services, Empowerment and Development (IJECSED) International Journal of Engagement and Empowerment (IJE2) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Afrida Helen
Teknik Informatika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Padjadjaran
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Other

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Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia Intan Nurma Yulita; Afrida Helen; Mira Suryani
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.58505

Abstract

In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.
THE COMMUNITY EMPOWERMENT THROUGH THE DIGITAL OF ARABIC APPLICATIONS HAJJ & UMRAH AT RAUDHOTUL QURAN ISLAMIC BOARDING SCHOOL, JATINANGOR, WEST JAVA Rustiman, Uus; Helen, Afrida; Firmansyah, Eka Kurnia
International Journal of Engagement and Empowerment (IJE2) Vol. 3 No. 3 (2023): International Journal of Engagement and Empowerment
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ije2.v3i3.137

Abstract

This community service activity aims to introduce the Hajj & Umrah The Digital of Arabic Application for the Hajj & Umrah Guidance Group (KBIH) at the Raudhotul Quran Islamic Boarding School, Jatinangor, West Java. This community service aims to provide socialization, training, and mentoring for teachers, Santri, and Hajj and Umrah congregants at the Raudhotul Quran Islamic Boarding School in Jatinangor, West Java. This service is carried out in collaboration with the Hajj and Umrah Guidance Group (KBIH). The material is delivered using Infocus and a projector in PowerPoint form. Results. The results of the activity carried out on November 24 2023 found that the Teachers, Santri and Hajj & Umrah Jama'ah of the Hajj & Umrah Guidance Group (KBIH) Raudhotul Quran Islamic Boarding School Jatinangor West Java were more enthusiastic and motivated to recognize the Hajj & Arabic Digital Application Umrah is further in its application and implementation in daily life, especially for Hajj and Umrah pilgrims as preparations in terms of language towards Mecca al Mukarromah. As a result of this community service activity, it is possible to conclude that this outreach activity has the potential to increase enthusiasm and high motivation to recognize the Hajj & Umrah Arabic Language Digital Application further among teachers, Santri and Hajj & Umrah Congregants, Hajj & Umrah Guidance Groups (KBIH) Islamic Boarding Schools Raudhotul Quran Jatinangor West Java. The participants' enthusiasm was a defining characteristic of the event
Klasifikasi Topik Berita Deutsche Welle Indonesia dengan Kata Kunci Indonesia Menggunakan Metode Multinomial Naive Bayes Ayuni, Amalia Qurrota; Helen, Afrida; Yuliawati, Susi
Jurnal Linguistik Komputasional Vol 6 No 1 (2023): Vol. 6, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v6i1.93

Abstract

Penerapan klasifikasi berita berdasarkan topik dan sub-topik dapat membantu pengguna media berita dalam menemukan informasi yang dibutuhkan secara lebih spesifik sehingga meningkatkan efisiensi waktu. Deutsche Welle Indonesia merupakan salah satu media berita asing yang terkenal akan publikasinya mengenai perkembangan politik dan teknologi. Penelitian ini bertujuan untuk mengklasifikasi topik berita khususnya mengenai Indonesia yang dipublikasikan oleh media berita Deutsche Welle Indonesia untuk mengetahui fokus pemberitaan dari media tersebut. Sebanyak 682 data dikumpulkan dan topik berita dengan jumlah terbanyak adalah berita politik, sosial budaya, dan kesehatan. Dengan tahapan preprocessing, labelling, training, testing, pembobotan tf-idf, dan klasifikasi data dengan algoritma multinomial naïve bayes didapatkan hasil akurasi tertinggi sebesar 88,3%. Klasifikasi topik diprediksi dengan confusion matrix dengan hasil berupa sebagian besar label berhasil dideteksi dan terdapat beberapa data yang mengalami kesalahan prediksi karena mesin tidak dapat mengidentifikasi judul dengan kata yang sama namun memiliki konteks berbeda.
PENGEMBANGAN PLATFROM RUANG KERJA DIGITAL BAHASA ARAB DAN UMRAH DALAM MENINGKATKAN INOVASI BIMBINGAN HAJI DI EL HARAMAIN WISATA BANDUNG, JAWA BARAT Rustiman, Uus; Helen, Afrida; Firmansyah, Eka Kurnia
Indonesian Journal of Engagement, Community Services, Empowerment and Development Vol. 4 No. 1 (2024): Indonesian Journal of Engagement, Community Services, Empowerment and Developme
Publisher : Yayasan Education and Social Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53067/ijecsed.v4i1.141

Abstract

The community service activity carried out is the development of an Arabic and Umrah digital workspace platform to increase innovation in Hajj guidance at El Haramain Wisata Bandung. The platform includes various key features such as Arabic learning modules, Umrah and Hajj guides, interactive maps, and discussion forums. Testing was conducted to ensure the functionality of the platform, which resulted in positive feedback from users. The training held ensures optimal use of the platform by congregants and mentors. This activity proves that digital technology can provide significant benefits in providing Hajj and Umrah guidance services, increasing information accessibility, structured information management, as well as time and cost efficiency
NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs Najma Rafifah Putri Syallya; Anindya Apriliyanti Pravitasari; Afrida Helen
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 1 (2025): February 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i1.6276

Abstract

Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missed questions. This study aims to develop a chatbot model capable of addressing questions about the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick, accurate information retrieval. The research focuses on optimizing the IndoBERT model for intent classification and addresses challenges that arose due to imbalance data, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students. To address data imbalance, we tried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. We achieved the best model with hyperparameters batch size of 8, learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer, resulting in 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model in intent classification for academic chatbots, achieving excellent performance.
Scientific reference style using rule-based machine learning Helen, Afrida; Pradana, Aditya; Afif, Muhammad
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1056

Abstract

Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing.
Automatic Abstractive Summarization Task for New Article Helen, Afrida
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (36.352 KB) | DOI: 10.24003/emitter.v6i1.212

Abstract

Understanding the contents of numerous documents requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produce Extractive summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This study discusses the tasks of generating summary. The conclusion is we can find that there are still opportunities to develop better outcomes that are better coherence and better accuracy.
Rule-based Sentiment Degree Measurement of Opinion Mining of Community Participatory in the Government of Surabaya Putra, Berlian Juliartha Martin; Helen, Afrida; Barakbah, Ali Ridho
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (423.153 KB) | DOI: 10.24003/emitter.v6i2.275

Abstract

Diskominfo Surabaya, as a government agency, received much community participatory for improvement of governmental services, with increasing number of 698, 2717, 4176 and 4298 participatory data respectively in 2011, 2012, 2013 and 2014. It is challenging for Diskominfo Surabaya to set a target by giving the response back within 24 hours. Due to task complexity to address the degree of participatory and to categorize the group of participatory, they faced difficulty to fulfill the target. In this research, we present a new system for measuring the sentiment degree of community participatory. We provide 5 functions in our system, which are: (1) Data Collection, (2) Data Preprocessing, (3) Text Mining, (4) Sentiment Analysis and (5) Validation. We propose our rule-based technique for the sentiment analysis of opinion mining with detection of 8 important parts, which are (1) Verb, (2) Adjective, (3) Preposition, (4) Noun, (5) Adverb, (6) Symbol, (7) Phrase, and (8) Complimentary. For applicability of our proposed system, we made a series of experiment with 410 data of community participatory in Twitter for Diskominfo Surabaya and compared with other sentiment classification algorithms which are SVM and Naive Bayes Classifier. Our system performed 77.32% rate of accuracy and outperformed to other comparing algorithms.
Co-Authors Aditya Pradana, Aditya Ali Ridho Barakbah Anindya Apriliyanti Pravitasari Annisa Rasyid Ardinur Mahyuzar Arna Fariza Ayu Purwarianti Ayuni, Amalia Qurrota Dwi H. Widyantoro Fahri Delfariyadi Faradhiba Salsabila Firmansyah, Eka Kurnia Fitri Setyorini Intan Nurma Yulita, Intan Nurma Mira Suryani Muhammad Afif Najma Rafifah Putri Syallya Nur Rosyid M Nur Rosyid Mubatada'i Putra, Berlian Juliartha Martin Rully Soelaiman Rustiman, Uus Setiowati, Yuliana Tita Karlita Yuliana Setiowati Yuliawati, Susi