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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) EMITTER International Journal of Engineering Technology Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INOVTEK Polbeng - Seri Informatika
Afrida Helen
EEPIS
Computer Science & IT Engineering

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Performansi Neuro Fuzzy untuk Peramalan Data Time Series Arna Fariza; Afrida Helen; Annisa Rasyid
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2007
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

ANFIS (Adaptif Neuro Fuzzy Inference System) adalah metode jaringan neural yang fungsinya samadengan sistem inferensi fuzzy. Pada ANFIS, proses belajar pada jaringan neural dengan sejumlah pasangandata berguna untuk memperbaharui parameter-parameter sistem inferensi fuzzy. Metode ANFIS menggunakanalgoritma Error backpropagation yang memiliki beberapa keunggulan, yaitu baik dari segi kekonvergenanmaupun dari segi lokal minimumnya yang sangat peka terhadap perbaikan parameter ANFIS. Metode inidiimplementasikan pada peramalan data time series untuk 4 jenis tipe data yaitu stasioner (data sunspot),random (data saham), non stasioner (airline), musiman (beban listrik). Proses learning data dengan ANFISmemiliki hasil yang sempurna dimana nilai error proses training mampu mencapai 0 (nol). Metode ANFISmemiliki hasil yang sangat baik untuk peramalan data saham dimana didapatkan nilai MSE 2.27 pada time lag320. Hasil peramalan untuk data sunspot dan data beban listrik memiliki hasil yang lebih kecil dari ARIMAyaitu 10.96 untuk time lag 30 dan 24885 untuk time lag 210. Pada metode ANFIS nilai time lag sangatberpengaruh pada hasil peramalan dimana semakin besar time lag maka hasil peramalan semakin baik.Kata kunci: Peramalan Time Series, neural network, ANFIS.
Automatic Abstractive Summarization Task for New Article Afrida Helen
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.
Semantic Information Retrival for Scientific Experimental Papers with Knowlege based Feature Extraction Nur Rosyid Mubatada'i; Ali Ridho Barakbah; Afrida Helen
Jurnal Inovtek Polbeng Seri Informatika Vol 4, No 1 (2019)
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1205.402 KB) | DOI: 10.35314/isi.v4i1.885

Abstract

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.
Co-Authors Ali Ridho Barakbah Anindya Apriliyanti Pravitasari Annisa Rasyid Arna Fariza Najma Rafifah Putri Syallya Nur Rosyid M Nur Rosyid Mubatada'i Rully Soelaiman Tita Karlita