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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Afrida Helen
Universitas Padjadjaran
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Penentuan Aspek Implisit dengan Ekstraksi Knowledge Berbasis Rule pada Ulasan Bahasa Indonesia Yuliana Setiowati; Fitri Setyorini; Afrida Helen
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1066.375 KB) | DOI: 10.22146/jnteti.v9i1.145

Abstract

Determination of implicit aspects is one of the important things in opinion sentences. This study proposes a new approach for developing rule-based knowledge by forming a relation between opinion words with aspect categories. The relationship is obtained from the combination of rules, based on Opinion Word Similarity (OWS). Evaluation for rule-based knowledge extraction is in the form of threshold values of frequency and confidence to produce the best precision, recall, and f-measure values. The knowledge extraction consists of two phases: training phase and testing phase. The training phase is described as the process to extract rule-based knowledge. The testing phase is described as the process to obtain the implicit aspects of opinion sentences by referring to rule-based knowledge. To extract rule-based knowledge on user reviews, it is necessary to identify opinion sentences with explicit aspects and get pairs of aspects and words of opinion with rules generated from regular expressions. The evaluation result of rule-based knowledge with confidence using OWS showed better results compared to rule-based knowledge without using OWS. By using OWS, precision value increased by 0.25%, recall value increased by 1.15%, and precision value increased by 0.83%.
Klasifikasi Topik terhadap Judul Berita Kasus Covid-19 dengan Multilayer Perceptron Faradhiba Salsabila; Afrida Helen; Susi Yuliawati
Techno.Com Vol 21, No 4 (2022): November 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i4.6617

Abstract

Peran media massa berpengaruh dalam meningkatkan kesadaran masyarakat terhadap penyebaran Covid-19. Berdasarkan laporan Reuters Institute Digital News Report 2022, media daring cenderung dikonsumsi oleh masyarakat Indonesia sebagai sumber berita dengan persentase 88%. Hal tersebut menunjukkan media daring merupakan tempat penyebaran informasi yang penting. Penelitian ini bertujuan untuk mengklasifikasikan topik yang ada dalam berita terkait kasus Covid-19 dalam media massa Kompas dengan menggunakan multilayer perceptron. Berdasarkan hasil penelitian, berita kasus Covid-19 dapat dikategorikan menjadi empat label, yaitu kebijakan pemerintah, pemberitahuan informasi, internasional, dan masyarakat umum. Tingkat akurasi yang didapat dari pemodelan dengan multilayer perceptron adalah 75%. Kemiripan pada kata-kata dalam data menyebabkan adanya kesalahan dalam membedakan antara satu topik dengan topik lainnya.
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.
Co-Authors Faradhiba Salsabila Fitri Setyorini Intan Nurma Yulita, Intan Nurma Mira Suryani Yuliana Setiowati Yuliawati, Susi