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All Journal JOIN (Jurnal Online Informatika) Jurnal Informatika Journal of Maternal and Child Health Jurnal Sistem Cerdas Indonesian Journal of Electrical Engineering and Computer Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics Jurnal Informatika
Nurmawati, Erna
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Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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Pemodelan Topik Opini Publik Terhadap Pelayanan Haji 2024 Menggunakan Latent Dirichlet Allocation (LDA) pada Data Twitter Maharani, Regita Pramiswari Hadi; Nurmawati, Erna; Apriliani, Nur Hidayah
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 3 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i3.90696

Abstract

Salah satu kewajiban umat muslim adalah melaksanakan ibadah haji. Indonesia, salah satu negara dengan jemaah haji terbanyak di dunia, terus berupaya meningkatkan mutu penyelenggaraan haji sebagaimana diamanatkan dalam Undang-Undang Nomor 8 Tahun 2019. Hasil Survei Kepuasan Jemaah Haji 2024 yang dilakukan BPS menunjukkan tingkat kepuasan sebesar 88.20 poin. Meskipun demikian, analisis kepuasan melalui media sosial menjadi penting karena memungkinkan masyarakat menyampaikan opini secara spontan dan terbuka, berbeda dengan survei tradisional yang memiliki batasan struktur dan respon. Media sosial terbukti menjadi sarana yang efektif untuk komunikasi dan koordinasi antara jemaah dan penyelenggara karena informasi disebarkan secara cepat dan langsung. Penelitian ini bertujuan untuk mengeksplorasi topik-topik utama yang dibicarakan terkait pelayanan haji Indonesia Tahun 2024 pada dataset sentimen positif, negatif, dan netral mengenai pelayanan haji 2024 menggunakan Latent Dirichlet Allocation (LDA) terhadap data Twitter. Hasil menunjukkan bahwa jumlah topik terbaik yang diklasifikasikan dalam analisis adalah 4 topik untuk sentimen negatif (coherence score 0.416) dan 5 topik untuk sentimen positif (0.475). Temuan ini memberikan gambaran rinci mengenai aspek pelayanan yang mendapat apresiasi maupun keluhan dari jemaah. Dengan demikian, hasil penelitian ini dapat digunakan sebagai masukan praktis oleh pemangku kebijakan, khususnya Kementerian Agama dan instansi terkait, untuk merumuskan perbaikan layanan haji yang lebih responsif terhadap kebutuhan dan pengalaman jemaah secara langsung.
Nowcasting Hotel Room Occupancy Rate using Google Trends Index and Online Traveler Reviews Given Lag Effect with Machine Learning (Case Research: East Kalimantan Province) Rahmawati, Adelina; Nurmawati, Erna; Sugiyarto, Teguh
Scientific Journal of Informatics Vol. 11 No. 2: May 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i2.5553

Abstract

Purpose: The presence of a two-month lag in Hotel Room Occupancy Rate (TPK) data necessitates an alternative method to accommodate adjustments in the economic circumstances of the tourism industry. In this context, TPK is connected to the influx of tourists, making the data a valuable resource for assessing the tourism potential of a particular area. The information can be used to make informed decisions when considering investments in the local tourism industry. Therefore, this research aimed to formulate predictions for future trends using now-forecasting. The variables of Google Trends Index (IGT) and online traveler reviews considered were obtained from big data. Methods: This research used machine learning methods with Random Forest, LSTM, and CNN-BiLSTM-Attention models in determining the best model. Meanwhile, the datasets were acquired from diverse secondary data sources. Hotel Occupancy Rooms Rate was derived from BPS-Statistics Indonesia, while additional data were collected through web scraping from online travel agency websites such as Tripadvisor.com, IGT with keywords “IKN”, “hotel”, and “banjir”. For the sentiment variable from online reviews, lag effects of one, two, and three months were analyzed to determine the correlation with TPK. The highest correlation was selected for inclusion in the prediction model across all machine learning methods. Result: The results showed that the use of IGT and online traveler reviews increased the precision of forecasting models. The best model of hotel TPK nowcasting was Random Forest Regression with the lowest MAPE value and accuracy of 5.37% and 94.63%, respectively. Novelty: The proposed method showed great potential in improving the prediction of hotel TPK by leveraging new technology and extensive data sources. The correlation with TPK decreases with an increasing time lag of sentiment. Therefore, the sentiment of reviews in the current month has the highest correlation with TPK, compared to the previous one, two, or three months.
Prediction-based Stock Portfolio Optimization Using Bidirectional Long Short-Term Memory (BiLSTM) and LSTM Putra, Raditya Amanta; Nurmawati, Erna
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.5941

Abstract

Purpose: Investment is the allocation of funds with the aim of obtaining profits in the future. An example of the investment instruments with high returns and high risks are stocks. The risks associated with the investment can be reduced by forming a portfolio of quality stocks optimized through mean-variance (MV). This is necessary because successful selection of high-quality stocks depends on the future performance which can be determined through accurate price prediction. Methods: Stock price can be predicted through the adoption of different forms of deep learning methods. Therefore, BiLSTM and LSTM models were applied in this research using the stocks listed on the LQ45 index as case study. Result: The utilization of LSTM and BiLSTM models for stock price prediction produced favorable outcomes. It was observed that BiLSTM outperformed LSTM by achieving an average MAPE value of 2.1765, MAE of 104.05, and RMSE of 139.04. The model was subsequently applied to predict a set of stocks with the most promising returns which were later incorporated into the portfolio and further optimized using the Mean-Variance (MV). The results from the optimization and evaluation of the portfolio showed that the BiLSTM+MV strategy proposed had the highest Sharpe Ratio value at k=4 compared to the other models. The stocks found in the optimal portfolio were BRPT with a weight of 19.7%, ACES had 16.9%, MAPI 11.8%, and BMRI at 51.6%. Novelty: This research conducted a novel comparison of LSTM and BiLSTM models for the prediction of stock prices of companies listed in the LQ45 index which were further used to construct a portfolio. Past research showed that the development of portfolios based on predictions was not popular.
Predicting Stock Price Movements with Technical, Fundamental, and Sentiment Analysis Using the LSTM Model Saputra, Muhammad Ighfar; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Informatika Vol. 12 No. 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v12i1.12248

Abstract

The challenge of minimizing risk and maximizing profit is what traders in the stock market have been endeavoring to solve for years. Stock prices typically exhibit the characteristic of volatility, influenced by various factors and necessitate a substantial amount of data to identify patterns in price movements. Considering the significant data requirements and the rapid advancement of big data and artificial intelligence, the LSTM (Long-Short Term Memory) model stands as a suitable approach for utilization in Deep Learning. The independent variables employed encompass technical indicator variables, currency exchange rates, interest rates, the Jakarta Composite Index (IHSG), and sentiment data extracted from Twitter tweets. The results indicate that sentiment analysis using the IndoBERT model achieved an accuracy of 0.69, while LSTM analysis produced the model with the smallest error for the fourth (4th) combination of variables, comprising closing price, technical indicators, IHSG, exchange rate, and Twitter sentiment, as well as the twelfth (12th) combination of variables, encompassing closing price, technical indicators, and IHSG. These combinations yielded average RMSE errors of 1.765E-04 and 1.978E-04, respectively. Hyperparameter optimization is done to six hyperparameter, number of unit layer, dropout rate, learning rate, batch size, optimizer, and timestamps. Following hyperparameter optimization, the best-identified model was the fourth (4th) combination of variables, yielding a minimal error of 7.580E-05 and an RMSE of 332.66 in the evaluation of test data. 
Co-Authors Abilawa, Rakai Abyasa, Rayhan Apriliani, Nur Hidayah Artiari, Navika Finmansyah Akbar, Edo Hasabi, Rafif Maharani, Regita Pramiswari Hadi Napitupulu, Joseph Gabriel Putra, Raditya Amanta Rahmawati, Adelina Ribut Nurul Tri Wahyuni Robert Kurniawan, Robert Saputra, Muhammad Ighfar Sugiarto S Sugiyarto, Teguh Vashellya, Zhasa