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All Journal JOIN (Jurnal Online Informatika) Journal of Maternal and Child Health Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan Scientific Journal of Informatics
Sugiyarto, Teguh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

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The Impact of Exposure to Information Technology in Determining Women’s Knowledge of Complication during Pregnancy, Labor, and Postnatal Nurmawati, Erna; Napitupulu, Joseph Gabriel; Sugiyarto, Teguh
Journal of Maternal and Child Health Vol. 7 No. 3 (2022)
Publisher : Masters Program in Public Health, Universitas Sebelas Maret, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (34.587 KB) | DOI: 10.26911/thejmch.2022.07.03.05

Abstract

Background: Indonesian Demographic Health Survey (IDHS) 2012 reveals that Maternal Mortality Ratio (MMR) in Indonesia is 359 per 100,000 live births or increase from 228 per 100.000 live births in 2007. Compared to the other ASEAN countries, the risk of maternal death in Indonesia is also relatively high, 1 in 65 mothers. Some researches depict that maternal death is caused by inadequate care during pregnancy and delivery (labor). This condition is caused by the availability of health facility or improper health seeking behaviors. Considering the crucial role of knowledge to determine people behaviors, this paper is aimed to discuss the impact of information technology to shape people knowledge on maternal health. Subjects and Method: Some variables in Indonesian Health Demographic Survey 2017, identify the subject’s  knowledge on danger sign of complication during pregnancy, labor/delivery and postnatal periods. In digitalized era, the knowledge may come from many sources. Therefore, the discussion will focus on the impact of subject’s  accessibility and utilization of internet, mobile phone, radio, TV and newspaper in shaping knowledge of maternal health.             Results: This study found that women with primary education (OR= 1.57; p < 0.001), did not access the internet (OR= 2.49; p= 0.110); the frequency of accessing the internet for health (OR= 1.94; p= 0.083) increased women's knowledge about dangerous signs during pregnancy. While not reading newspapers (OR= 0.66; p < 0.001); not watching television (OR= 0.66; p<0.001), having or not having television (OR= 0.59; p < 0.001); do not have a mobile phone (OR= 0.64; p<0.001); not having a radio (OR= 0.88; p < 0.001) decreased women's knowledge of the danger signs during pregnancy, but this result was statistically significant. Conclusion: This result reveal that the effectiveness of internet to influence women knowledge on maternal health must be improved because of its potential in this digitalized era and the progressive trend of internet penetration in Indonesia.
Analyzing Instagram Engagement to Forecast Domestic Tourist Trips in Lake Toba and North Sumatra: A Dual Approach with Conventional Statistics and Machine Learning Techniques Nurmawati, Erna; Sugiyarto, Teguh; Artiari, Navika; Rahmawati, Adelina
Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan Vol. 8 No. 2 (2024)
Publisher : Politeknik Pariwisata NHI Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34013/jk.v8i2.1619

Abstract

The tourism industry is well known as one booster for economic development. The advance of the tourism industry will lead to the improvement of other economic sectors. Therefore, the Indonesian government is taking steps to ensure the development of its tourism industry by launching 10 super-priority destinations (DSP). Despite numerous efforts and interventions, evidence suggests that the demand for the tourism industry in certain DSPs remains unsatisfied. This also holds true for Lake Toba in North Sumatra. Therefore, it is important to understand how to promote the destination site effectively and increase the number of domestic visitors. This study is aimed at assessing the impact of digital marketing through Instagram to determine the number of domestic tourist trips. The engagement rate (ER) on Instagram posts represents the impact of digital marketing. The result reveals that the topic 'cultural tourism and its activities that develop the economy' has the highest average ER, reaching 692.48. Further analysis reveals that the LSTM model, with independent variables TPK, GTI, and ER on the topic of 'ticket information and vacation packages', is the most effective model for predicting the number of domestic tourist trips to North Sumatra. This analysis emphasizes the crucial role of digital marketing to shape the demand for the tourism industry. The conclusion is based on the significant influence of the Google Trends Index (GTI) and ER on Instagram posts, which serve as a gauge for domestic visitor numbers. The related stakeholders must consider this aspect to sustain its business.
The Impact of Online Reviews to Predict The Number of International Tourists Vashellya, Zhasa; Nurmawati, Erna; Sugiyarto, Teguh
JOIN (Jurnal Online Informatika) Vol 10 No 1 (2025)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v10i1.1409

Abstract

The tourism sector is a potential resource for advancing the Indonesian economy. The development of the tourism industry is represented by the number of international tourist arrivals. Therefore, this indicator becomes an objective in development programs. To accomplish this goal and assess the demand aspect of the tourism sector, it is a must to have a precise forecast of the number of international visitors. This research attempts to develop precise methods and models for estimating the number of international tourists based on this premise. This study chooses Bali Province as its focus since nearly half, or 47%, of the tourists who visit Indonesia arrive through the entry point in Bali Province. This research uses the LSTM method and big data online reviews in building prediction models. The results of this study show that sentiment analysis of tourist attractions in Bali using the BERT model has an accuracy of 75%. The results also depict that reviews by visitors about tourist attractions in Bali Province during the period 2012-2023 contain more positive sentiments. Furthermore, the best model to predict the number of international tourists, with the smallest RMSE and MAPE values (39,470.64 and 11.25%, respectively), includes inflation, rupiah exchange rates, TPK, monthly sentiment scores, and the number of reviews as dependent variables. The prediction model also show that the review variables (sentiment score and number of reviews) can improve prediction accuracy.
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.
Co-Authors Artiari, Navika Napitupulu, Joseph Gabriel Nurmawati, Erna Rahmawati, Adelina Vashellya, Zhasa