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All Journal World Psychology Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan Scientific Journal of Informatics Seurune : Jurnal Psikologi Unsyiah
Rahmawati, Adelina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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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.
An Integrated Mindfulness Intervention For Emotional Numbness In Major Depression: A Case Study Rahmawati, Adelina
Seurune : Jurnal Psikologi Unsyiah Vol 8, No 2 (2025): Juli 2025
Publisher : Program Studi Psikologi, Fakultas Kedokteran, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/s-jpu.v8i2.47272

Abstract

This single-case study aims to examine the effectiveness of an integrated mindfulness-based intervention in reducing symptoms of emotional numbness in major depressive disorder. A 30 year old female participant with severe depression (BDI-II = 29) received 12 individual sessions combining mindfulness-based cognitive therapy, body-centered mindfulness, and compassion-focused therapy at a private psychology clinic. Data were collected through clinical interviews, systematic observation, and the BDI-II instrument. The result showed a significant reduction in depressive symptoms, with the BDI-II score decreasing to 14 (mild depression) after the intervention and further decreasing to 12 at the one-month follow up session. Thematic analysis revealed enhanced emotional awareness, improved emotional expression, and reduced self-criticism. These findings suggest that a structured mindfulness approach can effectively address emotional numbness in depression, offering promising implications for clinical practice. However, single case findings require replication before generalization.Abstrak: Penelitian studi kasus tunggal ini bertujuan untuk mengkaji efektivitas intervensi berbasis mindfulness dalam mengurangi simptom emotional numbness pada individu dengan gangguan depresi mayor. Partisipan adalah seorang Perempuan berusia 30 tahun dengan tingkat depresi berat (skor BDI-II = 29). Data dikumpulkan melalui wawancara klinis, observasi sistematis, dan instrumen BDI-II. Intervensi terdiri dari 12 sesi individual yang mengintegrasikan mindfulness-based cognitive therapy, body-centered mindfulness, dan compassion-focused therapy. Hasil penelitian ini menunjukkan penurunan signifikan pada gejala depresi dengan skor BDI-II menjadi 14 (depresi ringan) setelah intervensi dan skor 12 pada sesi follow up setelah satu bulan sesi selesai. Selain itu, partisipan menunjukkan peningkatan kesadaran emosional, ekspresi emosi yang lebih baik, dan penurunan sikap kritik diri. Temuan ini menunjukkan bahwa pendekatan mindfulness yang terstruktur efektif dalam mengatasi emotional numbness dan mendukung pemulihan psikologis pada gangguan depresi mayor.
"I Don't Know Why I'm Crying": Understanding Emotional Experiences of Premenstrual Syndrome in Women Rahmawati, Adelina; Arinda, Fiska Puspa
World Psychology Vol. 4 No. 1 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/wp.v4i1.919

Abstract

Premenstrual Syndrome (PMS) impacts nearly half of women worldwide, with mood changes being a primary concern. However, research on young women's subjective experiences, especially in non-Western contexts, remains limited. This qualitative study examined how young adult women experience and cope with premenstrual mood fluctuations, investigating their management strategies and influencing factors. Using interpretative phenomenological analysis, six women aged 18-25 years with consistent premenstrual mood symptoms for minimum six months were interviewed. Data analysis revealed three key themes: women's experiences and perceptions of premenstrual mood changes, including identity struggles and contextual influences; diverse coping mechanisms including behavioral, cognitive, and social strategies; and the role of socioeconomic factors, education, and technology in PMS management. Participants employed various adaptive approaches, ranging from self-care routines and exercise to seeking social support and using digital resources. Results demonstrate that PMS experiences are complex and multifaceted, highlighting the importance of personalized, culturally appropriate methods for understanding and addressing premenstrual mood fluctuations in young women. These findings contribute to better comprehension of PMS management in early adulthood.
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 Fiska Puspa Arinda Nurmawati, Erna Sugiyarto, Teguh