Article Per Year (5 Year)
p-Index From 2020 - 2025
1.959
P-Index
This Author published in this journals
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
Nurmawati, Erna
Unknown Affiliation
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

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
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.
Quality Analysis of JAKLITERA Website with Webqual 4.0 Method and Importance-Performance Analysis Abilawa, Rakai; Nurmawati, Erna
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i1.68598

Abstract

Public libraries must develop a library service system based on information and communication technology. In an effort to realize this, the Jakarta Library launched the JAKLITERA website which is an integrated library website to facilitate borrowing library collections anywhere and anytime. To achieve this goal, it is necessary to pay attention to the quality of the website, because it will be a reflection of the library's services. Good website quality will have an impact on user satisfaction in visiting the website. To measure, the right method is needed according to the purpose of assessing website quality. Webqual 4.0 method is considered appropriate to use because basically the quality measurement technique with this method takes user perception as a basis. The analysis used to determine indicators/attributes that need to be improved or have met user expectations and website quality is IPA and gap analysis, while the CFA method will be used to see indicators that contribute less. Based on the results of gap analysis and IPA, there are 21 indicators with negative values and 4 indicators that are prioritized for improvement, while the CFA results show that there is 1 indicator that is categorized as less contributing to the JAKLITERA website.
Prediksi Volume Ekspor Udang Menggunakan Indeks Google Trend dan Faktor Berpengaruh Lainnya dengan Machine Learning Abyasa, Rayhan; Nurmawati, Erna
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 3 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i3.78900

Abstract

Udang merupakan salah satu komoditas ekspor unggulan Indonesia pada sektor perikanan yang berkontribusi sebesar 34,57% dari nilai ekspor perikanan pada tahun 2022. Indonesia juga masuk kedalam enam negara pembudidaya dan eksportir udang terbesar di dunia. Untuk memantau dan mengevaluasi target yang telah ditentukan oleh pemerintah, dibutuhkan model peramalan yang akurat. Untuk meningkatkan akurasi peramalan, indeks google trend, kurs rupiah, dan harga udang internasional ditambahkan sebagai variabel eksogen. Kata kunci indeks google trend yang digunakan diambil dari sisi eksportir dan importir seperti "ekspor udang" untuk sisi eksportir dan terjemahan kata "udang indonesia" untuk sisi importir. Penelitian ini menggunakan machine learning dengan model XGBoost dan LSTM. Model XGBoost menghasilkan nilai MAPE sebesar 10,08% sedangkan model LSTM menghasilkan nilai MAPE sebesar 12,40%. Penelitian ini menghasilkan kesimpulan bahwa model terbaik untuk volume ekspor udang Indonesia adalah model XGBoost berdasarkan nilai MAPE.
Urban traffic congestion and its association with gas station density: insights from Google Maps data Hasabi, Rafif; Kurniawan, Robert; Sugiarto, Sugiarto; Tri Wahyuni, Ribut Nurul; Nurmawati, Erna
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1618-1626

Abstract

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions.
Penggunaan Sentimen Berita, Indeks Google Trends, dan Faktor yang Berpengaruh Lainnya untuk Memprediksi Harga Gabah Kering Panen (GKP) dengan Deep Learning Apriliani, Nur Hidayah; Nurmawati, Erna
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.78913

Abstract

Indonesia merupakan negara agraris yang mayoritas pekerjaan utama penduduknya adalah bertani, khususnya bertani padi. Keputusan mengenai harga gabah dapat mempengaruhi pendapatan petani serta kelangsungan usaha mereka. Hasil penjualan gabah yang sering tidak stabil ditambah dengan biaya produksi yang semakin meningkat menyebabkan petani mengalami kerugian karena harga jual gabah tidak dapat menghasilkan cukup pendapatan untuk mencakup biaya produksi mereka. Pemerintah menetapkan Harga Pembelian Pemerintah (HPP) untuk menstabilkan harga dan mendorong produksi. Prediksi harga Gabah Kering Panen (GKP) dapat membantu pemerintah dalam pengambilan keputusan terkait stabilisasi harga, subsidi, dan insentif bagi petani untuk kesejahteraan masyarakat. Metode yang digunakan untuk memprediksi harga gabah pada penelitian ini adalah metode LSTM, CNN, dan LSTM-CNN. Model yang telah dibangun dievaluasi berdasarkan nilai MAE, MAPE, MSE, dan RMSE untuk menguji efektivitas kerangka kerja yang diusulkan. Hasil dari penelitian ini menunjukkan bahwa model terbaik yang dipilih untuk memprediksi harga Gabah Kering Panen (GKP) pada penelitian ini adalah model LSTM dengan 7 (tujuh) variabel independen paling berpengaruh dengan nilai MAE, MAPE, RMSE, dan MSE sebesar 438,68, 7,71%, 600,37, dan 360439,91. Variabel tersebut diantaranya adalah Harga Pembelian Pemerintah (HPP), total impor beras, harga eceran beras, rata-rata curah hujan bulanan, Indeks Google Trends "harga gabah", dan jumlah berita dengan sentimen negatif pada bulan tersebut.
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.
Web Service Integration: Data Exchange among Area Sampling Framework, Paddy Sampling, and CAPI Cropping Systems Finmansyah Akbar, Edo; Nurmawati, Erna; Abyasa, Rayhan
Jurnal Sistem Cerdas Vol. 7 No. 3 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i3.411

Abstract

Statistics Indonesia (BPS) is responsible for providing agriculture data. BPS collects statistics on paddy paddy production by performing a survey that involves sampling paddy plots using the Area Sampling Framework (ASF). The ASF survey is conducted monthly. The ASF System receives information from the Paddy Commodity Cropping Sampling System to prepare the sample frame and withdraw samples. This is done by the Sub-Directorate of Sample Frame Development (PKS Sub-Directorate). The existing system requires human processing of ASF results to modify the paddy observation code. This processing is carried out by the Sub-Directorate of Food Crops and the data is prepared by the Sub-Directorate of Sample Frame Development (PKS) before being uploaded into the Paddy Commodity Cropping Sampling System. The findings of sample retrieval by the Paddy Commodity Cropping Sampling System will be transmitted to the Sub-Directorate of Data Processing Integration (Sub-Directorate of IPD) and thereafter uploaded into the CAPI System for Paddy Cropping. The PKS Sub Directorate has identified many processes in the existing system that are deemed to be less efficient. The current inefficiency of the business process is caused by the manual execution of various tasks in the ASF system, such as sending data via email, modifying the paddy observation code, and sending the modified code results. Additionally, the data preparation process relies on additional applications, and sample documents from the Paddy Crop Sampling System are manually sent to the CAPI Cropping Sampling System. Hence, there is a requirement for enhancing the process flow of paddy harvesting sample. The lack of integration across systems necessitates manual execution of the process. This research proposes enhancing the Paddy Commodity Crop Sampling System by introducing new functionalities for modifying the paddy observation code and data preparation. Additionally, it suggests utilizing web services to integrate the ASF System, Paddy Commodity Crop Sampling System, and CAPI Cropping System
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.
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/inf.v12i1.22299

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. 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. 
Optimalisasi Portofolio Saham Syariah Berbasis Prediksi Menggunakan Long Short-Term Memory (LSTM) Nurmawati, Erna; Abyasa, Rayhan; Putra, Raditya Amanta
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8421

Abstract

Saham merupakan salah satu jenis investasi aset finansial yang berpotensi untuk memberikan tingkat imbal balik yang tinggi sehingga menjadi salah satu instrument investasi yang popular. Salah satu jenis saham yang popular di Indonesia adalah saham syariah yang didukung kuat dengan ajaran agama islam (shariah compliant). Saham syariah mempunyai kinerja yang baik jika dibandingkan dengan saham konvensional ketika terjadi krisis keuangan ditandai dengan risiko indeks yang lebih kecil. Investor saham selalu menginginkan hasil timbal balik yang maksimal dengan risiko seminimal mungkin. Keinginan tersebut dapat tercapai dengan menyeleksi saham dengan return terbesar lalu melakukan optimalisasi pada potofolio saham. Salah satu metode seleksi saham yang dapat dilakukan adalah dengan memprediksi harga saham dengan menggunakan model LSTM pada indeks JII. Saham dengan return terbesar sesuai dengan hasil prediksi akan dimasukkan ke dalam satu portofolio yang akan dioptimalisasi dengan metode Mean-Variance (MV) dan Equal Weight (EW) yang akan diambil metode terbaik. Sebagai pembanding, portofolio dengan saham yang dipilih secara acak akan dibentuk dan dibandingkan hasilnya. Hasil penelitian menunjukkan portofolio yang dibentuk dengan menggunakan prediksi model LSTM dan metode optimalisasi MV memiliki keseimbangan dalam nilai mean return bulanan, standar deviasi bulanan, sharpe ratio bulanan, serta simulasi investasi sepanjang tahun 2023.
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