Article Per Year (5 Year)
p-Index From 2021 - 2026
4.498
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Simetris Mediator Jurnal Teknologi Informasi dan Ilmu Komputer KOMUNIKA Sinkron : Jurnal dan Penelitian Teknik Informatika Lontar : Jurnal Ilmu Komunikasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROSIDING KOMUNIKASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Ilmu Teknik dan Komputer Dinasti International Journal of Economics, Finance & Accounting (DIJEFA) International Journal of Educational Research and Social Sciences (IJERSC) International Journal of Community Service International Journal of Qualitative Research Electronic Journal of Education, Social Economics and Technology International Journal of Health, Engineering and Technology West Science Business and Management Bulletin of Social Studies and Community Development Indonesian Journal of Community Services Laporan Upaya Nyata Inovasi Ilmu Komputer (LUNIK) Siber International Journal of Education Technology Journal of Public Relations and Digital Communication (JPRDC) JPM (Jurnal Pengabdian Masyarakat) Ruwa Jurai Nemui Nyimah
Ida Nurhaida
Universitas Lampung
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 36 Documents
Search

 1   2   3   4 
Sentiment Analysis of US-China Tariffs using IndoBERT and Economic Impact on Indonesia Khaqqi, Fitriyana Nuril; Alfat, Lathifah; Nurhaida, Ida
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11544

Abstract

The US-China trade war has influenced public perception due to its potential economic impact on developing countries like Indonesia. This study analyses Indonesian sentiment towards the tariff policies and their correlation with economic indicators. The dataset consisted of 38,739 social media comments collected through web scraping. The data were processed through data cleaning, case folding, stopword removal, normalization, and stemming. Each comment was labeled as positive, negative, and neutral. The dataset was split into 80% training and 20% testing sets, followed by an oversampling process to balance the class distribution. The data is fine-tuned using the IndoBERT model with the Python programming language. The model achieved its highest performance with an accuracy of 93.03%, precision of 93.42%, recall of 93.03%, and F1-score of 92.94%. Spearman correlation revealed a weak to moderate positive and significant correlation (ρ = 0.434, p-value < 0.05) between public sentiment and global soybean prices. These findings underscore the effectiveness of combining a deep learning model like IndoBERT with statistical analysis to link digital discourse to tangible economic indicators, highlighting the method's potential as a data-driven tool for policy evaluation.
A Hybrid YOLOv11 and LightFM Model for Emotion-Driven Anime Recommendation Ramadityo, Kafka; Nurhaida, Ida
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15579

Abstract

Existing anime recommendation systems focus on genre preferences and viewing history without considering users' emotional states, leading to context-blind recommendations that may exacerbate negative moods and reduce satisfaction. Most existing systems employ outdated architectures with limited accuracy and lack diversification mechanisms to prevent filter bubbles. This study develops an emotion-based anime recommendation system integrating YOLOv11 for facial emotion recognition with hybrid collaborative filtering using LightFM and Maximum Marginal Relevance diversification. The primary novelty lies in seamlessly combining YOLOv11's superior emotion recognition, LightFM's hybrid matrix factorization for cold-start mitigation, and MMR diversification for preventing filter bubbles while maintaining emotional congruence. The methodology employed the KDEF dataset (3,597 images, five emotion classes) for training YOLOv11 with data augmentation, and the MyAnimeList dataset (744,330 interactions) for recommendation modeling. Emotion-to-genre mappings informed by survey data from 51 participants were implemented with MMR diversification to balance relevance and variety. The YOLOv11 model achieved 93.70% validation accuracy, outperforming CNN-LSTM approaches by 37.55 percentage points. The hybrid recommendation model demonstrated test AUC of 0.8567 and Precision@10 of 0.1457, representing 417% improvement over pure collaborative filtering, while diversification increased genre representation by 20.9% with minimal precision loss. This system demonstrates real-time applicability for streaming platforms through camera-based emotion capture and immediate recommendation generation, enhancing user engagement and emotional well-being. The integration represents a significant advancement toward affective computing in entertainment media.
Utilizing Demographic, Ethnic, and Human Emotional Variables to Enhance Compassion Feeling: Basis for Slow Lorises Conservation Extension Media Development Christine, Wulandari; Nurhaida, Ida; Sugeng Prayitno , Harianto; Andi, Windah; Samsul, Bakri
Jurnal Manajemen Hutan Tropika Vol. 32 No. 1 (2026)
Publisher : Institut Pertanian Bogor (IPB University)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.7226/jtfm.32.1.86

Abstract

Slow lorises, listed as endanger under CITES Appendex I, are increasingly found outside forest habitate, including the buffer zone of Wan Abdul Rachman Grand Forest Park (Tahura WAR) in Lampung Province. While this coexistence support ex-situ conservation, it also raises risks of illegal hunting and trafficking. This study investigates how demographics, education, ethnicity, and emotion influence compassion (COMP) toward slow lorises. A log-linear model was applied at a 95% confidence level.  The response variable [COMP] was scored as 1 if respondents expressed compassion, and 0 otherwise. Explanatory variables included esmotions (affection, neutral, disgust), prior direct sightings, education level, and ethnic background. Data were collected through door-to-door survey of 150 respondents across three villages in the Tahura WAR buffer zone during October–November 2023. Each respondent was shown a 20 cm × 30 cm photograph of slow loris before answering. Results suggest that compassion increases significantly among women, those with fisthand sightings, high school gradustes, and respondents with Lampung or Sundanese parental backgrounds. Affection strongly boost COMP, while digust reduces it. These findings highlight the importance of fostering empathy through conservation education programs that complement law enforcement. These results also support the SDG 15 and 16 pillars implementation.
Dermascan: Convolutional Neural Network-Based Skin Cancer Early Detection System Agustin, Arellia; Nurhaida, Ida
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.1243

Abstract

Skin cancer continues to show a significant global increase in incidence, and early detection remains essential to reducing mortality rates. Conventional diagnostic techniques such as biopsy are invasive, require considerable processing time, and are not always accessible, particularly in remote or resource-limited healthcare environments, indicating the need for an intelligent and efficient diagnostic support system. This study develops a lightweight Convolutional Neural Network (CNN) model designed to classify seven types of skin lesions using the HAM10000 dataset consisting of 10,015 dermatoscopic images. The preprocessing pipeline involved resizing, normalization, oversampling, and dataset splitting. The training process was conducted for a maximum of 40 epochs and concluded automatically at epoch 29 using early stopping to prevent overfitting. The experimental results demonstrated that the proposed model achieved an accuracy of 98%, and surpassed common pretrained architectures including ResNet50V2 (83%) and VGG19 (67%), with precision, recall, and F1-score metrics showing consistent performance across all lesion classes. The final trained model was integrated into the Dermascan web platform, enabling real-time automated lesion classification from user-uploaded images. These findings confirm that the lightweight CNN model offers a reliable, fast, and accessible tool for early skin cancer detection that can be beneficial for both clinical decision-support and wider public healthcare applications.
Cash Flow Prediction System of PT Gudang Garam Using ERP-Integrated LSTM Novandi, Muhammad Ananta Arya; Nurhaida, Ida; Sofia, Irma Paramita; Nurhidayah, Fitriyah
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.1235

Abstract

Enterprise Resource Planning (ERP) applications such as Odoo generally do not have predictive analytics capabilities for time series data and are limited to recording historical financial data. The limitations of ERP systems make companies dependent on traditional statistical models such as ARIMA, which often fail to capture complex non-linear patterns in financial data. However, the ability to accurately predict cash flow is crucial for strategic financial management in companies. This study aims to develop and evaluate a predictive model using a Long Short-Term Memory (LSTM) approach that is accurate and integrated into Odoo ERP. The research method includes designing a microservices architecture with FastAPI as a bridge between Odoo ERP, the predictive model, and prediction graph visualization. The LSTM model is evaluated by comparing it with the ARIMA model using 3,740 Daily cash flow data, with evaluation metrics MAE, MAPE, R2. System testing will use Black Box Testing and White Box Testing. The research results show that LSTM significantly outperforms the ARIMA model with an R2 evaluation of 0.8801 and an accuracy of 96.62%. The system testing results also yielded positive outcomes as the integration architecture runs stably and functionally. This research contributes by providing an Odoo ERP system that has predictive analysis capabilities with interactive graphical visualizations through Grafana, which helps companies make decisions effectively.
Empirical Study of the Dinamics Contribution of Public Communication Based on Local Wisdom to Development Windah, Andi; Hasan, Haryanto; Kartika, Tina; Nurhaida, Ida
LONTAR: Jurnal Ilmu Komunikasi Vol. 10 No. 2 (2022): Lontar : Jurnal Ilmu Komunikasi
Publisher : Program Studi Ilmu Komunikasi Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/lontar.v10i2.5604

Abstract

Communication is one of the crucial indicators in development, especially to build consensus and facilitate knowledge sharing to achieve positive change in development initiatives. One form of development communication that emphasizes the ability of communicators is public communication. In short, public communication can be interpreted as a strategic interaction to channel information, ideas, programs, presentations, data, propaganda, and many other contexts of development messages to the masses, the public, or a specific audience. This research uses a qualitative approach with a literature study to collect data. This study found that the experience of developing a geopark tourist area in Pangandaran, West Java, can be used as the first best practice as its articulation of local wisdom in public communication during the development process showed a significant effect. In this study also shows various phenomena of successful communication integration based on local wisdom and the development process in the economic field using social media, such as the Government of Kutai Kartanegara Regency, Purwakarta Regency, Sumenep Regency. It can be concluded that the use of local wisdom-based public communication is considered capable of supporting development in terms of economic, social, or cultural
Co-Authors ., Karomani Abdul Haris Agustin, Arellia Ahmad Riza Ahmad Riza Faizal, Ahmad Riza Akhmad Riza Faizal Akmal Junaidi Ali, Husain Andi Windah Andi Windah* Andi, Windah Arif Sugiono Arnila Purnamayanti Aryanti Nina Yudha Asep Sukohar Ayumi, Vina Azis, Diky Luqman Christine, Wulandari Darojat, Agus Dhamanti, Inge Djauharie, Arlyandi S Djuadi, Noverman Fikri, Muhammad Huri Fitri Yakub Fitriyah Nurhidayah Giovanni, Netaniel Halim, Yeru Dwi Pratama Hapzi Ali Hasan, Haryanto Hazni, Elvira Hestin Oktiani Hidayat, Januar Putra Ichsan Ichsan Irma Paramita Sofia Khaqqi, Fitriyana Nuril Kurnia Muludi Lathifah Alfat, Lathifah Maryani, Eri Mediansyah, Aulian Nina Yudha Aryanti Novandi, Muhammad Ananta Arya Olova, Khansa Ranbia Audreyzovna Pairulsyah Pairulsyah Pratiwi, Chelomitha Arsy Putra, Purwanto Ramadityo, Kafka Reza Gustifa, Adinda Samsul, Bakri Saputra, Muhammad Azi Sirat, Abdul Hadi SITI MARIAM Slamet Riyadi Sugeng P Hariyanto Sugeng Prayitno , Harianto Sugiyanta Sugiyanta Sugiyanto, Puspandari Setyowati Suharyani Suharyani Tina Kartika Tina, Kartika Tjahjono, Benny Yudha Aryanti, Nina