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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Journal of Applied Data Sciences Warta LPM
Dinar Ajeng Kristiyanti
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Health Professions Social Sciences

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Systematic Literature Review: Analisa Sentimen Penerimaan Masyarakat Terhadap Jenis Vaksin Covid-19 Di Dunia Sri Hardani; Dinar Ajeng Kristiyanti
ikraith-informatika Vol 6 No 3 (2022): IKRAITH-INFORMATIKA Vol 6 No 3 November 2022
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v6i3.2204

Abstract

Covid-19 menjadi hal yang banyak menyita perhatian dunia tiga tahun terakhir. Wabah yang dengancepat menyebar ke berbagai negara ini telah memakan jutaan korban dan memberikan dampak buruk diberbagai sektor. Berbagai upaya dilakukan guna menanggulangi wabah virus Covid-19, salah satunyadengan pemberian vaksin pada masyarakat. Namun ternyata solusi ini tidak langsung mendapat responpositif dari masyarakat. Adanya efek samping dan berbagai kejadian yang mengiringi pelaksanaan programvaksin Covid-19, memicu warga masyarakat memberikan opini yang beragam terkait penggunaan vaksinCovid-19. Penelitian ini menyampaikan hasil tinjauan literatur sistematis terkait opini masyarakat terhadappenggunaan vaksin Covid-19. Penelitian ini merupakan studi litratur yang menggunakan literasi terbitantahun 2019–2022 dimana Covid-19 melanda dunia. Tahapan yang dilakukan dalam penelitian ini antaralain perencanaan review, implementasi protokol review, dan penyampaian hasil review. Tujuan daripenelitian ini adalah memberikan gambaran umum mengenai teknologi yang banyak digunakan dalamanalisa sentimen masyarakat terhadap penggunaan vaksin Covid-19 baik metode, algoritma ataupun jenismachine learning, negara mana yang banyak dijadikan objek penelitian, serta jenis vaksin apa yang banyakmendapat perhatian masyarakat. Penelitian ini diharapkan mampu membantu penelitian yang akan datanguntuk mengembangkan metode dan teknik baru sehingga memberikan hasil yang lebih akurat.
Sentiment Analysis of Public Acceptance of Covid-19 Vaccines Types in Indonesia using Naïve Bayes, Support Vector Machine, and Long Short-Term Memory (LSTM) Dinar Ajeng Kristiyanti; Sri Hardani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4737

Abstract

The Covid-19 vaccination is a government program during the pandemic to create herd immunity so that people become more productive in their activities. In Indonesia, the Covid-19 vaccination campaign employs a range of vaccines and has sparked a range of responses from the public on social media, particularly Twitter. Users can tweet and communicate with one another on the social networking site Twitter. This study uses a Sentiment Analysis technique using the Nave Bayes (NB), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) algorithms to conduct a sentiment analysis of public acceptance of the type of Covid-19 vaccine used in Indonesia using Twitter data. Various types of vaccines in Indonesia include Sinovac, Vaksin Covid-19 Bio Farma, AstraZeneca, Pfizer, Moderna, Sinopharm, Novavax, Sputnik-V, Janssen, Convidencia, Zifivax, often confuse the public in determining the objectivity of this opinion. In addition, theoretically, this study also seeks to contrast the NB, SVM, and LSTM algorithms with experimental techniques to obtain the best algorithm model. The stages of the research involved gathering information based on Twitter user opinions about the type of Covid-19 vaccine on Twitter from January 2021 to January 2022. The researcher used Indonesian language tweet data with the keywords #vaksincorona, #vaksincovid19, #vaksinasi, #ayovaksin, #lawancovid19, and #vaksinindonesia. Before modelling, the pre-processing stage consists of case folding, tokenizing, filtering, stemming, and word weighting using TF-IDF. After that, model testing was carried out using Cross Validation with the Python programming language, and evaluation and validation of the test results using the Confusion Matrix. The results showed that the accuracy score of the SVM method for the best model was 84.89%, while for the Naïve Bayes and LSTM algorithms, they were 84.65% and 82.97%, respectively.
Development of a Web-Based and Mobile Application System for Posyandu Services in Curug Sangereng Village Monica Pratiwi; Irmawati; Nabila Husna Shabrina; Dinar Ajeng Kristiyanti; Monika Evelin Johan
Warta LPM WARTA LPM, Vol. 29, No. 1, Maret 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/warta.v29i1.8775

Abstract

Pos Pelayanan Terpadu (Posyandu) is a form of Upaya Kesehatan Bersumberdaya Masyarakat (UKBM) managed by and for the community to facilitate access to basic health services. Desa Curug Sangereng, located in Kecamatan Kelapa Dua, Tangerang, Banten, has nine posyandu that serve and monitor the health of 14,311 residents. This activity is supported by posyandu members who act as health promoters and educators to encourage a clean and healthy lifestyle while recording residents' health data. In addition to on-site recording at the posyandu, members also conduct home visits for residents who are absent during service hours or undergoing outpatient care. Up to the present, the process of recording community health data has still been carried out manually using notebooks and simple forms. This practice often poses challenges, such as difficulties in data recapitulation and analysis, as well as limited access when data are required for reporting to Puskesmas. To address these challenges, an e-Posyandu application was developed as a web-based and mobile platform integrated with the village’s main website (curugsangereng.id). The application was developed using the waterfall method and equipped with features such as data input, health record history, and data export in multiple file formats. Following the development and deployment stages, socialization and training sessions were conducted to introduce the application to Posyandu members. User Acceptance Testing (UAT) was carried out with 35 members of Curug Sangereng Village through a questionnaire. The results showed that 94.3% of members on the website version and 88.6% on the mobile version could easily use the application. These findings indicate that the majority of posyandu members are able to utilize e-Posyandu effectively in supporting the process of health data recording and monitoring, although a small proportion still requires further assistance, particularly in using the mobile version.
Optimized Hybrid CNN-LSTM Model for Predicting Transportation Sector Stock Prices Using Optimizer and Activation Function Tuning Fiena Gunawan; Dinar Ajeng Kristiyanti
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1323

Abstract

Stock price prediction is inherently complex due to nonlinear dynamics and high volatility, particularly in Indonesia’s transportation sector, which experienced significant inflationary pressure and extreme instability during and after the COVID-19 pandemic. These disruptions introduced structural breaks and regime shifts, intensifying non-stationary market behavior and increasing forecasting uncertainty. Such conditions create an urgent need for robust predictive information systems capable of supporting investment decision-making and risk management in highly volatile environments. However, standalone recurrent models such as Long Short-Term Memory (LSTM) often struggle to capture local micro-patterns and long-term dependencies. Moreover, prior studies have rarely implemented systematic hyperparameter optimization, resulting in inconsistent predictive performance across stocks with heterogeneous volatility. In contrast, Convolutional Neural Networks (CNN) extract local patterns and short-term nonlinear features, making them effective for modeling high-frequency fluctuations. This study proposes a systematically optimized hybrid CNN-LSTM model to forecast transportation sector stock prices using daily OHLC data from 2020-2025. The research framework follows the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, encompassing business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Prior to modeling, preprocessing includes data cleaning, Min-Max normalization, and sliding window transformation to construct supervised learning sequences. CNN is employed to extract localized nonlinear features and reduce noise, while LSTM models long-term temporal dependencies. Model performance is evaluated using MAE, MSE, RMSE, MAPE, and R². Results show that the optimized CNN-LSTM model outperforms the baseline across all stocks. The highest R² of 0.9725 is obtained from one stock, indicating strong performance. In addition, the average R² improves from 0.8736 to 0.9483, an increase of 0.0747 (8.55%). The best results are achieved using ReLU with Adam and Nadam optimizers, demonstrating improved convergence and generalization. These findings highlight the effectiveness of optimized hybrid deep learning models for forecasting in nonlinear and non-stationary financial markets.
Enhancing Support Vector Machines Accuracy Through Firefly Algorithm-Driven Feature Optimization for Forest-Fire Sentiment Classification Wafa Salma Sentanu; Dinar Ajeng Kristiyanti
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1144

Abstract

Forest fires are a growing global environmental issue that significantly impact ecosystems, human health, and exacerbate climate change. Data from 2023 indicate that approximately 11.91 million hectares of forest were lost due to fires, highlighting the urgent need for technological approaches in addressing this issue. One relevant approach is public sentiment analysis based on the social media platform X, which enables the rapid and real-time capture of public perception. In text-based sentiment analysis, key challenges include high-dimensional feature space and class imbalance, which can degrade the performance of machine learning algorithms. Therefore, this study applies feature selection methods based on swarm intelligence, namely Particle Swarm Optimization (PSO) and Firefly Algorithm (FA), to enhance classification efficiency and accuracy. The models evaluated include Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), using the Knowledge Discovery in Databases (KDD) approach, which involves selection, preprocessing, transformation with Term Frequency-Inverse Document Frequency (TF-IDF) and Synthetic Minority Oversampling Technique (SMOTE), and data splitting with an 80:20 ratio. Model performance was evaluated using four metrics: accuracy, precision, recall, and F1-score. The models achieved strong performance on the balanced training data, indicating effective learning after feature selection, with the fastest execution time recorded by FA optimization at 0.0071 seconds. To ensure a fair assessment of generalization, the main conclusions of this study are based on the testing results. On the testing data, SVM with FA achieved the highest accuracy of 96.36% with an execution time of 0.0064 seconds. Overall, swarm intelligence-based feature selection (PSO and FA) enhances the efficiency of conventional classifiers by reducing high-dimensional feature representations and execution time while maintaining strong predictive performance for forest-fire sentiment classification.
Grid search vs Bayesian optimization for intensity scoring classification and channel recommendation prediction Kelly Mae; Dinar Ajeng Kristiyanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 4: August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i4.26341

Abstract

Technological advancement has spurred financial technology growth, transforming traditional financial operations into digital. Peer-to-peer (P2P) lending is a key fintech solution offering online loans, though it struggles with repayment issues due to customer financial instability. To overcome these challenges, XYZ is a startup that focuses on enhancing the efficiency of collections and communication with customers. XYZ necessitates the implementation of a collection intensity scoring (CIS) model and a prediction model for interaction on recommended communication channels in order to optimize the collection process. This study evaluates the performance of grid search and Bayesian optimization on random forest (RF) classification models and K-nearest neighbors (KNN) regressor prediction models. RF and KNN regressor algorithms optimization are necessary to enhance their performance in CIS classification and channel recommendation prediction. The research stages follow the cross industry standard process-data mining (CRISP-DM) framework, which consists of business understanding, data understanding, data preparation, modeling, and evaluation. The model performance is assessed by accuracy and mean absolute error (MAE). The results of this study show that Bayesian optimization surpasses grid search, enhancing the accuracy of the RF model to 98.34% and reducing the MAE of the KNN regressor model to 0.24530.
Co-Authors Fiena Gunawan Irmawati Kelly Mae Monica Pratiwi Monika Evelin Johan Shabrina, Nabila Husna Sri Hardani Wafa Salma Sentanu