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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Optimizing Support Vector Machine (SVM) for Sentiment Analysis of Blu by BCA Reviews with Chi-Square Widodo, Aldi; Herlambang, Bambang Agus; Renaldy, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10541

Abstract

One of the products resulting from the development of financial technology is the blu by BCA application. This app can be downloaded by BCA bank users via the Google Play Store and has received various user responses in the form of reviews. Analyzing these user reviews can serve as a valuable reference for further development and decision-making by BCA regarding the blu app. Sentiment analysis is conducted using the Support Vector Machine (SVM) algorithm, with SMOTE and TF-IDF techniques, and feature selection via Chi-Square. Sentiment classification using the SVM algorithm and feature selection has produced various outcomes in previous studies. Therefore, further research is necessary to analyze reviews of the blu application. This study aims to optimize the SVM method in analyzing user sentiment on the blu by BCA application by applying Chi-Square feature selection to improve sentiment classification performance. The research method includes the following stages: scraping, preprocessing, labeling, TF-IDF transformation, Chi-Square feature selection, SMOTE, data splitting, data mining, and evaluation. The testing results show that the RBF kernel achieved the highest performance with an accuracy of 0.8623, precision of 0.8623, recall of 0.8623, and F1-score of 0.8623. After applying Chi-Square feature selection, the accuracy improved to 0.8726, with precision of 0.8747, recall of 0.8725, and F1-score of 0.8723. This optimization successfully increased the accuracy by 0.0103 or 1.03%, while also improving precision, recall, and F1-score, indicating that feature selection contributes significantly to sentiment classification performance.
Evaluating Fasttext and Glove Embeddings for Sentiment Analysis of AI-Generated Ghibli-Style Images Sentana Putra, I Gusti Ngurah; Yusran, Muhammad; Sari, Jefita Resti; Suhaeni, Cici; Sartono, Bagus; Dito, Gerry Alfa
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10600

Abstract

The development of text-to-image generation technology based on artificial intelligence has triggered mixed public reactions, especially when applied to iconic visual styles such as Studio Ghibli. This research aims to evaluate public sentiment towards the phenomenon of Ghibli-style AI images by comparing two static word embedding methods, namely FastText and GloVe, on three classification algorithms: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN). Data in the form of Indonesian tweets were collected from Twitter using hashtags such as #ghibli, #ghiblistyle, and #hayaomiyazaki during the period 25 March to 25 April 2025. Each tweet was manually labelled with positive or negative sentiment, then preprocessed and represented using pre-trained FastText and GloVe embeddings. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, both macro and weighted. Results showed that FastText consistently performed the best on most models, especially in terms of precision and overall accuracy, thanks to its ability to handle sub-word information and spelling variations in social media texts. The combination of CNN with FastText yielded the highest performance with a macro F1-score of 76.56% and accuracy of 84.69%. However, GloVe still showed competitive performance in recall on the Logistic Regression model, making it relevant for contexts that prioritise sentiment detection coverage. This study emphasizes the importance of selecting embeddings and models that are appropriate to the characteristics of the data and the purpose of the analysis in informal social media-based sentiment classification.
Integrating IndoBERTweet and GRU for Opinion Classification on X Towards Public Transportation in Jakarta Nafiah, Fajria Ulumin; Panglima, Talitha Fujisai; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10723

Abstract

Jakarta, the capital of Indonesia, faces persistent challenges with its public transportation system due to rapid urbanization, increased use of private vehicles, and poor service quality. While social media platforms such as X (formerly Twitter) offer valuable insights into public opinion, their unstructured nature complicates analysis. This study uses deep learning models to categorize user sentiments into six labels that cover positive and negative aspects of comfort, safety, and punctuality. The results show that IndoBERTweet achieved the highest performance, with 95.43% accuracy and a macro F1-score of 0.9545. It also required the shortest training time, at six minutes and 30 seconds. IndoBERTweet+GRU followed closely behind with an accuracy of 94.62% and a macro F1-score of 0.9460 in six minutes and 50 seconds. This shows that adding a GRU layer provides competitive results, but does not surpass the baseline model. Error analysis revealed that, while the models performed well with explicit sentiments, the models struggled with implicit expressions, such as sarcasm and mixed opinions. These results demonstrate the potential of sentiment analysis in real-time monitoring systems, which could help policymakers identify urgent issues and support data-driven improvements in Jakarta’s urban transportation services.
Knowledge Discovery on E-Commerce Customer Churn Using Interpretable Machine Learning: A Comparative Study of SHAP-Based Classifiers Amanda Ardhani, Dhita; Tania, Ken Ditha
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10811

Abstract

Customer churn remains one of the most pressing issues in the e-commerce sector, as it directly erodes revenue and reduces customer lifetime value. This study proposes an interpretable machine learning approach designed not only to predict churn but also to uncover practical insights that can inform retention strategies. The analysis draws on a publicly available dataset containing customer behavior and transaction records. Data preparation involved handling missing values, applying label encoding, and addressing class imbalance with SMOTE. Five classification models—Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Gradient Boosting—were trained on an 80:20 stratified split, with performance assessed through accuracy, precision, recall, F1-score, and AUC. Among these, XGBoost delivered the most consistent results, achieving 96% accuracy, 95% precision, 92% recall, and a near-perfect AUC of 0.999, followed closely by Random Forest. Logistic Regression produced the lowest AUC at 0.886. To ensure transparency in decision-making, SHAP (SHapley Additive exPlanations) was applied, revealing Tenure, Complain, and CashbackAmount as the most influential predictors. Longer customer relationships were linked to reduced churn risk, while frequent complaints and higher cashback usage indicated a greater likelihood of leaving. These findings contribute knowledge by blending robust predictive performance with interpretability, enabling e-commerce businesses to design more targeted and proactive customer retention measures.
Sentiment Analysis of Economic Policy Comments on YouTube Using Ensemble Machine Learning Nandini, Kety; Rahardi, Majid
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10897

Abstract

Public sentiment analysis of economic policies is increasingly important in the digital age, as social media platforms have become the main arena for public discussion. This study analyzes YouTube comments related to Tom Lembong's economic policies to address the lack of policy sentiment analysis tools in Indonesian. A dataset containing 1,029 comments was collected and systematically processed using normalization, stop word removal, and stemming techniques tailored to Indonesian. To overcome data scarcity and class imbalance, advanced data augmentation methods—synonym replacement, random insertion, and random deletion—were applied, expanding the dataset to 2,169 samples. Feature extraction used TF-IDF vectorization (unigram, bigram, trigram) and CountVectorizer, followed by an 80:20 split into training and testing sets. Several machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and Naïve Bayes, were evaluated with hyperparameter tuning through grid search. The results showed that SVM with TF-IDF bigrams achieved the best performance (accuracy: 96.08%, F1-score: 96.03%). Class-level evaluation showed high performance for negative sentiment (F1-score: 0.97) and positive sentiment (F1-score: 0.97), while neutral sentiment was more challenging (F1-score: 0.90) due to ambiguity, sarcasm, and fewer samples. The ensemble model, which combines several optimized SVM variants with soft voting, achieved robust and stable performance (accuracy and F1-score: 95.16%). These findings confirm the effectiveness of the ensemble-based approach for Indonesian sentiment analysis, while providing valuable insights into public perceptions of economic policy in the digital space.
Identification of Buzzers in Skincare Reviews Using a Lexicon-Based Sentiment Analysis Method Pramesti, Arfiana Diah; Umam, Khothibul; Handayani, Maya Rini
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.11005

Abstract

Along with the rapid development of digital technology, social media has become the main platform for consumers to share experiences about products, including skincare products. However, it is not uncommon for reviews provided by users to not reflect authentic experiences, but rather reviews created by certain parties, or buzzers, to manipulate public perception. The presence of buzzers in skincare reviews is important to consider, as they can affect consumer trust and influence purchasing decisions. This study aims to identify the presence of buzzers in skincare product reviews using a lexicon dictionary-based sentiment analysis. Of the 529 comments analyzed, 75 comments showed negative sentiment and 454 comments showed positive sentiment. The classification results revealed that 85.8% of the comments belonged to the non-buzzer category, while 14.2% were indicated as buzzers. Evaluation of the classification model showed high accuracy, reaching 93%, but performance in detecting buzzers was limited, with a recall metric of only 0.50. This shows that while the model managed to classify non-buzzer comments well, there are still difficulties in identifying buzzer comments, mostly due to data imbalance. This research emphasizes the importance of a proper analytical approach in detecting inauthentic reviews to ensure the information consumers receive remains accurate, transparent, and accountable.
Forecasting coconut production in West Aceh Using GIS and SARIMAX Ridha, Arrazy Elba; Azwanda, Azwanda; Adib, Adib
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.8706

Abstract

Coconut (Cocos nucifera) is a strategic commodity for agro-industrial development in Indonesia, especially in Sumatra, which is home to 34.5% of national coconut plantations. One of the major producers, with a coastal geography and tropical climate that is highly suitable for coconut plantations, Aceh Barat, is currently facing the threat of degradation of coconut plantation land loss due to the government's Regional Action Plan for Sustainable Palm Oil Plantations (RAD KSB Aceh 2023-2026). This study aims to look at the total coconut plantation land by integrating geospatial analysis (QGIS) and SARIMAX time series modelling to map coconut plantations in 2024, estimate production trends, and assess the viability of the agro-industry amidst land use conflicts. Results from mapping with QGIS software showed a drastic decrease in coconut area from 3,330.25 hectares in 2022 to 928.2 hectares in 2024. The reduction in coconut plantation area is signalled by RAD KSB's oil palm expansion target of 1,078,728 hectares by 2026. In addition, the results of the mapping obtained several sub-districts with the largest contribution in West Aceh, namely Kaway XVI (234.82 ha) and Muereubo (217.46 ha) of coconut plantation area, while Bubon (16.67 ha) and West Woyla (38.42 ha) experienced significant land conversion. The study also calculated coconut fruit production of 1,229,267 kg (1,229 tonnes) per month from 12 sub-districts, and generated revenue from selling only coconuts of IDR 2.23 billion. SARIMAX forecasts showed high accuracy (RMSE: 700-704; MAPE: 0.19-1.05%) for 10 sub-districts, except Bubon (MAPE: 2.13%) and West Woyla (MAPE: 1.05%) due to data volatility. Furthermore, projections for the next five periods were carried out and obtained results, namely, Period 1 (104,425.88 kg), Period 2 (94,851.07 kg), Period 3 (97,399.50 kg), Period 4 (96,721.21 kg), and Period 5 (96,901.75 kg) which were dominated by stable production in the core area of Kaway XVI: 311,870 kg/month, but volatile in smaller areas. Spatial analysis prioritises Samatiga (58.53 ha) and Arongan Lambalek (79.27 ha) for agro-industrial development, with potential for value-added products.
Mapping the Polarity of Tourist Opinions on Indonesian Destinations through Google Maps Reviews Using Supervised Learning Methods Sa’adah, Siti Miftahus; Umam, Khothibul; Handayani, Maya Rini; Mustofa, Mokhammad Iklil
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.9836

Abstract

The advancement of information technology has transformed how individuals seek information and plan their travels, notably through online reviews of tourist attractions on platforms like Google Maps. However, these reviews do not always align with visitors' expectations, necessitating further analysis to comprehend the underlying sentiments. The objective of this research is to inspect the performance of multiple machine learning algorithms in executing sentiment analysis on user generated reviews related to tourist attractions in Indonesia. The algorithms examined include Multinomial Naïve Bayes, Random Forest Classifier, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Extra Trees Classifier. The research process encompasses data collection and labeling, data preprocessing, exploratory data analysis (EDA), Word Cloud visualization, feature extraction, classification implementation, and performance evaluation. Experimental results indicate that the K-Nearest Neighbors (KNN) algorithm attain the most accuracy and F1-score of 97%, indicating its effectiveness in categorizing text-based sentiment reviews sourced from the Google Maps platform.
Adaptive File Integrity Monitoring for Container Virtualization Environments using OSSEC with Real-Time Alerting Wowiling, Gerry; Sinambela, Eka Stephani; Simatupang, Frengki; Siagian, Fabert Jody Manuel; Sibarani, Aisyah Ayu; Batubara, Indah Sari
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10006

Abstract

In this ever-evolving digital age, container technology has become one of the main solutions in cloud computing due to its efficiency and flexibility. However, the dynamic and ephemeral nature of containers poses new challenges in terms of security, especially regarding data integrity. The implementation of OSSEC in container environments requires a tailored approach, as it lacks native support for automatically detecting new containers. Agents must be embedded within container images or installed at the host level. These agents activate each time a container runs and send monitoring data to the OSSEC server. With orchestration and automated configuration, monitoring results are stored externally, and real-time email alerts can be triggered upon detecting suspicious file changes. Container environments are increasingly targeted by cyber threats such as malware and ransomware, which pose risks of unauthorized data access or encryption. Limited file integrity monitoring within containers creates a security gap that can be exploited undetected. This research addresses the issue by implementing a File Integrity Monitoring (FIM) mechanism using OSSEC, an open-source Host Intrusion Detection System (HIDS) capable of real-time file and log monitoring, malware detection, and automated threat response. OSSEC is deployed within a Docker-based setup and integrated with a Web User Interface for visualizing logs and monitoring activity. The system includes real-time email notifications for immediate alerts. Testing through file modification scenarios confirmed OSSEC’s accuracy in detecting changes and notifying administrators. This implementation effectively strengthens data security and provides timely threat detection in containerized environments.
IoT-Based Adaptive Room Temperature Monitoring and Energy Optimization System Using NodeMCU ESP8266 Aswandi, Sakti; Rizal, Rizal; Afrillia, Yesy
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10052

Abstract

This study presents the development of an IoT-based room temperature monitoring and AC control system at the Faculty of Engineering, Universitas Malikussaleh, using NodeMCU ESP8266, DHT11 sensor, PIR sensor, and IR LED for real-time automation via a Firebase web interface. The system automatically adjusts AC operation based on room temperature and occupancy, with daily logic resets to accommodate dynamic conditions. Testing conducted over one week demonstrated effective temperature stabilization within 25–26°C with ±2°C fluctuations and significant energy savings by deactivating the AC when the temperature drops below 25°C or the room is unoccupied. The PIR sensor supports a detection range of up to 7 meters, allowing scalability for different room sizes. User evaluation involving five respondents reported satisfaction scores of 4.2 for comfort and energy efficiency, though aspects such as the web interface (3.6) and system information display (2.6) require improvement. Overall, the system effectively enhances energy efficiency, ensures room comfort, and provides flexible control for users, supporting the smart classroom concept. Future development is directed toward the use of more accurate sensors like DHT22 or DS18B20, improved network stability, and integration with virtual assistants for voice-controlled operation.

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