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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Ilmiah Informatika Komputer Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer International Journal of Engineering and Emerging Technology International Journal of Engineering, Science and Information Technology Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Krisnadana Paradharma: Jurnal Aplikasi IPTEK KOMET INOVTEK Polbeng - Seri Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Saraswati, Ni Wayan Sumartini
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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COVID-19 Chest X-Ray Detection Performance Through Variations of Wavelets Basis Function I Gusti Ayu Agung Diatri Indradewi; Ni Wayan Sumartini Saraswati; Ni Wayan Wardani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1089

Abstract

Our previous work regarding the X-Ray detection of COVID-19 using Haar wavelet feature extraction and the Support Vector Machines (SVM) classification machine has shown that the combination of the two methods can detect COVID-19 well but then the question arises whether the Haar wavelet is the best wavelet method. So that in this study we conducted experiments on several wavelet methods such as biorthogonal, coiflet, Daubechies, haar, and symlets for chest X-Ray feature extraction with the same dataset. The results of the feature extraction are then classified using SVM and measure the quality of the classification model with parameters of accuracy, error rate, recall, specification, and precision. The results showed that the Daubechies wavelet gave the best performance for all classification quality parameters. The Daubechies wavelet transformation gave 95.47% accuracy, 4.53% error rate, 98.75% recall, 92.19% specificity, and 93.45% precision.
Recognize The Polarity of Hotel Reviews using Support Vector Machine Ni Wayan Sumartini Saraswati; I Gusti Ayu Agung Diatri Indradewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1848

Abstract

A brand is very dependent on consumer perceptions of the product or services. In assessing consumer perceptions of products and services, companies are often faced with data analysis problems. One of the data that is very useful to produce a picture of consumer perceptions of the products and services is review data. So that the company's ability to process review data means that the company has a picture of the strength of the brand it has. Some of the most popular machine learning algorithms for creating text classification models include the naive Bayes family of algorithms, support vector machines (SVM) and deep learning algorithms. In this research, SVM has been proven to be a reliable method in pattern recognition. In particular, this study aims to produce a model that can be used to classify the polarity of hotel reviews automatically. The experimental data comes from review data on hotels in Europe sourced from TripAdvisor with a total of 38000 reviews. We also measure the quality of the classification engine model. The test results of the SVM model built from hotel review data are quite good. The average accuracy of the classification engine is 92.48%. Because the recall and precision values ​​are balanced, the accuracy value is considered sufficient to describe the quality of the classification.
Recognizing Pneumonia Infection in Chest X-Ray Using Deep Learning Ni Wayan Sumartini Saraswati; I Wayan Dharma Suryawan; Ni Komang Tri Juniartini; I Dewa Made Krishna Muku; Poria Pirozmand; Weizhi Song
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3197

Abstract

One of the diseases that attacks the lungs is pneumonia. Pneumonia is inflammation and fluid in the lungs making it difficult to breathe. This disease is diagnosed using X-Ray. Against the darker background of the lungs, infected tissue shows denser areas, which causes them to appear as white spots called infiltrates. In the image processing approach, pneumonia-infected X-rays can be detected using machine learning as well as deep learning. The convolutional neural network model is able to recognize images well and focus on points that are invisible to the human eye. Previous research using a convolutional neural network model with 10 convolution layers and 6 convolution layers has not achieved optimal accuracy. The aim of this research is to develop a convolutional neural network with a simpler architecture, namely two convolution layers and three convolution layers to solve the same problem, as well as examining the combination of various hyperparameter sizes and regularization techniques. We need to know which convolutional neural network architecture is better. As a result, the convolutional neural network classification model can recognize chest x-rays infected with pneumonia very well. The best classification model obtained an average accuracy of 89.743% with a three-layer convolution architecture, batch size 32, L2 regularization 0.0001, and dropout 0.2. The precision reached 94.091%, recall 86.456%, f1-score 89.601%, specificity 85.491, and error rate 10.257%. Based on the results obtained, convolutional neural network models have the potential to diagnose pneumonia and other diseases.
Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms Dewa Ayu Kadek Pramita; Ni Wayan Sumartini Saraswati; I Putu Dedy Sandana; Poria Pirozmand; I Kadek Agus Bisena
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4254

Abstract

Predicting the correct hotel occupancy rate is important in the tourism industry because it has a major impact on the level of revenue and maintenance of a hotel’s reputation. With accurate predictions, hotel performance can be optimized regarding resources, staff, and hotel facilities. The linear regression method has been proven to perform causal predictions well. However, this method has several weaknesses, such as the function of the relationship between dependent variables and independent variables that are not linear, overfitting, or underfitting in building the prediction model. The purpose of this study was to optimize the linear regression model in predicting hotel occupancy rates. The method used in this study was a Linear Regression method optimized with Polynomial Regression and regularization techniques to reduce overfitting using Ridge Regression and Lasso Regression. The results of the model evaluation showed that linear regression, which was optimized with Polynomial Regression and Ridge Regression in the model with the historical data of the Adiwana Unagi occupancy rate, historical data of the hotel occupancy rate in Bali, and the number of tourist visits in Bali, gave the best performance, with a mean absolute error score of 1.0648, root mean square error of 2.1036, and R-squared of 0.9953. The conclusion of this research was optimization using polynomial regression, achieving the best evaluation scores, where the prediction model performance indicates that variable X7 (tourist visit numbers) strongly influences the prediction of the occupancy rate.
Sentiment Analysis of Tourists’ Perceptions of Ubud as a World Gastronomy Destination Using the Lexicon-SVM Method Ni Wayan Sumartini Saraswati; I Wayan Dharma Suryawan; I Kadek Agus Bisena; I Dewa Made Krishna Muku; Dewa Ayu Kadek Pramita
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 11 No. 2 (2026): May
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/zxfzmr57

Abstract

The development of Ubud as a sustainable gastronomic tourism destination requires understanding tourist perceptions expressed on digital platforms. This study analyzes tourist sentiment toward Ubud’s gastronomy using English-language reviews from TripAdvisor and X (formerly Twitter) through a hybrid Lexicon–Support Vector Machine (SVM) approach. A total of 28,550 pieces of textual data were analyzed, consisting of 23,647 TripAdvisor reviews and 4,903 X posts. The methodology includes data collection, text preprocessing, sentiment labeling using the VADER lexicon, TF-IDF feature extraction, and SVM classification. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that positive sentiment dominates on both platforms, with accuracies of 89.7% for X and 92.31% for TripAdvisor. Word cloud analysis further indicates that tourist perceptions are influenced by food quality, service, atmosphere, and pricing. These findings demonstrate the potential of the hybrid Lexicon–SVM approach for supporting sustainable gastronomic tourism development in Ubud. The study also contributes comparative insights into sentiment characteristics between structured reviews and real-time social media platforms.
Comparative Analysis of the Performance of Machine Learning and Deep Learning Methods in Detecting Hate Speech in Indonesia Priscilla Desinta Achelya; Ni Wayan Sumartini Saraswati; I Putu Agus Eka Darma Udayana
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 3 (2026): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i3.2635

Abstract

The rapid expansion of social media usage in Indonesia has increased the spread of harmful online communication, including hate speech, which may contribute to social conflict and discrimination. As a result, automated hate speech identification has become an important research area in Indonesian natural language processing. Although many studies have applied machine learning and deep learning techniques for this task, comprehensive comparisons between conventional algorithms and transformer-based models in the Indonesian context remain limited. This study evaluates several machine learning algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF), alongside the transformer-based IndoBERT model for Indonesian hate speech classification. All models were trained and evaluated using the same dataset, identical preprocessing stages, and consistent evaluation metrics consisting of accuracy, precision, recall, and F1-score to ensure fair comparison. Experimental findings show that IndoBERT achieved the strongest overall performance, reaching an accuracy of 87.45% and an F1-score of 84.92%. Among the classical machine learning approaches, Logistic Regression produced the highest result with an accuracy of 84.49% and an F1-score of 84.32%. While several machine learning models obtained relatively competitive recall values, IndoBERT demonstrated more stable performance across evaluation metrics and showed stronger capability in understanding contextual language patterns commonly found in Indonesian social media content. Overall, the study highlights the advantages and trade-offs between conventional machine learning and transformer-based deep learning approaches in Indonesian hate speech detection, while also providing practical insights for developing automated content moderation systems.
Unveiling Ubud’s Gastronomic Image through Comparative Aspect-Based Sentiment Analysis as Cross-Platform Insights and Strategic SWOT Perspectives Ni Wayan Sumartini Saraswati; Ketut Jaya Atmaja; I Wayan Dharma Suryawan; Christina Purnama Yanti
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1843

Abstract

Gastronomy is a strategic element in developing tourism destinations, and Ubud occupies a position as a cultural center that offers culinary riches of traditional value. This research reveals the gastronomic image of Ubud through a cross-platform analysis of TripAdvisor restaurant reviews and public opinion on sentiment labels at the micro level, implemented with VADER before fine-tuning aspect sentiment analysis (with BERT) to obtain a more granular representation. An in-depth analysis was conducted using wordcloud for cross-platform insights and SWOT recommendations. The research results show that the highest accuracy model evaluation results reached 98.87% for the term extraction aspect and 97.48% for the sentiment analysis aspect for TripAdvisor. Based on insight analysis, the food aspect is the main focus on both platforms, but with different narrative patterns: TripAdvisor emphasizes detailed evaluations such as taste, presentation, and service, while X is more oriented towards promotions, cultural identity, and participatory experiences, especially related to events such as the Ubud Food Festival. The integration of ABSA results into the SWOT framework resulted in four strategic findings: main strengths in the form of culinary authenticity, service quality, atmosphere, and experiential value; weaknesses related to inconsistencies in taste and chef quality; opportunities obtained from organizing festivals, street food, and culinary diversification; and threats in the form of overtourism and the perception of high prices. Overall, this study provides conceptual, methodological, and strategic contributions. The resulting findings strengthen the scientific basis for developing Ubud as a sustainable gastronomic destination that emphasizes taste, nature, and culture, as well as supporting data-based decision-making for tourism stakeholders.
Integrating WebQual 4.0 and Machine Learning for Evaluating JDIH Tabanan Regency Website Gusti Putu Sutrisna Wibawa; Putu Sugiartawan; Ni Wayan Sumartini Saraswati
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/pfefaf94

Abstract

The Legal Documentation and Information Network (JDIH) website of Tabanan Regency is a digital public service platform that provides access to regional regulations and legal documents. Evaluating website quality is important to ensure that the services provided are able to meet user needs and expectations. Previous studies on website evaluation generally applied either WebQual 4.0 or sentiment analysis separately, while studies integrating both approaches remain relatively limited. Therefore, this study combines WebQual 4.0 and machine learning–based sentiment analysis to provide a more comprehensive website evaluation. This study employed a mixed-methods approach involving 120 respondents and 120 user comments. Website quality evaluation was conducted using the WebQual 4.0 framework through three dimensions: usability, information quality, and service interaction. Sentiment analysis was performed using preprocessing techniques, TF-IDF feature extraction, and the Random Forest algorithm. The results showed that all WebQual dimensions achieved excellent categories, with usability obtaining the highest mean score of 4.487. Positive sentiment dominated user comments at 59.2%, while the Random Forest model achieved an accuracy rate of 79%. This integrated approach provided a more comprehensive evaluation and identified several technical aspects that still require improvement, including system performance, feature optimization, and interface consistency.
Real-Time Classification of Coconut Oil Quality using Smartphone Imaging and Deep Learning I Putu Budi Astawa; Putu Sugiartawan; N. W. Sumartini Saraswati
Jurnal Krisnadana Vol 5 No 3 (2026): Jurnal Krisnadana May - July 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/yntsh472

Abstract

Coconut oil quality assessment is important for ensuring product safety, commercial value, and consumer acceptance; however, conventional laboratory-based evaluation methods are often time-consuming, costly, and impractical for real-time applications. This study proposes a real-time coconut oil quality classification system using smartphone imaging integrated with a Convolutional Neural Network (CNN) model. A total of 60 coconut oil images were collected under controlled conditions and categorized into High Quality, Medium Quality, and Low Quality classes. The dataset underwent preprocessing stages including resizing, normalization, and augmentation before CNN training and evaluation. Experimental results showed that the proposed model achieved an accuracy of 93.33%, precision of 93.20%, recall of 92.80%, and F1-score of 93.00%, demonstrating reliable multi-class classification performance. In addition, the system achieved an average real-time prediction time of approximately 0.82 seconds per image on a smartphone device, indicating fast and efficient mobile-based implementation. The findings confirm that smartphone imaging combined with deep learning provides a portable, low-cost, non-destructive, and practical solution for intelligent coconut oil quality monitoring in food and agricultural applications.
Recency, frequency, quality: novel feature from sentiment analysis for clustering and ranking in tourism big data analytics Ni Wayan Sumartini Saraswati; I Ketut Gede Darma Putra; Made Sudarma; I Made Sukarsa; I Gusti Ayu Agung Mas Aristamy
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10709

Abstract

Understanding tourist perceptions has been a key benefit of sentiment analysis in tourism data. However, its outcomes can be further utilized to gain insights into the characteristics of tourist attractions and hotels. This study aims to develop a new feature, called recency, frequency, quality (RFQ), derived from sentiment analysis results to cluster and rank tourist attractions and hotels in Bali. RFQ consists of three components: review recency, review frequency, and review quality. These dimensions reflect the recentness of reviews, the popularity based on the number of reviews, and the review quality measured by the ratio of positive to negative sentiment polarity. Using big data analytics through clustering and ranking, the study finds that the quality of tourist attractions and hotels is primarily concentrated in Badung and Gianyar regencies. More tourist attractions are found in the silver cluster than in the gold, indicating the need to enhance quality. In the hotel sector, the diamond cluster dominates among star-rated hotels, suggesting overall high quality. Budget hotels show fairly good quality, with most falling under the gold cluster.
Co-Authors Alvin Limawan Susanto Andika, I Gede Atmaja, Ketut Jaya Baehaqi Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Dewa Ayu Putu Rasmika Dewi Dewa Ayu Putu Rasmika Dewi Dewa Ayu Putu Rasmika Dewi Dewi Natalia, Sang Ayu Made Krisna Dewi, Dewa Ayu Putu Rasmika Dewi, Yesi Ratna Eddy Hartono Eddy Hartono Eddy Hartono Eddy Hartono Eddy Hartono Gusti Putu Sutrisna Wibawa I Dewa Made Krishna Muku I Dewa Made Krishna Muku I Dewa Made Krishna Muku I Gede Adi Sudi Anggara I Gusti Ayu Agung Diatri Indradewi I Gusti Ayu Agung Mas Aristamy I Kadek Agus Bisena I Kadek Agus Bisena I Kadek Agus Bisena I Kadek Putra Agung Darmawan I Ketut Gede Darma Putra I Ketut Setiawan I Made Andi Kertha Yasa I Made Bayu Hadi Irawan I Made Sukafona I Made Sukarsa I Made Sukarsa I Nyoman Tri Anindia Putra I Nyoman Yudha Chandra Dinata I Putu Agus Eka Darma Udayana I Putu Agus Eka Darma Udayana I Putu Agus Eka Darma Udayana I Putu Budi Astawa I Putu Dedy Sandana I Putu Dedy Sandana I Putu Krisna Suarendra Putra I Wayan Agustya Saputra I Wayan Dharma Suryawan I Wayan Juli Budiarta Ida Bagus Ary Indra Iswara Ida Bagus Gede Sarasvananda Juniartini, Ni Komang Tri Kadek Budi Sandika Ketut Gede Darma Putra, I Ketut Jaya Atmaja Ketut Jaya Atmaja Ketut Laksmi Maswari Ketut Sepdyana Kartini Krismentari, Ni Kadek Bumi Krisna, Gede Gana Eka Made Sudarma Made Sudarma MADE WAHYU ADHIPUTRA Maria Osmunda Eawea Monny Melinia Hutari Natalia, Sang Ayu Made Krisna Dewi Ni Komang Tri Juniartini Ni Luh Pangestu Widya Sari NI LUH PUTU AGETANIA . NI LUH PUTU MERY MARLINDA Ni Made Lisma Martarini Ni Made Sintya Noviana Utami Ni Wayan Mirah Senja Pertiwi Ni Wayan Wardani Nirwana, Ni Kade Ayu Pirozmand, Poria Poria Pirozmand Poria Pirozmand Poria Pirozmand Poria Pirozmand Pramana, I Gusti Kadek Candra Adi Cahya Pramest, Ni Luh Gede Sintia Pramita, Dewa Ayu Kadek Pramitha, Gede Dana Priscilla Desinta Achelya Putu Ananda Sitarasmi Putu Sugiartawan Putu Sugiartawan Putu Wirayudi Aditama Sandhiyasa, I Made Subrata Sari, Ni Luh Pangestu Widya Suryawan, Wayan Dharma Waas, Devi Valentino Wardani, Ni Wayan Weizhi Song Welda