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All Journal Mimbar Sekolah Dasar Journal of Information Systems Engineering and Business Intelligence JIEET (Journal of Information Engineering and Educational Technology) Journal of Robotics and Control (JRC) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Abdimas: Papua Journal Of Community Service Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika J-Icon : Jurnal Komputer dan Informatika Ranah Research : Journal of Multidisciplinary Research and Development PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Emerging Information Systems and Business Intelligence (JEISBI) Journal of Education Technology and Information System
Wiyli Yustanti
Prodi Manajemen Informatika, Fakultas Teknik, Universitas Negeri Surabaya
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Transportation Other

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Web-based Profanity Detection Using a Combination of Lexicon and Support Vector Machine: Web-based Profanity Detection Using a Combination of Lexicon and Support Vector Machine Ainandita Riwipapusa; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71408

Abstract

Advances in information and communication technology, particularly the internet and social media, have made it easier for people to express their opinions openly, but have also increased the potential for the spread of profanity and hate speech. This study proposes a web-based profanity detection solution by combining lexicon-based methods and Support Vector Machine (SVM). The Knowledge Discovery in Database (KDD) process was implemented for data extraction and analysis, starting from Twitter data collection, preprocessing (cleaning, case folding, tokenizing, stemming), transformation using TF-IDF, to manual labeling. The SVM model was trained using a 3-fold cross-validation scheme, and evaluation was conducted using a classification report and confusion matrix. The results of the study showed a model accuracy of 93% on the test data with an average F1-score of 0.93, as well as optimal performance in detecting sentences categorized as profanity. The developed web application prototype successfully ran all profanity word detection and sensing features automatically, as proven by the black box testing results. The analysis test also ran smoothly, with a test using 10 sentences containing profanity words achieving 100% accuracy, and a test using 10 sentences without profanity words achieving 95% accuracy. This system is expected to contribute to creating a more positive digital space through adaptive and accurate profanity word detection.
Classification Agorithm Analysis For Predicting The Type Of Senior High School On Alumni Smp 2 Balong Ponorogo: Classification Agorithm Analysis For Predicting The Type Of Senior High School On Alumni Smp 2 Balong Ponorogo Kurnia Putri, Nabiilah Winda; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.71676

Abstract

This study aims to analyze the performance of various classification algorithms in predicting the type of Senior High School (SLTA) that students choose based on academic scores and achievements. The study was conducted at SMPN 2 Balong Ponorogo using the SEMMA (Sample, Explore, Modify, Model, Assess) approach. Secondary data from 1,113 students were used and processed through the stages of data exploration, normalization, feature selection (using Pearson Correlation, Mutual Information, Random Forest, and Lasso Logistic Regression), and dimension reduction using Principal Component Analysis (PCA). Eight classification algorithms were tested, namely Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, XGBoost, LightGBM, CatBoost, and Naïve Bayes. Model evaluation is done using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the Random Forest and KNN models with the Hybrid Feature Selection approach provide the best performance, with the F1-score value reaching 84%. This research contributes to data-based decision making for student guidance in choosing the right further education pathway.
Multivariate Time Series Forecasting on Sales Using Recurrent Neural Network (Case Study: Aqiqah Almeera) Purwani, Susi; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 6 No. 4 (2026): Vol. 06 Issue 04
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v6i4.72100

Abstract

Sales forecasting is a crucial component in business decision-making, particularly in inventory management and marketing strategies. Accurate sales predictions can help companies maintain stock balance, design effective promotions, and minimize the risk of losses. This study examines the application of Multivariate Time Series Forecasting using Recurrent Neural Networks (RNNs) to more accurately predict product sales. By considering multiple variables such as product price, inventory levels, promotional activities, and temporal features, this approach aims to capture complex and interrelated patterns in historical data. RNNs are chosen for their ability to handle sequential data and learn temporal relationships among variables, thereby improving prediction accuracy. This research adopts a quantitative method with a causal-associative approach, utilizing secondary data from the company’s sales records over the past two years. The data is analyzed using various preprocessing techniques such as data normalization, feature encoding, and correlation analysis for optimal feature selection before being fed into the RNN model. The model is trained using specific validation techniques to prevent overfitting. Model performance is evaluated using MAE and RMSE metrics to measure prediction accuracy and reliability. The results of this study are expected to produce an accurate and practical sales forecasting system that can be implemented by business practitioners to support more efficient, data-driven, and well-targeted decision-making processes.
Implementation of Xception Algorithm with Convolutional Block Attention Module (CBAM) for Waste Type Detection in Visual Images Shofa, Ahmad Khoiru; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.72502

Abstract

The increasing volume of waste each year poses a serious challenge in waste management, particularly in the waste sorting process, which remains suboptimal. The lack of public awareness and limited manual sorting facilities are major obstacles to creating an effective waste management system. To address this issue, this study developed a waste classification system based on visual images by utilizing the Xception algorithm integrated with the Convolutional Block Attention Module (CBAM) to improve classification accuracy. The dataset used in this study includes various categories of organic and anorganic waste. The experiments involved several stages, including the integration of CBAM into the Xception architecture, testing different data splitting schemes for training and validation, and hyperparameter tuning using the Random Search method with 10 combinations. The model was trained using the Keras and TensorFlow libraries, and the trained model was saved in the .h5 format commonly used for deploying deep learning models into web applications. The results showed that the addition of CBAM improved the model's accuracy from 88.38% to 91.29% without significantly increasing training time. Furthermore, the best hyperparameter combination obtained from tuning was Dense = 128, Dropout = 0.3, Optimizer = Adam, and Learning Rate = 0.0001. When retrained using this configuration, the model achieved a highest accuracy of 93.37%. The best-performing model was then integrated into a Flask-based web application. This application allows users to upload images of waste through a simple web interface and instantly receive the predicted waste type classification. With the implementation of this technology, the system is expected to assist the public in sorting waste more easily and to increase active participation in environmentally conscious waste management. Keyword: Waste Classification, Xception, CBAM, Deep Learning, Flask
Sentiment Analysis and Topic Modeling of Tourist Attractions in Gresik Regency Using the BERT Method Saharani, Salsabilla Putri; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.72873

Abstract

Tourism is one of the key sectors in national economic growth as well as a pillar of regional community welfare. Gresik Regency in East Java has considerable tourism potential, with more than 100 destinations covering religious tourism, natural attractions, and family recreation. However, tourist visit data from 2022–2024 shows a declining trend that requires an in-depth evaluation of visitor perceptions and experiences. This study aims to analyze public sentiment toward tourist destinations in Gresik Regency and identify the main topics of concern for tourists. The research data was collected from Twitter and Google Maps within the period of 2021–2024 using crawling techniques. Sentiment analysis was carried out with IndoBERT, while topic modeling was conducted using BERT. The results indicate that tourism reviews are dominated by positive sentiments highlighting the uniqueness of religious destinations, natural beauty, and family recreation atmosphere. However, negative sentiments were also found, emphasizing issues related to facilities, cleanliness, staff services, and accessibility to the sites. Topic modeling successfully grouped tourist opinions into coherent themes, and evaluation with coherence scores demonstrated good quality outcomes. The study concludes that although Gresik has strong tourism appeal, challenges in facility management, services, and digital promotion need to be addressed immediately. The integration of sentiment analysis and BERT-based topic modeling has proven effective in providing comprehensive insights into tourist perceptions and can serve as a basis for formulating regional tourism development strategies.
Sentiment Analysis and Topic Modeling Using BERT And LDA Methods (Case Study of Free Meal Program on Twitter) Yanna, Siti Mahmudah Putri; Yustanti, Wiyli
Journal of Emerging Information Systems and Business Intelligence (JEISBI) Vol. 7 No. 1 (2026): Vol. 07 Issue 01
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jeisbi.v7i1.75819

Abstract

The Free Meal Program is one of the Indonesian government's strategic efforts to structurally address poverty and malnutrition (stunting). As a new policy with massive social and fiscal impacts, an in-depth evaluation is required to measure public acceptance. This study aims to categorize public sentiment into positive and negative categories and identify the dominant topics discussed on Twitter (X) regarding the program. The methodology involved crawling Twitter data, resulting in a total of 8,307 datasets. Sentiment labeling was performed automatically using the IndoBERT deep learning model, followed by topic modeling using the Latent Dirichlet Allocation (LDA) method for each sentiment category. The results of the topic modeling were validated through topic coherence tests using word instruction task and topic instruction task techniques. The results showed an imbalanced sentiment distribution, with 7,034 negative sentiments and 1,273 positive sentiments. LDA modeling successfully extracted 5 optimal topics for both sentiment categories. Positive sentiments included topics such as budget efficiency, the role of government institutions (National Police), technical implementation, and local economic empowerment. Meanwhile, negative sentiments encompassed concerns regarding state budget (APBN) priorities, health/poisoning issues, and the comparative urgency between the free meal program and the education and health sectors. The coherence test results showed an interpretation accuracy rate of 93% for keywords and 79% for topic relevance, indicating that the developed LDA model was optimal in extracting public opinion.
Co-Authors Ainandita Riwipapusa Akbar, Rafy Aulia Alpiana, Intan Andi Iwan Nurhidayat ANITA QOIRIAH ARI KURNIAWAN Ariyanto, Savira Rahmania Putri Atmaja, Raden Mas Rizqi Wahyu Panca Kusuma Aulia Akbar, Rafy Aulia, Novi Rosidhatul Aviana, Anisah Nurul Ayuningtyas, Nimas Bayu Budi Prakoso choirullah, Sultan CHOIRUN NISA Dani, Andrea Dini Amalia, Dini Ervin Yohannes FAHRIYA, KHUSNIATUL Farid Baskoro Fitriani, Erlina Eka Haristyarini, Raniar Hartanto, Unung Istopo Hasanah, Rohmatul I Gusti Putu Asto Buditjahjanto I Nyoman Budiantara Iqbal, Kevin Satria Muhammad IRMA FEBRIYANTI Iskandar Java, Muhammad Istianah, Eva Istopo Hartanto, Unung Karputri, Diah Leni Kurnia Putri, Nabiilah Winda Kurniasari, Calycha Lumban Gaol, Gebryana Hotmida Lamtiar Maulidia, Ridhotul Meidyan, Martinus Ade meilita, Bunga Mohammad Akbar, Mohammad Muhammad Risalah Naufal Mutmainah Mutmainah Nabila Putri Listyanto Naim Rochmawati Nautika, Puji Septiyana Nuraini, Ulfa Siti Nurlyan, Reynisa Beta Prasetyo, Andhika Edo Pratiwi, Enggarbela Ogi Intan Priadana, Benny Widya Purwani, Susi Putra, Fachrian Bimantoro Putri, Windy Chikita Cornia Putu Asto Buditjahjanto, I Gusti Rachmaddhani, Gilang Raden Mohamad Herdian Bhakti Rahayu, Aulia Anisa Puji Rahman, Naufal Aditya Rahmawati, Naim Ricky Eka Putra Rina Harimurti Rizal, Mochammad Rochmawati, Naim Saharani, Salsabilla Putri Saputra, Andika Dermawan Shofa, Ahmad Khoiru Sifriyani, Sifriyani Suroto Suroto Syandika, Novliyan Dimas Vebriani, Mutiara Widi Aribowo Wulandari, Rahmah Yanna, Siti Mahmudah Putri YUNI YAMASARI