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Sentiment Analysis of Ijen Crater Reviews using Decision Tree Classification and Oversampling Optimization Hizham, Fadhel Akhmad; Asyari, Hasyim; Urrochman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1399

Sentiment analysis is a text mining technique that classifies content as positive, negative, or neutral polarity in each sentence or document. These lines or papers may be user reviews assessing the quality of a product or material supplied to them. The purpose of this study is to better understand the function of sentiment analysis in assessing evaluations of the Ijen Crater tourist destination based on Google Maps user comments. This study is conducted in four steps, beginning with data gathering in the form of Google Maps evaluations obtained by data scraping. Following data collection, text preparation includes case folding, tokenization, stopword elimination, and stemming. Following text preprocessing, the next stage is imbalaced data optimization, which involves modifying the minority class samples to be nearly equal to the majority class by randomly duplicating minority class samples. Then, each review is categorized according to sentiment using the Decision Tree (DT) method. Testing has done by comparing DT without optimization and DT with SMOTE-ENN and ADASYN optimization. The result shown DT with SMOTE-ENN optimization has the best accuracy improvement with 1.62%, from 96.94% to 98.56%.

PERFORMANCE COMPARISON OF RANDOM FOREST REGRESSION, SVR MODELS IN STOCK PRICE PREDICTION Urrochman, Maysas Yafi; Asy'ari, Hasyim; Hizham, Fadhel Akhmad
Jurnal Pilar Nusa Mandiri Vol. 21 No. 1 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i1.6072

The stock market is characterized by high volatility and complexity, making it an intriguing and challenging subject for researchers and practitioners. This study aims to predict stock prices by comparing the performance of two machine learning models: Random Forest Regression and Support Vector Regression (SVR). These models were selected for their ability to handle complex data and high volatility. The dataset used in this study consists of BNI stock data over the last five years (2019–2024), comprising a total of 1,211 data points. Testing was conducted using a cross-validation approach, and model performance was evaluated based on several metrics, including MSE, R², RMSE, MAPE, MAE, and Score. The results indicate that Random Forest Regression outperforms SVR. The model achieved an MAE of 17.766, an RMSE of 22.376, and an R² of 0.997. These findings suggest that Random Forest Regression is more effective in predicting stock prices, particularly in unstable market conditions. This study recommends Random Forest Regression as a reliable model for stock price prediction, with potential applications in other stock markets with similar characteristics.

AnalysisSentimentAlun-Alun LumajangReviewusingSupportVector Machine Urrochman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1555

Alun-Alun Lumajangis one of room the public that becomes center activity community and tourists . Perception public to place Thiscan measured through analysis sentiment to reviews available on digital platforms such as Google Maps. Research This aiming For classifysentiment review the use Support Vector Machine (SVM) method , one of the effective machine learning algorithms Fortask classification text . Data used in the form of review collected text fromGoogle Maps, then through pre-processing data such as cleaning text , tokenization , and deletion stopword . Sentiment label determined manually to be three categories : positive, negative , and neutral . Next , the data is extracted use TF-IDF technique before classified using SVM. Research results showthat SVM algorithm is capable of classify sentiment with level high accuracy , making it proper method For analysis opinion public based on text . Findings This expected can give input for government area in increase quality services and management room public in Lumajang.

Appropriate Technology Innovation “Biopharmaka Grinder” as a Means of Optimizing the Economy of the Senduro Community – Lumajang Ermawati, Emmy; Istichomah, Istichomah; Urrochman, Maysas Yafi
IMPOWERMENT SOCIETY Vol 8 No 1 (2025): February
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/eps.v8i1.1392

Lumajang Regency, Senduro District, East Java is famous as a center for the production of spices and herbs. Due to its abundant natural potential, various biopharmaceutical plants have been developed and are now major players in the local agricultural industry. The area is rich in natural resources, including vast agricultural lands and a climate that supports the cultivation of many medicinal plants. However, the challenge often faced by farmers is the lack of efficient processing equipment, which hinders the realization of the full potential of these biopharmaceutical components. Appropriate technological innovation The tool known as the “Biopharmaceutical Grinder” is intended to maximize the grinding of several biopharmaceutical raw materials, including spices, herbal plants, and other natural components. Biopharmaceutical Grinder can increase the output and market value of local goods by creating an instrument that is easy to use and friendly to rural communities. The creation and distribution of the Biopharmaceutical Grinder is expected to enhance Senduro's position as a center for biopharmaceutical processing in East Java and make it a model for other regions that want to utilize their natural resources in an environmentally responsible manner.

Aspect-Based Sentiment Analysis of Tumpak Sewu Waterfall Tourist Reviews Using the Naive Bayes Classifier (NBC) Method Urrochman, Maysas Yafi; Asy’ari, Hasyim; Ro’uf, Abdur
Journal of Informatics Development Vol. 4 No. 1 (2025): Oktober 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v4i1.1758

With the increasing popularity of Tumpak Sewu Waterfall, the volume of visitor reviews on Google Maps continues to grow. These reviews contain valuable insights into tourists’ experiences; however, conducting an in-depth manual analysis is inefficient. This study aims to perform aspect-based sentiment analysis on visitor reviews of Tumpak Sewu Waterfall using the Naive Bayes Classifier (NBC) method. This approach enables the classification of sentiments positive, negative, and neutral based on specific aspects such as facilities, accessibility, and natural scenery. Review data were collected from online platforms and processed through stages of text preprocessing and feature extraction before being trained using the NBC model. The results show that the model effectively classifies review sentiments with a high level of accuracy and provides detailed insights into which aspects most influence visitor satisfaction. These findings not only demonstrate the effectiveness of the Naive Bayes Classifier in aspect-based sentiment analysis tasks but also offer data-driven strategic recommendations for tourism managers to enhance service quality and improve visitor experience in the future.

Analisis Sentimen Ulasan Kawah Ijen Menggunakan Naïve Bayes Classification dan Optimasi Oversampling Hizham, Fadhel Akhmad; Asy'ari, Hasyim; Urrochman, Maysas Yafi
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4490

Sentiment analysis is a method that applies text mining concepts to provide classifications that have polarity that is positive, negative, or neutral from each sentence or document. In this context, the purpose of this research is to analyse the sentiment of user reviews related to the Ijen crater tourist attractions found on the Google Maps platform. This research is conducted in three main stages: first, Data Collection and Preprocessing by taking data samples obtained from Ijen Crater reviews contained on Google Maps; second Optimisation and Classification by changing the minority class samples to be almost equal to the majority class by randomly duplicating the minority class samples, third, classification performance measurement using confusion matrix. The test is conducted by comparing the performance between NBC classification without optimisation and NBC classification with SMOTE and ADASYN optimisation. The performance results show that SMOTE-optimised NBC classification provides the best improvement in accuracy by 6.74% compared to the performance of ordinary NBC and NBC added with ADASYN.

Using Machine Learning Techniques to Predict Financial Distress in Rural Banks in Indonesia Urrochman, Maysas Yafi; Asy’ari, Hasyim; Ro’uf, Abdur
Journal of Informatics Development Vol. 2 No. 2 (2024): April 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v2i2.1341

LPS liquidated about 100 people's Rural Banks between 2015 and 2019, indicating that these banks are facing significant issues, particularly financial distress. This study seeks to forecast financial distress through a two-stage classification and regression approach. Researchers used financial report data from Rural Banks in Indonesia from 2015 to 2019, covering a total of 150 banks, with 50 financial ratios from bankrupt banks and 100 from those that remained operational. Data was analyzed for two consecutive years prior to any bankruptcy declarations. The classification targets are categorized into five classes: very healthy, healthy, quite healthy, unhealthy, and distressed. The study results demonstrate that the two-stage classification and regression method can effectively predict the onset of financial distress. This is validated by the classification outcomes using the Decision Tree Algorithm, which achieved an f1-score accuracy of 88%. The evaluation of timing predictions using Random Forest Regression revealed a mean absolute error of 1.2 months and a mean absolute percentage error of 3%. These predictions can assist regulators, bank management, and investors in making better-informed decisions to address financial distress risks in Rural Banks. The superior performance of the Decision Tree Algorithm over Naïve Bayes in classifying financial distress highlights the potential of machine learning techniques in providing robust tools for early warning systems, aiding stakeholders in making informed decisions to mitigate risks.

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