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Peningkatan Kinerja Metode Random Forest Berbasis Smote-Tomek Link Pada Sentimen Analisis Pariwisata Lombok Marzuki, Khairan; Rady Putra, Lalu Ganda; Hairani, Hairani; Mardedi, Lalu Zazuli Azhar; Guterres, Juvinal Ximenes
Jurnal Bumigora Information Technology (BITe) Vol 5 No 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v5i2.3166

Background: Tourists visiting Lombok Island can access various sources of tourist information and can share their views and tourist experiences through social media such as positive and negative experiences. Objective: This research aims to analyze the sentiment of Lombok tourism reviews using the Smote-Tomek Link and Random Forest algorithms.Methods: The research was carried out in several stages, namely collecting the Lombok tourism dataset, text preprocessing, text weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, data sampling using SMOTE-Tomek Link, text classification using Random Forest, and the final stage was performance testing based on accuracy. Result: The research results obtained using the Smote-Tomek Link and Random Forest methods in sentiment analysis analysis of tourist reviews about Lombok were 94%. Conclusion: The use of the Smote-Tomek Link and Random Forest methods in Lombok tourism sentiment analysis produces very good accuracy.

Enhancing User Experience in Private Bank Mobile Banking: Insights from Management Information System Analysis Hartawan, Muhammad Syarif; Jauhari , Burhanuddin; Vandika, Arnes Yuli; Guterres, Juvinal Ximenes
Journal of Engineering and Science Application Vol. 1 No. 2 (2024): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v1i2.11

This research explores the impact of Management Information Systems (MIS) on user experience in private bank mobile banking, focusing specifically on the effectiveness of m-BCA. Utilizing a descriptive quantitative approach, the study aims to understand how mobile banking features, including accessibility, user interface, security, and functionality, influence user satisfaction. Data were collected through direct experimentation with the m-BCA application, complemented by analysis from secondary data sources and a survey of 30 users. Results indicate that 93.3% of users find m-BCA effective for routine banking needs, attributing high ratings to its convenience and functionality. However, while 80% of respondents appreciate the current security measures, 20% cite the absence of advanced security features such as fingerprint and facial recognition as a drawback. The study also reveals mixed feedback on the user interface; while the straightforward design aids usability, 10% of users express a desire for a more visually appealing interface. Despite these areas for improvement, m-BCA’s comprehensive set of features is generally seen as effective, supporting users in performing essential transactions with ease and reliability. The findings suggest that enhancing interface aesthetics and integrating advanced security options could further boost user engagement and trust in mobile banking applications, underscoring MIS’s role in meeting evolving customer expectations in digital banking.

Pembuatan Aplikasi Menu Pemesanan untuk Arny Cafe Berbasis Android Siswanto, Yoyok Adi; Sucipto, Sucipto; Muzaki, Muhammad Najibulloh; Guterres, Juvinal Ximenes
JSITIK: Jurnal Sistem Informasi dan Teknologi Informasi Komputer Vol. 3 No. 1 (2024): Desember 2024
Publisher : Cipta Media Harmoni

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53624/jsitik.v3i1.544

Latar Belakang: Perkembangan teknologi mendorong kebutuhan akan solusi digital di berbagai bidang, termasuk industri kuliner. Urgensi penelitian ini adalah untuk menjawab kebutuhan mendesak akan solusi digital di industri kuliner, khususnya dalam mengatasi permasalahan proses pemesanan manual yang sering tidak efisien, rawan kesalahan, dan memengaruhi kepuasan pelanggan. Tujuan: Tujuan penelitian ini adalah untuk merancang dan mengembangkan aplikasi menu pemesanan berbasis Android untuk Arny Cafe agar proses pemesanan lebih efektif, akurat, dan cepat. Metode: Metode pengembangan yang digunakan adalah model Waterfall, yang meliputi tahap analisis kebutuhan, perancangan, implementasi, dan pengujian sistem. Hasil: Hasil penelitian menunjukkan bahwa aplikasi ini dapat mempercepat proses pelayanan, meningkatkan akurasi pemesanan, serta menyediakan pengelolaan data menu dan laporan penjualan secara real-time untuk mendukung pengambilan keputusan manajemen. Kesimpulan: Implementasi aplikasi ini dapat meningkatkan efisiensi operasional, kepuasan pelanggan, dan mendukung pertumbuhan bisnis yang berkelanjutan di Arny Cafe.

OPTIMASI CHATBOT DALAM SISTEM PENGADUAN PELAYANAN PUBLIK BERBASIS ANDROID Tholib, Abu; Andi, Moh syaiful; Sukron, Moh; Shudiq, Wali Ja'far; Hairani, Hairani; Guterres, Juvinal Ximenes
Insand Comtech : Information Science and Computer Technology Journal Vol 10, No 1 (2025): Insand Comtech
Publisher : Universitas Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53712/jic.v10i1.2637

This study presents the development of an Android-based public service complaint application integrated with chatbot technology to improve service responsiveness. The system aims to facilitate community members in submitting complaints and receiving immediate responses through an interactive interface. A user-friendly mobile application was developed using the Kotlin programming language, and chatbot functionality was implemented via API integration to respond to frequently asked questions. The implementation followed the Waterfall model, encompassing stages of analysis, design, implementation, testing, and maintenance. Results show that the application effectively streamlines the complaint process, increases efficiency in complaint management, and enhances communication between the public and local government. The chatbot proved to be reliable in delivering relevant and timely responses, significantly reducing the time needed for initial interactions. This integration demonstrates the potential of artificial intelligence to support e-government services in rural setting

PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.

Optimization of MySQL Database in the Development of Solo Batik Mall Srirahayu, Agustina; Pamekas, Bondan Wahyu; Guterres, Juvinal Ximenes
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2025: Proceeding of the 6th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/kjy3qe33

This study aims to design and optimize a MySQL database for an online batik mall system to support the digitalization of batik micro, small, and medium enterprises (MSMEs). The research employed three stages: analysis, design, and implementation. The analysis phase identified actors (sellers, buyers, and administrators) and business process needs. The design stage focused on database structures and optimization strategies, including indexing, query optimization, caching, normalization, and denormalization. The implementation involved building the database, applying optimization techniques, and evaluating performance. The optimization of MySQL significantly improved query execution speed, reduced system response time, and enhanced resource efficiency. The system was able to manage transactions, product searches, and reporting more effectively, supporting both operational and strategic needs of the online batik mall. The MySQL-based online batik mall system provides an efficient solution for data management, thereby enhancing the competitiveness of batik MSMEs in the digital era.

The Role of Parents in Forming Children's Character in Sruwen Village and Duren Village Solihin, Solihin; Guterres, Juvinal Ximenes
Afeksi: Jurnal Penelitian dan Evaluasi Pendidikan Vol 5, No 1 (2024)
Publisher : Pusat Studi Penelitian dan Evaluasi Pembelajaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35672/afeksi.v5i1.217

The objectives of this research are 1) To analyze how parents' roles are used in shaping children's character in Duren Village and Sruwen Village; 2) To find out how parents' roles differ in shaping children's character in Duren Village and Sruwen Village; 3) To find out how the role of parents is constructed for children in Duren Village and Sruwen Village and its relevance in facing today's millennial generation. The results of the research are: 1) The role of parents in Duren Village is an authoritative parenting style, namely parents who apply a democratic parenting style have clear rules and expectations, and the role of parents in Sruwen Village is an authoritarian parenting style where parents are the responsible party. . center, namely the center of everything. the words/words and wishes of parents are used as benchmarks that must be obeyed by children; 2) The role of parents in Duren Village in shaping children's character through behavior that is very effective in directing children, while the role of parents in Sruwen Village in caring for their children is more concerned with the wishes of parents, children are supervised quite closely, parents always maintain their distance from each other. their children rather than considering their children's wishes; 3) The construction of a democratic parental role in Duren Village will give birth to an assertive personality which is the ideal type so that there are not many weak people. Meanwhile, the construction of the role of authoritarian and permissive parents in Sruwen Village can make children feel unloved, alienated, even hated by their parents, will become individuals who are not independent, easily offended, have a negative view of other people, feel inferior and lack self-confidence. and feel free to act.

Peningkatan Kinerja Metode Random Forest Berbasis Smote-Tomek Link Pada Sentimen Analisis Pariwisata Lombok Marzuki, Khairan; Rady Putra, Lalu Ganda; Hairani, Hairani; Mardedi, Lalu Zazuli Azhar; Guterres, Juvinal Ximenes
Jurnal Bumigora Information Technology (BITe) Vol. 5 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v5i2.3166

Background: Tourists visiting Lombok Island can access various sources of tourist information and can share their views and tourist experiences through social media such as positive and negative experiences. Objective: This research aims to analyze the sentiment of Lombok tourism reviews using the Smote-Tomek Link and Random Forest algorithms.Methods: The research was carried out in several stages, namely collecting the Lombok tourism dataset, text preprocessing, text weighting using the Term Frequency-Inverse Document Frequency (TF-IDF) method, data sampling using SMOTE-Tomek Link, text classification using Random Forest, and the final stage was performance testing based on accuracy. Result: The research results obtained using the Smote-Tomek Link and Random Forest methods in sentiment analysis analysis of tourist reviews about Lombok were 94%. Conclusion: The use of the Smote-Tomek Link and Random Forest methods in Lombok tourism sentiment analysis produces very good accuracy.

PREDICTION AND PREVENTION OF DISEASE DIAGNOSIS DELAY USING DATA MINING METHODS IN HEALTHCARE QUALITY MANAGEMENT Maulindar, Joni; Guterres, Juvinal Ximenes; Rosita, Riska
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2023: Proceeding of the 4th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v4i1.3376

This study analyzes the issue of disease diagnosis delay in healthcare quality management using data mining methods. The aim is to understand the relationship between several key variables and diagnosis delay for various diseases. The study focuses on the variables of Age, Symptom Duration, Physician Experience, and Diagnosis Delay. Advanced data mining methods are employed to predict and prevent disease diagnosis delays. The results of this study present the findings from the analysis of the collected dataset. The dataset consists of patient information, including attributes such as Patient ID, Age, Symptom Duration, Physician Experience, Diagnosis Delay, and Treatment Initiation. Each attribute plays a crucial role in understanding and predicting diagnosis delay. The approach using linear regression yields coefficients [0.03260123, 0.24605912, 0.01765057, 1.09631713], indicating the influence of each variable on Diagnosis Delay. The Mean Squared Error (MSE) value of 0.7926 signifies the model's ability to predict Diagnosis Delay accurately. The scatter plot illustrates the linear relationship between actual Diagnosis Delay and predicted Diagnosis Delay. The Pearson's Correlation Coefficient of 0.5222 indicates a moderate positive correlation between the two. However, the residual plot indicates a tendency for underestimation of Diagnosis Delay for higher values.

Patient Prevention Prediction and Diagnosis Using Data Mining in Healthcare Quality Management Noviyanty; guterres, juvinal Ximenes; Gusmao, Adozinda Soares; Soares, Domingas; Guterres, Anita; da Silva, Recardina Freitas
Jurnal Sains Informatika Terapan Vol. 4 No. 3 (2025): Jurnal Sains Informatika Terapan (Oktober, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar

The expansion of digital medical records and clinical data has strengthened the development of intelligent analytical systems to support early disease detection and improve diagnostic accuracy. This study aims to evaluate the performance of three classification algorithms, namely Random Forest, Support Vector Machine, and Logistic Regression, in predicting stroke risk using multidimensional patient clinical information. The dataset consists of 224 patient records derived from the Kaggle Stroke Dataset and additional questionnaire data collected from hospitals and primary health centers. The variables include demographic characteristics, clinical history, lifestyle factors, and physiological indicators. The research methodology involves several stages, including data preprocessing, feature selection using ANOVA F value, class balancing through the Synthetic Minority Oversampling Technique, model training, and performance evaluation using Accuracy, Precision, Recall, F1 Score, Matthews Correlation Coefficient, and Area Under the Curve. The results indicate that the Random Forest model achieves the highest performance, with an accuracy of 0.91 and an Area Under the Curve of 0.91, outperforming Support Vector Machine and Logistic Regression. This outcome confirms the effectiveness of ensemble based approaches in identifying complex nonlinear patterns and managing imbalanced data. The study contributes to healthcare quality improvement by providing a reliable prediction framework that supports early clinical decision making, reduces diagnostic delays, and enhances patient care outcomes.

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