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Naive Bayes Classifier untuk Analisis Sentimen Ulasan Pelanggan pada Domo Coffee and Resto Puji Hartini; Nana Suarna; Willy Prihartono
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10315

Domo Coffee and resto is one of the well-known cafes located on Jl. DR Sudarsono No.45 Kesambi, Kesambi District, Cirebon City. Domo Coffee and Resto has a variety of food and drinks served and the place is designed to be beautiful and comfortable to visit for various purposes. Of course, there are many kinds of problems related to unsatisfactory service, uncomfortable atmosphere or bad taste of food as well as several other disappointments and dissatisfaction that give rise to negative comments or reviews. Café Domo often receives mixed reviews from customers on the Google review platform. This research aims to analyze the sentiment of customer reviews on Domo Coffee and restaurant and will be completed using the Naïve Bayes Classifier method, namely a classification method based on Bayes' theorem. In this research, based on the author's understanding of sentences regarding sentiment analysis, the author received 374 positive reviews and 58 negative reviews regarding food. 469 positive reviews and 40 negative reviews regarding the atmosphere and 253 positive reviews and 99 negative reviews regarding the service. The highest number of positive comments was obtained by the atmosphere aspect with 469 reviews and the highest negative comments were obtained by the service aspect with 99 reviews. In testing the split data values of 0.8 and 0.2, the highest accuracy was obtained by the service technician with an accuracy of 98.22%, precision of 97.58%, recall of 100% and an F1-score value of 98.78%. The results of this research provide in-depth insight into customers' views of Domo cafe. Cafe owners and stakeholders can use these findings to understand aspects that need to be improved or improved.

Analisa Pengaruh Jumlah Penerima dan Penyaluran Pinjaman melalui Finansial Teknologi (fintech) terhadap Pertumbuhan Ekonomi Masyarakat melalui Regresi Linear Sri Farida Utami; Willy Prihartono; Mohamad Alif Dzikry
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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

This study looks into the relationship between the number of loans received and the amount funnelled by the technology financial platform and the economic growth of a population. Fintech. The method used in this study is linear regression. In recent years, fintech has grown to be a significant component of the financial system, particularly in emerging nations like Indonesia where traditional financial services are still unavailable. The study begins with the hypothesis that fintech can improve financial inclusion; theoretically, this will raise economic growth by improving the distribution of financial resources and the ease with which credit can be obtained. Important variables that were examined in the study were the total number of borrowers, the distribution of loans overall, and measures of economic growth. The outcomes of the linear regression demonstrated a strong positive association between the quantity of borrowing, the availability of fintech loans, and the population's economic expansion. The report emphasizes the significance of rules that enable healthy and inclusive fintech growth and offers pertinent policy implications for decision-makers and players in the fintech industry. The study's finding supports the claim that, by facilitating better access to credit and a more fair distribution of credit, fintech may significantly contribute to economic growth. According to the report, in order to maximize fintech's beneficial effects on the economy, policies that foster its growth are necessary.

Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe Widisa Adi Kumara; Rini Astuti; Willy Prihartono; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.772

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.

Website Based Digital Branding Strategy for Increase Sales of Gunung Puntang Coffee In Mekarjaya, Bandung Regency Juliyanti; Rini Astuti; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.802

This research aims to develop a branding strategy through optimizing a website-based digital company profile to increase sales of Gunung Puntang coffee. Gunung Puntang coffee is a high-quality local product that requires a digital approach to marketing to reach a broader market and enhance competitiveness. In today's digital era, a website plays a crucial role as a promotional and informational medium, providing customers with easy access to product information and enabling online purchases. The research employs the Prototype method, consisting of problem identification, planning, requirements analysis, system design, implementation, and testing phases. Data collection was conducted through observation, interviews with the coffee business owner, and documentation studies related to business processes and branding strategies. The collected data serves as a basis for designing a website system that optimizes the company's profile and supports coffee sales transactions. The system development includes creating use case diagrams, activity diagrams, and system architecture designs to outline functional and non-functional requirements. The research outcome is a website functioning as a digital information medium for branding Gunung Puntang coffee products and supporting sales transactions. Key features include customer registration, product selection, quantity adjustment, payment methods, order confirmation, and order cancellation. Testing results indicate that the system operates effectively and meets user needs. This website enhances operational efficiency, expands market reach, and improves the shopping experience for customers. It serves as an effective medium for strengthening branding and marketing strategies in the digital era, ensuring the sustainability of local businesses in the global market. Regular evaluations and feature upgrades are recommended to maintain system relevance to customer needs and technological advancements..

Implementation of Naive Bayes in Sentiment Analysis of CapCut App Reviews on the Play Store Oka Alvianto; Willy Prihartono; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.805

The CapCut video editing application has gained significant popularity among mobile users. This study aims to analyze user sentiment towards CapCut reviews on the Play Store using the Naive Bayes algorithm. User reviews were collected and preprocessed to clean and prepare the text for analysis. The Naive Bayes algorithm was employed to classify the reviews into positive and negative sentiment categories. Findings indicate that the majority of user reviews are positive, highlighting features such as ease of use, attractive visual effects, and the ability to share videos on social media. However, negative reviews were also identified, primarily criticizing issues like bugs, intrusive advertisements, and limitations in specific features. This research provides valuable insights into user sentiment towards CapCut, which can be utilized by developers to enhance application quality and user experience.

Web-Based Chatbot Development and User Satisfaction Analysis Using the Naive Bayes Method Through Online Questionnaires Nurholis; Willy Prihartono; Fathurrohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.823

This study aims to develop a web-based chatbot using Natural Language Processing (NLP) technology and the Naive Bayes algorithm to enhance digital interaction quality. User satisfaction was evaluated through an online survey involving 202 university students, focusing on ease of use, response speed, and relevance. The research followed the CRISP-DM framework, including data preprocessing (case folding, tokenization, stopword removal, and stemming), text transformation using the TF-IDF method, and implementation of a Naive Bayes classification model. an F1-score of 84%. Sentiment analysis revealed predominantly positive feedback, reflecting user satisfaction with the chatbot’s ease of use and response accuracy. However, some limitations, such as insufficient contextual understanding, were identified. These findings provide valuable insights into NLP-based chatbot development to support effective digital interactions. The proposed chatbot demonstrates potential applications in customer service, education, and e-commerce, with future improvements suggested to enhance contextual comprehension and scalability.

Application of K-Means for Product Grouping Best Sellers at Planet Tire Jatibarang Branch Risnawati; Rini Astuti; Willy Prihartono
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.845

This research aims to identify the best-selling products at Planet Tire Workshop Jatibarang Branch using the K-Means Clustering method. Understanding product sales patterns is important in designing effective marketing strategies and managing stock efficiently. This research uses sales transaction data for one year, including the number of sales, product types, and total transaction value. The analysis process includes data preprocessing, selection of relevant attributes, application of the K-Means algorithm, and validation of the optimal number of clusters with the Elbow method. As a result, products were grouped into three categories: high, medium, and low sales. The high sales cluster contributes significantly to revenue, while the medium sales cluster shows potential for improvement through promotion, and the low sales cluster requires further evaluation. This research helps management manage stock, prioritize promotions, and optimize resource allocation. However, the research has limitations as it has not considered external factors such as seasonal trends and promotions, and focuses on one branch. Development of the research in other branches can expand its benefits. The results of this study are expected to improve operational efficiency, support data-driven strategies, and enrich academic literature related to the application of K-Means in retail management and sales data analysis.

Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.

Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.

ALGORITMA RANDOM FOREST UNTUK PREDIKSI STATUS PINJAMAN BERDASARKAN SKOR KREDIT Attaufiqqurrohman, Hadit; Ade Irma Purnamasari; Denni Pratama; Nining Rahaningsih; Willy Prihartono
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

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

The rapid development of financial technology has encouraged financial institutions to adopt data-driven credit scoring systems in order to minimize the risk of default. However, many loan eligibility prediction models still face challenges such as data imbalance (class imbalance) and the limited capability of traditional models to capture non-linear relationships among variables. This study aims to develop a loan status prediction model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and One-Hot Encoding (OHE) to improve model accuracy and generalization capability. The data used in this study are secondary data obtained from the public Kaggle platform, consisting of 45,000 records with 14 demographic and financial attributes. The research method employs a supervised learning approach with several stages, including data acquisition and preprocessing (data cleaning, normalization, encoding, and data balancing), Random Forest model training, and performance evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the combination of Random Forest, SMOTE, and OHE achieves high predictive performance, with an accuracy of 94.8%, precision of 95.6%, recall of 93.7%, F1-score of 94.6%, and an AUC value of 0.972. The most influential variables in loan status prediction are credit_score, person_income, and loan_amnt. This approach is proven to be effective in addressing data imbalance issues and improving classification accuracy in identifying creditworthy and non-creditworthy borrowers.

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