cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Lisnawita
Contact Email
indoaijurnal@gmail.com
Phone
+6281268655436
Journal Mail Official
indoaijurnal@gmail.com
Editorial Address
Jl. Soekarno Hatta
Location
Unknown,
Unknown
INDONESIA
IndoAI: Journal of Artificial Intelligence and Computational Logic
Published by CV. LENTERA NALAR CEMERLANG
ISSN : -     EISSN : 31250114     DOI : -
Core Subject :
Journal of Artificial Intelligence and Computational Logic (IndoAI) publishes original research articles, review papers, and applied studies in the fields of Artificial Intelligence (AI), Computational Intelligence, Data Science, and Intelligent Computing. The journal aims to disseminate innovative theories, methodologies, algorithms, and practical applications that advance intelligent systems and computational technologies across various domains. The journal welcomes original research in, but is not limited to, the following areas: Artificial Intelligence and Machine Learning Computational Logic and Soft Computing Data Science and Data Analytics Natural Language Processing and Computer Vision Decision Support Systems and Expert Systems Information Systems and Software Engineering Cloud Computing, Internet of Things (IoT), and Distributed Systems Robotics and Intelligent Systems Cybersecurity and AI Applications Smart Technologies and Intelligent Applications in Education, Business, Healthcare, Government, Industry, and Society
Arjuna Subject : -
Articles 5 Documents
 1 
Analisis Sentimen Terhadap Ulasan Hotel XYZ Dengan Metode Naive Bayes Classifier Rahul Rinaldo Siagian; M. Sadar
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

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

Abstract

The development of information technology and the increasing use of the internet have encouraged people to utilize online reviews as a consideration when choosing hotel accommodations. Customer reviews on platforms such as Traveloka contain opinions regarding service quality, facilities, and overall stay experience; however, they are presented in unstructured text form, making them difficult to analyze manually in large quantities. This study aims to analyze the sentiment of reviews for XYZ Hotel using the Naive Bayes Classifier (NBC) method and to determine the accuracy level of the resulting model. The data were collected through web scraping on April 29, 2025, resulting in a total of 2,091 reviews. The data processing stages included preprocessing steps consisting of case folding, cleansing, normalization, tokenizing, stopword removal, and stemming. Subsequently, sentiment labeling was conducted into two categories, namely positive and negative, resulting in 1,194 positive reviews and 897 negative reviews. The word weighting process used the TF-IDF method to identify dominant terms, which were then visualized using a word cloud to facilitate interpretation of text patterns. Classification was carried out using three data-splitting scenarios: 70%:30%, 80%:20%, and 90%:10% (training and testing). The results showed that the best performance was achieved with a 90% training and 10% testing data split, yielding an accuracy of 77.14%. The model performed better in identifying positive sentiment compared to negative sentiment. Overall, the Naive Bayes Classifier method is sufficiently effective for analyzing hotel review sentiment and can serve as a basis for decision-making in improving service quality.
Analisis Sentimen Pasien Pada Ulasan Layanan Puskesmas Sekota Pekanbaru Menggunakan Metode Naive Bayes Classifier Michael Jordan Sirait
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

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

Abstract

The rapid development of information technology has encouraged the public to share opinions regarding healthcare services through digital platforms such as Google Maps. These patient reviews can be utilized to evaluate the quality of public health center (Puskesmas) services in a more objective and data-driven manner. This study aims to analyze patient sentiment toward Puskesmas services across Pekanbaru City using the Naïve Bayes Classifier (NBC) method. The research data were collected through a web scraping technique from Google Maps, resulting in 856 patient reviews. The research stages included data preprocessing (case folding, cleansing, normalization, tokenizing, stopword removal, and stemming), sentiment labeling (positive, negative, and neutral), TF-IDF weighting, classification using NBC, and model evaluation. The initial sentiment distribution consisted of 480 neutral reviews, 259 positive reviews, and 117 negative reviews. To address data imbalance, the SMOTE method was applied. The evaluation results using a 70% training and 30% testing split showed an accuracy of 71.60%. After applying SMOTE, the accuracy increased to 78.21%, while the implementation of Chi-Square feature selection produced the highest accuracy of 80.56%. Meanwhile, PCA and LDA achieved accuracies of 71.06% and 57.41%, respectively. Word cloud visualization and TF-IDF analysis revealed dominant words such as “service,” “friendly,” “good,” and “bad.” The findings indicate that the Naïve Bayes method is effective for classifying patient review sentiments, particularly when combined with Chi-Square feature selection. This study is expected to provide a basis for improving the quality of Puskesmas services in Pekanbaru City.
Penerapan Metode Apriori dalam Implementasi E-Commerce pada Toko Gorden Taufiq Hidayah; Ahmad Zamsuri
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

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

Abstract

Curtains are pieces of fabric or textiles used to block light and enhance the appearance of doors, windows, and the room as a whole as a decorative element of the home interior. The sale of curtains in Pekanbaru, especially at Toko Ola Gorden, still uses conventional methods that limit market reach and store development. This research aims to create an e-commerce system for Toko Ola Gorden and apply the Apriori Algorithm to determine product recommendations based on transaction data. The results of this study successfully designed and built an e-commerce system using the FAST method and applied the Apriori Algorithm to provide product recommendations based on sales transactions at Toko Ola Gorden. The results of product recommendations are obtained with a minimum support of 30% and confidence of 50%, where one of the results shows that the purchase of vitrase is followed by the purchase of curtains with a support value of 57.14% and confidence 80%.
Klasifikasi Kematangan Buah Kelapa Sawit Berdasarkan Warna Menggunakan Metode K-Nearest Neighbor Nova Elija Barutu; Dafwen Toresa
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

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

Abstract

The K-Nearest Neighbor (K-NN) algorithm is a simple machine learning algorithm used for classification and regression. This study aims to implement the K-NN algorithm in classifying the ripeness level of oil palm fruit based on color. The data used consisted of 270 images of dura, tenera, and pisifera oil palm fruits taken using a smartphone camera. The results showed that the K value in the K-NN algorithm plays an important role in determining the classification performance. With K = 3, the model achieved the highest accuracy of 93.67%, while the lowest accuracy was 80.05% with a value of K = 25. Compared to previous studies that obtained the highest accuracy of 92% at K = 7, this study shows an increase in classification performance. Classification data analysis showed that 56 image data were correctly classified and 25 image data were incorrectly classified from a total of 81 test image data. This study proves that K-NN with RGB color images can be effectively used for classification of the ripeness level of oil palm fruit.
Penerapan Optimasi Gradient Boosting dalam Prediksi Nilai Transaksi Pelanggan di E-CRM Fluffy Cat Shop Tiara; Lisnawita; Lucky Lhaura Van FC
IndoAI: Journal of Artificial Intelligence and Computational Logic Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)
Publisher : IndoAI: Journal of Artificial Intelligence and Computational Logic

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

Abstract

The development of e-commerce encourages companies to optimally leverage customer data through an Electronic Customer Relationship Management (E-CRM) system. Fluffy Cat Shop, an online store for cat supplies, faces challenges in accurately predicting customer transaction value. This study aims to optimize the Gradient Boosting algorithm for predicting customer transaction value within the E-CRM system of Fluffy Cat Shop. The research methods include collecting customer transaction data, data preprocessing (cleaning, encoding, and normalization), building a Gradient Boosting model, and optimizing hyperparameters using the Grid Search method. Model evaluation is conducted using the MAE, RMSE, and R² Score metrics. The results show that after optimization, the model’s performance improves with an R² Score of 0.8, indicating that the model can explain 80% of the variation in customer transaction value. The error values also decrease compared to the initial model.

Page 1 of 1 | Total Record : 5

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 1 No. 1 (2026): IndoAI: Journal of Artificial Intelligence and Computational Logic (I-JAICL)