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All Journal Jurnal Eksplora Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Dan Sistem Informasi Bisnis
Setiawan, Gede Herdian
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Computer Science & IT Electrical & Electronics Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Improving Helpdesk Chatbot Performance with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine Similarity Models Setiawan, Gede Herdian; Adnyana, I Made Budi
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6527

Abstract

Helpdesk chatbots are growing in popularity due to their ability to provide help and answers to user questions quickly and effectively. Chatbot development poses several challenges, including enhancing accuracy in understanding user queries and providing relevant responses while improving problem-solving efficiency. In this research, we aim to enhance the accuracy and efficiency of the Helpdesk Chatbot by implementing the Term Frequency-Inverse Document Frequency (TF-IDF) model and the Cosine Similarity algorithm. The TF-IDF model is a method used to measure the frequency of words in a document and their occurrence in the entire document collection, while the Cosine Similarity algorithm is used to measure the similarity between two documents. After implementing and testing TF-IDF and Cosine Similarity models in the Helpdesk Chatbot, we achieved a 75% question recognition rate. To increase accuracy and precision, it is necessary to increase the knowledge dataset and improve pre-processing, especially in recognition and correct inaccurate spelling
Utilization of ResNet Architecture and Transfer Learning Method in the Classification of Faces of Individuals with Down Syndrome Pranatha, Made Doddy Adi; Setiawan, Gede Herdian; Maricar, M Azman
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8474

Abstract

Classifying the faces of individuals with Down Syndrome poses a significant challenge in image processing and genetic anomaly detection. This study leverages the ResNet34 architecture and transfer learning methods to improve classification accuracy for Down Syndrome facial recognition. Three experiments were conducted, varying the batch size, learning rate, and number of epochs. In the first experiment, the model achieved an accuracy of 82.83%, precision of 0.8362, recall of 0.8350, and an F1 score of 0.8348, showing promising performance but falling short of the target accuracy of 85%. The second experiment yielded the best results, with an accuracy of 87.88%, precision of 0.8956, recall of 0.8956, and an F1 score of 0.8956, indicating an optimal balance between correct predictions and errors. The third experiment resulted in the lowest accuracy, at 80.47%, with a precision of 0.8272, recall of 0.8249, and an F1 score of 0.8247, signifying a decline in performance compared to the other trials. Among the three experiments, the best configuration was achieved in the second trial, as the high recall value is crucial in medical contexts to ensure that as many individuals with Down Syndrome are correctly detected as possible, minimizing the risk of serious consequences due to false negatives.
Topic Clustering of Student Complaints Based on Semantic Meaning Using the indoBERT and K-Means Models Setiawan, Gede Herdian; Pranata, Made Doddy Adi; Arimbawa, Ida Bagus Alit; Giri, I Wayan Paramarta; Carisa Dayani, Ni Putu Leona
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10080

Abstract

This study applies Natural Language Processing (NLP) technology to extract and cluster information from student complaint text data. The model used is IndoBERT, a variant of BERT (Bidirectional Encoder Representations from Transformers) that has been adapted for the Indonesian language. The main objective of this research is to perform topic clustering based on semantic similarity. The process begins with data collection and cleaning, followed by tokenization and text normalization. Each complaint is transformed into a vector representation through IndoBERT embeddings, which are then used as input for the K-Means clustering algorithm. Evaluation is conducted using various metrics, and the results of the Silhouette Score and Elbow Method indicate that the optimal number of clusters is four. Cluster visualization using the t-distributed Stochastic Neighbor Embedding (t-SNE) method reinforces these findings by displaying four fairly distinct groups of complaints, although one cluster appears dispersed and less well-defined, indicating possible topic overlap. The quality of topics within each cluster is evaluated using the Topic Coherence (c_v) metric, where Cluster 3 achieved the highest score of 0.7084. The topics in this cluster highlight critical issues such as campus facilities, lecturer quality, and information delivery systems. Overall, the four resulting clusters reflect central themes: Facilities, Expectations or Impressions, Services, and Academic Lectures. These results are expected to serve as a reference for institutions in formulating service improvement policies based on student complaint analysis.
Co-Authors Adiputra, I Komang Nopan Arimbawa, Ida Bagus Alit Carisa Dayani, Ni Putu Leona Gde Sastrawangsa, Gde Giri, I Wayan Paramarta I Made Budi Adnyana, I Made Budi Made Doddy Adi Pranatha, Made Doddy Adi Maricar, M Azman Pranata, Made Doddy Adi Pranatha, Made Doddy