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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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Game Tower Defense sebagai Media Pengenalan Mitologi Bali Berbasis Android Adiputra, I Komang Nopan; Sastrawangsa, Gde; Setiawan, Gede Herdian
Jurnal Eksplora Informatika Vol 12 No 1 (2022): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v12i1.978

Abstract

Game merupakan permainan berbasis multimedia pada alat elektronik yang memberikan hiburan kepada yang memainkannya. Seiring dengan perkembangan teknologi, fungsi game tidak hanya dijadikan sebagai sarana hiburan saja, namun game telah menjadi luas fungsinya. Misalnya, sebagai sarana pembelajaran atau pengenalan sebuah budaya tertentu yang dikemas secara unik dan menarik. Mitologi adalah ilmu mengenai bentuk sastra yang mengandung konsepsi dan dongeng suci tentang kehidupan dewa dan makhluk halus dalam suatu kebudayaan. Di Indonesia khususnya di Bali sendiri cerita-cerita serta keberadaan makhluk-makhluk mitologi masih sangat dihormati di kalangan masyarakat. Salah satu cerita mitologi yang menarik adalah kisah Samudramanthana. Cerita Samudramanthana masih berbentuk teks saja namun dengan diadaptasi ke dalam bentuk multimedia yaitu game maka diharapkan cerita akan menjadi menarik dan lebih mudah dipahami terutama bagi generasi muda. Penelitian ini bertujuan untuk merancang dan membangun game cerita mitologi tentang kisah Samudramanthana disertai dengan informasi mengenai tokoh-tokoh mitologi Bali yang dikemas dengan menarik. Penelitian ini mengunakan metode (MDLC), dijalankan pada android dengan menggunakan software Unity 3D, Berdasarkan rancang bangun yang sudah dilakukan, diperoleh kesimpulan yaitu hasil pengujian Heuristic Evaluation didapat bahwa seluruh tombol sudah berjalan sesuai dengan yang dirancang pada Game Tower Defense Sebagai Media Pengenalan Berbasis Android
Implementasi Arsitektural Resnet-34 Dalam Klasifikasi Gambar Penyakit Pada Daun Kentang Pranatha, Made Doddy; Maricar, M Azman; Setiawan, Gede Herdian
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 3 (2024): Juli 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i3.1431

Abstract

This research develops and implements an image classification method using the Residual Network (ResNet) architecture to identify potato leaf diseases, achieving an accuracy of around 97%. The dataset used consists of 2152 potato leaf images, categorized into three classes: early blight, late blight, and healthy. The selected model is ResNet-50, known for its ability to address the vanishing gradient problem, allowing for the training of very deep networks. The model training process involves data augmentation to enhance dataset diversity and prevent overfitting. Additionally, hyperparameter optimization was performed to maximize the model's performance. Evaluation of the model shows that ResNet-50 can achieve an accuracy of approximately 97% on the test data, indicating the model's high capability in accurately recognizing and classifying the condition of potato leaves. These results demonstrate the significant potential of using ResNet in plant disease image classification applications, which is crucial for decision-making in agricultural management. This research underscores the importance of deep network architectures and data augmentation techniques in improving the performance of deep learning models.
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