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All Journal Jurnal Matematika Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIEET (Journal of Information Engineering and Educational Technology) JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal Matematika UNAND Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Informatika (JUPITA) International Journal of Applied Mathematics and Computing. Konstanta : Jurnal Matematika dan Ilmu Pengelatuan Alam Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Wijayakusuma, I Gusti Ngurah Lanang
Universitas Udayana
Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Penerapan Metode Saw dan Topsis pada Pemilihan Lokasi Kuliner di Kota Denpasar Ulfatun Farika Novitasari; Eka N. Kencana; I GN Lanang Wijayakusuma
Konstanta : Jurnal Matematika dan Ilmu Pengetahuan Alam Vol. 2 No. 4 (2024): Desember : Jurnal Matematika dan Ilmu Pengetahuan Alam
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/konstanta.v2i4.4193

Abstract

Bali is a renowned tourist destination that attracts visitors from around the world, particularly for its natural beauty, rich culture, and delicious cuisine. The increasing number of tourists in Bali has driven rapid growth in the culinary industry. In Denpasar City, selecting the right location is a key factor for the success of culinary businesses, as each location has different characteristics and potentials. This study employs the Multiple Attribute Decision Making (MADM) model, combining the Simple Additive Weighting (SAW) and Technique for Orders Preference by Similarity to Ideal Solution (TOPSIS) methods, to determine the optimal location for culinary businesses in Denpasar City. Data were collected through surveys of 154 culinary business owners, considering eight criteria: Accessibility, Visibility, Traffic, Facilities, Expansion, Environment, Competition, and Regulations. The study's findings indicate that both SAW and TOPSIS methods identify high population density areas as the best choice. The SAW and TOPSIS method provides the highest preference value of 0,8815 and 0.7082 respectively, making it the more effective method for recommending optimal culinary locations in Denpasar City.
ANALYSIS OF CONSUMER PREFERENCES IN CONSUMING PROCESSED COFFEE PRODUCTS AT CAFE NECTAR BALI Isabel Divya Georgiana Walewangko; I Komang Gde Sukarsa; I Gusti Ngurah Lanang Wijayakusuma; I Putu Eka Nila Kencana; I Gusti Ayu Made Srinadi; Ratna Sari Widiastuti
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 3 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i3.2104

Abstract

Coffee beverages have become a highly sought-after product, particularly in tourist areas that are favoured by both local and foreign tourists. For this reason, there are many business owners who want to expand their business with coffee as the main menu. Cafe Nectar Bali, not far from the tourist attraction Garuda Wisnu Kencana (GWK), is one of the places frequented by both locals and foreign tourists. The purpose of this study is to identify the characteristics consumers often consider when consuming processed coffee products at Cafe Nectar Bali and to understand the preferences of local residents and foreign tourists regarding processed coffee products offered. The research method used is the analysis of local and foreign tourist preferences using conjoint analysis techniques. The findings show that consumers are prioritizing the type of coffee and how it is served. Both locals and foreign tourists value the diversity feature more than the presentation method feature. Local consumers choose the stimulus of latte variant and hot serving methods. On the other hand, foreign tourists chose the stimulus of latte variant and the cold serving method. Coffee; Conjoint Analysis; Consumer Preferences
Comparison of Online Gambling Promotion Detection Performance Using DistilBERT and DeBERTa Models Pratama, Halim Meliana; Wijayakusuma, IGN Lanang; Widiastuti, Ratna Sari
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11293

Abstract

Online gambling promotions on social media have become a serious concern in Indonesia, where perpetrators use ambiguous and disguised language to evade detection. This study compares two transformer-based models, DistilBERT and DeBERTa, in detecting such content within Indonesian YouTube comments. Using a balanced dataset of 6,350 comments, both models were fine-tuned with optimized hyperparameters (learning rate 1e-5, batch size 32, 5 epochs) and evaluated through five-fold cross-validation. Results show that DeBERTa achieves superior performance with 99.84% accuracy and perfect recall, while DistilBERT achieves 99.29% accuracy. Error and linguistic analyses indicate that DeBERTa’s disentangled attention and Byte-Pair Encoding provide better understanding of non-standard and ambiguous language. Despite requiring higher computational cost, DeBERTa is ideal for high-accuracy applications, whereas DistilBERT remains suitable for real-time and resource-limited environments.
Development of Secure API to Support ICD-10 Based Electronic Medical Records Interoperability I G N Lanang Wijayakusuma; Made Sudarma; I Ketut Gede Darma Putra; Oka Sudana; Minho Jo; I Putu Winada Gautama
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p06

Abstract

Previous research in 2021 and 2022 has yielded a revolutionary health examination system. This system seamlessly integrates the World Health Organization's International Classification of Diseases-10 (ICD-10) data, ensuring diagnoses align with global standards and thereby enhancing the quality of healthcare provision. A pivotal achievement is the creation of a sophisticated doctor's examination interface, designed for precision and efficiency. Complementing this interface, a search engine autonomously generates relevant keywords, successfully passing the rigorous black-box test, which attests to its robustness and reliability in retrieving critical medical information. A new challenge arises in enabling seamless access to the stored medical record data for various stakeholders, including the Ministry of Health, BPJS, insurance companies, and other relevant entities. To address this, the research team has devised the Application Programming Interface (API). Functioning as a crucial bridge, this API facilitates interoperability among diverse systems. Adherence to the stringent security standards set by the Open Web Application Security Project (OWASP) ensures that the exchange of medical data occurs within a secure environment. Consequently, sensitive patient information can be shared across platforms without compromising confidentiality or integrity.
Implementation of LSTM for Gold Price Prediction in Indonesia Sibannang, Maria Oktaviani Giska; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11860

Abstract

Gold is a significant investment instrument that serves as a safe-haven asset; nevertheless, its price dynamics are inherently nonlinear and highly volatile due to the influence of various economic factors. This study aims to develop a predictive model for daily gold prices denominated in Indonesian Rupiah. The proposed methodology employs a Long Short-Term Memory (LSTM) neural network architecture. Historical gold price data covering the period from January 1, 2015, to October 1, 2025, were obtained from investing.com. The dataset underwent a preprocessing phase, which included normalization using the MinMaxScaler and the construction of input sequences with a sliding window of 60 time steps. The implemented LSTM model consists of two stacked layers, each comprising 16 units, and is equipped with a dropout rate of 0.2 as well as an early stopping mechanism to improve generalization and prevent overfitting. The evaluation results demonstrate that the proposed model achieved a Mean Absolute Percentage Error (MAPE) of 5.08% and an accuracy of 94.92%, with a Mean Squared Error (MSE) of 0.00203. Furthermore, the visualization of prediction outcomes confirms the model’s capability to effectively capture actual price fluctuations, including during periods of heightened market volatility. Overall, these findings indicate that a relatively simple LSTM architecture is effective for forecasting gold price movements in the Indonesian market. The results of this study provide a robust foundation for the future development of more sophisticated predictive systems and potential real-time applications.
A Fine-Tuned Transfer Learning Vision Transformer Framework for Lungs X-Ray Image Classification Wijayakusuma, I Gusti Ngurah Lanang; Sudarma, Made; Dian Astutik, Ni Putu
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11865

Abstract

Lung diseases constitute a significant source of morbidity and therefore require diagnostic frameworks that provide both high accuracy and operational efficiency. This study proposes the development of a Vision Transformer (ViT)-based classification model for lung X-ray images, employing transfer learning and fine-tuning techniques to improve detection performance across five disease categories. Experimental results demonstrate stable and effective model convergence, as reflected by the consistent decrease in loss metrics throughout the learning process. Evaluation on an independent test dataset shows that the proposed approach achieves an accuracy of 0.958, indicating strong and balanced generalization performance. Further analysis using a confusion matrix reveals that the ViT model is capable of recognizing subtle and complex radiographic patterns with low misclassification rates, particularly achieving high recall for major pathological classes, which is critical for minimizing false negatives in clinical screening scenarios. Overall, this study demonstrates that the application of transfer learning with fine-tuning on a Vision Transformer architecture yields competitive performance for multi-class lung X-ray classification when trained on a balanced dataset. These findings are consistent with prior evidence highlighting the effectiveness of ViT in capturing global contextual information in medical imaging tasks.
Public Sentiment Analysis on Demonstration Actions Using IndoBERT Based on Transfer Learning Tentriajaya, I Dewa Ayu Pradnya Pratiwi; Agustina, Ni Putu Dina; Wijayakusuma, I Gusti Ngurah Lanang
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12116

Abstract

Sentiment analysis based on language modeling plays a crucial role in mapping public perception of socio-political dynamics in Indonesia. This study aims to evaluate public sentiment toward the House of Representatives of the Republic of Indonesia (DPR RI) in response to the August 2025 demonstrations using the IndoBERT model based on transfer learning. The dataset comprises 1,815 Indonesian-language opinion texts classified into positive and negative sentiments. Due to a substantial class imbalance dominated by negative opinions, a hybrid sampling strategy combining oversampling and undersampling was employed to obtain a balanced dataset of 650 samples per class. The research methodology included text preprocessing, an 80:20 training–testing split, and fine-tuning the IndoBERT-base-p1 model. Experimental results indicate that the proposed model achieves robust and balanced performance, with an overall accuracy of 85%. Precision and F1-score for both sentiment classes reached 0.85, while recall values were 0.86 for negative sentiment and 0.85 for positive sentiment, demonstrating the model’s ability to identify both classes effectively without bias toward the majority class. Despite the dominance of negative sentiment in the original dataset, the application of data balancing techniques successfully mitigated class imbalance effects, enabling fair and proportional sentiment classification. These findings confirm that the IndoBERT-based transfer learning approach is effective in capturing public sentiment related to mass demonstrations and can provide valuable, data-driven insights for policymakers in understanding societal concerns in the digital era.
Perancangan Inovatif UI/UX Fitur Kesehatan BNI Mobile Banking Berbasis User Centered Design Karina Maharani Bernis; I Gusti Ngurah Lanang Wijayakusuma
Jurnal Ekonomi Manajemen Sistem Informasi Vol. 6 No. 3 (2025): Jurnal Ekonomi Manajemen Sistem Informasi (Januari - Februari 2025)
Publisher : Dinasti Review

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/jemsi.v6i3.3724

Abstract

Salah satu aspek krusial dalam perjalanan kehidupan manusia adalah kesehatan. Integrasi antara layanan kesehatan dan keuangan melalui penciptaan fitur kesehatan di BNI Mobile Banking tidak hanya memberikan kemudahan, tetapi juga mendorong masyarakat untuk lebih memperhatikan kesehatan mereka sekaligus memiliki pengaturan keuangan yang baik dan terstruktur serta bisa dilacak. Penelitian ini menciptakan fitur kesehatan pada aplikasi BNI Mobile Banking dengan menerapkan metode User Centered Design. Melalui wawancara dan survei yang dilakukan terhadap sejumlah pengguna aplikasi perbankan tersebut, didapatkan bahwa lebih dari 50% pengguna menginginkan sebuah integrasi BNI Mobile Banking dengan aplikasi kesehatan dan gaya hidup yang mereka gunakan sehari-hari. Pengujian prototype yang dikembangan menggunakan aplikasi Figma memberikan suatu hasil penelitian bahwa tercetak skor sebesar 80 serta skor SUS yang didapat setelah perancangan  fitur  baru  meningkat  dari angka 52,5 menjadi 80. User yang melakukan testing menyatakan cukup puas dengan rancangan UI/UX yang dihasilkan dan memberikan kemudahan serta efektivitas dalam pengeluaran aspek kesehatan.
Neural Machine Translation of Balinese-Indonesian Using T5 Architecture with QLoRA Optimization Leonard Kumaro; I Gusti Ngurah Lanang Wijayakusuma; IPW Gautama
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12771

Abstract

This study proposes a Neural Machine Translation (NMT) system for Balinese–Indonesian translation by integrating the T5 architecture with Quantized Low-Rank Adaptation (QLoRA) to address low-resource constraints. The model is trained using the NusaTranslation dataset, consisting of 140,972 parallel sentence pairs, and optimized through parameter-efficient fine-tuning with 4-bit quantization and low-rank adaptation. Unlike conventional full fine-tuning, the proposed approach updates only a small fraction of parameters, significantly improving computational efficiency. Experimental results show that the proposed model achieves a BLEU score of 27.93%, ROUGE-1 of 18.94%, ROUGE-2 of 11.96%, ROUGE-L of 18.54%, and BERTScore F1 of 70.49%, indicating competitive performance in lexical, structural, and semantic evaluation aspects. These results demonstrate that QLoRA can maintain translation quality while reducing computational costs. Furthermore, qualitative analysis reveals that the model is capable of generating fluent and contextually appropriate translations, although challenges remain in handling complex sentence structures and linguistic variations. This study highlights the effectiveness of parameter-efficient fine-tuning for low-resource language translation and provides practical implications for developing scalable translation systems for regional languages.  
ConvNeXt with Transfer learning for Microscopic Canine Skin Disease Classification Made Ardika Mertha Putra Vaikuntha; I Gusti Ngurah Lanang Wijayakusuma
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12792

Abstract

Canine skin diseases represent a significant health concern in veterinary practice, with accurate diagnosis often requiring specialized expertise and microscopic examination. This study presents an implementation and evaluation of the ConvNeXt architecture for classifying microscopic images of four canine skin diseases: Demodex, Kokus, Malassezia, and Scabies. The dataset consists of microscopic images obtained from skin scraping preparations photographed under 400× digital microscopy and annotated by a certified veterinary. Two dataset scenarios were evaluated: a small dataset (356 images; Demodex: 104, Kokus: 47, Malassezia: 102, Scabies: 103) and a large dataset (2,963 images with near-balanced class distribution). Using transfer learning with pre-trained weights from ImageNet, the ConvNeXt model was evaluated across three input sizes (224×224, 180×180, 150×150). Augmentation balancing, including rotation, flipping, zoom, translation, shear, and color jitter, was applied to address class imbalance while preserving biological validity of morphological features Augmentation balancing was applied to address class imbalance, ensuring equal representation across all classes. Experimental results demonstrate that ConvNeXt with 224×224 input size trained on the large dataset achieved the best overall performance with 97.22% test accuracy, 0.9524 F1-score, 0.9624 Matthews Correlation Coefficient (MCC), and a perfect Area Under Curve (AUC) of 1.0000. Analysis of input size effects revealed that 224×224 is optimal for detecting small pathogens like Malassezia (3-8 μm) and Kokus (0.5-1 μm), while 150×150 better preserves spatial context for large pathogens such as Demodex (300 μm) and Scabies (200-400 μm). Visualization of feature maps provided insights into how the architecture extracts diagnostic features, producing dense, hierarchical feature representations that benefit from abundant data. This research demonstrates that transfer learning with the ConvNeXt architecture, combined with appropriate augmentation balancing, can achieve high classification accuracy for automated diagnosis support of canine skin diseases. However, clinical deployment requires further validation by veterinary and prospective clinical studies before these results can be considered clinically applicable.
Co-Authors Agustina, Ni Putu Dina Anak Agung Kompiang Oka Sudana Arta Wiguna, I Putu Chandra Astutik, Dian Br Sebayang, Virna Dalira Chandra, Veronica Celine Damayanthi, Ni Wayan Rita Desak Made Sidantya Amanda Putri Dian Astutik, Ni Putu Driyandita, Bernadeta EKA N. KENCANA Fransisca Emmanuella Aryossi G K Gandhiadi I Gusti Ayu Made Srinadi I Ketut Gede Darma Putra I ketut Gede Darma Putra I Ketut Restu Wiranata I KOMANG GDE SUKARSA I MADE EKA DWIPAYANA I Nyoman Widana I Putu Eka Nila Kencana I Putu Winada Gautama I Putu Winada Gautama I PUTU WINADA GAUTAMA I WAYAN SUMARJAYA IM Suyana Utama IPW Gautama Isabel Divya Georgiana Walewangko Jocelynne, Charlotte Karina Maharani Bernis Karina Maharani Bernis Kencana, Eka N. Ketut Jayanegara Khairunisa, Mutiara Komang Dharmawan Leonard Kumaro LUH PUTU IDA HARINI LUH PUTU IDA HARINI Made Andini Maharani Made Ardika Mertha Putra Vaikuntha Made Ireina Dwiandra Divayanti Made Sudarma Made Sudarma Manurung, Mikael Triartama Maylianti, Ni Putu Minho Jo Minho Jo Ni Kadek Emik Sapitri Ni Kadek Jegeg Anastasya Dwipayanti Ni Luh Putu Suciptawati Ni Nyoman Bintang Marscelina Ni Putu Leony Putri Paramita Oka Sudana Pratama, Halim Meliana Pratiwi Tentriajaya, I Dewa Ayu Pradnya Putri, Desak Putri, Desak Made Sidantya Amanda Putri, Made Ayu Asri Oktarini Putu Veri Swastika Raharja, Made Agung Ratna Sari Widiastuti Riandika Fathur Rochim Sagun Chandra Yowani Sibannang, Maria Oktaviani Giska Siden, Hagia Sofia Swastika, Putu Veri Syahril Alamsyah Zainuddin Tentriajaya, I Dewa Ayu Pradnya Pratiwi Tobing, Charlotte Jocelynne L Ulfatun Farika Novitasari Widiastuti, Ratna Sari Yohanes Kristianto Kristianto