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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Pengabdian Masyarakat Tapis Berseri
Wulandari, Dewa Ayu Putri
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Computer Science & IT Education

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PKM Pemutakhiran Data Penduduk di Desa Kukuh Kerambitan Tabanan: Indonesia Ginantra, Ni Luh Wiwik Sri Rahayu; Yanti, Christina Purnama; Wulandari, Dewa Ayu Putri; Hendrawati, Theresia
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 2 No. 1 (2023): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v2i1.49

Abstract

Kukuh Village is a village located in Kerambitan District, Tabanan Regency, Bali. Regional government has been promoting routine population data collection but most people do not yet have awareness of the importance of such data collection. Village population data stored in the Kukuh Village Information System is currently inaccurate, because the population of Kukuh Village is increasing every year, causing population data stored in the Kukuh Village Information System to be less accurate. Because of this, a population data update was carried out in Kukuh Village, Kerambitan, Tabanan. This program is realized in order to make population data on the official website of the Kukuh Village Information System accurate, up-to-date, integrated, of good quality so as to create accurate population data on the official website of the Kukuh Village Information System.
Penerapan Metode Stable Diffusion Dengan Fine Tuning Untuk Pola Endek Bali Ginantra, Ni Luh Wiwik Sri Rahayu; Hendrawati, Theresia; Wulandari, Dewa Ayu Putri
TEMATIK Vol. 11 No. 2 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i2.2069

Abstract

Endek Bali fabric is a cultural heritage of Bali renowned for its traditional decorative motifs, including floral, fauna, patra, and diamond patterns. Although rich in cultural value, artisans often face challenges in creating new designs that align with market trends while preserving cultural authenticity. Artificial Intelligence (AI) technology, particularly text-to-image generation models, offers a solution to this issue by streamlining the design process and enabling the exploration of new motifs. The Stable Diffusion model, introduced by Stability.AI in 2022 and open source, can be utilized to generate Endek Bali patterns through Fine Tuning techniques. Fine Tuning allows the model to be adapted to specific domains, enhancing its performance in generating textile patterns based on textual descriptions. This study aims to apply the Stable Diffusion model and Fine Tuning techniques to create new patterns and motifs. By using this model, it is hoped that innovative designs can be produced while maintaining the authenticity and local cultural values of Bali. The research demonstrates that the Fine-Tuned Stable Diffusion model is effective in creating Endek Bali patterns with high accuracy, as evaluated by Clip Similarity, with the highest scores achieved for Floral Patterns (92.43), followed by Decorative (free-form motifs) Floral (88.77), Decorative (free-form motifs) Geometric (87.94), and Decorative (free-form motifs) (85.79). These findings indicate the model’s flexibility and effectiveness in producing intricate textile designs, enabling designers and artisans to generate complex and innovative patterns solely from textual descriptions while preserving Bali’s cultural values.
Analysis and Evaluation of SIDUN Mobile Application in UEQ-Based User Experience Perspective Wulandari, Dewa Ayu Putri
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5932

Abstract

This study analyzes and evaluates the user experience (UX) of SIDUN, a mobile-based Village Information System designed to manage community contributions digitally in Dusun Tegal Kori Kaja, Denpasar, Bali. The system aims to address limitations of the previous manual process by enabling digital interaction among villagers, pecalang, and administrative staff. The evaluation method applies the User Experience Questionnaire (UEQ), assessing six core dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. A total of 30 active users participated in the study by completing the UEQ instrument. The results indicate that all six UX dimensions received positive scores, ranging from 1.58 to 2.00. The highest ratings were observed in Stimulation (2.00), Attractiveness (1.96), and Efficiency (1.92), reflecting high user engagement, visual appeal, and operational speed. Perspicuity and Novelty also showed strong performance, while Dependability, though positive, revealed opportunities for improvement in system reliability and consistency. Compared to the UEQ benchmark, all dimensions achieved “Excellent” ratings, placing them within the top 10% of evaluated applications. These findings affirm that SIDUN offers a satisfying user experience and supports effective community-level digital transformation. The study underscores the value of user-centered design and continuous UX assessment in enhancing public digital services in rural communities.
Convolutional Neural Network Algorithm Implementation for Classifying Traditional Wood Carving Motifs of Patra Bali Widyatama, I Dewa Gede Surya; Sudipa, I Gede Iwan; Fittryani, Yuri Prima; Wulandari, Dewa Ayu Putri; Jayanegara, I Nyoman
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14841

Abstract

This research develops an automatic classification system to recognize Balinese Patra carving motifs using deep learning method based on Convolutional Neural Network (CNN). The data used are images of Cina Patra, Mesir Patra, Punggel Patra, and Sari Patra motifs, which have gone through preprocessing stages such as cropping, resizing, and augmentation in the form of flip and rotation to increase data variation. Three pre-trained CNN models were used in testing, namely DenseNet169, InceptionResNetV2, and MobileNetV2. The training process was performed with Adam optimization, batch size 32, and 100 epochs. Model performance evaluation was performed using accuracy and confusion matrix metrics. The results show that all three models were able to achieve 100% accuracy on the test data, with MobileNetV2 recording the lowest loss of 0.75%, followed by DenseNet169 (1.14%) and InceptionResNetV2 (1.18%). Based on the confusion matrix, all motifs were recognized very well, although there was a slight misclassification of the Patra Sari motif by the InceptionResNetV2 model. These findings prove that CNN is effectively used in the recognition of traditional carving motifs and has the potential to support cultural preservation through interactive visual technology.
MARKERLESS AUGMENTED REALITY UNTUK PEMBELAJARAN SISTEM SARAF PUSAT Yanti, Christina Purnama; Wulandari, Dewa Ayu Putri; Putra S., I Ketut Yama Cahyana
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 5, No 2 (2025): JEIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol5no2.777

Abstract

The research aims to design and build a learning media application for the Human Central Nervous System at SMA Negeri 1 Pupuan, utilizing Augmented reality technology. This Augmented reality provides students with a new experience that seems practical and effective in supporting learning activities. The Augmented reality method used in the research is Markerless (i.e., without markers). After designing the application, Black Box Testing was carried out, material expert testing with results of 96% in the strongly agree category, as well as UEQ testing with the Attractiveness value (allure) of 1.88 in the outstanding category, Perspicuity (Clarity/Readability) of 1.81 in the good category, Efficiency of 1.85 in the good category, Dependability (Reliability) of 1.91 in the outstanding category, and Stimulation of 1.98 in the exceptional category. Good and structured system design, careful testing of the application, and positive user responses show that the application has the potential to become a learning medium that can support learning activities in the Central Nervous System material.Penelitian ini bertujuan untuk merancang dan membangun aplikasi media pembelajaran Sistem Saraf Pusat Manusia pada SMA Negeri 1 Pupuan, dengan memanfaatkan teknologi Augmented Reality. Hal ini memberikan pengalaman yang baru pada siswa yang terkesan praktis dan efektif untuk mendukung kegiatan pembelajaran. Metode yang digunakan dalam penelitian adalah MDLC (Multimedia Development Life Cycle). Setelah perancangan aplikasi, dilakukan pengujian Black Box Testing, pengujian ahli materi dengan hasil 96% dalam kategori sangat setuju, serta pengujian UEQ dengan nilai Attractiveness (Daya Tarik) sebesar 1,88 dengan kategori sangat baik, Perspicuity (Kejelasan/Keterbacaan) sebesar 1.81 dengan kategori baik, Efficiency (Efisiensi) sebesar 1,85 dengan kategori baik, Dependability (Keandalan) sebesar 1,91 dengan kategori sangat baik, dan Stimulation (Stimulasi) sebesar 1,98 dengan kategori sangat baik. Hasil pengujian diatas menunjukan bahwa aplikasi berpotensi dalam menjadi sebuah media pembelajaran yang bisa mendukung kegiatan pembelajaran dalam materi Sistem Saraf Pusat.
IndoBERT-Based Pediatric Disease Classification and Symptom-Based Traditional Medicine Recommendation from Lontar Usada Rare Winata, I Putu Erick Prawira; Sudipa, I Gede Iwan; Meinarni , Ni Putu Suci; Wulandari, Dewa Ayu Putri; Yanti, Christina Purnama
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15507

Abstract

This study aims to develop a Balinese traditional text-based pediatric disease classification model using a fine-tuned IndoBERT model on the Lontar Usada Rare dataset. The dataset used consists of 422 entries containing disease symptoms, disease types, medicinal ingredients, and treatment procedures obtained from transliteration of lontar manuscripts and interviews with traditional medicine experts. Pre-processing was done through case folding, cleansing, and normalization, followed by label encoding on 35 disease classes. The IndoBERT model was fine-tuned using the AdamW optimizer with a learning rate of 5e-5, batch size 8, and 15 epochs. Evaluation results showed the model was able to achieve 90.59% accuracy, 94.71% precision, 90.59% recall, and 90.99% F1-score, indicating excellent performance in understanding the linguistic context of traditional medical text. The developed recommendation system integrates model prediction with TF-IDF-based cosine similarity method to provide the most relevant treatment recommendations based on user symptom input. This research makes an important contribution to the digitization and preservation of Balinese traditional medical knowledge through the development of a structured and widely accessible digital knowledge base.
Enhanced Stacked GRU Model for Monthly Rice Production Forecasting in Bali Province Gotama, I Gusti Agung Raditiya; Sudipa, I Gede Iwan; Brahma, Anak Agung Gede Raka Wahyu; Ariantini, Made Suci; Wulandari, Dewa Ayu Putri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15715

Abstract

Rice production has a seasonal pattern that depends on the planting cycle and environmental conditions, requiring forecasting methods that can accurately model temporal dynamics. This study aims to predict monthly rice production in Bali Province using the Stacked Gated Recurrent Unit (GRU) architecture. Monthly rice production data from 2018 to 2024 from the Central Statistics Agency (BPS) was used as the main source. The preprocessing stage includes data cleaning, Min-Max normalization, and feature engineering in the form of creating sin_month and cos_month features to capture seasonal patterns, as well as a 3-month rolling mean to extract short-term trends. The proposed stacked design with dual-layer GRU combined with seasonal features improves temporal pattern extraction compared to single-layer GRU baselines. The model was tested using three configurations, and Scheme 3 provided the best performance with an MAE value of 1610.21, an RMSE of 2055.90, and a MAPE of 14.29%, which is considered good accuracy. The model was able to follow seasonal production trends, including an increase at the beginning of the year and a decrease during the planting period. Long-term predictions for the next 12 months and quarterly forecasts per district/city also showed patterns consistent with historical data. The results of the study indicate that Stacked GRU is effective in forecasting seasonal rice production and can be used as a basis for decision support in food security planning in Bali.
Co-Authors Brahma, Anak Agung Gede Raka Wahyu Christina Purnama Yanti Gotama, I Gusti Agung Raditiya Hendrawati, Theresia I Gede Iwan Sudipa I Nyoman Jayanegara Made Suci Ariantini Meinarni , Ni Putu Suci Ni Luh Wiwik Sri Rahayu Ginantra Putra S., I Ketut Yama Cahyana Widyatama, I Dewa Gede Surya Winata, I Putu Erick Prawira Yuri Prima Fittryani, Yuri Prima