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All Journal International Journal of Electrical and Computer Engineering JURNAL MEDIA INFORMATIKA BUDIDARMA JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Indonesian Journal of Data and Science Tematik : Jurnal Teknologi Informasi Komunikasi IJISTECH JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) JPM: JURNAL PENGABDIAN MASYARAKAT Journal of Informatics Management and Information Technology PROFICIO: Jurnal Pengabdian Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) International Journal Of Computer, Network Security and Information System (IJCONSIST) JOMLAI: Journal of Machine Learning and Artificial Intelligence Jurnal Pengabdian Masyarakat Sains dan Teknologi Jurnal Pengabdian Masyarakat Tapis Berseri Jurnal Krisnadana Journal of Social Work and Empowerment Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Ni Luh Wiwik Sri Rahayu Ginantra
Institut Bisnis Dan Teknologi Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

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Sistem Informasi Pengelolaan Travel Agent Berbasis Website Sri Rahayu Ginantra, Ni Luh Wiwik; Penanta, Aditya
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 3 (2019): March
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (655.634 KB) | DOI: 10.33173/jsikti.25

Abstract

Travel agent is a company engaged in the travel agency. Business management processes such as making sales invoices that still use the handwriting method along with sales and purchase reports that are still used Microsoft Excel. Travel agent requires a website-based system where the management of its business to be more effective , safe and accessible anywhere and anytime. In the design phase Data Flow Diagram notation is used, Conceptual Data Models, Physical Data Models and Mockups. The development of the system uses Sublime Text 3 and SQLyog as a Database Management System and is tested using blackbox testing. The results have successfully built an information system for managing a travel agent where the system was built aimed at helping the business management process of Travel agent to be faster, neater, avoid the risk of losing and unnecessary errors.
Meningkatkan Literasi Numerisasi Siswa Literasi Media Pembelajaran Nilai Tempat Dan Bangun Datar SD Pelangi Jimbaran Ginantra, Ni Luh Wiwik Sri Rahayu; Dewi, Ni Wayan Jeri Kusuma; Indra Pratistha
Darma Abdi Karya Vol. 4 No. 2 (2025): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/darmaabdikarya.v4i2.2773

Abstract

Numeracy literacy skills are a fundamental foundation in mathematics learning at the elementary school level, particularly in topics such as place value and plane geometry. However, initial observations at SD Pelangi Jimbaran indicate that fifth-grade students still experience difficulties in understanding numerical concepts both conceptually and contextually. Learning tends to be procedural and oriented toward rote memorization, with limited support from interactive, technology-based learning media. This community service activity aims to enhance students’ understanding and learning interest through the development of interactive learning media based on Canva. The implementation methods include observation and needs analysis, media design, production and pilot testing, as well as evaluation and dissemination. The media were developed in the form of educational visual presentations and digital activity worksheets that present concrete and contextual visualizations of place value and plane geometry. The results of the implementation show increased student engagement, improved conceptual understanding, and positive responses from partner teachers. The program also enhances teachers’ capacity in utilizing digital media and strengthens collaboration between higher education institutions and elementary schools. Thus, Canva-based learning media have proven to be an effective innovative solution for strengthening numeracy literacy in elementary schools
Optimization of Performance Traditional Back-propagation with Cyclical Rule for Forecasting Model Anjar Wanto; Ni Luh Wiwik Sri Rahayu Ginantra; Surya Hendraputra; Ika Okta Kirana; Abdi Rahim Damanik
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1826

Abstract

The traditional Back-propagation algorithm has several weaknesses, including long training times and significant iterations to achieve convergence. This study aims to optimize traditional Back-propagation using the cyclical rule method to cover these weaknesses. Optimization is done by changing the training function and standard Back-propagation parameters using the training function and cyclical rule parameters. After that, a comparison of the two results will be carried out. This study uses quantitative method of time-series data on coronavirus cases sourced from the Worldometer website, then analyzed using three forecasting models with five input layers, one hidden layer (5, 10, and 15 neurons) and one output layer. The results showed that the 5-10-1 model with the training function and cyclical rule parameters and the tansig and purelin activation functions could perform well in optimization, including faster training time and smaller iterations (epochs), MSE training performance, and better tests. Low and high accuracy (92%) with an error rate of 0.01. So it was concluded that the training function and cyclical rule parameters with the tansig and purelin activation functions were able to optimize the traditional Back-propagation method, and the 5-10-1 model could be used for forecasting active cases of the coronavirus in Asia
Comparison of Naïve Bayes and SVM in Sentiment Analysis of ChatGPT for Learning on X and YouTube Ni Putu Eka Swari; Ni Wayan Jeri Kusuma Dewi; Ni Ketut Utami Nilawati; Aniek Suryanti Kusuma; Ni Luh Wiwik Sri Rahayu Ginantra
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.382

Abstract

The rapid development of artificial intelligence technology has encouraged users to actively express opinions on social media platforms such as X and YouTube, including discussions on the use of ChatGPT as a learning support tool. This study aims to analyze public sentiment toward the use of ChatGPT in learning contexts by comparing the performance of the Naïve Bayes and Support Vector Machine (SVM) classification methods. A total of 5,500 comments from platform X and 5,543 comments from YouTube were collected through a crawling process using relevant keywords during the period from January 2023 to December 2025. The data were preprocessed and labeled into three sentiment classes (positive, negative, and neutral) using a lexicon-based approach with the INSET Lexicon. Feature extraction was conducted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, and the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score. The results show that the SVM classifier consistently outperformed the Naïve Bayes method on both platforms. On platform X, SVM achieved an accuracy of 76.67%, while Naïve Bayes obtained 74.60%. On YouTube, SVM achieved an accuracy of 73.10%, significantly higher than Naïve Bayes at 62.04%. These findings indicate that SVM is more effective for sentiment analysis of social media data related to the use of ChatGPT in learning environments
PEMANFAATAN TIK UNTUK PENINGKATAN KEMAMPUAN PENGGUNAAN MICROSOFT WORD DI SDN 1 TAMPAKSIRING Ginantra, Ni Luh Wiwik Sri Rahayu; Hendrawati, Theresia; Yanti, Christina Purnama
Jurnal Pengabdian Masyarakat FKIP UTP Vol 7 No 2 (2026): PROFICIO : Jurnal Abdimas FKIP UTP
Publisher : PROFICIO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36728/.v7i2.6518

Abstract

Perkembangan teknologi informasi dan komunikasi (TIK) menuntut siswa sekolah dasar menguasai keterampilan digital fundamental, termasuk pengoperasian aplikasi pengolah kata Microsoft Word. Akan tetapi, observasi di SDN 1 Tampaksiring menunjukkan sebagian besar siswa masih kesulitan mengoperasikan aplikasi tersebut karena keterbatasan paparan praktis. Kegiatan pengabdian masyarakat ini bertujuan meningkatkan kompetensi siswa dalam penggunaan Microsoft Word melalui pelatihan berbasis TIK yang terstruktur. Kegiatan dilaksanakan dengan pendekatan kualitatif yang melibatkan tiga puluh siswa kelas IV hingga VI sebagai peserta. Data dikumpulkan melalui observasi, wawancara, dokumentasi, serta asesmen kinerja siswa sebelum dan sesudah pelatihan. Pelatihan dilaksanakan dalam lima tahap: analisis kebutuhan, penyusunan materi, pelaksanaan pelatihan, pendampingan, dan evaluasi. Hasil kegiatan menunjukkan rata-rata kompetensi siswa meningkat secara signifikan dari 42,3% pada pra-tes menjadi 84,7% pada pasca-tes. Siswa mengalami peningkatan keterampilan dalam pembuatan dokumen, pemformatan teks, penyisipan tabel, dan tata letak halaman dasar. Kegiatan juga menumbuhkan motivasi belajar serta kepercayaan diri digital siswa. Temuan ini menegaskan bahwa pelatihan TIK yang aplikatif berkontribusi nyata terhadap literasi digital siswa sekolah dasar.
ANALISIS SENTIMEN CHATGPT DATA SOSIAL MEDIA X(TWITTER) DENGAN MENGGUNAKAN FINE TUNING XL NET Theresia Hendrawati; Ni Luh Wiwik Sri Rahayu Ginantra
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.vol5no2.782

Abstract

ChatGPT (Generative Pre-training Transformer) is an artificial intelligence technology designed to mimic human conversation in text form and has become an important tool in various fields, including education. This study aims to analyze public sentiment toward the use of ChatGPT, which can be categorized into positive and negative sentiments. The data for the study was obtained from 5,686 user reviews on the Twitter platform, collected through Google Colaboratory and processed with pre-processing steps. The data was labeled as positive and negative, then classified using fine-tuning on the XLNet model, a Transformer-based language model. The results show that the fine-tuned XLNet model achieved an accuracy of 88.45%, a precision of 89%, a recall of 88%, and an F1-score of 89%, as measured using the Confusion Matrix. This study demonstrates that fine-tuning XLNet is effective in classifying the sentiment of ChatGPT user reviews related to education.ChatGPT (Generative Pre-training Transformer) adalah teknologi kecerdasan buatan yang dirancang untuk menirukan percakapan manusia dalam bentuk teks dan telah menjadi alat penting di berbagai bidang, termasuk pendidikan. Penelitian ini bertujuan untuk mengalisis akurasi kinerja model fine tuning XL Net  dari sentimen masyarakat terhadap penggunaan ChatGPT, yang dapat dikategorikan menjadi sentimen positif dan negatif. Data penelitian diperoleh dari 5.686 ulasan pengguna di platform Twitter, dikumpulkan melalui Google Colaboratory dan diproses dengan tahap pre-processing. Data diberi label positif dan negatif, lalu diklasifikasikan menggunakan metode fine-tuning pada model XLNet, model bahasa berbasis Transformer. Hasil penelitian menunjukkan bahwa model fine-tuning XLNet mencapai akurasi 88,45%, precision 89%, recall 88%, dan F1-score 89%, yang diukur menggunakan Confusion Matrix. Penelitian ini membuktikan bahwa fine-tuning XLNet efektif dalam mengklasifikasikan sentimen ulasan pengguna ChatGPT terkait pendidikan
Hybrid Deep Learning Models For Gold Price Prediction: Enhancing Forecast In Volatile Financial Markets Ni Luh Wiwik Sri Rahayu Ginantra; Ni Wayan Yeni Pratiwi; Christina Purnama Yanti; Wayan Gede Suka Parwita
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.394

Abstract

Introduction: Gold is widely regarded as a long-term store of value and a hedge against inflation, yet its short-term price volatility creates significant challenges for investment decision-making and requires accurate forecasting methods. This study evaluates a hybrid deep learning approach combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to capture both short-term fluctuations and long-term temporal dependencies in gold price movements. Method: Historical daily gold closing-price data comprising 2,735 observations from 2015 to 2025 were collected and normalized using Min-Max Scaling. The data were divided chronologically into 80% training and 20% testing sets. A hybrid CNN–LSTM model was trained using the Adam optimizer with a learning rate of 0.0001, dropout of 0.2, a timestep of 30, and batch sizes of 16, 32, and 64. Model performance was evaluated using Root Mean Square Error (RMSE). Results and Discussion: The batch size of 16 achieved the best performance, producing the lowest validation RMSE of 0.0929 and an RMSE of 11.518535% after denormalization, outperforming batch sizes of 32 and 64. The model also followed actual gold-price trends more closely, while the inclusion of Dense and Dropout layers improved generalization. Conclusion: The CNN–LSTM hybrid model, particularly with a batch size of 16, provides an effective approach for forecasting volatile gold prices by integrating local pattern extraction with long-term temporal modeling.
Klasifikasi Tingkat Keparahan Penyakit Diabetic Retinopathy menggunakan Convolutional Neural Network Ni Luh Wiwik Sri Rahayu Ginantra; Theresia Hendrawati; I Kadek Diksa Bayu Prasetya
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7432

Abstract

Diabetic Retinopathy is an eye condition in Diabetes sufferers that causes damage to the retina, which can result in permanent blindness if not treated properly. The initial stage of this disease is the widening of the blood vessels in the eye which, if left untreated, can cause the formation of new blood vessels which can cover the retina of the eye, thereby increasing the risk of vision loss. There are several classes of Diabetic Retinopathy disease; to determine the class you can use the Deep Learning method which can model various data such as images. The classification process is carried out by training a Convolutional Neural Network model on a disease image dataset taken from the Kaggel repository with a total of 5 classes. This research uses a Fine Tuning strategy and the EfficientNetB1 model to determine the performance of the CNN model in the Diabetic Retinopathy Classification process. Based on training results, the EfficientNetB1 model produces 92.51% accuracy in detecting Diabetic Retinopathy. These results show that the model can provide optimal results in the dataset training process.
Emotional Detection of Students During The Learning Process Using Yolo V8 Ni Luh Wiwik Sri Rahayu Ginantra; Suyud Setiawan Al Arif; Ni Wayan Jeri Kusuma Dewi
Jurnal Krisnadana Vol 5 No 2 (2026): Jurnal Krisnadana- January 2026
Publisher : Yayasan Sinergi Widya Nusantara (Sidyanusa)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58982/krisnadana.v5i2.1061

Abstract

This research develops an emotional detection system for students during learning using YOLOv8 model N and Computer Vision technology. The main objective measures accuracy of facial expression recognition across happy, sad (murung), neutral (normal), and confused classes while evaluating lighting and camera position impacts. Dataset of 1500 Kaggle images was labeled on Roboflow with augmentations including flip, crop, blur, and noise, then trained on Google Colab for 100 epochs using pre-trained YOLOv8n weights. Model validation employed confusion matrices, precision-recall curves, and real-time webcam testing on 10 students at 5-15 cm distances. Key findings show accuracies of 75% (happy), 62% (sad), 69% (neutral), and 33% (confused), averaging 59.75% under >39 lux frontal lighting. Optimal performance occurred at 0°-10° angles, but backlit conditions reduced efficacy by 25-35% due to shadow occlusion. The system enables real-time classroom monitoring with 4.2 ms inference latency, supporting cognitive engagement assessment despite bingung class limitations from dataset imbalance. Future improvements recommend expanded ambiguous samples and lighting augmentations.
Performance Comparison of MobileNetV2 and NASNetMobile Architectures in Soybean Leaf Disease Classification I Gede Rian Lanang Oka; Anak Agung Gede Bagus Ariana; Wayan Sauri Peradhayana; Ni Luh Wiwik Sri Rahayu Ginantra; I Ketut Sutarwiyasa
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.243

Abstract

Soybean is one of the essential commodities in Indonesia, commonly used as a raw material for tofu and tempeh, making it highly sought after. However, soybean production has decreased by up to 30% due to disease attacks, necessitating preventive measures. This study aims to compare two Convolutional Neural Network (CNN) architectures, MobileNetV2 and NASNetMobile, in classifying soybean leaf diseases. The models were trained using a leaf image dataset collected directly from agricultural fields and categorized into five classes. The dataset underwent augmentation to increase its size, resulting in a total of 6,000 images, which were then split with an 80:10:10 ratio. The models were trained using the Adam optimizer with a learning rate of 0.001, optimized using ReduceLROnPlateau, and a dropout rate of 0.2 to prevent overfitting. Evaluation results using a confusion matrix indicated that MobileNetV2 performed better with an accuracy of 96.67%, precision of 96.70%, recall of 96.67%, and an F1-score of 96.68%, compared to NASNetMobile, which achieved an accuracy of 86.33%, precision of 86.91%, recall of 86.33%, and an F1-score of 86.40%.
Co-Authors Abdi Rahim Damanik Afriliansyah, Teuku Anak Agung Gede Bagus Ariana Anandita, Ida Bagus Gede Aniek Suryanti Kusuma Asana, I Made Dwi Putra Atmaja, Ketut Jaya Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Christina Purnama Yanti Dewa Ayu Putri Wulandari Dewa Ayu Putri Wulandari Dewi, Ni Wayan Jeri Kusuma Dwipayana, Ida Bagus Rama Faisal Amir Gede Surya Mahendra GS , Achmad Daengs Hendrawati, Theresia I Gede Iwan Sudipa I Gede Rian Lanang Oka I Gusti Agung Indrawan I Gusti Ayu Agung Diatri Indradewi I Kadek Diksa Bayu Prasetya I Ketut Sutarwiyasa Ika Okta Kirana Ilham Tri Maulana Indra Pratistha Jasmir Krismentari, Ni Kadek Bumi Made Suci Ariantini Mahmuda Lailiya Meri Aryati, Ni Wayan Mhd Ali Hanafiah Ni Kadek Ariasih, Ni Kadek Ni Kadek Nita Noviani Pande Ni Ketut Utami Nilawati NI LUH PUTU MERY MARLINDA Ni Putu Adelia Indah Paramita Ni Putu Eka Swari Ni Putu Suci Meinarni Ni Wayan Eva Agustini Ni Wayan Jeri Kusuma Dewi Ni Wayan Jeri Kusuma Dewi Ni Wayan Suardiati Putri Ni Wayan Wardani Ni Wayan Wardani Ni Wayan Yeni Pratiwi Okprana, Harly Pande Putu Sukma Awantari Penanta, Aditya Pressa Persana Surya Saputra Putra, I Kadek Andika Putu Surya Wedra Lesmana Saiman, Clarita Mutiara Samsul Lutfi Sandi, Victor Ari Sandika, I Kadek Budi Sastaparamitha, Ni Nyoman Ayu J Silfia Andini Surya Hendraputra Sutarwiyasa, I Ketut Suyud Setiawan Al Arif Theresia Hendrawati Toraja, Dewa Gede Victor Ari Sandi Wanto, Anjar Wardani, Ni Wayan Wayan Gede Suka Parwita Wayan Gede Suka Parwita Wayan Sauri Peradhayana Widiartha, I Komang Kurniawan Widiartha, Komang Kurniawan Wiguna, I Komang Arya Ganda Winatha, Komang Redy Wulandari, Dewa Ayu Putri