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All Journal Jurnal Pendidikan Teknologi dan Kejuruan Techno.Com: Jurnal Teknologi Informasi Ekuitas: Jurnal Pendidikan Ekonomi JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING WIDYABHAKTI Jurnal Ilmiah Populer IJISTECH (International Journal Of Information System & Technology) Indonesian Journal of Data and Science Journal of Computer Networks, Architecture and High Performance Computing IJISTECH Jurnal Puan Indonesia Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Sains dan Teknologi Ekuitas Jurnal Pnedidikan Ekonomi KOMET
I Gusti Agung Indrawan
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Journal : Indonesian Journal of Data and Science

 1 
Classification of Cavendish Banana Ripeness With CNN Method Tjokorda Istri Agung Pandu Yuni Maharani; I Gusti Agung Indrawan; Gede Dana Pramitha; Christina Purnama Yanti; I Made Marthana Yusa
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.v6i2.259

Abstract

Cavendish bananas are one of the most widely consumed tropical fruits in Indonesia due to their sweet taste and high nutritional content. However, as they ripen, the sugar content in bananas increases, which can be a problem for diabetics. To help diabetics choose bananas with the right level of ripeness, this study developed a Cavendish banana ripeness classification model using artificial intelligence technology, namely the ResNet50 Convolutional Neural Network (CNN) architecture. The banana data is divided into five ripeness categories: green, yellowish green, yellow, spotted yellow, and spotted brownish yellow. The model was trained with two approaches, with and without data augmentation, using two types of training algorithms (optimizers), namely Adam and SGD, as well as a k-fold cross-validation method to ensure accurate results. The results showed that the ResNet50 model produced the highest accuracy of 98% when trained using data augmentation and the Adam optimizer with a learning rate setting of 0.0001.
Classification Of Bougainvillea Flower Varieties Using Variant Of CNN: Resnet50 I Gede Agung Chandra Wijaya; I Gusti Agung Indrawan; I Nyoman Anom Fajaraditya; Ayu Gede Wildahlia; Ida Bagus Ary Indra Iswara
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.v6i2.266

Abstract

Bougainvillea is a tropical ornamental plant renowned for its vibrant colors and variety of cultivars, yet classifying its species remains challenging due to morphological similarities. This study aims to develop an automated classification system using the ResNet50 deep learning architecture to identify Bougainvillea flower varieties based on visual imagery. The dataset consists of 700 images from seven distinct classes, captured under natural lighting using a smartphone camera. The research process includes image preprocessing (resizing to 224x224 pixels), geometric data augmentation to increase dataset diversity, and evaluation using K-Fold Cross Validation to ensure robust model assessment. The model was trained using transfer learning, and its performance was compared between augmented and non-augmented datasets through evaluation metrics such as accuracy, precision, recall, and F1-score. The results show that augmentation significantly improved the model's performance, achieving an average accuracy of 99.67% on augmented data compared to 93.39% on non-augmented data. The augmented model also exhibited greater consistency across all folds, with several achieving perfect scores. These findings highlight that combining ResNet50 with transfer learning and image augmentation produces a highly accurate and reliable Bougainvillea classification system. This research contributes to the field of AI-based plant phenotyping and lays the groundwork for future applications in horticulture, biodiversity conservation, and education. Further development is recommended to explore larger and more diverse datasets, investigate advanced architectures such as EfficientNet or Vision Transformers, and build real-time mobile-based classification tools for practical field usage
Classification Of Organic And Inorganic Waste Using Resnet50 Qinantha, I Kadek Mahesa Chandra; Indrawan, I Gusti Agung; Putra, I Putu Satria Udyana; Aristamy, I Gusti Ayu Agung Mas; Willdahlia, Ayu Gede
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.v6i2.267

Abstract

Waste generation, particularly from organic and inorganic sources, has become a growing environmental issue, especially in culturally unique regions like Bali where traditional offerings contribute to organic waste volumes. Despite regulations such as Gianyar Regency Regulation No. 76 of 2023 mandating source-level separation, on-ground implementation remains inconsistent due to low public awareness and operational limitations. This study addresses the challenge by developing an automated image-based classification system using the ResNet50 deep learning architecture to distinguish between organic and inorganic waste. A total of 200 images were collected 100 per class using smartphone cameras, and the dataset was expanded to 1,400 images through geometric data augmentation techniques such as rotation, flipping, and zooming. Images were resized to 224x224 pixels and evaluated using K-Fold Cross Validation to ensure model stability. The model was trained using transfer learning and tested under two conditions with and without augmentation while optimizing hyperparameters such as learning rates (0.0001 and 0.00001) and optimizers (Adam and SGD). The results demonstrate that augmentation significantly enhanced model performance, with the augmented model achieving an average accuracy of 99.25%, precision of 99.32%, recall of 99.25%, and F1-score of 99.25%, compared to 89.88% accuracy in the non-augmented model. These findings confirm that ResNet50, when combined with geometric augmentation and proper preprocessing, offers a robust, accurate, and scalable solution for waste classification tasks. This research contributes to the advancement of AI-driven environmental technologies and offers a potential framework for smart waste management systems, with future directions including real-time deployment, multi-class classification, and expansion to more diverse and real-world datasets.
Comparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTubeComparison of Naïve Bayes and Random Forest in Sentiment Analysis of State-Owned Banks Management by Danantara on X and YouTube Ni Wayan Indah Juliandewi; Kusuma, Aniek Suryanti; Putri, Kompiang Martina Dinata; Indrawan, I Gusti Agung; Aristamy, I Gusti Ayu Agung Mas
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.366

Abstract

The advancement of digital technology has increased public engagement in expressing opinions and responding to issues on social media platforms such as X and YouTube. A prominent topic of recent public debate concerns Danantara's management of state-owned banks. This study analyzes public sentiment regarding this issue by comparing the performance of the Naïve Bayes and Random Forest classification methods. A dataset comprising 25,565 entries was collected from both platforms between January 2025 and May 2025. The data underwent text pre-processing, labeling with the InSet Lexicon, and feature weighting using term frequency-inverse document frequency (TF-IDF). The dataset was split at 80:20, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE) prior to classification. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results demonstrate that Random Forest performed stably, achieving 84% accuracy both before and after sampling. In contrast, Naïve Bayes achieved 74% accuracy before sampling, which increased to 79% after sampling. These findings suggest that Random Forest is more robust to data imbalance than Naïve Bayes, which is more susceptible to bias toward the majority class.
Co-Authors Aniek Suryanti Kusuma, Aniek Suryanti Aprianti, Ni Nyoman Aranta, Arik Aristamy, I Gusti Ayu Agung Mas Atmaja, Ketut Jaya Ayu Gede Wildahlia Ayu Manik Dirgayusari Christina Purnama Yanti I Gede Adi Sudi Anggara I Gede Agung Chandra Wijaya I Gusti Ayu Anom I Ketut Setiawan I Komang Arya Ganda Wiguna I Made Marthana Yusa I Nyoman Agus Suarya Putra I Nyoman Anom Fajaraditya I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Dwi Guna Ambara Putra I Putu Eka Giri Gunawan I Wayan Agus Surya Darma Ida Bagus Ary Indra Iswara Kompiang Martina Dinata Putri Libraeni, Luh Gede Bevi Lie Jasa Made Sudarma Murpratiwi, Santi Ika Ni Kadek Ariasih Ni Kadek Ariasih, Ni Kadek NI LUH PUTU MERY MARLINDA Ni Luh Wiwik Sri Rahayu Ginantra Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Sutramiani Ni Wayan Indah Juliandewi Pramitha, Gede Dana Prawira, I Made Karang Satria Putra, I Putu Dwi Guna Ambara Putra, I Putu Satria Udyana Putu Andy Novit Pranartha Putu Surya Wedra Lesmana Putu Wirayudi Aditama Qinantha, I Kadek Mahesa Chandra Radhitya, Made Leo Sandika, I Kadek Budi Santi Ika Murpratiwi Setiawan, Antonio Henry Sutarwiyasa, I Ketut Tjokorda Istri Agung Pandu Yuni Maharani Welda Widiartha, Komang Kurniawan Willdahlia, Ayu Gede Yuri Prima Fittryani, Yuri Prima