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All Journal Jurnal Pendidikan Teknologi dan Kejuruan BERKALA SAINSTEK Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal TAM (Technology Acceptance Model) Indonesian Journal of Data and Science JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Riset Rumpun Seni, Desain dan Media (JURRSENDEM) Jurnal Sistem Informasi Journal of Social Work and Empowerment Jurnal TAM (Technology Acceptance Model) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Aristamy, I Gusti Ayu Agung Mas
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Mathematics Physics Social Sciences Other

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Journal : Indonesian Journal of Data and Science

 1 
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.
Public Response on X to the Revocation of Indonesia’s 3-Kg LPG Retail Ban: A Support Vector Machine Study Wahyuni, Ni Nyoman Asti Sri; Sudipa, I Gede Iwan; Sastaparamitha, Ni Nyoman Ayu J.; Willdahlia, Ayu Gede; 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.v6i3.349

Abstract

This study examines public responses on X to the 3-Kg LPG retail ban implemented on February 1, 2025, and revoked on February 4, 2025, which caused widespread shortages, long queues, and limited access, particularly for citizens living far from official distribution points. A total of 2,524 Indonesian-language tweets were collected via crawling and systematically processed through text cleaning, tokenization, normalization, stopwords removal, and stemming, followed by automatic labeling using the Indonesian Sentiment (InSet) Lexicon. After removing 229 neutral tweets, 1,405 tweets (61.2%) were classified as negative and 890 tweets (38.8%) as positive, with the study focusing on these two sentiment classes. Text features were extracted using TF-IDF, and classification was conducted using a linear-kernel Support Vector Machine (C = 0.1) with an 80:20 train-test split. The model achieved an overall accuracy of 84%, with precision, recall, and F1-score of 0.82, 0.94, and 0.88 for the negative class, and 0.87, 0.68, and 0.76 for the positive class. Results indicate that negative sentiment was dominated by criticism related to LPG shortages and insufficient policy communication, while positive sentiment reflected user relief over restored supply and hopes for fairer distribution in the future. These findings suggest that revoking the ban did not fully restore public perception, highlighting the necessity for more effective policy dissemination and stricter monitoring of 3-Kg LPG distribution. The study also emphasizes the importance of leveraging social media, particularly X, as a real-time source for monitoring public opinion and evaluating the effectiveness of energy distribution policies in Indonesia.
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 Afifah Nurul Izzati Al Lukman, Moh Alif Alaik, Zain Anggarawati, Putu Ratna Aniek Suryanti Kusuma, Aniek Suryanti Ardinata, I Putu Adi Ayu Gede Willdahlia Azmi Pratama Bhawanaputra, I Gusti Made Ngurah Ari Christina Purnama Yanti Devi Valentino Waas Dewi, Ni Luh Ervina Sunari Dharsika, Eka Hendrawati, Theresia I Gede Iwan Sudipa I Gusti Agung Indrawan I Kadek Dwi Mahendra Putra I Komang Arya Ganda Wiguna I Made Adi Swardana I Nyoman Agus Suarya I Nyoman Anom Fajaraditya I Wayan Yoga Widya Putra Ida Bagus Ary Indra Iswara Ida Bagus Nyoman Pascima Indra Pratistha Iswardani, Putu Risanti Izzati, Afifah Nurul Komang Redy Winatha Kompiang Martina Dinata Putri Libraeni, Luh Gede Bevi Mahardika, Putu Arya Aditya Meinarni, Ni Putu Suci Najwa, Nina Fadilah Naratama, I Made Edo Netty Amelia Ni Kadek Nita Noviani Pande Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Wayan Indah Juliandewi Nina Fadilah Najwa Nur Aini Rakhmawati Permana, I Kadek Adi Pramudita, I Gusti Ngurah Agus Ega Patria Pratama, Azmi Putra, I Putu Satria Udyana Qinantha, I Kadek Mahesa Chandra Rahayu, Ni Made Astini Rizkita Ayu Mutiarani Rizkita Ayu Mutiarani Sanjaya, Wayan Putra Adi Santi Ika Murpratiwi Sastaparamitha, Ni Nyoman Ayu J. Sephiarini, Ni Putu Satya Thalib, Emmy Febriani Wahyuni, Ni Nyoman Asti Sri Wayan Gede Suka Parwita Willdahlia, Ayu Gede Winatha, Komang Redy Yuri Prima Fittryani, Yuri Prima Zain Alaik