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INDONESIA
Indonesian Journal of Data and Science
Published by yocto brain
ISSN : -     EISSN : 27159930     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
IJODAS provides online media to publish scientific articles from research in the field of Data Science, Data Mining, Data Communication, Data Security and Data Representation
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 159 Documents
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Medium Range Meteorological Drought Prediction Based on SPEI-3 Using Ensemble Machine Learning and Deep Learning in North West Province, South Africa Phiri, Reatlegile; Bukohwo Michael Esiefarienrhe; Ibidun Christiana Obagbuwa
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.v6i3.354

Abstract

Meteorological drought monitoring is a pivotal action in everyday humankinds’ activities around the globe. It evaluates atmospheric conditions using weather observation instruments to measure atmospheric variables. Due to the highly sophisticated atmospheric environment, errors in drought monitoring and uncertain observation have been observed. Therefore, this research paper develops a lightweight Machine Learning (ML) and Deep Learning (DL) framework to forecast medium term meteorological drought in North West, South Africa using Standardized Precipitation Evapotranspiration Index at 3 -months (SPEI-3) timescale. This time scale reflects moisture deficits directly impacting agricultural production, early warning decisions and water management. The dataset used in this research study was obtained from South African Weather Services through a formal data request submission and not publicly accessible over a period of 10 years. Furthermore, the dataset consists of 20085 data entries and 11 data columns collected from 10 weather stations. The proposed models include SVR-RF, and, CNN-LSTM-ANN, compared to benchmark models, such as SVR, RF, CNN, LSTM, ANN, CNN-LSTM evaluated using statistical metrics, such as MSE, MAE, and . The results demonstrated irregular drought patterns during the defined period with SPEI-3 values clustered below normal conditions. Similarly, validation results showed that SVR demonstrated strong predictive performance with competitive MSE of 0.28, low MAE of 0.34 and  of 0.86. Although, the proposed CNN-LSTM-ANN and SVR-RF models did not exhibit competitive performance compared to benchmarking models, the result provides valuable comprehension, data collection, distribution, architecture, and computational power
Transfer Learning with VGG-16 for Image Classification of Endemic Papuan Orchids Mardhiyyah, Iftinan; Suhendra, Christian D.; Arobaya, Agustina Y. S.
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.v6i3.355

Abstract

This study applies a transfer-learning approach using the VGG16 architecture to classify three Papuan endemic orchid species—Dendrobium spectabile, Dendrobium lineale, and Dendrobium mirbelianum. A total of 810 field-photographed images were collected, followed by preprocessing and data augmentation to enhance data diversity. The VGG16 model pretrained on ImageNet was used as a fixed feature extractor by freezing its convolutional layers and removing the fully connected layers, while a custom classification head was added to distinguish among the three species. Experimental results demonstrated a validation accuracy of 94.44% and a macro-average F1-score of 0.94, confirming the robustness of the model under limited-data conditions. These findings suggest that transfer learning using VGG16 can effectively support orchid species recognition and serve as a foundation for developing AI-based biodiversity monitoring and conservation systems in Indonesia
Information Extraction from Makassar Culinary Images Using Vision Transformers and Cahya GPT-2 (Visual Question Answering Case Study Sharief, Tirta Chiantalia; Hazriani; Syamsul; Anas; Yuyun
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.v6i3.357

Abstract

This study examines the development of a Visual Question Answering (VQA) system to extract information from images of Makassar culinary specialties by combining the Vision Transformer (ViT) and Cahya_GPT-2 models. The main objective is to integrate visual and natural language understanding so that computers can recognize visual objects (food images) and generate relevant text descriptions. The research method uses an experimental approach with a fine-tuning process of the pre-trained ViT model as a visual encoder and Cahya_GPT-2 as a text decoder. The dataset used includes images of Makassar culinary specialties such as Coto, Konro, Pisang Epe, Barongko, and Jalangkote with question and answer (QnA) annotations. Evaluation is carried out using the ROUGE metric to assess the semantic match between the model's answers and the actual answers. The results show that the developed multimodal model is able to accurately understand the image context with an average ROUGE-L score of 0.63, indicating a good level of closeness between the model's answers and the annotations. In conclusion, the combination of ViT and Cahya_GPT-2 can be an effective approach for natural language-based visual information extraction systems, especially in the Indonesian local culinary domain
Classification of Gamelan Selonding Music Using Convolutional Neural Network Savitri, Ni Putu Diah Pradnya; Ariana , Anak Agung Gde Bagus; Pande, Ni Kadek Nita Noviani; Asana, I Made Dwi Putra; Indrawan , I Gusti Agung
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.v6i3.358

Abstract

Introduction: Balinese Selonding gamelan is an endangered sacred repertoire, and automatic recognition of its musical pieces can support documentation and preservation. Method: This study investigates the automatic classification of Selonding gamelan music using a Convolutional Neural Network (CNN). The dataset consists of 10 traditional Selonding compositions. Recordings were segmented into fixed 15-second excerpts, converted to WAV, normalized, and transformed into time–frequency features using two approaches: Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Transform (CQT). A CNN-based classifier was trained and evaluated using 5-fold cross-validation for each feature representation. Results: The MFCC-based model achieved stable high performance, with mean accuracy of 94.67% (±2.11%), mean precision of 94.97% (±1.90%), mean recall of 94.67% (±2.11%), and mean F1-score of 94.63% (±2.12%) across folds. In contrast, the CQT-based model performed notably worse, reaching only 58.04% mean accuracy and 53.28% mean F1-score, with large variance across folds. These results indicate that MFCC features capture the discriminative timbral characteristics of Selonding more effectively than CQT under the current experimental setting. Conclusion: Overall, the findings show that a CNN trained on MFCC features can reliably distinguish Selonding compositions using only short (15-second) audio segments, despite limited data. This suggests that deep learning is a feasible strategy for indexing, retrieval, and long-term preservation of Balinese gamelan repertoires.
Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN Jelni, Olivia Sutriani; Radhitya, Made Leo; Wardhana, Gede Wirya; Ni Wayan Jeri Kusuma; Desmayani, Ni Made Mila Rosa
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.v6i3.365

Abstract

The rapid growth of digital banking has increased user interaction through mobile banking apps such as BRImo (Bank Rakyat Indonesia). Google Play Store reviews provide valuable insight into app quality, but their unstructured format makes manual analysis inefficient. This study analyzes user sentiment toward BRImo and compares the performance of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for sentiment classification. Reviews were collected using Google Play Scraper from May 2024 to May 2025, yielding 15,945 raw reviews. After cleaning (removing duplicates, symbols, links, emojis) and language filtering, 15,233 valid reviews remained. Sentiment labels were generated using two lexicon-based methods: INSET and VADER. Using INSET, the data consisted of 6,238 positive, 4,987 negative, and 4,383 neutral reviews, producing 11,225 reviews for modeling. Using VADER, 10,496 positive, 2,903 negative, and 1,834 neutral reviews were obtained, totaling 13,399 reviews. Datasets were split into 80% training and 20% testing with stratified sampling. Features were extracted using TF-IDF unigrams. Classification was performed using linear SVM and KNN, with the optimal K=3 selected via Grid Search. Models were evaluated using 5-fold cross-validation, reporting mean accuracy, precision, recall, and F1-score (macro-average for INSET; weighted-average for VADER due to class imbalance). Results show SVM consistently outperforms KNN, achieving 98.36% mean accuracy and 98.34% mean F1-score on INSET, and 95.59% mean accuracy and 95.56% mean F1-score on VADER. Overall, BRImo user sentiment is predominantly positive, and findings can guide developers in improving app stability and quality
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.
Sales Forecasting Analysis Using Fuzzy Time Series and Simple Linear Regression Methods at Toko Ari Ni Luh Sri April Yanti; Ni Wayan Jeri Kusuma Dewi; I Gede Made Yudi Antara; Desak Made Dwi Utami Putra; Putu Wirayudi Aditama
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.v6i3.368

Abstract

Introduction: Forecasting, often referred to as prediction, can actually help assess conditions or predict future sales. In the business world forecasting is crucial because it can help companies plan their future operations especially when faced with sudden increases and decreases in sales and stockpiles. Especially in retail forecasting is extremely helpful in purchasing merchandise, managing inventory in the warehouse, and reducing losses due to changing customer preferences. Ari's shop, located on Jalan Raya Samu, Singapadu Kaler, Gianyar, Bali, also experiences increases and decreases in monthly sales. Therefore, it is hoped that this sales forecasting can help maintain more stable and smooth operations. Methods: This study used two methods to forecast sales: Fuzzy Time Series (FTS) and Simple Linear Regression (SLR), to predict figures from Ari's shop's monthly sales data. Both methods use the same dataset, which is Ari's Store sales data for 13 months, from January 2024 to January 2025. The forecast results are then compared using the Mean Absolute Percentage Error (MAPE), which measures the model's accuracy in predicting results. Results: Based on the sales forecasts performed, both models produced fairly accurate predictions due to their low MAPE values, below 10%. Of the two methods, Simple Linear Regression provided more accurate results with a MAPE of 3.57%. Meanwhile, the Fuzzy Time Series method produced a MAPE of 5.53%. This difference in values indicates that the linear regression model is more appropriate for Ari's Store sales data, especially since the data pattern tends to follow a linear trend.
A Website-Based Management Information System For Pratama Sidhi SAI Clinic Pangestu, Ni Made Diah Nandita; Radhitya, Made Leo; Adhiputra, Made Wahyu; Sandhiyasa, I Made Subrata; Udayana, I Putu Agus Eka Darma
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.v6i3.370

Abstract

Healthcare services in Indonesia currently need to be improved given Indonesia's dense population, which results in patient queues at health service facilities. This is due to several factors, one of which is the manual processing of health data, as is the case at the Sidhi Sai Pratama Clinic. This research aims to improve healthcare services and provide easy access to information for both clinic staff and patients. The stages of this research method are needs analysis, system design, implementation, and testing. In the needs analysis stage, data was collected through direct observation and interviews with one of the clinic staff. The system design stage was carried out by creating a system flowchart and database model required to ensure the clinic's needs for the system were met. The results of the study showed that the system can run effectively in terms of managing patient data, patient medical records, and managing medication data. Based on the results of testing using the black box testing method, all features in the system are functioning well according to the objectives. With this system, it is hoped that the problem of patient queues can be overcome by providing effective and efficient healthcare services
Sentiment Analysis of Student Comments on Facilities and Infrastructure at Instiki Using Retrieval Augmented Generation Ni Putu Juliana Dewi; I Kadek Dwi Gandika Supartha; I Putu Yoga Indrawan; Ketut Jaya Atmaja
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.v6i3.377

Abstract

This research was conducted to analyze the sentiment of student comments on infrastructure facilities at the Indonesian Institute of Business and Technology (INSTIKI) to overcome the problem of comment analysis that was previously done manually. The data used is in the form of student comments in 2024. The method used in this study is Retrieval Augmented Generation (RAG) with data labeling using Lexicon-Based. The test was carried out on three Large Language Models (LLMs), namely indobenchmark/indobert-base-p1, TinyLlama/TinyLlama-1.1B-Chat-v1.0, and w11wo/indonesian-roberta-base-sentiment-classifier. The test results showed that the indobenchmark/indobert-base-p1 model produced the highest accuracy of 80% in both test sessions compared to other models. The TinyLlama/TinyLlama-1.1B-Chat-v1.0 model produced 60% accuracy in session 1 and 65% in session 2, while the w11wo/indonesian-roberta-base-sentiment-classifier model produced 60% accuracy in both test sessions. The difference in the performance of these three LLMs shows that the model's understanding of Indonesian can affect the results of sentiment predictions.

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