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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science JUTIK : Jurnal Teknologi Informasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) JE (Journal of Empowerment) Journal of Engineering and Scientific Research (JESR) Indonesian Journal of Data and Science Jurnal Scientia Journal of Computer Networks, Architecture and High Performance Computing Jurnal Krisnadana Jurnal Krisnadana TECHNOVATE KOMET Jurnal Nasional Teknologi Informasi dan Aplikasinya
Radhitya, Made Leo
Institut Bisnis Dan Teknologi Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences

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Journal : indonesian journal of data and science

 1 
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Performance Comparison of MobileNet and EfficientNet Architectures in Automatic Classification of Bacterial Colonies Wahyudi, I Putu Alfin Teguh; Sudipa, I Gede Iwan; Libraeni, Luh Gede Bevi; Radhitya, Made Leo; Asana, I Made Dwi Putra
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.v6i2.218

Abstract

Bacterial colony classification is crucial in microbiology but remains labor-intensive and time-consuming when performed manually. Deep learning, particularly Convolutional Neural Networks (CNNs), enables automated classification, improving accuracy and efficiency. This study compares MobileNetV2 and EfficientNet-B0 for bacterial colony classification, evaluating the impact of data augmentation on model performance. Using the Neurosys AGAR dataset, preprocessing techniques such as histogram equalization, gamma correction, and Gaussian blur were applied, while data augmentation (rotation, noise addition, luminosity adjustments) improved model generalization. The dataset was split (80% training, 20% testing), and models were trained with learning rates (0.0001, 0.001) and epochs (100, 150, 200). Results show EfficientNet-B0 outperforms MobileNetV2, achieving higher validation accuracy and stability, with optimal performance at 150–200 epochs and a lower learning rate (0.0001). Data augmentation significantly improved accuracy and reduced overfitting. While MobileNetV2 remains a lightweight alternative, its performance is heavily reliant on augmentation. These findings highlight EfficientNet-B0 as the superior model, supporting the automation of microbiological diagnostics. Future research should explore hybrid CNN architectures, Vision Transformers (ViTs), and real-time implementation for improved classification efficiency.
Sentiment Analysis of BRImo Reviews on Google Play Store Using SVM and KNN Olivia Sutriani Jelni; Made Leo Radhitya; Gede Wirya Wardhana; Ni Wayan Jeri Kusuma; Ni Made Mila Rosa Desmayani
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
A Website-Based Management Information System For Pratama Sidhi SAI Clinic Ni Made Diah Nandita Pangestu; Made Leo Radhitya; Made Wahyu Adhiputra; I Made Subrata Sandhiyasa; I Putu Agus Eka Darma Udayana
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
Co-Authors Admojo, Fadhila Tangguh Agus Harjoko Aprianti, Ni Nyoman Asana, I Made Dwi Putra Asana, Made Dwi Putra Atmaja, I Made Ari Dwi Suta Atmaja, Ketut Jaya Bagus Tresna Bramstya Felix Andika Dwiyanto Gede Wirya Wardhana Giri, Gst. Ayu Vida Mastrika Gst. Ayu Vida Mastrika Giri Gunawan, I Komang Agus Bryan Hamada Zein I Dewa Gede Agung Pandawana I Gede Iwan Sudipa I Gusti Agung Indrawan I Made Subrata Sandhiyasa I Made Yoga Apriliana I Nyoman Alit Arsana I Nyoman Gede Arya Astawa I Nyoman Tri Anindia Putra I Putu Agus Eka Darma Udayana, I Putu Agus Eka I Putu Eka Andika Cakra I Wayan Raka Ardana I Wayan Supriana Ida Bagus Gede Sarasvananda Ketut Laksmi Maswari Libraeni, Luh Gede Bevi Made Wahyu Adhiputra Naswin, Ahmad Ni Made Diah Nandita Pangestu Ni Made Mila Rosa Desmayani, Ni Made Mila Rosa Ni Putu Widantari Suandana Ni Wayan Jeri Kusuma Olivia Sutriani Jelni Putra, Putu Satria Udyana Raharja, Made Agung Saputra, I Wayan Kelvin Widana Selivan, Diana Semadi, Ketut Ngurah Siti Marlina, Siti Subawa, I Gede Arika Triadi, Wilfridus Bambang Waas, Devi Valentino Wahyudi, I Putu Alfin Teguh Widiantari, Ni Komang Mira Wiguna, I Komang Arya Ganda Wiguna, I Wayan Darma Wijaya, Bagus Kusuma