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Bulletin of Informatics and Data Science
Published by Asosiasi Peneliti Data Science Indonesia
ISSN : -     EISSN : 25808389     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Bulletin of Informatics and Data Science journal discusses studies in the fields of Informatics, DSS, AI, and ES, as a forum for expressing research results both conceptually and technically related to Data Science
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 4, No 2 (2025): November 2025" : 5 Documents clear
Weighted Multi-Criteria Assessment of Rice Quality Using The TOPSIS Method Satria, Budy; Fadilah, Sandi
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.145

Abstract

Rice is a staple food for the Indonesian people, and its availability must be guaranteed by the government. The background of this research is based on the increasing demand for high-quality rice from consumers, thus challenging producers to set optimal rice quality standards. The process of selecting quality rice is still carried out using conventional methods in Bulog warehouses, namely by checking every rice data received by the quality control team tasked with assessing the quality of incoming rice. To overcome this problem, a decision support system is needed that can provide fair, objective, and efficient decisions. This study aims to evaluate the quality of rice from 10 alternatives using five criteria: milling degree, head grain, moisture content, broken grain, and grit grain, with a total weight of 100%. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is applied. This research was conducted by following a series of steps, including building a Decision Matrix, Normalizing the Decision Matrix, Calculating the Weighted Normalized Decision Matrix, Determining the Ideal Positive and Negative Solutions, Calculating the Distance to the Ideal Positive and Negative Solutions, and Calculating the Preference Score. The results of the study showed that from 10 alternative data, 5 types of rice were obtained with the highest preference values, namely Harum Solok Rice (0.8363), Anak Daro Rice (0.7955), Kuruik Kusuik Rice (0.7210), Ampek Angkek Rice (0.6919), and Saganggam Panuah Rice (0.6727). The conclusion of this study is that the application of the TOPSIS method is effective in objectively assessing rice quality. In further research, it is recommended to utilize a combination of other decision support methods to acquire new knowledge and refine preference values, as well as to develop these methods into user-friendly interfaces
Hybrid Chaos-Isolation Forest Framework for Anomaly Detection in Indonesia’s Public Procurement Ambarsari, Erlin Windia; Desyanti, Desyanti; Fathudin, Dedin
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.137

Abstract

This study proposes and empirically evaluates a Hybrid Chaos-Isolation Forest (HC-iForest) framework for detecting anomalies in Indonesia’s public procurement datasets. The purpose of this research is to address the difficulty of identifying irregular procurement patterns, as existing assessment mechanisms remain largely descriptive and retrospective. The framework integrates chaos-based temporal descriptors—permutation entropy, turning points, and volatility—with statistical indicators to enhance sensitivity to nonlinear and irregular time series. Using monthly procurement data from the Open Contracting Data Standard (OCDS) covering the period from 2019 to 2024, the model identified anomalous fiscal patterns associated with year-end budget adjustments and procurement surges. Empirical evaluation using correlation, ablation, and statistical validation shows that the hybrid model introduces non-redundant anomaly information, achieving a Spearman rank correlation of approximately 0.75 compared to the baseline Isolation Forest, with reduced overlap at intermediate thresholds (Jaccard similarity of 0.20 at the Top 5%). These results confirm that chaos-driven features improve model stability and interpretability. The findings reveal that anomalies are systemic manifestations of institutional and fiscal behavior rather than random deviations. The HC-iForest framework offers a data-driven early-warning mechanism for oversight agencies such as LKPP and ICW, strengthening transparency and accountability in public spending. Future studies may extend this framework through neural or spatiotemporal hybrid architectures to support intelligent and adaptive fiscal monitoring systems
Classification Model Optimization using Grid Search and Random Search in Machine Learning Algorithms Parinduri, Syawaluddin Kadafi; Alkhairi, Putrama; Irawan, Irawan; Qurniawan, Hendry
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.136

Abstract

The performance of a machine learning model is highly dependent on the selection and tuning of appropriate hyperparameters. The main problem in this study is how to improve the accuracy and stability of a classification model without sacrificing computational time efficiency, especially in the case of kidney disease classification that requires accurate and fast prediction results. This study aims to optimize the classification model by applying two hyperparameter search methods, namely Grid Search and Random Search, to the Random Forest algorithm. The kidney disease dataset is used as a case study with preprocessing processes including data cleaning, missing value imputation, categorical variable encoding, and normalization. Each model is tested using accuracy, precision, recall, and F1-Score metrics. The results show that the Grid Search_RF model produces the highest performance with perfect accuracy, precision, recall, and F1-Score values (1.0000), while Random Search_RF provides results close to (accuracy 0.9875 and F1-Score 0.9900) with more efficient training time. Meanwhile, the standard Random Forest without tuning still shows competitive performance (accuracy 0.9917 and F1-Score 0.9930). Based on these results, it can be concluded that hyperparameter optimization, using both Grid Search and Random Search, can significantly improve the performance of the classification model, with Random Search being the most efficient method for practical implementation in machine learning-based disease detection systems.
Comparison of Case-Based Reasoning and Hybrid Case-Based Methods in Expert System for Diagnosing Rice Plant Diseases Roznim, Roznim; Mesran, M.Kom, Mesran; Setiawansyah, Setiawansyah; Ambarsari, Erlin Windia
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.132

Abstract

Rice plants are susceptible to various types of diseases that can reduce productivity and quality of the harvest. Therefore, an expert system is needed that can help the disease diagnosis process quickly and accurately. This study compares two approaches in expert systems, namely the Case-Based Reasoning (CBR) method and the Hybrid Case-Based method, to diagnose rice plant diseases based on the symptoms experienced. Data on symptoms and types of diseases were analyzed using both methods to see the level of suitability of the resulting diagnosis. The test results showed that the Hybrid Case-Based method produced a higher level of certainty for all types of diseases compared to the CBR method. For example, Bacterial Leaf Blight disease has a certainty value of 99.5% in the Hybrid method, higher than 83.8% in the CBR method. These findings indicate that the Hybrid method is more effective and accurate in the process of diagnosing rice plant diseases. Thus, an expert system based on the Hybrid Case-Based method is recommended to support decision making in the agricultural sector, especially in early detection of rice diseases
Deep Learning–Based Pneumonia Classification on Chest X-Ray Images Mustakim, Mustakim; Lestari, Danur; Hartono, Hartono
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.130

Abstract

Pneumonia is a lung infection and one of the leading causes of mortality worldwide. Early and accurate diagnosis is essential to reduce death rates, with chest X-ray (CXR) imaging being the most commonly used diagnostic tool. However, CXR-based pneumonia identification remains challenging due to limited image quality and the shortage of experienced radiologists. To address this issue, this study proposes a hybrid deep learning framework that integrates Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN) to enhance the classification of bacterial and viral pneumonia from CXR images. The dataset comprises 7,927 CXR images, including 3,270 normal cases, 3,001 cases of bacterial pneumonia, and 1,656 cases of viral pneumonia. Four CNN architectures, Xception, InceptionV3, ResNet50V2, and DenseNet201, are evaluated using RMSprop and Stochastic Gradient Descent (SGD) optimizers. Model development and training are conducted using the TensorFlow framework. Experimental results demonstrate that ResNet50V2 with the RMSprop optimizer achieves the highest classification accuracy of 0.85, while also yielding the fastest training time of 2,215 seconds. These findings indicate that the proposed approach can support faster and more accurate pneumonia screening, particularly in healthcare facilities with limited diagnostic resources

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