Jurnal Informasi dan Teknologi

Implementation Clustering Diabetes Suffering Areas Using Web-Based Dbscan Algorithm North Aceh District

Ahmad Fauzi Abdillah — 2025-05-31

Diabetes has shown a significant increase in Indonesia, including in the North Aceh District. This research implements the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) web-based method to map diabetes distribution patterns in 27 North Aceh sub-districts. This system was built using the PHP programming language and database MySQL. Proses clustering utilizing data on population, number of sufferers, and number of deaths from 2021-2023 obtained from Prima Inti Medika Hospital and Cut Meutia RSU, with parameters epsilon = 0.5 and MinPts = 3. Results clustering shows an increase in high-risk areas from year to year. In 2021, 2 high-risk sub-districts were identified, Dewantara and Lhoksukon, increasing to 3 sub-districts in 2022 Dewantara, Lhoksukon, and Nisam, in 2023 to 4 sub-districts Dewantara, Lhoksukon, Nisam and Muara Batu. The resulting web-based system succeeded in visualizing diabetes distribution patterns and can be used to plan more effective and targeted health programs.

Prediction Of Industrial Waste Using The Autoregressive Integrated Moving Average Method

Roslaini Roslaini — 2025-05-31

This study presents the development of a web-based industrial waste prediction system using the Autoregressive Integrated Moving Average (ARIMA) method to forecast the volume of liquid and solid waste generated by PT Pupuk Iskandar Muda (PIM). The predictive model is built upon historical waste data collected between 2020 and 2023, serving as the foundation for the statistical analysis. The system is developed using the Flask web framework, offering an interactive and user-friendly interface, while SQLite3 is employed as a lightweight local database solution for efficient data handling. The ARIMA (1,1,1) model was selected based on stationarity testing and examining ACF and PACF patterns. The results suggest that the model can moderately capture prediction trends, although limitations in accuracy are evident. For 2024, liquid waste is projected to decrease from 30,600 tons in January to 29,400 tons in December. In contrast, solid waste displays a more stable trend, with an average monthly generation of approximately 23.2 tons. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) method, yielding high error rates—166.11% for liquid waste and 100% for solid waste, highlighting the significant impact of data quality and completeness on prediction accuracy. The system generates visual outputs through interactive graphs and tables accessible via a web browser, supporting data-driven decision-making. This research is a predictive tool for PT PIM and a reference for future development of technology-driven waste management systems to promote environmental sustainability.

Text Data Classification Using the SVM Model on the LMDB Minecraft Dataset

Bayu Yoga Astario — 2025-05-31

Text classification is a fundamental task in Natural Language Processing (NLP) aimed at categorizing text data into predefined classes. This study implements a Support Vector Machine (SVM) model to classify text data from the LMDB Minecraft Dataset, which contains user reviews of the Minecraft movie. The research involves text preprocessing, TF-IDF feature extraction, and SVM model training. The classification results are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. The comment data is also analyzed based on the timing of their appearance in the movie. All processes are visualized in diagrams; the final results are saved in Excel format. The SVM model performs adequately on informal and domain-specific language data, providing a foundation for future research in similar text classification contexts.

The Influence of Economic Factors on Investment Decisions in Property & Real Estate Sub-Sector Companies Listed on the Indonesia Stock Exchange for the Period 2020-2023

Dina Oktavia — 2025-06-15

This study aims to determine the effect of economic factors on investment decisions, especially in property and real estate companies listed on the Indonesia Stock Exchange for 2020-2023. The financial factors that are the focus of this research are inflation and interest rates, with investment decisions proxied by the Price Earning Ratio (PER). The sample in this study consisted of 8 companies selected using a non-probability sampling method with a purposive sampling technique. Meanwhile, the research population includes all property and real estate companies listed on the Indonesia Stock Exchange, including as many as 94 companies. The method used in this research is a quantitative method with an associative approach. Data analysis was carried out using multiple linear regression techniques to measure the effect of inflation and interest rates on investment decisions. The results showed that inflation positively and significantly influences investment decisions as measured by Price Earning Ratio (PER). This means that an increase in inflation drives an increase in PER, which indicates that investors still have optimism about the prospects for investment in the property and real estate sector despite inflationary pressures. Conversely, interest rates have a positive and significant effect on investment decisions, which means that an increase in interest rates causes a decrease in PER. This shows that when interest rates increase, investors tend to shift their investments to safer instruments, thereby reducing interest in property and real estate stocks.