Journal of Informatics Development

Statistical Application Using Visual Basic For Application (VBA) Excel

Subandi Subandi — 2024-04-30

BandiStats, a statistical application, was developed with the aim of being a simple and easy-to-use statistical analysis tool thanks to its base on Microsoft Excel, a well-known platform. The development method used the Visual Basic for Application (VBA) programming language. The application test results showed the success of all implemented menus. This research produced an application that can facilitate users in analyzing statistical data. It is hoped that this application can be a useful tool for those who need statistical analysis in their daily work without having to have in-depth knowledge of statistics or computer programming. With its easy-to-use interface and comprehensive features, BandiStats provides an efficient and effective solution for statistical data analysis.

Classification of Chili Fruit Diseases Using Deep Convolutional Neural Network Transfer Learning

Masrur Anwar — 2024-04-30

Chili peppers are among the highest-value agricultural commodities, often experiencing significant price fluctuations due to supply constraints. The rainy season frequently leads to crop failures caused by diseases affecting chili plants. Existing methods often struggle to accurately differentiate between similar symptoms on leaves and fruits, leading to misdiagnosis and ineffective disease management strategies. Early detection of these diseases, which manifest as symptoms on the leaves and fruits, is crucial for effective pest management. Common diseases include anthracnose, characterized by dry brown spots on the fruit, and fruit rot, where the interior of the fruit decays while the skin remains intact. Identifying these diseases promptly is essential for applying appropriate treatments to ensure optimal yields.In this study, a comprehensive approach is taken to classify diseases in chili pepper plants (Capsicum annuum L.) by incorporating both leaf and fruit segmentation. The research employs Deep Convolutional Neural Networks with Transfer Learning (DCNN) to enhance detection capabilities. The findings reveal that for leaf disease classification, fewer neurons in additional layers yield better accuracy and reduced loss, while for fruit disease classification, a more complex model with additional neurons is necessary. This underscores the need for balancing model complexity to achieve optimal performance and prevent overfitting, particularly in distinguishing between leaf and fruit diseases.

Fuzzy Logic Algorithm Optimization for Safe Distance Control on Arduino-Based Reverse Parking System and SRF04 Sensor

Achmad Firman Choiri — 2024-04-30

This research aims to develop a smart parking system that can accurately control the distance between vehicles and obstacles during reverse parking maneuvers. By integrating fuzzy logic algorithms into the system, this study seeks to improve the precision and reliability of distance control, thereby improving the overall safety of parking operations. The utilization of the Arduino platform and the SRF04 sensor allows real-time data processing and accurate distance measurement, i.e. this research contributes to the effectiveness of the proposed system. The application of fuzzy logic optimization in this context is expected to provide a powerful solution for safe reverse parking, offering potential benefits in terms of comfort and accident prevention in parking scenarios, especially for cars that still do not have obstacle detection sensors at the rear of the car

Using Machine Learning Techniques to Predict Financial Distress in Rural Banks in Indonesia

Maysas Yafi Urrochman — 2024-04-30

LPS liquidated about 100 people's Rural Banks between 2015 and 2019, indicating that these banks are facing significant issues, particularly financial distress. This study seeks to forecast financial distress through a two-stage classification and regression approach. Researchers used financial report data from Rural Banks in Indonesia from 2015 to 2019, covering a total of 150 banks, with 50 financial ratios from bankrupt banks and 100 from those that remained operational. Data was analyzed for two consecutive years prior to any bankruptcy declarations. The classification targets are categorized into five classes: very healthy, healthy, quite healthy, unhealthy, and distressed. The study results demonstrate that the two-stage classification and regression method can effectively predict the onset of financial distress. This is validated by the classification outcomes using the Decision Tree Algorithm, which achieved an f1-score accuracy of 88%. The evaluation of timing predictions using Random Forest Regression revealed a mean absolute error of 1.2 months and a mean absolute percentage error of 3%. These predictions can assist regulators, bank management, and investors in making better-informed decisions to address financial distress risks in Rural Banks. The superior performance of the Decision Tree Algorithm over Naïve Bayes in classifying financial distress highlights the potential of machine learning techniques in providing robust tools for early warning systems, aiding stakeholders in making informed decisions to mitigate risks.

Monitoring System Indoor Mushroom Cultivation via Telegram Bot

Rio Ridho Sandikyawan — 2024-04-30

Mushroom cultivation in Indonesia has significant potential due to its tropical climate, which is ideal for growing various types of mushrooms. However, maintaining optimal environmental conditions, such as temperature and humidity, is crucial for successful cultivation. This study aims to design and develop an Internet of Things (IoT)-based monitoring system for indoor mushroom farming, utilizing NodeMCU and Telegram Bot for real-time data management. IoT is used in mushroom cultivation to monitor and manage environmental conditions in real-time, considering that mushroom growth is very sensitive to changes in temperature and humidity. The main challenges faced are ensuring the stability of environmental conditions and reducing communication delays in data transmission to maintain the quality and quantity of mushroom production. The system employs a DHT11 sensor connected to a NodeMCU 8266 microcontroller to monitor temperature and humidity. Data is transmitted to farmers via the Telegram app, allowing for remote monitoring and early warning alerts when environmental parameters exceed safe limits. Field testing and performance evaluations were conducted, comparing mushroom growth between crops cultivated with and without the monitoring system. The results show that mushrooms grown under the IoT-based system achieved better growth, with the system maintaining optimal conditions between 24°C to 27°C for temperature and 80% to 90% for humidity. Communication delays averaged 9 seconds, which impacted the successful rate of real-time monitoring. Overall, the system improved the control of environmental conditions and supported enhanced mushroom growth, demonstrating its effectiveness in optimizing cultivation practices. transmission rates and maintaining environmental parameters to further improve cultivation results.