Jurnal Informatika dan Bisnis

Aplikasi Assistive Technology Parkinson dengan FSM dan Risk Stratification Berbasis Mobile

William Susilo — 2026-06-03

The development of mobile application-based health technology provided opportunities to support the care of patients with Parkinson’s disease who experienced progressive motor disorders. Within the family context, common challenges included medication non-compliance, a high risk of falls associated with freezing of gait without early monitoring, and limited caregiver involvement in continuous supervision. Therefore, mobile-based assistive technology was required to support medication management, fall risk monitoring, and patient–caregiver coordination. This study applied assistive technology for Parkinson’s management by implementing a Finite State Machine as a multi-stage model for detecting movement conditions, combined with risk stratification to translate sensor signals into interpretable risk levels. The system also involved caregivers through monitoring and notification mechanisms to enable early intervention. The system was developed iteratively using Extreme Programming, including planning, design, coding, and testing phases. Implementation was conducted on Android using Kotlin and Jetpack Compose, utilizing accelerometer and gyroscope sensors, as well as Firebase for authentication, data storage, synchronization, and notifications. Functional testing was performed using black-box testing. The results indicated that the application provided medication management and fall risk monitoring, while improving coordination between patients and caregivers. The system demonstrated potential as an assistive technology to support Parkinson’s care within the family environment.

Implementasi Semantic Retrieval Berbasis Embedding dan LLM pada Aplikasi Web Keuangan Pribadi

Jimmy Putra Alam — 2026-06-03

This study developed a web-based personal finance application integrated with semantic retrieval using embedding and Large Language Models to address limitations in conventional keyword-based search. The system was developed using the Extreme Programming approach to ensure iterative development, rapid feedback, and adaptability to user requirements. The application was designed using a client–server architecture and implemented to support transaction recording, financial management, and contextual data retrieval. Financial transaction data were preprocessed and converted into vector embeddings to capture semantic meaning, enabling more flexible and accurate search using natural language queries. The system utilized a vector database with pgvector extension to store embeddings and perform similarity search. Evaluation was conducted using the RAGAs framework with 100 financial transaction data and 20 query scenarios. The results showed that the system achieved a context recall of 0.81, context precision of 0.70, faithfulness of 0.83, and answer relevancy of 0.84. These results indicate that the system was able to retrieve relevant contextual information and generate consistent and accurate responses. Furthermore, the implementation demonstrated improved effectiveness in organizing, managing, and retrieving financial data compared to traditional methods. Overall, the proposed system successfully provided a more efficient and context-aware solution for personal financial management and information retrieval.

Analisa dan Perbaikan Proses Bisnis Pelayanan Pasien Rumah Sakit dengan Pendekatan Business Process Improvement

Anita Sari Wardani — 2026-06-17

Fast and efficient patient service increases patient satisfaction, whereas slow and complicated service leads to dissatisfaction. RSUD Gambiran, as a healthcare provider, is committed to delivering high-quality healthcare service. However, several problems exist wihtin patient service business process. If these problems are ignored without improvement, they will hinder operations and reduce patient satisfaction. The primary problems identified include morning patient registration queues, medication backlogs in the hospital pharmacy, and cashiers having to check and combine payment receipts, which is prone to errors. An in-depth analysis and business process improvements are needed. This study aims to improve the patient service business process by applying the BPI method. The business process improvement phase begins with modeling the business process using BPMN to understand the current workflow. Root cause analysis is conducted to identify the source of the problem. Business process improvement solutions are designed using streamlining tools. Finally, a simulation is conducted to compare the performance of the as-is model with the to-be model. The results show that the cycle time of the patient service business process model is reduced. The patient registration process is 1.6 minutes faster, medication returns are 7.6 minutes, and outpatient services are 11 minutes faster.

Analisis Sentimen Publik terhadap Purbaya Yudhi Sadewa di X menggunakan Metode Naive Bayes

Rian Nurdiana — 2026-06-17

The rapid growth of the social media platform X (Twitter) has made it an essential medium for observing how the public responds to influential figures and economic policy issues. This study investigates public sentiment toward Purbaya Yudhi Sadewa by applying the Naive Bayes Classifier to tweet data collected from X. A total of 500 tweets containing the keyword “Purbaya Yudhi Sadewa” were gathered through API crawling between January and November 2025, and 451 tweets were retained after filtering irrelevant and duplicate entries. The workflow of this research consists of text preprocessing steps—such as case folding, tokenization, stopword removal, and stemming—followed by TF-IDF–based feature extraction, model training using Naive Bayes, and performance evaluation. The classifier produced an accuracy of 91.1%, with precision and recall values of 0.92 and 0.97 for negative sentiment, and 0.60 and 0.33 for positive sentiment. Further analysis showed that negative opinions dominated the dataset (427 tweets), while positive sentiments appeared far less frequently (32 tweets), primarily criticizing economic performance, inflation, and the stability of the rupiah. These findings highlight the capability of the Naive Bayes algorithm in effectively categorizing sentiment toward economic figures in Indonesian-language social media content.

Implementasi Penjadwalan Produksi dengan Visualisasi Gantt Chart Berbasis Web Pada PT. IPPI

Matthew Kenneth Lee — 2026-06-17

Production scheduling problems often result in delays, disorganized workflows, and inefficient resource utilization. At PT. Inti Pantja Press Industri (IPPI), the scheduling process is still conducted manually using spreadsheet tools, which limits real-time updates and complicates schedule adjustments when production priorities change. These limitations reduce data integration and hinder fast and accurate decision-making. This study aims to develop a web-based production scheduling system integrated with Gantt Chart visualization. The system is designed to manage production data centrally and display scheduling information in a structured timeline format. The research applies a qualitative method with data collection through observation and documentation. Software development adopts the Personal Extreme Programming (PXP) approach, which is iterative and adaptive. The result of this research is a web-based application capable of generating production schedules dynamically based on input data such as machines, manpower, and production items. The system provides real-time updates, supports schedule adjustments, and improves monitoring through visual representation. The implementation shows that the system successfully enhances scheduling efficiency and reduces manual errors. In conclusion, the proposed system effectively improves production scheduling management by providing structured visualization, integrated data processing, and flexible adjustment capabilities, thereby supporting better operational decision-making.