This study compares the performance of Linear Regression and Neural Network algorithms in predicting stock prices using historical data from PT Bank Central Asia Tbk (BBCA) for the period from January 1, 2019, to February 17, 2025. The dataset includes daily open, high, low, and close prices, as well as trading volume. Linear Regression is employed as a conventional statistical approach, while Neural Networks are applied as a machine learning method based on deep learning. Performance evaluation is conducted using three error metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The experimental results show that the Linear Regression model consistently produces more accurate predictions with lower error values compared to the Neural Network. Although Neural Networks are more flexible in capturing non-linear patterns, Linear Regression demonstrates greater stability under market conditions present in the observed data period.

Evaluation of service satisfaction is essential for decision-making related to efforts to improve service quality. PT. BKZ’s expedition service conducts customer satisfaction evaluations as part of its competitive strategy. The satisfaction assessment for PT. BKZ's import goods delivery service is carried out using five criteria: Reliability, Responsiveness, Assurance, Empathy, and Tangibles. The evaluation process employs the Profile Matching method, which functions to identify the competency gap between actual data values and the predefined profile benchmarks. The questionnaire was distributed online and filled out by PT. BKZ customers. The results indicate that the implementation of the Profile Matching method is effective in supporting decision-making related to service improvement and enhancement strategies at PT. BKZ. The three lowest-scoring criteria that require follow-up action are Empathy (38), Assurance (38.65), and Responsiveness (39.3). These aspects must be improved, particularly among frontline staff at PT. BKZ. The next policy direction should focus on maintaining current staff performance and providing motivation to foster continuous improvement. This can be achieved by offering performance-based bonuses to employees with the highest service satisfaction evaluation scores and providing training programs for those with the lowest performance scores.

The design of User Interface (UI) and User Experience (UX) plays a crucial role in weather forecast applications, which often face issues such as unstructured layouts, inaccurate information, and delayed notifications. Using a Human-Centered Design (HCD) approach, this study aims to design the UI/UX of the WeatherWise application to enhance user comfort and satisfaction. The methodology follows the stages of inspiration, ideation, and implementation, involving data collection through observations and interviews to understand user needs, followed by prototyping for evaluation. The results are expected to produce a more intuitive and responsive design that effectively addresses existing issues, positioning WeatherWise as a more effective and preferred weather forecast application compared to others. Thus, this research not only improves the user experience in weather applications but also serves as a guide for developers in applying HCD principles to UI/UX design.

Retrieving contextually relevant verses from the Al-Qur'an translation dataset presents significant challenges due to the linguistic richness and semantic variation of the text. This study aims to enhance the accuracy and relevance of information retrieval in the Al-Qur'an translation dataset by combining Latent Semantic Indexing (LSI) and FastText word embeddings. The proposed method involves several steps: text preprocessing (lowercasing, punctuation removal, stopword elimination, and stemming), tokenization and vocabulary creation, Bag-of-Words (BoW) representation, creation of LSI models, conversion of FastText vectors, and combining LSI and FastText vectors. A similarity index is then created from the combined vectors to process user queries and rank documents based on cosine similarity. Testing on the dataset, consisting of 6236 translated verses from 114 surahs, showed promising results. The combined approach effectively captures both broader semantic structures and detailed word meanings, providing more accurate and contextually relevant search results. Key findings include high similarity scores, with 90% of retrieved verses being highly relevant to the user query, an accuracy improvement to 85%, and enhanced handling of synonyms and morphological variations at 88%. Further development is recommended, including parameter optimization, advanced preprocessing techniques, real-time search optimization, integration of contextual embeddings, and multilingual support to improve search performance and accuracy.

Lung cancer remains one of the leading causes of death worldwide, with many sufferers unaware of their condition until it is too late for treatment. Therefore, high-accuracy prediction methods are urgently needed for early detection of lung cancer. This research uses the Random Forest algorithm, known for its excellent performance in medical data classification. In this study, modeling was optimized by implementing hyperparameter optimization using Optuna. The results of the generated model show an accuracy rate of 98.6%, which is highly significant in the context of early lung cancer detection. Additionally, this algorithm demonstrated 100% recall for the positive class and 97% for the negative class, indicating that the model is highly effective in identifying patients who truly have lung cancer. Another advantage of this model is seen in the AUC (Area Under the Curve) value reaching 1, indicating 100% accurate predictions. With these results, this research affirms the importance of using the Random Forest algorithm in developing early detection systems for lung cancer. This not only can improve treatment success rates but also significantly reduce mortality rates from lung cancer.

As the number of people using computers has increased, the risks to digital systems have increased too. That has necessitated the development of new techniques to defend against hackers. Authentication systems based on text passwords are an example of digital systems that face risks through internet use. Therefore, it was necessary to provide security and reliability to users while protecting their passwords. In the authentication database, A technique known as honeywords was used. In the digital world, honeywords are a popular technique used to enhance the security of users' actual passwords and are an additional strong layer of security. The benefit of this technique is its ability to detect unauthorized access attempts to the systems. In this paper, three of the most popular techniques for honeyword generation and other well-known intelligence algorithms are put in comparison using some evaluation metrics to study the performance of each one. The results illustrate a contrast in the performance of different techniques and intelligence algorithms based on the Hamming distance value.

Dengue fever is one of the most common infectious diseases in Indonesia. However, information regarding its prevention and treatment remains fragmented, with limited accessibility and availability. This study developed a chatbot integrating the Long Short-Term Memory (LSTM) algorithm to answer various questions about dengue fever, including its symptoms, prevention, and treatment. The dataset used consists of questions and answers related to dengue fever, sourced from both primary and secondary data. The data undergoes a series of preprocessing steps before being used for model development, training, and evaluation. The test results indicate that the developed model achieved an accuracy of 100% during validation with a loss function value of 0.0221. These findings demonstrate that the LSTM-based chatbot can provide accurate and relevant responses, making it an effective tool for educating the public in an interactive and efficient manner. This implementation is expected to offer an innovative solution for increasing public awareness of dengue fever prevention and management.

PT XYZ is a company engaged in the liquefied petroleum gas (LPG) processing industry. In recent years, the company has faced significant fluctuations in production, deviating from its established targets. This issue has led to a decline in productivity over time. To address this, the study applies the Objective Matrix (OMAX) method to analyze key performance factors measuring LPG productivity from October 2023 to September 2024. The OMAX calculations reveal fluctuations, with the highest achievement recorded in August 2024 at 961.8 and the lowest in February 2024 at 173. Root Cause Analysis (RCA) was conducted to identify the primary causes of productivity decline, uncovering three critical factors: raw materials (Ratio 1), machine operating hours (Ratio 3), and idle machine hours (Ratio 4). These factors significantly contributed to reduced productivity due to unstable feed gas supply, high contamination levels affecting feed gas quality, machine downtime, and insufficient supervision and employee training. To enhance LPG productivity in the future, this study recommends several improvement strategies, including the development of gas exploration and extraction, strengthening raw material quality monitoring systems, implementing a more structured maintenance and repair plan, and providing intensive training programs for workers.

The selection of thesis titles by students is an important thing to do as part of the graduation requirements in completing undergraduate studies. In general, the difficulty or complexity of the thesis can be reflected through the title of the thesis that is appointed. This can indicate that the more difficult a thesis title, the longer it will take to complete the thesis research. This study utilizes the data mining method using machine learning, namely linear regression, in predicting how long it will take to complete a thesis title. The data used is obtained from the words or text in the thesis title as a feature or independent variable and the completion time in days as the dependent variable to predict the time required for students starting from a thesis proposal seminar to a comprehensive seminar or thesis final session. The regression model produces an evaluation value of the coefficient of determination of 0.999, which is close to the maximum value equal to 1.

A review of research in the field of chemistry that incorporates machine learning is essential to identify recent developments and explore its potential applications. Published research articles provide an opportunity to analyze emerging research trends. The use of natural language processing (NLP) technology not only accelerates text data analysis but also enhances accuracy in understanding the content and context of scientific articles. Previously, trend analysis in ophthalmology research had been conducted using Zero-Shot Learning. In this study, an analysis of chemistry-related articles focusing on machine learning was carried out using a multi-turn prompting technique. The process began with data collection through web scraping of abstracts containing the keywords "machine learning" and "chemistry." The retrieved data was then tabulated and analyzed using a Large Language Model (LLM) with a Multi-Turn Prompting approach, where general prompts were initially used, followed by deeper exploration based on previous responses. Additionally, statistical descriptive analysis was performed using targeted prompts. Analysis of 200 article abstracts identified seven key terms related to the use of machine learning in chemistry: chemical (138 articles), protein (119 articles), drug (107 articles), structure (100 articles), molecular (96 articles), chemistry (91 articles), and quantum (84 articles). Furthermore, three dominant research topics were found in the intersection of chemistry and machine learning: protein and molecular structure, quantum chemistry, and drug discovery. The number of articles on machine learning in chemistry began to rise in 2012 and saw a significant increase in 2019. The findings suggest that there are still many opportunities for developing machine learning applications in chemistry, particularly in quantum chemistry. This field only began to gain attention in 2013, and the number of published articles remains relatively low each year, indicating that it is still in the early stages of exploration.