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User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

Given the great benefits of electrical energy, the availability of electrical energy sources is limited. Currently, the availability of electrical energy sources is not able to meet the increasing demand for electricity in Indonesia. The high use of electrical energy in daily life will have a negative impact on the environment. Therefore, to maintain the sustainability of energy sources, it is necessary to pursue strategic steps that can support the provision of electrical energy optimally and affordably, This study aims to find out how much total energy is used by the Energy Consumption Index (IKE) on electrical energy from the influence of electrical power and the length of time of use of electrical energy at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. This study aims to determine the value of energy consumption used or Energy Consumption Index (IKE) and energy saving opportunities at SMK Negeri 2 Lubuk Basung, Lubuk Basung District, Agam Regency. The results of this study are for the IKE value of the first floor which is 1.71 kWh/m2, for the IKE value of the second floor which is 0.03 kWh/m2, for the IKE value of one building, which is with a value of 1.74 kWh/m2, for the annual IKE of 0.022 kWh/m2/year and for the value of energy-saving opportunities of IDR 651 646/month IDR 7 819 755/year.

Indonesia has extraordinary resources and potential in developing renewable energy sources (RES), but various obstacles must be overcome in implementing RES. The purpose of this study is to analyze the gap in the application of RES. This gap includes energy knowledge, community participation, battery waste management, service quality, regulation, and legal policy. This study uses a mixed-methods approach, by conducting a structured questionnaire in quantitative data collection, while qualitative data collection through special interviews, focused group discussions, and conducting policy regulation analysis. The results show that 62% of people do not understand RES, 28% are involved in project planning, and 74% are unaware of SOP (standard operating procedures) regarding battery waste recycling. The results of the correlation analysis reveal a positive relationship between the level of knowledge and interest in RES (R = 0.56). Also, the developed community-based participation model includes initial involvement, transparency of information, and local incentives. These findings further strengthen the compatibility of the innovation diffusion theory, planned behavior theory, SERVQUAL, and the theory of public interest. This study will make a practical contribution through evidence-based strategies in increasing resilience, especially for policymakers and energy service providers. The impact of the policy aspects includes the need for large reforms, education, public campaigns, and the realization of battery waste management systems. This study also provides an opportunity for further study by expanding the geographical scope and related industrial sectors.

The increasing need for electrical energy in Indonesia encourages the development of new renewable energy (EBT), including hydroelectric power plants (PLTA) as an environmentally friendly solution. This research was conducted at the Mentawa Dam, West Toili District, Banggai Regency, Central Sulawesi, to analyze the potential of water energy that can be utilized as a source of micro-hydro power plants (PLTMH). This dam currently functions as irrigation and a tourist attraction, but has significant potential to generate sustainable electrical energy. The research methods include measuring water discharge using the float method, measuring the height of the water fall (head) with an altimeter and GPS, and analyzing the potential for electrical power using the formula P = η ⋅ ρ ⋅ g ⋅ Q ⋅ H. The measurement results show an average water discharge of 3.01 m³ / s and a water fall (head) of 6.56 meters. With a turbine efficiency of 80%, the potential electrical power that can be generated reaches 154.76 kW. This study recommends the use of Kaplan turbines in Mentawa Dam, which are ideal for large discharge conditions and low to medium heads, and can adapt to discharge fluctuations, making it an optimal choice to maximize energy potential. This research is expected to provide sustainable energy solutions for the people of Toili Barat District and support the development of the tourism sector.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.

Automatic Speech Recognition (ASR) for the Indonesian language faces significant challenges due to high Word Error Rate (WER), especially when using pre-trained models without fine-tuning. This study develops an optimized ASR system using a hybrid cloud architecture that integrates the Faster-Whisper large-v3 engine with advanced audio preprocessing techniques. The system adopts a distributed architecture, with Google Colab (Tesla T4, 15GB VRAM) as the GPU server and Ubuntu 22.04 LTS (8 core, 32GB RAM) as the client. Evaluation was conducted on five Indonesian audio samples covering formal news, informal conversations, and long-duration recordings. The system achieved an 80% success rate in processing, with WER ranging from 27.69% (formal news) to 645.16% (informal conversations). Resource utilization was also efficient, with 21.3% GPU usage and 35.4% RAM usage. Processing time remained stable for normal-sized files but experienced timeouts on large files (>50MB). The results indicate that hybrid cloud architecture is feasible for distributed ASR processing in Indonesian, with several areas still open for optimization toward production deployment.

CV. Apris Expressindo is a private company engaged in the construction and distributor of U-PVC frames. In its business process activities, CV. Apris Expressindo still does not utilize technology optimally, so that the transfer of information regarding offers, sales, purchases, product stock, suppliers, customers, and financial reports is still done manually. This causes inefficient business processes including: slow fulfillment of customer orders, product pricing errors between divisions, product marketing that is not optimal, financial reports that cannot be seen and controlled by company leaders. Therefore, it is necessary to implement Enterprise Resource Planning (ERP) which aims to overcome these problems. The methodology used in this study was carried out in several stages, namely, data collection: interviews, observation and literacy studies, business process analysis: activity diagrams and use case diagrams, implementation: installation, configuration and module customization, testing: blackbox and UAT (User Acception Test) . The results of this study indicate that the ERP software that has been selected and implemented, namely the Odoo application, can improve and simplify business process activities and can minimize any problems faced today. It is hoped that with this ERP system business operations can be more effective and efficient so that it is expected to provide more financial benefits for CV Apris Expressindo.

This study discusses the design of a goods storage information system at the Medan Class A Search and Rescue Office to improve the efficiency and accuracy of goods management management. This system is designed to automate the process of storing and tracking goods, which has been done manually and often causes various problems such as tracking difficulties and data inaccuracies. The results of the implementation show that this information system is able to minimize errors, reduce the risk of loss of goods, and ensure the availability of important equipment in real-time. Despite challenges such as user resistance and resource requirements, careful planning and proper training overcame these obstacles. In conclusion, this system contributes significantly in improving the operational performance of the Medan Class A Search and Rescue Office.

This study aims to examine the impact of a 6-phase design using an asymmetrical dual 3-phase design on the torque and speed of an induction motor, where the newly designed motor still uses a 3-phase power source. The research compares the performance of a conventional 3-phase induction motor with that of an asymmetrical 6-phase induction motor design, focusing on torque and speed. This motor is only designed in terms of its windings, so the motor's position remains unchanged. The 3-phase system applied to the 6-phase induction motor design ensures that the 6-phase motor operates within a 3-phase system. The study also explores performance improvements, particularly in torque and speed, by redesigning the windings of a 3-phase induction motor into an asymmetrical 6-phase motor. The induction motor used in this research has the following specifications: 3-phase, 2 A, 1390 RPM, 50Hz, 0.75 kW, and 380Y. The study aims to understand the effects of the asymmetrical 6-phase induction motor design, particularly regarding torque and speed. The results show that the torque and speed of the two motors differ due to the asymmetrical 6-phase winding design. In a conventional 3-phase induction motor, the winding coil layer distance is 120°, whereas, in the asymmetrical 6-phase induction motor, the distance between the first and second layers is 30°. Based on this analysis, the asymmetrical 6-phase induction motor design demonstrates better performance compared to the conventional 3-phase induction motor.

Electric vehicles use a power source from electrical energy to be converted into kinetic energy. The electrical energy source is stored in the battery. The battery charging process can be done using power sources from Solar Panels and PLN. Based on the analysis of the measurement results, it can be concluded that (1) recharging the battery using Solar Panels from minimum to maximum conditions takes 4 hours 38 minutes, and (2) recharging the battery using PLN from minimum to maximum conditions takes 2 hours.

This research aims to design and test a genset fuel conversion system from gasoline to HHO gas (brown gas) as a more environmentally friendly alternative. Conventional fossil-fueled generators face challenges in theform of limited resources and the impact of environmental pollution. In this study, HHO gas is produced through a water electrolysis process using an HHO generator that separates water molecules into hydrogen and oxygen using electric current. The test results show that increasing the voltage from 2.58 V to 5.12 V significantly increases the volume of gas produced. At a voltage of 2.58 V, the gas volume reached 110 ml, and increased to 750 ml at a voltage of 4.72 V. The generator set was successfully operated for 1 minute 15 seconds with HHO gas using a separate battery power source. These results demonstrate the potential of HHO gas as an efficient alternative fuel for generator sets. This study recommends further testing with voltage and current variations to find the optimal configuration, as well as the use of a larger power source to increase the efficiency of HHO gas electrolysis.

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.

This research uses a literature study, a descriptive qualitative research type, a literature review describing the function of digital technology-based learning educational innovation. Research uses written sources such as articles, journals and relevant documents. The literature study focuses on the function or role of educational innovation, educational digital technology, and the use of educational technology. The study results show innovation in the process of renewal and change. Innovation is very necessary in the world of education so that it continues to develop. The use of technology has positive and negative impacts. The impact generated is focused on positive things and minimizes negative impacts if there is good cooperation from various parties so that the use of technology meets its objectives. The function of educational innovation in digital technology-based learning is very important. So that the use of digital technology can be carried out optimally and comprehensively, good innovation needed.

In the era of the new normal, maintaining health and hygiene has become critically important, especially in preventing the spread of viruses. One effective way to achieve this is through regular handwashing. Schools are particularly susceptible to the transmission of viruses and bacteria, which is why it's crucial to implement an effective hand sanitizer system. However, the use of conventional hand sanitizer bottles that require manual pressing or opening still carries the risk of virus transmission. Therefore, there is a need for the development of an automatic hand sanitizer system to reduce the risk of virus transmission. This research aims to design an Arduino-based automatic hand sanitizer system at SD Kanisius Genuk. The automatic hand sanitizer system utilizes an ultrasonic sensor to detect the distance between an object, in this case, a hand, and the hand sanitizer. When the hand approaches within a certain distance, the hand sanitizer fluid is dispensed automatically through a mini 5V pump. Additionally, the device features a liquid level sensor that provides information about the hand sanitizer fluid level via an LCD screen. The theoretical significance of this research lies in enriching knowledge about programming using the C++ programming language in designing an automatic hand sanitizer system. Furthermore, the research is expected to enhance creativity, innovation, and knowledge development in response to technological advancements. The practical benefits of this research include facilitating students, teachers, and guests at SD Kanisius Genuk in using the automatic hand sanitizer, thus reducing the risk of virus transmission. Additionally, this research can serve as a reference for universities and contribute to the field of human resource management knowledge.

Public services in several districts have not utilized online information and communication technology. So that the service applicant has to go back and forth to the sub-district which takes a very long time. For this reason, with the development of the modern era, employees should provide good service to the community, such as running community service programs using applications. This research aims to create Community Public Service Application programs and to help people carry out services using Web-based Public Service Administration service applications in South Prabumulih District. This research uses descriptive research type. The type of data taken is quantitative data. Sources of data in this study using primary data sources and secondary data sources. Data collection techniques in this study were carried out by observation, interviews and literature studies. The system development method used in this research is the Water Fall Method, and the System Design Auxiliary Tool.

Rice plants (Oryza sativa L.) are rice-producing plants which are a source of carbohydrates for most of the world's population. Almost 95% of Indonesia's population consumes rice as a staple food, so every year the demand for rice increases as the population increases. Therefore, farmers must choose quality seeds. In this era of fast and efficient technological progress, this is a very good thing for all progress in various fields. more and more fields of knowledge are developing, one of which is the existence of a decision-making system. a set of model-based procedures for processing and valuing data to help managers make decisions. This decision-making system uses several variables as input consisting of: type of variety, seed shape, seed color, root, seed age. The method used by the author is Fuzzy Tsukamoto. In the Tsukamoto method, it is explained that each consequence in IF-Then must be explained with a fuzzy set that has a membership function that does not change or is monotonous and for programming it uses PHP. The results obtained from the research that the authors conducted were in the form of a decision-making system to get the best seed yields.

The combination of activities carried out by humans and technology is related to administrative systems that create information from organized work processes. PT. Farmhouse Production in the administration of data management is carried out with the help of Microsoft Excel and documents are written, and copied in the traditional way which results in the accumulation of accumulated paper resulting in complexity and difficulty in finding the required documents. Therefore, a balance of human resources, database, hardware, software, and a new strategy is needed to overcome the problems that can burden the company by realizing a website that is easy to apply so that any file is found quickly by paying attention to supervision, control, and methodical organization using PIESCES analysis as a method used to detect deficiencies and weakness in the old system. With this analysis, the new system is the right support in managing data that is collected in an orderly manner and stored neatly in large storage without being randomized and the work is more strategic because the information generated is valuable to the recipient or leader.