Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics.  Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability.   Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

Background: The field of clinical or medical imaging is beginning to experience significant advancements in recent years. Various medical imaging methods such as computed tomography (CT), X-radiation (X-ray), and magnetic resonance imaging (MRI) produce images with distinct resolution differences, goals, and noise levels, making it challenging for medical experts to diagnose diseases. Objective: The limitations of a single medical image modality have increased the necessity for medical image fusion. The proposed solution is to create a fusion method of merging two types of medical images, such as MRI and CT. Therefore, this study aimed to develop a software solution that swiftly identifies the precise region of a brain tumor, speeding up the diagnosis and treatment planning. Methods: The proposed methodology combined clinical images by using discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT). This strategy depended on a multi-goal decay of the image information using DWT, and high-frequency sub-bands of the disintegrated images were combined using a weighted averaging method. Meanwhile, the low-frequency sub-bands were straight-forwardly replicated in the resulting image. The combined high-quality image was recreated using the IDWT. This method can handle images with various modalities and resolutions without the need for previous data. Results: The results showed that the outcomes of the proposed method were assessed by different metrics such as accuracy, recall, F1-score, and visual quality. The method showed a high accuracy of 98% over the familiar neural network techniques. Conclusion: The proposed method was found to be computationally effective and produced high-quality medical images to assist professionals. Furthermore, the method can be stretched out to other image modalities and exercised by hybrid techniques of wavelet transform and neural networks and used for different clinical image analysis tasks.   Keywords: CT and MRI, Image fusion, brain tumor, wavelet transform methods, medical images, machine learning, CNN  

Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories.   Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation Background: In Agile software development, an essential initial stage is eliciting software requirements. This process engages stakeholders to achieve comprehensive results. However, a common issue is the variance in domain and technical knowledge among stakeholders, potentially impacting the quality of software requirements elicitation. Objective: Understanding the characteristics of user stories produced by stakeholders becomes crucial, particularly considering the differences in domain and technical knowledge. This study aims to compare the characteristics of user stories generated by stakeholders with varying backgrounds in domain and technical expertise. Methods: The initial step involves categorizing respondents into distinct stakeholder groups. Three stakeholders are involved in this study, constituting a combination of those with high and low technical and domain knowledge. Subsequently, data collection of user stories is conducted across various case studies. Finally, the acquired user stories are analyzed for further insights. Results: The analysis reveals variations in user stories generated by the three stakeholder categories across the three case studies. Stakeholders with domain knowledge tend to focus on 'what' aspects with task elements and 'why' aspects with hard-goal elements. Meanwhile, technical knowledge crafts user stories with capability elements in the 'what' aspect. Utilizing the QUS framework, it is evident that technical knowledge consistently produces a higher number of high-quality user stories across all quality categories, Conclusion: The contribution offered by this study lies in determining the distinct characteristics of user stories produced by different types of stakeholders, focusing on disparities in domain and technical knowledge. The study highlights the comparison of various characteristics of user story elements, such as hard-goals, soft-goals, tasks, or capabilities, and assesses the quality of user stories based on the user story framework. Additionally, it endorse the importance of process innovation in shaping the requirements gathering process and subsequently influencing the quality of user stories.   Keywords: User story, Agile Software Development, Requirements Elicitation, Stakeholder, Domain Knowledge, Process Innovation

Background: This study focuses on the latest knowledge regarding Micro, Small and Medium Enterprises (MSMEs) as a current central issue. These enterprises have shown their significance in providing employment opportunities and contributing to the country’s economy. However, MSMEs face various challenges that must be addressed to optimize their outcomes. Understanding the characteristics of this group was crucial in formulating effective strategies. Objective: This study proposed to cluster or combine micro, small, and medium enterprises (MSMEs) data in a particular area based on asset value and turnover. As a result, this study aimed to gain insights into the MSME landscape in the area and provided valuable information for decision-makers and stakeholders. Methods: This study utilized two methods, namely the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method and the K-Means method. These methods were chosen for their distinct capabilities. DBSCAN was selected for its ability to handle noisy data and identify clusters with diverse forms, while K-Means was chosen for its popularity and ability to group data based on proximity. The study used a dataset containing MSME information, including asset values and turnover, collected from various sources. Results: The outcomes encompassed identifying clusters of MSMEs based on their closeness in the feature space within a specific region. Optimizing the clustering outcomes involved modifying algorithm parameters like epsilon and minimum points for DBSCAN and the number of clusters for K-Means. Furthermore, this study attained a deeper understanding of the arrangement and characteristics of MSME clusters in the region through a comparative analysis of the two methodologies. Conclusion: This study offered perspectives on clustering MSMEs based on asset value and turnover in a specific region. Employing DBSCAN and K-Means methodologies allowed researchers to depict the MSME landscape and grasp the business attributes of these enterprises. These results could aid in decision-making and strategic planning concerning the advancement of the MSME sector in the mentioned area. Future study may investigate supplementary factors and variables to deepen comprehension of MSME clusters and promote regional growth and sustainability.   Keywords: Asset Value, Clustering, DBSCAN, K-Means, Turnover

Background: The publicly available large dataset plays an important role in the development of the natural language processing/computational linguistic research field. However, up to now, there are only a few large Indonesian language datasets accessible for research purposes, including sentiment analysis datasets, where sentiment analysis is considered the most popular task. Objective: The objective of this work is to present sentiment analysis on a large Indonesian product review dataset, employing various features and methods. Two tasks have been implemented: classifying reviews into three classes (positive, negative, neutral), and predicting ratings. Methods: Sentiment analysis was conducted on the FDReview dataset, comprising over 700,000 reviews. The analysis treated sentiment as a classification problem, employing the following methods: Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), LSTM, and BiLSTM. Result: The experimental results indicate that in the comparison of performance using conventional methods, MNB outperformed SVM in rating prediction, whereas SVM exhibited better performance in the review classification task. Additionally, the results demonstrate that the BiLSTM method outperformed all other methods in both tasks. Furthermore, this study includes experiments conducted on balanced and unbalanced small-sized sample datasets. Conclusion: Analysis of the experimental results revealed that the deep learning-based method performed better only in the large dataset setting. Results from the small balanced dataset indicate that conventional machine learning methods exhibit competitive performance compared to deep learning approaches.   Keywords: Indonesian review dataset, Large dataset, Rating prediction, Sentiment analysis

Background: Mobile social commerce is a collection of e-commerce activities accessed via mobile devices and supported by users actively engaging in commercial activities on social media. As a country with a substantial number of social media users, Indonesia has sufficient opportunities to implement mobile social commerce as application for online shopping. Objective: This study aimed to identify the factors influencing the use of mobile social commerce for online shopping, using Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). In this context, some variables were excluded, namely user behavior, price value, and moderating variables (age, gender, and experience). Additional variables considered included price saving orientation (PSO), privacy concerns (PC), social commerce construct (SCC), social support (SS), and trust (TR). Methods: Data were collected by distributing questionnaires to respondents who had used mobile social commerce for shopping, resulting in 320 collected responses. Furthermore, the collected data were analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) method through SmartPLS 3.3.3 application. Results: The results showed that among the 17 proposed hypotheses, 6 were rejected, while 11 were accepted. Conclusion: In conclusion, the factors influencing the use of mobile social commerce consisted of effort expectancy, habit, hedonic motivation, SCC, SS, and PC. Therefore, future studies should concentrate on exploring the continued intention of users towards mobile social commerce application.   Keywords: Mobile Social Commerce, Privacy Concern, Social Construct, Social Support, UTAUT

Background: Insect vectors spread 80% of plant viruses, causing major agricultural production losses. Direct insect vector identification is difficult due to a wide range of hosts, limited detection methods, and high PCR costs and expertise. Currently, a biodiversity database named Global Biotic Interaction (GloBI) provides an opportunity to identify virus vectors using its data. Objective: This study aims to build an insect vector search engine that can construct an virus-insect-plant interaction knowledge graph, identify insect vectors using network analysis, and extend knowledge about identified insect vectors. Methods: We leverage GloBI data to construct a graph that shows the complex relationships between insects, viruses, and plants. We identify insect vectors using interaction analysis and taxonomy analysis, then combine them into a final score. In interaction analysis, we propose Targeted Node Centric-Degree Centrality (TNC-DC) which finds insects with many directly and indirectly connections to the virus. Finally, we integrate Wikidata, DBPedia, and NCBIOntology to provide comprehensive information about insect vectors in the knowledge extension stage. Results: The interaction graph for each test virus was created. At the test stage, interaction and taxonomic analysis achieved 0.80 precision. TNC-DC succeeded in overcoming the failure of the original degree centrality which always got bees in the prediction results. During knowledge extension stage, we succeeded in finding the natural enemy of the Bemisia Tabaci (an insect vector of Pepper Yellow Leaf Curl Virus). Furthermore, an insect vector search engine is developed. The search engine provides network analysis insights, insect vector common names, photos, descriptions, natural enemies, other species, and relevant publications about the predicted insect vector. Conclusion: An insect vector search engine correctly identified virus vectors using GloBI data, TNC-DC, and entity embedding. Average precision was 0.80 in precision tests. There is a note that some insects are best in the first-to-five order.   Keywords: Knowledge Graph, Network Analysis, Degree Centrality, Entity Embedding, Insect Vector

Background: As the population increases, the need for corn products also increases. Corn is needed for various purposes, such as food consumption, industry, and animal feed. Therefore, increasing corn production is crucial to support food availability and the food industry. Objective: The objective of this project is to create a model to increase corn farming productivity using scenarios from drip irrigation systems and farmer field school programs. Methods: A system dynamics approach is utilized to model the complexity and nonlinear behaviour of the corn farming system. In addition, several scenarios are formulated to achieve the objective of increasing corn productivity. Results: Simulation results showed that adopting a drip irrigation system and operating a farmer field school program would increase corn productivity. Conclusion: The corn farming system model was successfully developed in this research. The scenario of implementing a drip irrigation system and the farmer field school program allowed farmers to increase corn productivity. Through the scenario of implementing a drip irrigation system, farmers can save water use, thereby reducing the impact of drought. Meanwhile, the scenario of the farmer field school program enables farmers to manage agriculture effectively. This study suggests that further research could consider the byproducts of corn production to increase the profits of corn farmers.   Keywords: Corn Farming, Decision Support System, Modeling, Simulation, System Dynamics

Background: Over the years, the role of startups has experienced a significant increase in healthcare delivery, particularly in telemedicine. However, there are still some inherent challenges, including cultural factors, lack of digital literacy, and uneven internet network infrastructure that must be considered during implementation. Previous reports also showed that there was a knowledge gap regarding the factors influencing acceptance of telemedicine. Objective: This study aimed to introduce and investigate an adjusted model based on Technology Acceptance Model (TAM) to assess the influence of user dimensions, technological aspects, and socio-cultural elements on the intention to adopt telemedicine services. Methods: The hypothesized relationships between latent variables were examined through Structural Equation Modeling (SEM). In addition, data analysis was carried out using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results: Self-efficacy (β=-0.272, P=0.013), perceived usefulness (β=0.355, P=0.000), facilitating conditions (β=0.425, P=0.000), and cultural factors (β=0.421, P=0.001) were found to exert a significant influence on the intention to adopt telemedicine services. Meanwhile, trust, the variables of perceived ease of use, and social influence had no significant influences. Conclusion: This study emphasized the significance of comprehending the factors influencing the adoption of telemedicine services. In addition, the results showed that the extended TAM was applicable in assessing acceptance of telemedicine services.   Keywords: acceptance, telemedicine, TAM, SEM, intention to use

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin   Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization