Water saving rice cultivation using sheet-pipe subsurface irrigation.

Water saving in rice cultivation has assumed paramount importance, especially in the context of climate change. The introduction of sheet-pipe technology in Indonesia heralded as an innovative subsurface irrigation and drainage system, is poised to revolutionize how to manage this vital resource. Our study was designed with two primary objectives: first, to investigate how rice plants respond when water levels are deliberately reduced using the sheet-pipe technology; and second, to comprehensively analyze water productivity and water use efficiency in comparison to conventional flooded rice cultivation systems. We conducted two distinct experiments: one employing sheet-pipe subsurface irrigation (SSI) and the other utilizing conventional flooded irrigation (CFI). In the SSI setup, the water level was maintained at a depth of 5-10 cm below the soil surface 20 days after transplanting to harvesting. With this setting, the soil moisture was maintained at around 85-95 degrees of saturation. On the other hand, the CFI approach involved water flowing directly over the soil surface, with the water level consistently maintained at a mere 2-3 cm above it. Interestingly, while the SSI method did lead to a reduction in yield, it has significant benefits. Our results showed that a reduction in yield was observed for the SSI 15.5-18.6 % lower compared to the conventional method (CFI). However, the SSI is environmentally benefit compared to the conventional method by reducing 37.5-50.5 % in water irrigation, increasing water use efficiency (WUE) up to 70.8 %, and improving 3.2-10.4 % in water productivity. Our findings reveal that optimizing water conservation may have a disadvantageous effect on rice yield, indicating the importance of optimal water level. Future research to find the optimal water level that balances yield production and environment is required, especially to adapt to dry and warming climate change in the future.

Twitter dataset on public sentiments towards biodiversity policy in Indonesia.

In recent years, biodiversity has emerged as a prominent and pressing topic due to the urgent need to address biodiversity loss and the recognition of its connections to climate change and sustainable development. Additionally, increased public awareness and the consideration of economic factors have further underscored the significance of biodiversity conservation. To investigate the sentiment of the Indonesian people towards biodiversity, we conducted a comprehensive data collection on Twitter, focusing on keywords we have set. We amassed a substantial dataset of 500,000 Indonesian tweets from January 2020 to March 2023. These tweets encompassed a wide range of discussions on biodiversity, including its subdomains such as food security, health, and environmental management. Three annotators labeled each tweet with a sentiment class (positive, negative, neutral), or label none for unrelated tweet. The final label was determined using the majority voting method. The tweets with the final label none and those with undecided sentiment class were considered invalid and excluded in the subsequent process. Before labeling, a team of 18 experts jointly developed a labeling guide. This document served as a reference in labeling. After going through a series of processes, including cleaning (removing duplications, irrelevant tweets, and tweets written other than in Indonesian) and preprocessing, we prepared a dataset containing 13,435 tweets. We measured the inter-annotator agreement level, made several models using different algorithms and the K-Fold cross-validation method, and evaluated the models. The Fleiss' Kappa value of the dataset was 0.62187 as the value of the inter-annotator agreement level, and the F1-score value with the best model using the pre-trained IndoBERT model was 0.7959. The Fleiss' Kappa and F1-score values suggest that the annotators have a substantial comprehension and agreement of how to label a tweet, thus ensuring consistency and reliability of our dataset, and the reusability of our dataset is quite suitable for further research on sentiment analysis on biodiversity, respectively. This dataset will benefit various research, including topic modeling, sentiment analysis, public opinion analysis on Twitter, etc., especially biodiversity-related policies.

Hate speech and abusive language detection in Indonesian social media: Progress and challenges.

Nowadays Hate Speech and Abusive Language (HSAL) have spread extensively over social media. The easy use of social media allows people to abuse the media to spread HSAL. Hate speech and abusive language in social media must be detected because they can trigger conflict among citizens. Not only in social media, but HSAL also often trigger conflict in real life. In recent years, many scholars have researched HSAL detection in various languages and media. However, there are still many tasks on HSAL detection that need to be done to develop a better HSAL detection system. This paper discusses a summary of Indonesian HSAL detection research, conducted by utilizing the Kitchenham systematic literature review method. Based on our summary, we found that most Indonesian HSAL research still uses the classic machine-learning approach with classic text representation features that experimented on the Twitter text dataset. We also found several challenges and tasks that need to be addressed to build a better HSAL detection system in Indonesian social media that can detect the hate speech target, category, and levels; and the hate speech buzzer, thread starter, and fake account spreader.

Usability factors that drive continued intention to use and loyalty of mobile travel application.

The expeditious development of mobile applications has transformed how travel e-commerce firms enhance customer relationships to implement their business strategies successfully. However, the literature has identified that a major determinant affecting customers' considerations to reject a mobile application is a deficiency of usability. Therefore, unlike the typical spotlight on general usability variables found in the prior literature, this study concentrated on investigating more specific usability factors that drive the continued intention to use and mobile application loyalty of a mobile travel e-commerce application. To achieve this, this study utilized the mobile application usability, which we adapted to form an Indonesian version. The dataset was obtained through a survey of 248 users of an Indonesian mobile travel e-commerce application, Traveloka. The collected data were analyzed using partial least square structural equation modeling (PLS-SEM) to test the hypotheses. The results indicated that application utility and the user interface structure emerge as significant drivers for both outcomes. This study could help travel e-commerce firms to develop their mobile strategies effectively, especially in terms of their usability, which may ensure that their customers continue to use the mobile application.

Detection of fintech P2P lending issues in Indonesia.

Financial technology (fintech) is a growing industry in Indonesia, supported by advances in the technological infrastructure. At the end of 2019, the Financial Services Authority (OJK), the financial authority in Indonesia, recorded 164 registered and licensed fintech (P2P lending) companies. However, since early 2018, the Investment Alert Task Force (SWI) and the Ministry of Communication and Information Technology have blocked 1,350 illegal fintech platforms. Illegal fintech lending practices have mechanisms beyond the responsibility and authority of the OJK, including the risk of collection and distribution of personal data. The essence of this study is to discuss the landscape of fintech P2P lending in Indonesia from Indonesian Online News data, explore cases of fintech p2p lending in Indonesia, and understand the rules and policies. Qualitative research with a case study approach and Focus Group Discussion techniques were used to obtain data from 4 stakeholders in the Fintech P2P Lending Industry in Indonesia. VOS Viewer software is used to build keywords from Indonesian Online News collections, NVIVO 12 qualitative software is used to assist data analysis. The research found the keyword clusters most frequently discussed in the Indonesian Online News collection and five case themes such as public awareness about P2P lending (user understanding), data leakage, and restriction of data access, including personal data protection, personal data fraud, illegal fintech lending, and Product marketing ethics.

User acceptance factors of hospital information systems and related technologies: Systematic review.

This study reviews the literature on the most important acceptance factors associated with Hospital Information Systems (HIS) and related technologies based on user groups' perspectives (medical staff, hospital management, administrative personnel, patient, medical student, and IT staff), which can assist researchers and hospital management to develop suitable acceptance models to improve the quality of HIS. We conducted searches in online databases with large repositories of academic studies, written in English and fully accessible by the authors. The articles being reviewed are related to health information technology (HIT), clinical information systems (CIS), HIS, electronic medical records (EMR), telemedicine or telehealth, picture archiving and communication systems (PACS), radio frequency identification (RFID), and computerized physician order entry (CPOE), where the use of most of those applications and technologies is highly integrated. A predefined string was used to extract 1,005 articles, and the results were reviewed and checked. The results of this study found 15 user acceptance factors related to HIS and related technologies that were frequently identified by a minimum of five previous studies. These factors were related to individual, technological, and organizational factors. In addition, HIS and related technologies' user acceptance factors in each user group describe different results.

Hospital information system institutionalization processes in indonesian public, government-owned and privately owned hospitals.

The Hospital Information System (HIS) could help hospitals as a public entity to provide optimal health services. One of the main challenges of HIS implementation is an institutional change. Using institutional theory as the analytical lens, this study aims to explain the institutionalization of HIS as an instance of e-health initiatives in Indonesia. Furthermore, this paper aims for hospital management and researchers to improve the understanding of the social forces that influence hospital personnel's HIS acceptance within an organizational context. We use case studies from four public, government-owned hospitals and four privately owned (public and specialty) hospitals to explain the HIS institutionalization process by exploring the three concepts of institutional theory: institutional isomorphism, institutional logic, and institutional entrepreneurship. This study reveals that differences exist between public, government-owned and private hospitals with regard to the institutionalization process: public, government-owned hospitals' management is more motivated to implement HIS to comply with the regulations, while private hospitals' management views HIS as an urgent requirement that must be achieved. The study findings also reveal that various institutional isomorphism mechanisms and forms of institutional logic emerge during the process. Finally, three factors-self-efficacy, social influence, and management support-have a significant influence on the individual acceptance of HIS.

Pixel color clustering of multi-temporally acquired digital photographs of a rice canopy by luminosity-normalization and pseudo-red-green-blue color imaging.

Red-green-blue (RGB) channels of RGB digital photographs were loaded with luminosity-adjusted R, G, and completely white grayscale images, respectively (RGwhtB method), or R, G, and R + G (RGB yellow) grayscale images, respectively (RGrgbyB method), to adjust the brightness of the entire area of multi-temporally acquired color digital photographs of a rice canopy. From the RGwhtB or RGrgbyB pseudocolor image, cyan, magenta, CMYK yellow, black, L*, a*, and b* grayscale images were prepared. Using these grayscale images and R, G, and RGB yellow grayscale images, the luminosity-adjusted pixels of the canopy photographs were statistically clustered. With the RGrgbyB and the RGwhtB methods, seven and five major color clusters were given, respectively. The RGrgbyB method showed clear differences among three rice growth stages, and the vegetative stage was further divided into two substages. The RGwhtB method could not clearly discriminate between the second vegetative and midseason stages. The relative advantages of the RGrgbyB method were attributed to the R, G, B, magenta, yellow, L*, and a* grayscale images that contained richer information to show the colorimetrical differences among objects than those of the RGwhtB method. The comparison of rice canopy colors at different time points was enabled by the pseudocolor imaging method.

Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma.

Previous studies have been conducted in gene expression profiling to identify groups of genes that characterize the colorectal carcinoma disease. Despite the success of previous attempts to identify groups of genes in the progression of the colorectal carcinoma disease, their methods either require subjective interpretation of the number of clusters, or lack stability during different runs of the algorithms. All of which limits the usefulness of these methods. In this study, we propose an enhanced algorithm that provides stability and robustness in identifying differentially expressed genes in an expression profile analysis. Our proposed algorithm uses multiple clustering algorithms under the consensus clustering framework. The results of the experiment show that the robustness of our method provides a consistent structure of clusters, similar to the structure found in the previous study. Furthermore, our algorithm outperforms any single clustering algorithms in terms of the cluster quality score.