1. Introduction

2. Literature Review

3. Materials and Methods

3.1. Research Area

In the 1990s, there is a fast conversion from dry land and swamp forests to residential neighborhoods and agricultural rice fields in the upstream regions of the Cidanau watershed and Rawadanau swamp (midstream of Cidanau watershed) due to poverty [40,41,42]. This generates chemical build-up from fertilizers because the lack of sewage treatment degrades the water quality [43,44,45]. According to [46], rehabilitation programs, security patrols, surveillance, and infrastructure upgrades are efforts made to prevent the problem related to a top-down policy. This helps to reduce the Cidanau watershed's role and satisfy water requirements, specifically during the dry season.

The Cidanau watershed assists to deliver sufficient and high-quality water to prevent harm to upstream ecosystems [47,48,49,50,51]. Cidanau watershed is established to generate economic growth in Banten Province's western section [40,52], by supplying industrial and domestic raw water, as well as becoming a reservoir with a suitable outflow [53].

The water quality and quantity become an impediment to industrial and household demands, socioeconomic, ecological, and environmental growth if there is available water [40,54,55]. In the middle of the Cidanau watershed, there is a 2,500-hectare Rawa Danau Nature Conservations area [56]. According to [56], this area is the main reservoir that exists on Java island. Based on previous study, indicated that the Cidanau watershed become a critical raw water supply that is far more than satisfying the societies because it has an average discharge of 80–100 liters/second.

Figure 1. Research area of CWM. Sources : Env. and Forestry Agency of Banten Province

Figure 1. Research area of CWM. Sources : Env. and Forestry Agency of Banten Province

3.2. Social Networks Analysis Method

In the context of Cidanau Watershed Management (CWM), the logical collection of real data necessitates the utilization of analysis tools and appropriate methodologies, owing to the diverse array of stakeholders involved. According to [14], social network analysis is conducted using six steps.

Figure 2. Procedures of social network analysis. Sources: [14]

Figure 2. Procedures of social network analysis. Sources: [14]

The first stage is to identify the actors (individuals, government, non-government organization, public organizations, industry, university, etc) involved in CWM. The Researchers identified 22 actors involved in CWM from upstream to downstream of the Cidanau watershed. To make it easier to analyze, the researcher uses an abbreviation (node variable) for each actor involved. Actor data involved in CWM, Table 1 displays the aforementioned information.

The second is to administer effective questionnaires or surveys to stakeholders for SNA data collection and analysis. he next step is to collect, record and store the data. After Data collection was carried out by means of a Limited Group Discussion Forum for participants/actors involved in CWM. The data generated through conversation to informant’s interview involved and understanding the trajectory on CWM to elicit facts or statements from the interviewer (see table 1). The Group Discussion Forum is conducted for representatives of actors who have knowledge and involved in CWM. Participants asked to fill in a matrix showing the relationship between one actor and another. The existence of a relationship between actors is given a score of 1, while there is no relationship between actors is given a score of 0.

The third step involves the collection of data from actors. Specifically, data is gathered from participants who are part of the identified network, utilizing the questionnaire that was developed in Step 2. This step aims to identify both the actors and participants and the ties (which serve as a common source of information). Currently, it is customary for Social Network Analysis (SNA) to be conducted using specialized software. In such cases, the data needs to be stored in a format that is compatible with the software. This often entails storing the data in commonly used software packages like Microsoft Excel or Access. However, in situations where specialist software like UCINET is being utilized, the data is directly input into that software package. Figure 3 provides a comprehensive overview of the relationships among actors in the Contextual Web Mining (CWM) framework.

Figure 3. Matrix data set of Cidanau watershed stakeholders

Figure 3. Matrix data set of Cidanau watershed stakeholders

The fourth step is input data to UCINET 6 (a user-friendly SNA package for analysis of social network data) [3,57]. This step will produce a matrix of residents and their sources of information for landscaping decisions and ties between them. Presenting data in picture or diagram form is normally the easiest part of SNA. Once the information has been collected and stored then computer software can be used to generate a map (or different maps) of the network. There are also other ways in which data can be presented, including matrices, graphs, tables and plots. These are described in the following section.

Furthermore, the fifth step is to draw the network and identify the interconnection between nodes. It is important to note that SNA software can help present and display data with a view to supporting analysis, but the analysis itself needs to come from human beings. The final step is to recommend appropriate action and analyze the data which is presented and displayed by the SNA. The Data collected through limited group discussion to key actors deliberated CWM.

The present study employed a social network as a scientific methodology to evaluate the nature of relationships that exist among actor [32,34,35]. UCINET software used to analysis complex relationships among diverse individuals in a single group or social network[13,57]. UCINET is a computer program of a general purpose designed for network analysis [10,13]. UCINET is a comprehensive software package that facilitates the analysis of social network data, along with other 1-mode and 2-mode data. While it can accommodate a maximum of 32,767 nodes (with certain exceptions), the practical efficiency of various procedures tends to diminish when dealing with 5,000 - 10,000 nodes. This user-friendly program encompasses fundamental graph theoretical concepts, positional analysis, and multidimensional scaling, making it a valuable tool for social network analysis.

4. Results

4.2. Sociogram Analysis

The sociogram visually represents the social interactions among actors in CWM by conceptualizing individuals or organizations as nodes and their relationships as ties between the nodes. In the sociogram, two nodes that have a relationship are connected by a tie, while nodes without a tie indicate the absence of a relationship. Nodes are differentiated by color, size, and shape, reflecting individual-level characteristics, whereas ties are represented by various characteristics of the relationship, such as communication frequency or relationship strength. Through the mapping of connections among actors, the network analysis reveals the variations in influence, interests, and objectives of each actor involved in CWM. Some actors possess significant influence, while others have limited influence within the CWM context. There are actors who have high influence, but there are those who have low influence in CWM. There are actors who have certain interests and there are actors who have less interest in CWM. However, all actors involved in the management of the Cidanau Watershed have made a common-sense commitment to preserve the Cidanau Watershed for sustainability.

4.2.1. Network Degree Centrality of CWM

A highly centralized network is one in which a small number of persons or organizations control a greater fraction of the connections than is proportional to their size [34]. When regarded in this node size, the metric is a useful tool for considering power and equity in a network in terms of how the network is structurally structured. Degree network centrality of CWM is measures how important a person or organization is to a network or the power of an actors in a network. Centrality used to identify essential members in a CWM network. Depending on the metric utilized, centrality measures reflect the extent to which a network is directly connected to numerous others (degree centrality), indirectly linked to many others (closeness centrality), or the degree to which an individual exercises control over information or resources within the network and acts as a critical intermediary between multiple nodes (betweenness centrality).

The centrality index identifies nodes that hold critical positions within a network, often associated with exceptional leadership, high popularity, or an excellent reputation within the network. Central positions always get equated with remarkable leadership, good popularity or excellent reputation in the network. Figure 4, demonstrates that Fkdc is the major node of interaction and network for all entities participating in CWM. The larger size of the Fkdc nodes signifies a higher degree centrality, indicating that node Fkfdc attained the maximum frequency. High centrality ratings define actors in networks with the most structural meaning, and these individuals are expected to play a prominent part in both simulated and real-world behaviors[5]. Furthermore, Fkdc demonstrated the highest level of trust among all other actors involved in CWM. The second highest degree centrality is Rg2, because it was the most connecting to others actor in CWM. Fkdc and RG2 had higher social influence, promoting trust links and influencing network dynamics in the management of the Cidanau watershed. The lowest degree centrality is node Hs (households), because only one ties connected to node Pdam (District water services company). Households is the passive actors and less network to other stakeholder, but it should be educated to be wise as water user.

Figure 4. Trust network degree centrality index of CWM

Figure 4. Trust network degree centrality index of CWM

4.2.2. Trust Network Due to Output Betweenness Centrality

Betweenness centrality reveals the presence of actors holding distinctive positions within a network, serving as communication bridges that other actors must pass through to connect with other network members. The greater the number of actors relying on such intermediaries for communication, the higher their influence. Nodes with high betweenness centrality values play a significant role in facilitating communication within the network. Betweenness centrality considers the number of shortest paths in the network that pass through a node, taking into account the connectivity of the node's neighbors and assigning greater value to nodes that bridge clusters.

Figure 5 illustrates that Fkdc attained the highest betweenness centrality, positioning it as one of the key actors within the trust network of stakeholders in CWM. Fkdc occupies a central position within the network, strategically positioned to exercise significant control and serve as a broker for transactions among other stakeholders. All actors are connected to Fkdc, this shows that Fkdc is trusted mediator for others in CWM network. Rg2 also has high betweenness centrality, beside Fkdc. Rg2 is a government institution at the provincial level, which acts as an intermediary for stakeholders, especially for other government institutions. Households have the lowest betweenness centrality, this is because households are passive actors (water users) and are not directly involved in CWM management. Households only indirectly connected to Pdam.

Figure 5. Trust network due to output Betweenness Centrality.

Figure 5. Trust network due to output Betweenness Centrality.

4.2.3. The trust network exhibits output closeness centrality as a determinant factor

Closeness centrality, which is defined as the reciprocal of the average geodesic distance, serves as a measure of proximity within a network. It represents the average of all geodesic distances between a given actor and other actors in the network. A higher value in closeness centrality indicates that the node is reachable with fewer links, reflecting the actor's ability to quickly access others within the network.

In Figure 6, it can be observed that Households (Hs) and University (Univ), represented by the purple color, have the shortest geodesic distance with other stakeholders in the network and possess the highest closeness centrality. Households, as water users, are directly connected to Pdam, the local public water services company. Similarly, the University is connected to Fkdc, which serves as the central node in the network. The high closeness centrality of these nodes indicates their effectiveness in exchanging content within the network, their easy accessibility to other nodes (stakeholders), and their crucial role in distributing information or content among other stakeholder groups in the CWM network. This implies that if an idea or information exists somewhere in the network, it is likely to reach a node with high closeness centrality after a few interactions.

Figure 6. The trust network exhibits output closeness centrality as a determinant factor

Figure 6. The trust network exhibits output closeness centrality as a determinant factor

Figure 7. The trust network demonstrates reciprocal degree as a determining factor

Figure 7. The trust network demonstrates reciprocal degree as a determining factor

Reciprocal degree serves as a metric to depict the presence of reciprocal relationships among actors and the level of sustainability and cohesion within the network. In networks where an actor can select another actor in multiple instances, the links between nodes can be assigned weights. The strength of a node is measured by the cumulative weight of its links and can be seen as analogous to the degree of a node. Typically, two types of strength are considered: in strength, which represents the sum of weighted links directed towards a node, and out strength, which represents the sum of weighted links originating from a node.

Fkdc has the largest number of links (in strength), in this case Fkdc can be a facilitator node for other actors. This shows that Fkdc is a trusted and important actor in the network because almost all actors involved in CWM have ties to Fkdc. Meanwhile, in strength, Fkdc only has 2 links, namely links to Il and Pht, because both actors are parties whose level of involvement in CWM is low.

Figure 8 displays the values of betweenness centrality, closeness centrality, and degree centrality. Fkdc has the highest centrality betweenness score but the lowest centrality closeness. This shows that Fkdc has the highest degree of a node, which means it is connected to the most nodes and has the highest geodesic distance. Whereas Hs (Household) has the lowest node degree, which means it has the fewest connected nodes with the shortest geodesic distance. In contrast, households have the lowest centrality betweenness but the highest centrality closeness. The inverse of the mean geodesic distance is centrality (closeness). This implies that if an idea exists somewhere in the network, some version of it will reach a node with a high degree of closeness after a few interactions.

Figure 8. Fkdc formalize as Cidanau watershed management.

Figure 8. Fkdc formalize as Cidanau watershed management.

The Fkdc was initiated by the local government elements (Rda, Rdb, Rdc), Rekonvasi Bumi (Rkb), and Krakatau Tirta Industri (Kti) to become a collective institution on performing the Cidanau watershed management. Meanwhile, decentralization is not always synonymous with the government's role in the CWM[58]. This makes the Banten Provincial government strengthen Fkdc in the connectivity network and driving the society to be involved in managing Cidanau water resources. However, the Banten government plays an important role by allowing public and private sectors to prevent conflicts and water crises in the future [59]. Certain parties were resistant to the government's domination to enable it to hinder or cause the failure of a watershed. In the CWM, FKDC is a collective institution that builds commitment and integration between stakeholders because it allows knowledge sharing and strengthens societal participation.

5. Discussion

5.1. Network Cluster of CWM

Total link strength can be used to measure the linkages between entities and determine how these entities are connected to other entities. For example, if an entity has many links that connect to other entities, then that entity may have a high total link strength. Conversely, if an entity only has a few links that connect to other entities, then that entity may have a low total link strength. Total link strength can be used to group entities based on their relatedness, or to discover relationships that might not be apparent in other ways

Based on Figure 9, the researcher found actors cluster network in CWM. Clustering is based on total strength networks of a node/s. Cluster one is the Fkdc (size and green color) nodes which are the center cluster with the largest number of networks. Fkdc is a source of information and a share network for almost all actors involved in CWM. The second cluster is Rg2, Rg1, Rd, Rd1, Rd2 which is moderate influence for collaborative center network of other actors. Rg1, Rg2, Rd, Rd1, Rd2 are government institutions in provincial level and district level that have authority in implementing policies and strengthening networks in watershed management. Government institutions still have a major role in building networks between CWM stakeholders. The 3rd cluster is Kti and Pdam, which is a cluster, building a network with water users, both industry and households into three clusters. Cluster 4 is only connected to a few actors' interactions and networks, and it comprises University (Univ), farmers (Fmr), NGO (Ingo), household, Rkb, as well as Perhutani (Pht).

Figure 9. Network cluster of CWM

Figure 9. Network cluster of CWM

6. Conclusions

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