Collective action : improving smallholder rice farmers' value chain in Yogyakarta, Indonesia : a thesis presented in partial fulfilment of the requirements for the degree of Master of AgriCommerce, at Massey University, Manawatu, New Zealand

Collective action has been widely accepted as one of the strategies to improve smallholder farmers’ capability to gain benefit from the agrifood value chain. This is also part of the working policy of the Government of Indonesia. Nevertheless, there is little empirical evidence for staple food farmers, particularly rice, in organising collective action and many such attempts have not met the policy’s implementation objectives. Considering the importance of rice agribusiness in Indonesia, therefore, there is a need to investigate experiences of smallholder rice farmers who work collectively and are able to improve their value chain and gaining benefit from it. The objectives of this study were to identify and describe what benefit captured through collective action and how, and; to identify and describe how these farmers act collectively within a group and why. The research question was answered and objectives addressed by using a qualitative single case study. A farmer group named Gapoktan Sidomulyo was selected, as it was identified by the central and local government as a well-developed collective farmers’ group. Data was collected through semi-structured interviews with farmers and other actors relevant to the group development. This study found that collective action helped smallholder rice farmers to build a competitive advantage. This action enabled them to improve production capacity and product quality, as well as human capability and bargaining power. This also helped them to reduce the number of intermediaries. Therefore, they can capture the potential value offered by the rice value chain. This study also highligted essential factors for smallholder rice farmers’ collective action: Firstly, this action required incentives and support as well as a motivated group of farmers. Even when collective action was supported by government, it was essential to motivate farmers to act collectively and see the benefits for doing so. Secondly, trust and a shared vision between members of the farmer group was important element for collective action. These formed the basis for building horizontal relationships between farmers. This affected the reciprocity between them and their commitment. Thirdly, in a group that was heterogeneous, in terms of religion and reliance on farming as an income source, group cohesion could be achieved through effective group management, which means management that promoting transparency and active communication between farmers and the leadership team, and giving an opportunity for each actor within the group to play their role. These reduced the potential of conflict and maintain the farmers’ awareness on the group so that they keep engaged within the group. Fourthly, leadership with strong motivation, good interpersonal skills, social awareness, as well as administration and marketing skills were essential for the group’s development. Unlike to what has been identified in many studies, the leadership could also be provided by a team of people, instead of relying on an individual. Fifthly, maintaining the active members and the leadership team’s participation was essential as they were the key actors within the group. For the active farmers, this was achieved through: facilitating members to raise their voice and be involved in decision making, involving them to enforce rules, and conducting activity that attract them to attend regular meetings. Meanwhile, for the leadership team members, this could be achieved through conducting an appropriate leadership team selection process and acknowledging their effort in fostering the group. Lastly, despite there was a culture to work as a group, it was important for having trusted external agents to facilitate farmers and motivate them to act collectively, particularly when this required money in initiating the action. The support from external agents, such as technology and finance, was also important to build farmers capability in improving the value chain. In addition, this case highlighted that only some farmers were able to gain benefit through this action and they were who can produce consistently volume beyond their household requirements. Keywords: Smallholder farmers, collective action, rice value chain, agriculture, rice, Yogyakarta, Indonesia

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient’s chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others

Energy Based Performance analysis of AODV Routing Protocol under TCP and UDP Environments

Mobile Ad hoc Network (MANET) is a combination of wireless nodes that share resources and information. One of the major issues in MANET is to minimize the energy consumption of wireless nodes. Higher energy consumption nodes minimize the network life while lower energy consumption nodes increase the network life. Various routing protocols have been proposed for energy saving. Ad hoc On-demand Distance Vector (AODV) is an energy efficient routing protocol. In this paper, the energy based performance of AODV routing protocol is evaluated under Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) by using different simulation scenarios. NS2 has been used for simulation purposes. An energy model is defined in which power for receiving and transmitting one packet, initial energy, sleep power, idle power, transition power and transition time values are kept constant for different simulation scenarios. The simulation results show that AODV routing protocol consumes less energy in TCP environment as compared to UDP environment

The Impact of Temperature and Frying Time on Tempe Chips’ Quality and Consumer Acceptance: The Impact Of Temperature And Frying Time On Tempe Chip’s Quality And Consumer Acceptance

As the main process in tempe chip production, frying can change food properties. This change will influence product quality and consumer acceptance. Temperature and frying time are one of the factors that affect the frying process. Therefore, it is important to understand the optimum temperature and length of time to produce tempe chip that is accepted by consumers. The aim of this research was to know the impact of temperature and length of frying time on water content, protein, crispiness, and consumer acceptance of this chip. This research applied a combination of temperature and frying time that are 140 oC, 150 oC, and 160 oC, and 3, 5, and 7 minutes. The analyzed parameter are water content, protein, and texture. This study also involved an organoleptic test: color, taste, aroma, and crispiness. This study shows that temperature and frying time affect the tempe chip characteristics. The best tempe chip based on its consumer acceptance was the chip that was fried at 160oC in 7 minutes (T3t3). The average acceptance score from T3t3 treatment was 4,2 and the characteristics of this tempe chip were: water content 4,09%; protein content 9,53%; and compression 0,87 N/cm2.Penggorengan sebagai proses utama dalam produksi keripik tempe dapat menyebabkan perubahan pada bahan pangan. Perubahan tersebut dapat mempengaruhi mutu produk dan tingkat penerimaan konsumen. Suhu dan waktu merupakan salah satu faktor yang dapat mempengaruhi mutu produk dalam proses penggorengan. Oleh karena itu perlu dikaji mengenai suhu dan lama waktu penggorengan yang terbaik untuk menghasilkan keripik tempe yang disukai konsumen. Tujuan dari penelitian ini adalah untuk mengetahui pengaruh suhu dan lama waktu penggorengan terhadap kadar air, kadar protein, tingkat kerenyahan serta penerimaan konsumen terhadap produk keripik tempe tersebut. Perlakuan yang digunakan adalah kombinasi dari suhu penggorengan 140 oC, 150 oC, dan160 oC dengan lama waktu penggorengan 3, 5, dan 7 menit. Sedangkan parameter yang dikaji adalah kadar air, protein, tingkat kerenyahan, dan uji organoleptik (warna, rasa, aroma, dan kerenyahan). Hasil penelitian menunjukkan bahwa keripik tempe terbaik adalah pada perlakuan penggorengan dengan suhu 160oC dan waktu penggorengan selama 7 menit (T3t3) dengan nilai kadar air sebesar 4.06%, tingkat kerenyahan sebesar 87.125,41 N/m2 dan kadar protein sebesar yaitu 9,53%, dengan rata-rata penerimaan uji organoleptik keseluruhan 4,2 (sangat suka)

LI-FI Become IOE: A Novel Architecture for LED

Abstract. Nowadays LI-FI attracts the intention of the whole world. With in no time, the idea of LI-FI spread in the field of wireless communication technology. The working of LI-FI is purely based upon visible light communication in the form of LED. Due to VLC and LED LI-FI not only become an important part of the internet of things (IOT) but also internet of everything (IOE). LI-FI deal with a large amount of data rate, which is in the range of several Mbs to Gbs. Different parameters are affected by the performance of data rate. The proper arrangement of LED play part and parcel role in this new technology so there needs to investigate the novel architecture and flow control protocol in LI-FI system. The dimension of LED and the position of LED lead this technology towards high data rate. So, this paper presents the actual technique and novel architecture that tells us how to increase the data rate in front of different parameters

Participants Ranking Algorithm for Crowdsensing in Mobile Communication

As mobile technology is becoming more advance, the uses of this technology is increasing day by day. The mobile phone is used by everyone and it became the necessity of life. Today, smart devices are flooding the internet with data that are everywhere and in any form. Crowd sensing is a new sensing model which depends on the strength of mobile devices. One of the key challenges in mobile crowd sensing system is multiple selections of participants with low priority to perform tasks. This research work presents the concept of Crowd sensing along with the raking process of participants from a large user pool. This article provides the efficient raking process of participants to assign the priorities for performing tasks in smooth manner

Advancing brain tumor detection: harnessing the Swin Transformer’s power for accurate classification and performance analysis

The accurate detection of brain tumors through medical imaging is paramount for precise diagnoses and effective treatment strategies. In this study, we introduce an innovative and robust methodology that capitalizes on the transformative potential of the Swin Transformer architecture for meticulous brain tumor image classification. Our approach handles the classification of brain tumors across four distinct categories: glioma, meningioma, non-tumor, and pituitary, leveraging a dataset comprising 2,870 images. Employing the Swin Transformer architecture, our method intricately integrates a multifaceted pipeline encompassing sophisticated preprocessing, intricate feature extraction mechanisms, and a highly nuanced classification framework. Utilizing 21 matrices for performance evaluation across all four classes, these matrices provide a detailed insight into the model’s behavior throughout the learning process, furthermore showcasing a graphical representation of confusion matrix, training and validation loss and accuracy. The standout performance parameter, accuracy, stands at an impressive 97%. This achievement outperforms established models like CNN, DCNN, ViT, and their variants in brain tumor classification. Our methodology’s robustness and exceptional accuracy showcase its potential as a pioneering model in this domain, promising substantial advancements in accurate tumor identification and classification, thereby contributing significantly to the landscape of medical image analysis

A Novel Routing Protocol Based on Elliptical Shaped Movement of Autonomous Underwater Vehicles in Data Gathering Process for Underwater Wireless Sensor Network

High end-to-end delay is a significant challenge in the data collection process in the underwater environment. Autonomous Underwater Vehicles (AUVs) are a considerably reliable source of data collection if they have significant trajectory movement. Therefore, in this paper, a new routing algorithm known as Elliptical Shaped Efficient Data Gathering (ESEDG) is introduced for the AUV movement. ESEDG is divided into two phases: first, an elliptical trajectory has been designed for the horizontal movement of the AUV. In the second phase, the AUV gathers data from Gateway Nodes (GNs) which are associated with Member Nodes (MNs). For their association, an end-to-end delay model is also presented in ESEDG. The hierarchy of data collection is as follows: MNs send data to GNs, the AUV receives data from GNs, and forwards it to the sink node. Furthermore, the ESEDG was evaluated on the network simulator NS-3 version 3.35, and the results were compared to existing data collection routing protocols DSG–DGA, AEEDCO, AEEDCO-A, ALP, SEDG, and AEDG. In terms of network throughput, end-to-end delay, lifetime, path loss, and energy consumption, the results showed that ESEDG outperformed the baseline routing protocols

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques

Global horizontal irradiance prediction for renewable energy system in Najran and Riyadh

Producing and supplying energy efficiently are important for many countries. Using models to predict energy production can help reduce costs, improve efficiency, and make energy systems work better. This research predicts solar electricity production in the Najran and Riyadh regions of Saudi Arabia by analyzing 14 weather factors. The weather factors that were considered in the study include date, time, Global Horizontal Irradiance (GHI), clear sky, top of atmosphere, code, temperature, relative humidity, pressure, wind speed, wind direction, rainfall, snowfall, and snow depth. GHI is the most important factor because it determines how much solar energy a system can produce. Therefore, it is important to be able to predict GHI accurately. This study used a variety of data-driven models to predict GHI, including the elastic net regression, linear regression, random forest, k-nearest neighbor, gradient boosting regressor, light gradient boosting regressor, extreme gradient boosting regressor, and decision tree regressor. The models were evaluated using a set of metrics, including the mean absolute error, mean squared error, root mean square error, coefficient of determination (R2), and adjusted coefficient of determination. This study found that the decision tree regression, Random Forest (RF), and Extreme Gradient Boosting (XGB) models performed better in the Riyadh region than in the Najran region. The R2 values for the Riyadh region were 99%, 99%, and 98%, while the R2 values for the Najran region were 89%, 94%, and 94%. This suggests that the Riyadh region is a more suitable location for solar energy conversion systems. These findings are important for policymakers and investors who are considering the development of solar energy projects in Saudi Arabia