Software Engineering (SE) is a discipline, concept, method that focus on all aspects related to software production. SE must adapt organized system and approach in building software. The objective of this article is to discuss needs elicitation in SE, so that the design can be done through utilizing appropriate tools and techniques based on the problem to be solved, restrictions and need that must be met, as well as resource availability. The method used in this article is literature review as a major source of problem analy-sis. In the next phase, the main topic of the article is clarified, validated, and verified by the model of the Focus Group Discussion (FGD). The respondents of FGD are lecturers of Informatics Department at UIN Sunan Gunung Djati Bandung. Requirements elici-tation is in-dept and comprehensive process of finding information from all stakeholders in relation to the built software. The result of requirements elicitation is the explanation from every stakeholder with natural language related to the will and interest of the built software. The mechanism of finding information can be done through observation, questionnaire, interview, and documentary study, which are then tested with data validation and verification in FGD. The product of requirements elicitation is the User Re-quirement Document (URD) that can be used as an early artifact in software development process. Furthermore, URD is used as a reference in tracking the suitability between the function of software and the needs of stakeholder. This article formulated analysis approach of SE need, so that it is easily modified, adaptive to growing software complexity, and connected with the analysis of hardware.

Requirements elicitation in software engineering

The agriculture being the major source of revenue for the people in the rural areas in a country like India many measures have been taken to improve the revenue of the farmers. The assessing the ripeness of the fruits is also one of the primary responsibilities of the farmer to fix the proper time to glean the fruits and taking them to the market. Computer vision and the image processing together remains as a key methodology in reducing the human labor and classifying the different stages of maturity is has become very prominent in examining the stages of the fruit growth. As the color seems to be the important feature to be observed in determining the different stages of the fruits maturity and color identification of the tropic fruits is difficult due to the various illuminations and the partial clogging up of the fruits. In order to identify maturity stage of the fruits the paper proposes a RGBD analysis to examine the stages of the fruits and identify the various stages of maturity.

https://www.irojournals.com/iroiip/V1/I2/06.pdf

Rgbd analysis for finding the different stages of maturity of fruits in farming

Higher education over the past decade has experienced several view changes that include: paradigm shift, management and competi-tion changes, and so forth. The only university supported by the excellent IT that will be able to enhance and maintain its ad-vantages. The purpose of this article is reviewing the strategic design of IT implementation at the university. The analysis used to reveal the fact, problem and problem-solving in this article adopts the model of Strategic Management of Information Technology (SMIT) developed by Flodstrom. The strategy design of IT implementation based on the competitive environment of UIN Sunan Gunung Djati Bandung is coordinated with its business strategy. The role of IT as an enabler or tool that allow the university to be able to create cheaper-better-faster educational process. In this article, there are designed two types of IT Functions: back office and front office. For back office application function, there is a software management with specific modules to support the implementa-tion of university management. In the front office function of IT usage, there are some types of IT usage concepts that directly and indirectly affect the way of establishing education that leads to quality improvement which can be implemented through: media simulation, course management, virtual class, computer-based training (CBT), knowledge portal and cyber community. The strate-gic design of IT implementation presented in this article is a generic model of the plan, design, and implementation of IT in the object of study.

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Strategic Design of Information System Implementation at University

This article aims to implement the algorithm model of k-Nearest Neighbor (k-NN) in analyzing, predicting, and classifying students who have potentials to get scholarships in universities. The k-NN algorithm works by making a prediction based on the closest data points between the old data history as training data and the new data as testing data. The data collected totals 1018 students with 24 scholarship receiver candidate students are used as the dataset for the test purposes. The attributes used in the prediction process are a semester, parents' income, number of family dependents, and Cumulative Grade Point Average. The distance calculation of the value from testing attribute to each training attribute uses Euclidean Distance equation, while the test of the model accuracy value is calculated using Confusion Matrix. The results of the simulation of the prediction model show that the determining factor of training data from both the number and the variation of different values can improve the performance of the k-NN algorithm with the best accuracy rate of 95.83 percent in predicting students who have the greatest chance of getting the scholarship.

https://iopscience.iop.org/article/10.1088/1757-899X/434/1/012039/pdf

The prediction of scholarship recipients in higher education using k-Nearest neighbor algorithm

The main enterprise of the Philippine agriculture sector is crop cultivation where tomato is deliberated as one of the major crops in the country. With the abundance on tomato production, ripeness classification becomes fairly laborious and challenging, not to mention the subjective visual interpretation of human graders grounded from practical experience that is easily influenced by the environment and prone to error. Thus, this study proposes an automatic tomato ripeness identification using Support Vector Machine (SVM) classifier and CIELab color space via a machine learning approach. Dataset used for modeling and validation experiment in a 5-fold cross-validation strategy was composed of 900 images assembled from a farm and various image search engines. Divided into six classes that represent tomato ripening stages, experimental results showed that the proposed method was successful with 83.39% accuracy in ripeness classification detection. With this machine learning approach and combination of image processing techniques, the agriculture industry could benefit by automating the ripeness estimation which then could save tomatoes from damage.

https://manuelgarcia.info/media/full_paper/tomayto-tomahto.pdf

Tomayto, Tomahto: A Machine Learning Approach for Tomato Ripening Stage Identification Using Pixel-Based Color Image Classification

Manual laxity of fruit ripeness classification is highly influenced by operator subjectivity, thus there is inconsistency for some periods in the classification process. Information Technology development allows fruit identification based on color characteristic by computer aids. A developed system was designed to work on a mobile device with the ability to detect four levels of ripeness of tomato and chili fruits. The acquisition of training data is done with a new approach. Training data came from objective observation of the same fruit of tomato and chili, captured since one month before harvesting until harvesting period. Image segmentation uses K-Means Clustering Method while ripeness detection uses fuzzy logic. The system output consists of types and level of ripeness grouped into four categories: unripe 1, unripe 2, medium, and ripe. This article explains preliminary results of the testing system in static and partial condition using a personal computer before being applied into a mobile-based integrated system. The results showed the level of success for fruit segmentation was 80% for tomato and 100% for chili. The fault is due to the similarity of fruit sample size. The level of success for detecting fruit ripeness is 80% for tomato and 90% for chili. By 10 training data of each, it is shown that the good result with an overall accuracy level of average ripeness detection is 85%.

https://iopscience.iop.org/article/10.1088/1757-899X/288/1/012018/pdf

K. Komarujaman

Papers

Design of smart system to detect ripeness of tomato and chili with new approach in data acquisition