Smart-SITA merupakan sebuah aplikasi berbasis web untuk membantu mahasiswa FTKI UNAS melakukan proses pengerjaan Tugas Akhir secara online. Dalam pembuatan suatu sistem, tiap-tiap aspek harus diterapkan dengan baik demi kenyamanan user dalam berinteraksi dengan aplikasinya yang dimana dapat dinilai dari usability User Interface (UI) dan User Experience (UX) nya. Penelitian ini memiliki tujuan, yaitu, menganalisis UI/UX dari Smart-SITA dan membuat desain solusi UI/UX nya untuk diterapkan dalam perancangan ulang Front-End Web Smart-SITA dengan menggunakan metode User Centered Design (UCD). Dalam tahapan evaluasi sistemnya, digunakan evaluasi User Experience Questionnaire (UEQ) yang diajukan kepada minimal 72 responden mahasiswa FTKI UNAS dan didapatkan hasil nilai mean dari total 6 aspek UEQ pada desain lama sebesar 0,20, lalu 1,28 untuk desain baru. Selanjutnya, testing menggunakan toolLigthouse untuk mengetahui kualitas halaman web dari Front-End WebSmart-SITA yang sudah dirancang ulang mendapat nilai meanPerformances sebesar 91, Accessibility 88,7, Best Practise 95,2.

Glaucoma is an eye disease that causes the second largest blindness after cataracts, this disease can cause decreased vision and can even be fatal, namely permanent blindness if it is not realized and treated immediately. Lack of information and education to the public to always maintain eye health is the basis for the purpose of making this expert system which aims to provide early diagnosis to people who are indicated to have glaucoma based on the symptoms or characteristics previously felt. The Naïve bayes method is a method that uses statistics and probability in predicting a person's chance of suffering from glaucoma based on the symptoms previously felt. It is made based on a website with PHP as the programming language and uses MySQL for the database. As for the comparison method used is the Certainty factor, which is a method that functions to determine a certainty value based on the calculation of the predetermined CF value by applying manual calculations. In the Naïve bayes method, the application can group symptom data and types of disease and can diagnose based on previous training data, while for the Certainty factor method based on the calculation of the value of the expert and the CF value that has been inputted by the user, it can produce a percentage of the diagnosis of the disease glaucoma in 96%.Keywords:Certainty factor, Expert System, Glaucoma, MySQL, Naïve bayes, PHP.

The Covid-19 pandemic that has occurred since 2020 entered Indonesia and has almost occurred in all parts of the world has resulted in the world being stopped due to the Covid-19 Pandemic, all its effects have been felt by all circles, one of which is business actors. Indonesia is one of the countries affected by the Covid-19 Pandemic, one of which is the Indonesian economy which is in the MSME business sector, especially the Micro sector, such as the perfume business, clothes distro, shoe shop, medium-scale traders. small down another. They are most affected by this Pandemic because since the implementation of the large social scale (PSBB) which has automatically reduced their sales turnover. Because consumers no longer come to them. Therefore, the authors created a website-based system to assist MSMEs in making sales. Which is where the buyer no longer has to come to the seller. The author hopes this website can help return to their sales turnover.

The purpose of this study is to design and develop a stunting symptom detection system in children using the Website-based certainty factor method which aims to create a stunting symptom detection system in children from an early age and help parents in maintaining nutrition in their children, indirectly said to be computerized to detect stunting means that when applied, it makes it easier for parents to monitor nutrition quickly and precisely. This study uses a system development method where the model used is Waterfall. The results of this study are in the form of an application that provides several symptoms of stunting in children then the user can conduct consultations to determine the calculation of the results of stunting symptoms correctly. The use of this certainty factor method is implemented in an expert system that is used to look for symptoms caused by stunting and in solving the case, the certainty factor obtains an accuracy of 80%.

Technology today is growing rapidly from year to year, not least started to spread to the agricultural sector. With the information technology making society more easily in search of information via the internet from your smart device. The goal of this study was made to facilitate the community, especially farmers in helping to diagnose diseases and pests in rice plants. Rice plants can be attacked by a wide variety of diseases and pests with a wide variety of symptoms experienced in rice plants. To know the kind of disease on rice plants in the era of technology, it takes an expert system that can help detect the disease in rice plants. In this study, Expert System-Based Website using Tsukamoto Fuzzy method and the Algorithm of K-Nearest Neighbor whose purpose is to help people, especially farmers in diagnosing diseases and pests in rice plants by looking at the symptoms of the attack on the rice plant. Data was obtained from the Research and the Ministry of Agriculture then taken some sample data for testing done. The results of the testing data of this expert system is the result of late diagnosis in diseases of the rice with the symptoms that already exist based on the data that have been obtained with an accuracy rate of 92,88%.