Nowadays technological developments are increasingly having a positive influence on the development of human life, including in the health sector. One of them is an expert system that can transfer an expert's knowledge into a computer application to simplify and speed up the diagnosis of a disorder or disease in humans. The purpose of this final project is to design an application to diagnose diseases that occur during pregnancy which is caused by the existence of these pregnancies to simplify and speed up the diagnosis of diseases experienced by pregnant women. This study uses the forward chaining method. By involving experts in this expert system analysis according to current needs. Users are given easy access to information on several types of pregnancy disorders and their symptoms, as well as consultation through several questions that the user must answer to find out the results of the diagnosis. While experts are facilitated in system management, both the process of adding, updating and, deleting data.

Errors that occur in solving problems in strawberry plants (Fragaria Xananassa) such as the presence of leaf patches, fruit rot, perforated leaves, and insect pests can be the cause of not maximum in harvest time. The farmers and the general public who planted strawberry (Fragaria Xananassa) need to know the proper treatment of diseases and pests so that future yields as expected. Therefore, it takes an application as a solution in the delivery of information related to the problems that are often encountered in strawberry plants (Fragaria Xananassa). Methods of production rules can be used to diagnose the disease strawberry (Fragaria Xananassa) based on signs or symptoms that occur in the parts of plants and strawberry, the results of diagnosis using this method are the same as we do Consultation on experts.  The purpose of this study was to determine the early diagnosis of disease in strawberry plants (Fragaria Xananassa) based on signs or symptoms that occur in the plant and fruit parts. The results of the analysis of this study showed that the validation of disease and symptom data in strawberry plants (Fragaria Xananassa) reached 99%, meaning that between the data of symptoms and disease understudy the accuracy was guaranteed with the experts.

Perkembangan teknologi yang sangat pesat membuat para pelaku bisnis mulai melakukan proses jual-beli secara online, hal ini membuat banyak e-commerce mulai bermunculan di Indonesia yang membuat persaingan bisnis pun berpindah menjadi online, salah satunya adalah e-commerce Wonosobo Mall. Wonosobo Mall adalah e – commerce yang digunakan penulis untuk melakukan studi analisis tentang user interface terhadap kenyamanan pengguna menggunakan metode regresi linier sederhana dan usability testing. Dari 79 responden yang berpartisipasi dalam penelitian ini, 47 responden berjenis kelamin laki – laki sedangkan 32 responden berjenis kelamin perempuan. Dari pengujian yang dilakukan dapat disebut e – commerce Wonosobo Mall adalah baik. Penelitian bertujuan untuk memperoleh hasil yaitu prosentase usability testing untuk mengukur penggunaan e – commerce Wonosobo Mall. Pada penelitian ini menggunakan pengujian secara objektif dengan pengujian secara langsung kepada responden pengguna e - commerce Wonosobo Mall dengan menggunakan kuesioner yang memiliki 5 skala yaitu: learnability, efficiency, memorability, errors dan satisfaction.

Chili is a variety of crop groups that have promising business prospects. To obtain optimal agricultural yield, then the process of plant care and how to planting should be maximal. Constraints often experienced by farmers in the process of planting chili in Magelang regency of Indonesia is a disease of yellow leaves. Some diseases in plants can be identified using precision technology, one of them is by using an image or image-based technology. In previous studies, no one has analyzed using feature extraction using ACE as an analysis to detect plant disease in chili. In this study will extract features using Automated Color Equalization (ACE) which is then classified using SVM (Support Vector Machine) for disease identification based on its leaves. With this method, the accuracy of the extraction results in a combination of 80% texture features, color feature extraction, and a combination of 80% color feature texture

The process of determining promotional class in acupuncture clinics is very common. Clinics also have data i.e form of primary data and customer secondary data. This happens repeatedly and generates a build up of customer data that affects information retrieval of the data. This study aims to grouping the clinic customer data in which those who are a greater contribution value get a valuable promotion as well. The Acupuncture Clinic uses decision support system by utilizing data mining process by using Clustering technique. The method used is research action through Diagnosing process, Action planning, Action taking, Evaluating, Reflection. The algorithm used for cluster formation is the K-Means algorithm. K-Means is one of the non-hierarchical clustering data methods that can group customer data into multiple clusters based on the similarity of the data, so the customer data with similar contribution values are grouped in one cluster and those with different contribution values are grouped into other clusters. Implementation using PHP is used to find accurate values. Attributes used are customer earnings, total clinical outcomes, repeat visits, product purchases, needle types and therapists. The customer cluster formed is four clusters, with the first cluster 5 customers, the second cluster 9 customers and the third cluster a total of 6 customers and the fourth cluster there are 5 customers. The results of this study are used as one of the basic decision-making to determine promotion based on the clusters formed by the administration of acupuncture clinics.