Sistem Pakar Diagnosa Penyakit pada Hewan Kucing Berbasis Web

Tahun 2018 Rakuten Insight menyurvei hewan jinak di Asia. Survei diikuti 97.000 responden dari benua asia timur dan asia tenggara. bahwa 59% orang memiliki binatang peliharaan. Dari banyaknya peliharaan, kucing menjadi pilihan, terutama diIndonesia. sebanyak 47% orang memelihara kucing. Kucing terkadang sering terkena penyakit, dan kita suka bingung apa yang terjadi dengan hewan kita, dan bagaimana cara kita bisa mengobatinya, terutama kota yang tidak memiliki rumah perawatan hewan. Untuk mengetahui penyakit apa yang diderita kucing, diperlukan informasi medis untuk mengetahui itu, sedangkan yang kita tahu masih sangat terbatas. akibatnya dibutuhkan sistem guna menyampaikan pengetahuan seperti seseorang pakar, penulis membuat sistem dimana berisi pengetahuan seseorang ahli penyakit pada kucing, agar masyarakat yang awam dapat mengetahui jenis penyakit serta penyembuhanya, rancangan sistem pakar menggunakan metode Naïve Bayes dimana pengklasifikasian probabilitasnya sederhana. keuntunganya naïve bayes hanya membutuhkan data kecil pelatihan untuk proses klasifikasi yang diperlukkan untuk parameter dalam membantu membuat sistem identifikasi penyakit. hasil contoh, kita menginputkan gejala-gejala seperti bulu rontok, lingkaran merah pada kulit, serta bercak putih seperti ketombe, dimana merupakan gejala pernyakit kadas seperti yang ada disystem. Hasil penelitian menunjukkan diagnosa penyakit kucing menggunakan naïve bayes dapat menghasilkan akurasi 93%.

Rancang Bangun Sistem Penilaian Produk Virtual Reality Berbasis Website Di PT.Shinta VR

In the era of increasingly modern technology as it a today industrial growth in the world is growing rapidly, the competition is getting tougher. One of the companies engaged on services, namely PT. Shinta VR. PT Shinta VR is engaged in serving and providing technology services for making learning videos in the field of education and as well as several types of games on interactive three dimensional form. Activities that have been running so far have shortcoming and weakness, especially in creating their product rating system, until now product assessment from customer data is still done manually or on writing. To overcome the problems that exist at PT.Shinta VR, the author intends to develop a website based product assessment application system as a VR product assessment system from customer data, product data and assessment data. The development model used is the Waterfall model. This application will describe the activities generated at each design stage. The result to be archived from this research is that the website application is able to manage customer data and assessment data more neatly, the conclusion of this study can help staff and admins optimize time and know that the system build can help staff and admins.

Sistem Pendukung Keputusan Penilaian Kinerja Tenaga Kependidikan (TENDIK) Dengan Menggunakan Metode SMARTER

The success of a university is not only seen from the role of Educators (Lecturers) but also the role of Education Personnel (TENDIK). Educational Personnel (TENDIK) includes Administrative Personnel in universities. Work performance is the main factor of the success of higher education performance. The results of good work given will have a good impact on universities. TENDIK who excels are also competent in higher education and contributes to the joint progress of both TENDIK and universities. Apart from lecturers, TENDIK is also an asset owned by universities. Rewards or awards given to TENDIK can be done by assessing the performance results of each TENDIK. The reward given to TENDIK is a form of appreciation for the performance carried out at the university. Giving rewards to TENDIK must be based on a proper and accurate performance assessment. The problem is that there is no definite reference used to evaluate the performance of TENDIK in giving rewards. The performance assessment carried out must be objective, of course, with an objective assessment the results obtained from the TENDIK performance assessment will not be a problem for other TENDIK. Decision Support System is a computer-based information system that is used to assist in decision making by utilizing certain data and models to support a solution in solving a semi-structured and non-structured problem. SMARTER is one of the methods in the Decision Support System that provides recommendations to decision makers based on relevant criteria, which in determining the criteria and sub-criteria and their weight values ​​use ROC (Rank Order Centroid). Of the several criteria used for the selection process using the alternative SMARTER method with the name A4 with a final utility value of 64.25%

Instance Selection dengan Naïve Bayes pada Klasifikasi Kanker Serviks

There are several deadly disease for woman, one of which is servical cancer. The growth and development of the disease is very slow, so that treatment if know form the beginning will facilitate the healing process, but conversely unknown cancers from the beginning will become dangereous and deadly disease due to relatively difficult healing. Biopsy action is one way to detect the presence of cancer. In the previous study, classification of cervical cancer had the bighest accuracy value of 97,515% using the decision tree method of several feature selection technique. for this reason, this research will use the decision tree or tree C4.5 classification method, logistic function and zeroR which were previously carried out processing with instance selection with Naïve Bayes by reducing the elimination of missing values with the aim of increasing the level of accuracy better than previous studies. C4.5 classification in this study has the most maximum results compared to other classification methods with an accuracy value of 99,69%.

Implementasi Metode Forward Chaining, Certainty Factor dan Dempster Shafer pada Sistem Pakar Diagnosis Penyakit Gigi dan Mulut

Dental and oral disease is one of the diseases that has been felt by most of the people. Insufficient information and the limited level of public awareness of the prevention of dental and oral diseases make the impact quite dangerous if not handled properly. An appropriate information system is needed in overcoming and providing solutions for handling a disease as early as possible. Expert systems can be used as a means of information on the treatment of dental and oral diseases. The manufacture of the expert system in this study initially used the forward chaining method, which is a method that searches based on information that is made into a set of rules so as to get a conclusion. However, after re-analysis, two other methods, namely certainty factor and dempster shafer, were also applied in this study with the aim of overcoming the shortcomings of the forward chaining method, one of which is uncertainty in producing a conclusion or diagnosis of disease. Determining the type of dental and oral disease can be known by looking at the symptoms experienced by the patient. The use of an expert system for diagnosing dental and oral diseases can be used as an initial solution in helping someone to treat the disease. The existence of this expert system can be used as consideration in making decisions to determine the type of dental and oral disease quickly, precisely and accurately.

Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine

Tourism is the sources of income which is influenced by customer satisfaction. One way to know customer satisfaction is feedback, one of which is a review using an application. One of the feedback applications is Google Review. Such applications are have been widely used, for example in this study in this case study, Summarecon Mal Bekasi, can reach 60,000 comments. To find out the sentiment of the large number of comments, it is necessary to use computational tools. The current research applies sentiment analysis using the Naïve Bayes method and the Support Vector Machine. Data retrieval is done by web scrapping technique. Furthermore, the comment data is processed by pre-processing and labelling using the Lexicon dictionary. The process of applying sentiment analysis is carried out to determine whether the comments are positive or negative. In this study, the accuracy of the Naïve Bayes and Support Vector Machine methods in conducting sentiment analysis on the Summarecon Mal Bekasi review with a data of 2,143 comments with an accuracy for Naïve Bayes and Support Vector Machine 80.95% and 100% respectively. A Jason-style application is built to show the implementation in Flask framework. Keywords:

Penerapan Data Mining Association Rule Menggunakan Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel

Many individuals are interested in starting a workshop. By responding to each customer's desires, the workshop company may continue to develop, and so the data mining technique can address this challenge. The FP-Growth algorithm is one of the methods that may be used to determine the stock availability of automotive spare components such as engine oil, spark plugs, oil filters, ac filters, batteries, tires, and so on. This research is divided into four stages: problem identification, data gathering, data processing, and data testing. Based on the results of the testing, AK (Battery), OM (Engine Oil), and BS (Spark plug) received support values of 33% and 80%, respectively. Furthermore, the BN (Ban) and KR (Kampas Bram) values were found with 33% support and 80% confidence. Furthermore, we obtain AK (Battery) and OM (Engine Oil) with 33% support and 80% confidence, and BN (Tires) and OM (Engine Oil) with 33% support and 80% confidence. OM (Engineering Oil), AK (Battery), and BS (Battery Storage) are the abbreviations for the terms OM (Engineering Oil), AK (Battery), and BS (Battery (Spark plug)).

Prediction of University Student Performance Based on Tracer Study Dataset Using Artificial Neural Network

In order to increase student performance, several universities use machine learning to analyze and evaluate their data so that it enables to improve the quality of education in the university. To get a new insight from the tracer study dataset as the relevance between university performance and student capability with business and industries work, the author will develop a model to predict student performance based on the tracer study dataset using Artificial Neural Network (ANN). For obtaining attributes that correspond to labels, Phi Coefficient Correlation will be used to select the attributes with high correlation as Feature Selection. The author is also performing the oversampling method using Synthetic Minority Oversampling Technique (SMOTE) because this dataset is imbalanced and evaluates the model using K-Fold Cross-Validation. According to K-Fold Cross Validation, the result shows that K = 3 has a low standard deviation of evaluation score and it's the best candidate of K to split the dataset. The average standard deviation is 0.038 for all score evaluations (Accuracy, Precision, Recall, and F-1 Score). After applied SMOTE to treating the imbalanced dataset with the data splitting 65 training data and 35 testing data, the accuracy value increases by 10% from 0.77 to 0.87.

Pengembangan Sistem Pemantauan dan Deteksi Serangan pada Ekosistem Rumah Cerdas

It is predicted that the growth of smart devices installed in the smart home ecosystem will increase in the future. Smart devices that are connected to each other using wired or wireless networks that have the potential for security holes are attacked by threat actors. Whereas a house is a place that provides comfort for its residents. An attack on the smart home ecosystem allows the operation of the smart home ecosystem to be disrupted or personal information stolen to be used by irresponsible parties. To face the high wave of smart home implementation in the future with potential security holes that always accompany it. This is in accordance with the principle that no system is 100% secure. In this study, attacks on wireless networks can be detected as anomalous traffic. Monitoring and detection in the smart home ecosystem involves supporting components such as sensors, access points, gateway collectors and analysis stacks.

Prediksi Perubahan Penggunaan Lahan dan Pola Berdasarkan Citra Landsat Multi Waktu dengan Land Change Modeler (LCM)

Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry