Windu Gata
Sekolah Tinggi Manajemen Informatika dan Komputer Nusa Mandiri

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PENERAPAN ALGORITMA DIJKSTRA DALAM PENENTUAN LINTASAN TERPENDEK MENUJU UPT. PUSKESMAS CILODONG KOTA DEPOK Cicih Sri Rahayu; Windu Gata; Sri Rahayu; Agus Salim; Arif Budiarto
JURNAL TEKNIK INFORMATIKA Vol 14, No 1 (2021): JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v14i1.18721

Abstract

One of the government's efforts in providing health to the community is the construction of health centers in each sub-district, and the community is expected to be able to take advantage of the health facilities provided by the government. One of the problems that exist in the community is determining the shortest distance to the puskesmas. In Depok City, there are 26 routes that can be passed from the 38 nodes or vertices to the Cilodong Health Center with the starting point of the Depok mayor's office. This study uses a survey research method to calculate the actual distance at each node or vertex, the purpose of this study is to determine the shortest path taken by the starting point from the Depok mayor's office to get to the Cilodong Health Center by applying the dijkstra algorithm. This dijkstra algorithm works by visiting all existing points and making a route if there are 2 routes to the same 1 point then the route that has the lowest weight is chosen so that all points have an optimal route. This quest continues until the final destination point. After doing this research and testing using a simple application to calculate the distance by applying the djikstra algorithm, it was found that the shortest path taken to the destination is through the GDC Main Gate or on the test results in Iteration 26. From the results of this study, people can choose this closest route to save time when viewed from the distance of the existing track. For further research, it is expected to be able to compare two other algorithms and other parameters so that the closest route with the fastest time is obtained.
SENTIMENT ANALYSIS DUE TO "MUDIK" PROHIBITED OF COVID-19 THROUGH TWITTER Sabar Sautomo; Noor Hafidz; Yuni Eka Achyani; Windu Gata
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 1 (2020): JITK Issue August 2020
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1192.563 KB) | DOI: 10.33480/jitk.v6i1.1357

Abstract

“Mudik” is a habit every year for the people of Indonesia to return to their hometowns before the Eid. The existence of the Corona Virus pandemic (COVID-19) hit all over the world, including Indonesia, resulting in a ban from the government to do Mudik. Social media such as Twitter is often used as an expression of some people in commenting on something like the ban on Mudik. Comments on Twitter that are often known as tweets can be used as material for sentiment analysis. However, it is not easy to do sentiment analysis on Twitter, especially comments in Indonesian, because the text is not structured. This study uses data from Indonesian-language tweets containing the word "Mudik," the algorithm model used in this study, Naïve Bayes Classifier and Support Vector Machine, is compared to get accuracy, precision, recall, and F1-score values. From this research, it was concluded that the Naïve Bayes algorithm and Support Vector Machine performed well enough to predict the sentiment of tweets about Mudik on Twitter social media. Naïve Bayes with an accuracy of 82% and f1-score 0.8, while Support Vector Machine with an accuracy of 87% and f1-score 0.87.
CLASSIFICATION OF LIVER DISEASE BY APPLYING RANDOM FOREST ALGORITHM AND BACKWARD ELIMINATION Irwan Herliawan; Muhammad Iqbal; Windu Gata; Achmad Rifai; Jajang Jaya Purnama
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol 6 No 1 (2020): JITK Issue August 2020
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1157.377 KB) | DOI: 10.33480/jitk.v6i1.1424

Abstract

Cancer is a type of disease that is not realized by most people because most people associated with this disease lack understanding of cancer itself and are doing early detection of cancer, due to the majority of cancers found at an advanced stage and difficult to overcome to facilitate large expenditure to help cancer. Early detection of liver or liver cancer is very important to overcome the very high risk of death caused by liver or liver cancer. This study aims to help classify liver or liver cancer based on data from routine examination results of patients summarized in the Indian Liver Data Patient (ILDP) dataset. The method used in the classification process in this research is backward elimination modeling for testing optimization and Random Forest algorithm and split validation to validate the model. The results of this study yielded 76.00% and value of AUC 0.758 results. These results indicate that the results of this study are good enough to help classify breast cancer
DIAGNOSIS OF HEART DISEASE USING AUTOMATA FINITE STATE ALGORITHM Tony Yudianto Pribadi; Kartika Handayani; Angelina Puput Giovani; Windu Gata
Jurnal Techno Nusa Mandiri Vol 18 No 1 (2021): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v18i1.1364

Abstract

The heart is an organ of the human body that has an important role in human life and is certainly very dangerous if our heart has problems remembering that many deaths are caused by heart disease. But with minimal knowledge and information, it is impossible to be able to maintain heart health. Therefore we need an expert who is an expert on the heart and various diseases. Based on the facts above, this research can help us to diagnose heart health and anticipate if there is a risk of heart disease by designing and implementing. This application was created using the web-based Finite State Automata algorithm which is still in the form of pseudocode. In this system several questions will be asked. After all the questions are answered, the results of the diagnosis will appear along with suggestions that can help anticipate the heart disease.