Cyberbullying is a form of bullying that takes place across virtually every social media platform. Twitter is a form of social media that allows users to exchange information. Bullying has been a growing problem on Twitter over the past few years. Sentiment analysis is done to identify the element of bullying in a tweet. Sentiments are divided into 3 classes, namely Bullying, Non-Bullying and neutral. There are three steps to classify cyberbullying i.e. collection of data set, preprocessing data, and classification process. This research used sentiStrength, an algorithm which uses a lexicon based approach. This SentiStrength lexicon contains the weight of its sentiment strength. The assessment results from 454 tweets data obtained 161 tweet non-bullying (35.4%), 87 tweet neutral (19.1%), and 206 tweet bullying (45.4%). This research produces an accuracy value of 60.5%.
Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.
Fakultas Sains dan Teknologi Universitas Jambi sebagai institusi yang bergerak dibidang pendidikan, saat ini telah mengunakan teknologi komputer dalam pengelolaan dokumennya. Akan tetapi teknologi tersebut belum digunakan secara optimal. Hal ini terlihat dari cara penyimpanan arsip dokumen fakultas yang masih dilakukan secara manual oleh staff tenaga kependidikan. Di Fakultas Sains dan Teknologi, dokumen disimpan dalam lemari arsip dan atau di dalam folder komputer yang dipisahkan berdasarkan jenis dokumen. Akibatnya terdapat kendala dalam pengaksesan dokumen, dimana staff tenaga kependidikan harus terlebih dahulu membongkar folder arsip untuk mencari dokumen yang dibutuhkan. Selain itu dokumen arsip berupa kertas menumpuk di ruangan, yang lama kelamaan akan membutuhkan ruang yang banyak. Untuk mengatasi masalah tersebut maka solusi yang dilakukan adalah dengan pengembangan sistem informasi manajemen arsip berbasis web. Sistem informasi manajemen arsip ini dikembangkan dengan menggunakan framework laravel dengan metode pengembangan software development life cycle (SDLC). Hasil yang diperoleh dari kegiatan pengembangan sistem ini adalah sebuah sistem informasi manajemen arsip Fakultas Sains dan Teknologi yang dapat diakses pada https://arsip.fst.unja.ac.id untuk digunakan secara langsung dalam penyimpanan data atau dokumen, serta dapat meningkatkan efektifitas dalam pengolahan dokumen
Dengue Hemorrhagic Fever (DHF) is one of the common and fatal diseases in Indonesia. Jambi city is one of the dengue-endemic areas in Jambi province. To reduce the incidence rate of dengue, an early warning based on forecasting is necessary. Time-series forecasting of DHF can provide useful information to support and help public health officers for planning on DHF prevention. This paper compares two methods for Time-series forecasting of DHF incidence, namely seasonal autoregressive integrated moving average (SARIMA) and Long Short-Term Memory (LSTM). The forecasting performance is assessed using the monthly number of DHF incidence data from January 2012 to April 2019 were acquired from the district health offices. To show the effectiveness of the model, the performances are evaluated based on two metrics: mean absolute error (MAE) and root mean square error (RMSE). In the first analysis, we found that the SARIMA ((1,0,0) (1,0,0)12) is the most suitable model to predict the number of monthly DHF incidence with RMSE value of 30.07 and MAE 18.97, and the second one used the LSTM with one hidden layer (1-64-1) architecture with RMSE of 30.41 and MAE 18.27. Based on the experiment between SARIMA and LSTM perform relatively well to predict the future.
This research is a mixed method research using an explanatory design. The purpose of this study is to use Elista to assess a lecturer's response to thesis guidance based on the lecturer's gender. This study was condi-cted in Jambi University, involving 330 female and 359 male lecturers respectively. The sampling method used was purposive sampling technique, with the sample criterion being academics who became thesis su-pervisors. Interviews and surveys on Elista were conducted and the responses were gathered. The goal of this study is to establish a technology-based final project guidance system in which it is explored if the thesis supervisor's speedy response to guidance at Elista is affected by the gender of the thesis supervisor. The findings of the study demonstrate that female supervisors respond faster than male supervisors when em-ploying technology such as Elista to carry out the guidance procedure. Elista improves the effectiveness and efficiency of the guidance process between supervisors and students in terms of implementation.
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