Digital Zone: Jurnal Teknologi Informasi dan Komunikasi <p>Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning (Online <a href=";1426561719&amp;1&amp;&amp;" target="_blank" rel="noopener">ISSN 2477-3255&nbsp;and Print ISSN 2086-4884</a>) This journal publish two periode in a year on May and November</p> <p><strong>Journal Digital Zone: Jurnal teknologi informasi dan Komunikasi has been accredited by National Journal Accreditation (ARJUNA) Managed by Ministry of Research, Technology, and Higher Education, Republic Indonesia since year 2021 to 2025 according to the decree No. 164/E/KPT/2021</strong></p> <p>&nbsp;</p> Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning en-US Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2086-4884 <div class="page"> <div class="pkp_footer_content"> <p><img src="/public/site/images/llisnawita/CC._6.jpg" width="63" height="23">&nbsp;Jurnal Digital Zone is licensed under a<a href="Creative%20Commons Attribution-ShareAlike 4.0 International License." target="_blank" rel="noopener">Creative Commons Attribution-ShareAlike 4.0 International License.</a></p> <p>&nbsp;</p> </div> </div> The Impact of Feature Extraction to Naïve Bayes Based Sentiment Analysis on Review Dataset of Indihome Services <p><em>&nbsp;</em><em>Indihome is a product of PT Telekomunikasi Indonesia as an internet service provider or internet service provider (ISP) in Indonesia. Every product or service offered to the public certainly has its advantages and disadvantages, as well as Indihome. From the advantages and disadvantages of Indihome services, </em><em>we</em><em> can do a technique, namely sentiment analysis. In this study, sentiment analysis was carried out regarding public responses or reviews about IndiHome services on Twitter social media. This study uses a comparison of TF-IDF and Word2Vec feature extraction, and the classification method used is the nave Bayes classifier. The accuracy results obtained in this study were 96% using the TF-IDF feature extraction and testing was carried out using an unseen data test that was selected randomly resulting in an accuracy of 92%. While the accuracy value obtained by using the Word2Vec feature extraction is 60% by testing using unseen test data that was selected randomly resulting in an accuracy value of 44%.</em></p> <p><em>&nbsp;</em></p> Salsabila Mazya Permataning Tyas Bagus Setya Rintyarna Wiwik Suharso Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-04-25 2022-04-25 13 1 1 10 10.31849/digitalzone.v13i1.9158 Quality Classification of Palm Oil Varieties Using Naive Bayes Classifier <p><em>As one of the leading commodities of the Indonesian economy, the ever-increasing production of palm oil has created intense competition among palm oil (CPO) producers. This causes CPO producers to increase their palm oil production without compromising the quality of the palm oil produced. CPO producers are required to be able to objectively determine the quality of superior and precise oil palm varieties in order to produce high economic value palm oil. Therefore, a model is needed to determine the quality of oil palm from several existing varieties. The Naive Bayes Classifier method in this study was used to classify the quality of oil palm based on predetermined variables using a data set of 28 oil palm varieties. Method testing is done by using a confusion matrix and K-fold cross-validation scheme. This study shows a reasonably high accuracy value of 64.25% and a low error rate of 35.7%, indicating that the Naive Bayes Classifier can classify the quality of oil palm varieties quite well.</em>&nbsp;</p> <p><br><br></p> Novianti Puspitasari Rosmasari Rosmasari Fhanji Wilis Pratama Heni Sulastri Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-27 2022-05-27 13 1 11 23 10.31849/digitalzone.v13i1.9773 Comparison of K-Means and K-Medoids Algorithms in Text Mining based on Davies Bouldin Index Testing for Classification of Student’s Thesis <p><em>The thesis is one of the scientific works based on the conclusions of field research or observations compiled and developed by students as well as research carried out according to the topic containing the study program which is carried out as a final project compiled in the last stage of formal study. A large number of theses, of course, will be sought in looking for categories of thesis topics, or the titles raised have different relevance. However, the student thesis can be by topics that are almost relevant to other topics so that it can make it easier to find topics that are relevant to the group. One of the uses of techniques in machine learning is to find text processing (Text Mining). In-text mining, there is a method that can be used, namely the Clustering method. Clustering processing techniques can group objects into the number of clusters formed. In addition, there are several methods used in clustering processing. This study aims to compare 2 cluster algorithms, namely the K-Means and K-Medoids algorithms to obtain an appropriate evaluation in the case of thesis grouping so that the relevant topics in the formed groups have better accuracy. The evaluation stage used is the Davies Bouldin Index (DBI) evaluation which is one of the testing techniques on the cluster. In addition, another indicator for comparison is the computation time of the two algorithms. According to the DBI value test carried out on algorithm 2, the K-Medoids algorithm is superior to K-Means, where the average DBI value produced by K-Medoids is 1,56 while K-Means is 2,79. In addition, the computational time required in classifying documents is also a reference. In testing the computational time required to group 50 documents, K-Means is superior to K-Medoids. K-Means has an average computation time for grouping documents, which is 1 second, while K-Medoids provide a computation time of 26,7778 seconds.</em></p> Siti Ramadhani Dini Azzahra Tomi Z Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-27 2022-05-27 13 1 24 33 10.31849/digitalzone.v13i1.9292 Application of Gaussian Filter and Histogram Equalization for Repair x-ray Image <p><em>The X-ray image is a medical examination procedure that uses electromagnetic wave radiation to get a picture of the inside of the body. However, in the process, there is noise that appears due to the exposure factor. This research builds a system to improve the X-ray image with noise by using Gaussian Filter and Histogram Equalization. In this study, in order to see the optimization of image enhancement, the two methods were combined. The data used are 60 x-ray images that have noise and each has an original image without noise as a comparison image to get system accuracy using PSNR and SSIM. Gaussian Filter method is used to reduce noise by determining the size of the kernel matrix and the standard deviation used. Histogram Equalization method is used to even out the value of the gray level of the image. Based on the test results from the combination of the two methods, the larger the size of the kernel matrix used, the faster the duration of time needed to repair the image. The PSNR value and accuracy obtained in the X-ray image repair are 31 dB and 71% on a 3x3 kernel matrix with an average time duration of 9 seconds, 32 dB and 77% on a 5x5 kernel matrix with an average duration of 9 seconds, 32 dB and 78% on a 7x7 kernel matrix with an average time duration of 8 seconds</em></p> Dandi Mulyana Tedy Rismawan Cucu Suhery Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-27 2022-05-27 13 1 34 43 10.31849/digitalzone.v13i1.9770 Implementation of Naïve Bayes for Classification of Learning Types <p><em>Learning is a process that is carried out by each individual from not knowing to knowing, or from bad behavior to being good, so that it has a good change for the individual, Each individual has a learning type in receiving the material presented by the teacher, but not all individuals understand what type of learning they need, The purpose of the research is to determine the type of learning of the students of the Faculty of Computer Science. The method used is nave Bayes for the accuracy of its calculations. The results of this study are the classification of visual learning types as many as 50 people, for audio as many as 24 people, while kinesthetic as many as 25 people, for the Informatics Engineering Study Program as many as 61, consists of 37 visual learning types, Auditory 14 people, Kinesthetic 10 people, While the Information Systems Study Program is 37 people, where is Visual 14 people, Auditory 9 people and Kinesthetic 14 people. With this classification, it can help lecturers apply learning methods that are suitable for their students. The best Naïve Bayes accuracy rate is 88.89%</em></p> Lisnawita Lisnawita Guntoro Guntoro Musfawati Musfawati Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-31 2022-05-31 13 1 44 54 10.31849/digitalzone.v13i1.9825 Spatial Mapping of Landslide Susceptibility Level in Pacitan District Using Analytical Hierarchy Process and Natural Break <p><em>Pacitan district has a high potential for landslides. Landslide is a hydrometeorological disaster that causes loss of life, property loss, and environmental damage. Disaster preparedness is very necessary for the wider community in dealing with landslide emergency response situations. Applications to determine the level of vulnerability to landslides are very useful to minimize the impact and losses on the Pacitan community. This study aims to make an application for assessing the level of landslide susceptibility using the analytical hierarchy process and natural break based on the factors that cause landslides in the sub-district or village of Pacitan district. The factors that cause landslides in Pacitan district consist of weather, history of landslides, land slope, and history of earthquakes. The results of the AHP and natural break classifications are visualized in the form of a spatial map into 3 categories, namely high, medium and low vulnerability levels. The results of the AHP classification and natural break in the 2016-2020 data have a good average GVF value of 0.77. This shows that in general, the results of the 2016-2020 data classification are correct. Mobile device-based applications provide convenience for the public in accessing information as an effort to improve landslide disaster preparedness.</em></p> Arna Fariza Arif Basofi Silfiana Nur Hamida Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-04-25 2022-04-25 13 1 55 66 10.31849/digitalzone.v13i1.8619 Web Based Application Wet Cake Snack Product Distribution Using Concept Business To Business To Consumer <p><em>Business To Business To Customers is part of E-Commerce which is a process of buying and selling transactions and distribution to consumers. Distribution of wet cakes from producers on Jl. Panglima Minal Senggoro is still manual by recording and monitoring products that run out from partners. Distribution from producers to retailers uses a profit-sharing system that has been agreed upon by both parties. The design of this system is designed using the Waterfall method and the Codeigniter Framework (CI) as well as with the design of the E-commerce Framework, namely B2B2C which produces information about cake manufacturers, knows the available products, and also helps producers in recapitulating sales to partners. The features in this system are Approval in user registration, the input of cake products, selection of payment methods, uploading proof of payment, updating of remaining products on the partner side, and dynamic reviews and ratings. So that producers can compete with other wet cake.</em></p> Mansur Mansur Dinda Nurul Mawardah Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-31 2022-05-31 13 1 67 78 10.31849/digitalzone.v13i1.9793 LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter <p><em>&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;</em><em>The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vaccination from the community. This attituded of pros and cons, which is also called sentiment, can influence people to accept or refuse to be vaccinated. Todays, people express their sentiment in social media in comments, post, or status. One of the methods used to detect sentiment on social media, whether positive or negative, is through a categorisation of text approach. This research provides a deep learning technique for sentiment classification on Twitter that uses Long Short Term Memory (LSTM), for positive, neutral and negative classes. The word2vec word embeddings was used as input, using the pretrained Bahasa Indonesia model from Wikipedia corpus. On the other hand, the topic-based word2vec model was also trained from the Covid-19 vaccination sentiment dataset which collected from Twitter. The data used after balanced is 2564 training data, 778 data validation data, and 400 test data with 1802 neutral data, 1066 negative data, and 566 positive data. The best results from various parameter processes give an F1-Score value of 54% on the test data, with an accuracy of 66%. The result of this research is a model that can classify sentiments with new sentences.</em></p> Miftahul Ihsan Benny Sukma Negara Surya Agustian Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-31 2022-05-31 13 1 79 89 10.31849/digitalzone.v13i1.9950 The Implementation of Simple Additive Weighting Method in deciding Apprentice Assistant <p><em>An internship is a mandatory course to be taken by a sixth-grader. Students should finish the course by either apprenticing or making a product in the form of software. The problem often is that students enroll and choose partners. The student files already stored should be matched to the data of the previous semester conventionally. Another problem is that students select partners based not on the field of interest but based on following their friends. Students have difficulty completing an apprenticeship. Therefore, the study examined the identification of an apprentice by using the simple, adapting method, the research object was the student of the semester VI apprentice, the method of storing data using literature, observation, and interviews. Research results from simple standard weighting show K1 criteria at 0.75, K2 at 0.5, structural criteria at 0.25, and requirement criteria of 1. </em><em>The results of the accuracy test are 80% so that the SAW method can be developed as a decision support system in determining internship lecturers based on the student's field of interest</em><em>.</em></p> Hamid Muhammad Jumasa Wahju Tjahjo Saputro Copyright (c) 2022 Digital Zone: Jurnal Teknologi Informasi dan Komunikasi 2022-05-31 2022-05-31 13 1 90 101 10.31849/digitalzone.v13i1.9880