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Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Published by Universitas Lancang Kuning
ISSN : 20864884     EISSN : 24773255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Digital Zone journal publish by Fakultas Ilmu Komputer Universitas Lancang Kuning (Online ISSN 2477-3255 and Print ISSN 2086-4884) This journal publish two periode in a year on May and November.
Arjuna Subject : -
Articles 198 Documents
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Implementation of Naïve Bayes for Classification of Learning Types Lisnawita, Lisnawita; Guntoro, Guntoro; Musfawati, Musfawati
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9825

Abstract

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%
LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter Ihsan, Miftahul; Benny Sukma Negara; Surya Agustian
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9950

Abstract

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.
Web Based Application Wet Cake Snack Product Distribution Using Concept Business To Business To Consumer Mansur, Mansur; Mawardah, Dinda Nurul
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9793

Abstract

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.
The Implementation of Simple Additive Weighting Method in deciding Apprentice Assistant Hamid Muhammad Jumasa; Wahju Tjahjo Saputro
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 1 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i1.9880

Abstract

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. 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.
Sentiment Analysis on the Construction of the Jakarta-Bandung High-Speed Train on Twitter Social Media Using Recurrent Neural Networks Method Kinan Salaatsa, Titan; Yuliant Sibaroni
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i12.10777

Abstract

During the construction of the Jakarta-Bandung high-speed train, many Indonesian people gave their responses to the public. The answers were also varied, with some giving positive and negative reactions. The purpose of this study is to analyze the sentiments of the responses given by the public to the construction of the Jakarta-Bandung high-speed train on Indonesian-language Twitter. To perform sentiment analysis, tweet data was collected utilizing data crawling based on keywords related to the construction of the Jakarta-Bandung high-speed train and given positive, negative, and neutral labels and then represented into numbers using the Keras tokenizer. The method used for sentiment classification of tweet data is the Recurrent Neural Networks method. The highest accuracy results were obtained using the GRU architecture with an accuracy of 69.62%.
SVM Method with FastText Representation Feature for Classification of Twitter Sentiments Regarding the Covid-19 Vaccination Program Mukti M Kusairi; Agustian, Surya
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i2.11531

Abstract

Covid-19 is a virus that has a high level of spread, making the government implement a mass vaccination program throughout Indonesia. This program received a lot of responses from the public, with positive and negative opinions or comments. Currently, the public's response through social media is also an input and consideration for the government to implement a program. Therefore, this study was conducted to produce a method approach to assessing the Covid-19 vaccination program by calculating the percentage of each sentiment class. The method used is the Support Vector Machine (SVM) and the fasttext language model feature as a representation of words in the Covid-19 vaccination sentiment dataset collected from Twitter. The data used has been dataset balancing, feature selection and parameter tuning, the optimal SVM model is obtained with a composition of 2536 training data, 778 development data and testing of 400 testing data, resulting in the best value of fi-1 score of 59% with an accuracy rate of 68%. The system is quite successful in detecting sentiment in tweets compared to before. Keywords: sentiment classification, FastText, SVM, Covid-19 vaccine.
Sentiment Analysis of Public Opinion Regarding Papuan Local Languages Condition Using Data Science Approach Hasan, Nur Fitrianingsih; Aisyah, Aisyah; Rahman, Rahman; Wonda, Herlin
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i2.11545

Abstract

Regional languages ​​can support economic empowerment and improvement through the tourism sector. Opinions from people's expressions in social media and online news collections in reporting the condition of regional languages ​​often become headlines in cyberspace that number in the thousands, which can be used as new knowledge as a basis for making decisions through the mining method. This study aims to explore public opinion sentiment related to the condition of the Papuan language, sourced from text data in cyberspace using a data science approach, namely the classification method with text mining techniques using the naïve bayes algorithm. Public opinion sentiments are processed and the results are presented using word cloud visualization through 4 stages of data science, namely data collection, data preprocessing, modeling exploration and visualization analysis. The result of 778 opinions, 92% tend to have a positive sentiment. The analysis of public opinion sentiment is carried out by the naïve bayes algorithm which has an algorithm model accuracy of 78% and a precision of 88%. The machine learning model that was built and the word cloud visualization analysis succeeded in providing new insights regarding the condition of the Papuan language.
Live Forensics Analysis Of Malware Identified Email Crimes To Increase Evidence Of Cyber Crime Prawira, Yudhi Prawira; Samsudin, Samsudin
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i12.11570

Abstract

Now days Email is the most important aplplication on the internet, this make email one of the industry’s most targeted sector for commiting cyber crimes. Email phishing and spam not only harm many parties but also consumes a lot of network bandwidth. Most spam are emotet malware. Trojan malware that targets internet users financial system to steal financial information and personal data by sending phishing. In this research, digital forensics analysis email crimes identified malware using live forensics and tools analyze digital evidence of email content, as wall as offVise, Wireshark, and Procmon to analyze malware activities. The results of the investigation of the email content carried out using software found digital evidence that could be used as a reference that attachment downloaded by the victim was Emoted type malware, when the victim opened it, this malware will be installed automatically on the victim’s computer. .
C4.5 Algorithm Implementation For Public Sentyment Analysis Covid-19 Vaccine Devi Astri Nawangnugraeni; Abdillah, M. Zakki; Suseno, Akrim Teguh
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i2.11658

Abstract

Corona virus disease is one of the dangerous diseases and has been prevented by giving vaccinations. In an effort to prevent, there must be a positive or negative public response. One of the media facilities used to convey public responses is Twitter. The public's reaction can be analyzed using public sentiment analysis using C4.5 algorithm. The purpose of paper for determine public's response to the administration of moderna and pfizer vaccinations. The implemented methodology starts from collecting data taken from tweets, pre-processing, classification using the C4.5 algorithm and validation using k-fold cross validation. Based on the results of the moderna keyword analysis, the positive sentiment response was 6% and negative sentiment was 94%, while the pfizer keyword positive sentiment was 12.4% and negative sentiment was 87.6%. The results of test iteration that have been carried out 3 times, the average error value is 38%.
Speech Recognition for English Sentences with Malay Accent Keumala Anggraini; Van FC, Lucky Lhaura; Darmayunata, Yuvi
Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Vol. 13 No. 2 (2022): Digital Zone: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Publisher: Fakultas Ilmu Komputer, Institution: Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/digitalzone.v13i2.10759

Abstract

Some countries conduct speech research using accents. One language that has a different accent is English. English is an international language that is often used to communicate with citizens of other countries. For beginner, many difficulty to translate English with accent, include malay accent. This study performs speech recognition using English and Riau Malay accents. This research use Google Recognizer to cut words in sentences, Mel Frequency Cepstral Coefficient for feature extraction, and Hidden Markov Model for classification. The accuracy of this research is 94.02%.

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