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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 926 Documents
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Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging Naufal Alvin Chandrasa; Sri Suryani Prasetyowati; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1896

Abstract

This research provides information about land prices in Jakarta by classifying using the Random Forest method. Where Random Forest is a data mining technique that is usually used to perform classification and regression. Random Forest is one of the best classification methods. It is found that classification accuracy will increase dramatically as a result of voting to select class types and ensemble tree growth. The method helps in providing information about the classification of land prices with the class of land prices per meter less than IDR 15 million, land prices per meter with a price range of IDR 15 to 25 million and land prices per meter more than IDR 25 million. With a fairly good accuracy of 82%, this method can classify where the permeter land price data that is tested will match the predicted classification accurately. Classification is performed on unbalanced data which is then oversampled using the ADASYN method. Assisted by doing spatial interpolation with the Ordinary Kriging method using Semivariogram, information about the classification of land prices can be seen on the distribution of the Jakarta area map. Ordinary Kriging can predict the estimated price per meter of land around the area of land that has a known price. The Root Mean Square Error (RMSE) results of the best Semivariogram model are obtained from the lowest RMSE value, namely the Spherical model with a value of 1.014896e7. The contribution of this research is to provide information about a reliable classification method, namely Random Forest and Ordinary Kriging performance as a spatial analysis method that can predict land prices per meter at unknown points so as to provide information about the distribution of land prices in Jakarta with each price class.
Rancang Bangun Alat Pemisah Buah Kopi Berdasarkan Tingkat Kematangan Menggunakan Sensor TCS3200 Berbasis Android Irma Salamah; Mega Muliawati; Mohammad Fadhli
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1901

Abstract

The processing of coffee before it can be drunk goes through a long process, namely harvesting coffee fruits that have matured either by machine or by hand, then processing coffee fruits and drying before they become spindle coffee. Before the roasting process, the coffee fruit is chosen first to be ripe in order to produce the last result of the best coffee process. After the coffee fruit is selected, the next process is stripping the ripe coffee fruit in order to speed up the post-harvest process of the coffee fruit. Harvesting will also affect the quality and taste of coffee, harvesting is usually done when the coffee fruit that has matured physiologically which is characterized by the color of the fruit skin becomes a red color. At the level of maturity of coffee fruits still occurs not simultaneously so that the harvesting process takes a long time. When the harvest period comes, the separation of coffee fruits is still mostly done manually by picking them in a simultaneous way. This simultaneous separation of coffee fruits makes the mixing of coffee fruits that are still raw, half-ripe, and already ripe, causing poor taste quality, that's why the author got the idea to make a tool that can make the coffee harvesting process simpler and more efficient, namely a coffee fruit separator tool based on maturity level aims to find out how the tool performs in detecting coffee fruits based on maturity levels and how performance from a coffee fruit peeler. This tool can produce separate coffee fruits based on the degree of maturity, namely in the category of ripe, half-ripe and unripe. When coffee fruits that are still not separated between ripe, half-ripe and raw, are placed on the conveyor, the TCS3200 color sensor will detect the coffee fruit according to its color. The coffee fruit will be sorted automatically into a container that has been provided which is controlled by a servo motor that has been pre-programmed by Arduino. After the data from the coffee fruit separator has been read by the sensor, the Arduino will send the data to android via bluetooth connection. Furthermore, red coffee fruits or coffee fruits in the ripe category will proceed to the stripping stage. At the stripping stage, ripe coffee fruits will be peeled on a peeling machine controlled by a servo motor
Analysis of Community Sentiment on Twitter towards COVID-19 Vaccine Booster Using Ensemble Stacking Methods Syifa Khairunnisa Salsabila; Jondri Jondri; Widi Astuti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1902

Abstract

The outbreak of the COVID-19 virus in Indonesia has not ended until the government has made various efforts to reduce this outbreak, such as the Large-Scale Social Restriction (PSBB) policy and the obligation of the entire community to vaccinate against COVID-19. The government has made a new policy for the community: booster vaccination for people who have already been vaccinated against COVID-19 1 and vaccinated against COVID-19 2. With this new policy, many people have given opinions on social media. One of them is Twitter social media. Positive and negative opinions given by Twitter users can be used as a source of information data. Because of these problems, researchers conducted a sentiment analysis of the booster vaccine using the Ensemble Stacking method. The dataset that has collected as many as 6,500 data from Twitter will be grouped into positive and negative class sentiments. The best results from this study using ensemble stacking and oversampling have an accuracy value of 80%.
Detection of Radicalism Speech on Indonesian Tweet Using Convolutional Neural Network Faiza Aulia Rahma Putra; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1907

Abstract

The ease of disseminating information today is inseparable from the rapid development of information technology. Unfortunately, radical groups also use this condition to spread propaganda and recruit members through social media such as Facebook and Twitter. Therefore, detecting radicalism on social media is essential, given the ease with which information can be spread that can affect social media users. Several studies to classify radicalism speech have been carried out using machine learning algorithms such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). However, only a few used the Indonesian language and even utilized a small dataset. This study proposed to detect radicalism speech in Indonesian tweets using Convolutional Neural Network (CNN) and Word2Vec as feature extraction. The dataset is a collection of Indonesian-language tweets obtained through tweet crawling. CNN modeling was conducted using several scenarios with the number of filter parameter values = 100 and 300, and kernel size parameter value = 3, 5, 7, 9. From the training process using the scenarios above, the most optimal model is obtained with parameter filters = 300 and kernel size = 7, producing the best accuracy of 87.87% and average accuracy of 86.93%. Based on the best model obtained, an evaluation was carried out on the test data, which resulted in an accuracy of 87.15%.
Chatbot-Based Movie Recommender System Using POS Tagging Muhammad Alwi Nugraha; Z K A Baizal; Donni Richasdy
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1908

Abstract

The movie recommender system is a technology designed to make it easier for users to provide recommendations quickly and among the many pieces of information. Because the number of movies is huge, it causes a person to be confused in determining the choice of the movie to watch. Many movie recommending systems have been developed, but users cannot interact intensively. Based on these problems, we developed a chatbot-based conversational recommender system, which can interact intensively with the system. The developed chatbot uses normal language handling to permit the framework to comprehend what the user enters as natural language. POS Tagging is used to find tags in the form of movie titles with patterns in the POS Tagging model. However, the algorithm of those used on POS Tagging does not pay attention to the sentence entity, so the predicted title must correspond to the provisions of POS Tagging. Multinomial Naive Bayes looks for similarities of user input to datasets on intents. The dataset with the highest probability value or almost equal to the sentence entered by the user can be used as a response to user input. The test results of the chatbot application showed that the match rate between response and user input was 89.1%. Thus, the developed chatbot can be used well to provide practical and interactive movie recommendations.
Question Answering System Using Semantic Reasoning on Ontology for The History of The Sumedang Larang Kingdom Silvia Atika Anggrayni; Z K A Baizal; Donni Richasdy
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1910

Abstract

Studying history can train us to understand the sequence of events and increase a sense of nationalism in the younger generation. However, today's young generation views studying history as boring and unimportant. Studying history is considered boring because it has the stereotype of having to learn by reading long writings in books. Therefore, in this study, a Question Answering System (QAS) was built using an ontology to get historical information and get to know the culture. With QAS, users don't have to read long sentences and spend a lot of time searching for historical information, users can also ask questions in natural language without having to pay attention to sentence structure. The ontology was chosen to be able to build a knowledge base on the historical domain and SPARQL was used to find answers in the ontology. The construction of this system is expected not only to help introduce the history of the Sumedang Larang Kingdom but also to be able to introduce the attraction of cultural tourism in Indonesia, especially the Sumedang Larang Kingdom. The results of the evaluation with the system accuracy test showed a result of 87%.
Prediction Retweet Using User-Based and Content-Based with ANN-GA Classification Method Edvan Tazul Arifin; Jondri Jondri; Indwiarti Indwiarti
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1931

Abstract

Current technological advances have caused rapid dissemination of information, especially on social media, one of which is Twitter. Retweeting or reposting messages is considered an easily available information diffusion mechanism provided by Twitter. By finding out why a user retweets a tweet from another person and by making this prediction we can understand how information diffuses on Twitter. In this study, Artificial Neural Network – Genetic Algorithm is used in the classification process and uses user-based and Content-Based features. Evaluation result obtained in this study are 90% accuracy, 72% precision, 83% recall, and 65% F1-Score value on the model by Oversampling.
Evaluation and Redesign of Telkom University’s Open Library Website Interface Using the Goal Directed Design (GDD) Method Zian Alfaen; Indra Lukmana Sardi; Monterico Adrian
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1949

Abstract

As a university with World Class University quality, Telkom University provides information services with a website as the main source of libraries for its students. The website is called Open Library which provides information on all library collections owned by Telkom University which are open and can be loaned. One of the missions of this website is "To play an active role in increasing interest in reading and writing in the community". Because, a good digital library website is a library website that can increase the reading interest of users. Interface design is one of the most important elements to support the quality of digital libraries. Therefore, this study aims to evaluate the design and provide design solutions for the interface of the Open Library website. . The final evaluation used in this study used the System Usability Scale method. The System Usability Scale (SUS) is a measure of the usability of a product using a 10-question questionnaire developed by John Brooke. Of the 20 respondents who had filled out the questionnaire in the final evaluation, the result was 74.45. Where 74.45 is a score that has passed the average standard value of SUS
The Organization Entity Extraction Telkom University Affiliated using Recurrent Neural Network (RNN) Muhammad Daffa Regenta Sutrisno; Donni Richasdy; Aditya Firman Ihsan
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1956

Abstract

In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%
Comparison of Ensemble Methods for Detecting Hoax News Delvanita Sri Wahyuni; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1957

Abstract

The spread of hoaxes in Indonesia has become a big concern for the public, especially now that the COVID-19 virus pandemic is hitting the whole world. Due to the large number of people who believe the hoax news regarding the COVID-19 vaccination that has spread on social media, many people refuse to carry out the COVID-19 vaccination as a form of government effort in dealing with this pandemic. Therefore, people need to be wiser when reading news on social networks. To help the public not to read hoaxes, it is necessary to classify the COVID-19 vaccine hoax. This study builds a system to classify hoax news on the COVID-19 vaccine. The model was built using the ensemble method by comparing the Random Forest and AdaBoost algorithms to choose a good classification for detecting hoaxes. In this research, there are use two test scenarios. The first scenario is an experiment using the Random Forest algorithm method and the second scenario is an experiment using the Adoboost algorithm method. The experimental results show that the first scenario produces a good accuracy value with the random forest algorithm method of 93.58%.

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