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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 889 Documents
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Penerapan Metode MOOSRA dan MOORA dalam Keputusan Pemilihan Produk Asuransi Terbaik Suryanto, Andik Adi; Alam, Sitti Nur; Widjaja, Warkianto; Wijaya, Hamid; Adhicandra, Iwan
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2938

Abstract

At present, public opinion regarding the selection of insurance products is very low because people still do not understand enough to insure themselves against a product that will guarantee their necessities of life. Especially now that there are so many insurance products out there, it's likely that people don't want to register for insurance. Therefore, people must be careful in choosing an insurance product so that it fits what they want. The Decision Support System is a computerized system and is designed to assist management in making decisions to solve semi-structured and unstructured problems so that the decision-making process can be of higher quality. This application that will be made is an application that is guided by the MOORA method. Therefore, an application that is guided by the MOORA method is very suitable for calculating insurance product selection. From the results of our research we conducted the MOOSRA and MOORA methods for selecting insurance products easier and more precise than the manual work method. applying the MOORA method produces an alternative A5, namely Prudential with a value of 0.217 as the best alternative.
Text Classification of Indonesian Translated Hadith Using XGBoost Model and Chi-Square Feature Selection Putri, Dita Julaika; Dwifebri, Mahendra; Adiwijaya, Adiwijaya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2944

Abstract

Aside from the Holy Qur'an, Hadith is indeed a life guide that every Muslims in this world must follow. The technology for classifying texts and sentences, including categorizing hadiths, is evolving in tandem with the advancement of the times. The model used to perform classification has also been developed and optimized such as the use of the XGBoost algorithm which is more optimized than the previous tree algorithm. This can also make it easier for us as Muslims to study hadiths by categorizing them according to recommendations, prohibitions, and information. This study conducted text classification of Indonesian translations of hadith texts based on recommendations, prohibitions, and information using the XGBoost algorithm, TF-IDF for its feature extraction, and Chi-Square for its feature selection. In this study, experiments were carried out by changing the order of the preprocessing process for the stopword removal and stemming parts, performing the classification process with and without using chi-square as a feature selection, and adding parameter value during the modeling process with XGBoost and the highest final results obtained were 79% for accuracy, 79% for precision, 78% for recall and 78% for F1-score.
Conversational Recommender System based on Functional Requirement using Knowledge Graph for Building Personal Computer Aryanta, Rafi Rizkya; Baizal, Z. K. A
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2978

Abstract

When a person wants to build a personal computer, this person needs to browse many kinds of computer components. Besides that, this person needs to consider the compatibility between hardware and an affordable price. This will be a problem for people who are still unfamiliar with the computer, due to their lack of understanding of how compatibility between computer components works and the time-consuming nature of market research. To deal with this problem, the recommender system will assist in finding and matching compatibility efficiently based on the functional requirements of the user. The recommender system will issue various products based on the preferences and interests of the user, but some recommendations still need to be checked for compatibility. With the help of developing a Conversational Recommender System by utilizing the Knowledge Graph, it will be easier to construct the relationship between component compatibility. We propose this research by using Knowledge Graph as alternative from ontology to build Conversational Recommender system in Building Personal Computer. This research will involve the user to prove whether the recommendations from this system meet the needs and accuracy of the recommendations requested. The main results of this study will issue a recommendation for the development of personal computers by considering compatibility using the Conversational Recommender System using the Knowledge Graph approach.
Mask Detection on Motorcyclists Using YOLOv4 Firdauz, Salma Salsabila; Rachmawati, Ema; Sulistiyo, Mahmud Dwi
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2980

Abstract

The use of mask is a mandatory for everyone in the pandemic regulation to prevent the spread of COVID-19 infection. This becomes a pandemic regulation for everyone, especially in public places like in traffic situation, such as pedestrian and motorcyclists. However, many motorcyclists ignore this rule or do not use the mask properly, let alone they have high risk in being infected by the virus; Thus, a computer vision-based solution is required to help monitoring it. This study aims to built a system to automatically detect the use of mask on motorcyclists. Here, we propose a YOLOv4 model, one of YOLO variants, which is popular in the object detection task and featured with a considerably high speed in real-time situation. This study also implements domain adaptation to discuss the object detection performances. Based on the experimental results in various scenarios, our model obtained average accuracy of 78.3% and IoU of 64.8% for class with_mask, average accuracy of 78.4% and IoU of 56.3% for class without_mask, and average accuracy of 87% and IoU of 55.5% for class incorrect_mask
Sign Language Translator Based on Raspberry Pi Camera Using The Haar Cascade Classifier Method Aji, Gempur Bayu; Yulianto, Fazmah Arif; Rakhmatsyah, Andrian
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2990

Abstract

Sign language is the main tool of communication for people with hearing impairments. Communication is very limited and difficult to understand between normal people who do not know sign language, so an interpreter is needed. Where not everyone, even a few normal people, learns sign language, especially the Indonesian Sign Language System (SIBI). Motion Detection is an important subject in the field of computer vision, which is used by many systems. Today's Internet of Things is very helpful and facilitates daily human activities. An internet network allows a device to be controlled from a considerable distance. This study described a sign language translator tool for the deaf and speech impaired using a raspberry-pi camera and displayed it on the other device monitor. This system was built using the Python programming language and the OpenCV Library. The system is using Haar Cascade Classifier algorithm, where there will be data on all hand shapes based on the letters to be translated. This application uses the OpenCV library and Visual Studio Code IDE software connected to the Raspberry Pi Camera. The publisher will send data to other devices using the MQTT Broker to connect and display detection results to other device monitors wirelessly using a local network. The research was conducted at various distances between the hand and the webcam, from 30cm to 150cm. The research results using the Haar Cascade Classifier method to detect sign language obtained an accuracy of 82%.
Perbandingan Algoritma K-Means dan Algoritma K-Medoids Pada Kasus Covid-19 di Indonesia Puspitasari, Novianti; Lempas, Gidion; Hamdani, Hamdani; Haviuddin, Haviluddin; Septiarini, Anindita
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.2994

Abstract

Analyzing Covid-19 data has been conducted in many types of research, but research on classifying each case from Covid-19 data in all provinces in Indonesia has yet to be available. This study uses two clustering algorithms, namely K-Means and K-Medoids, to classify positive cases recovered and died in the Covid-19 data into three clusters, namely low, medium and high. The research data is Covid-19 case data in all provinces in Indonesia from 2020 to 2021. In the clustering calculations, the three distance methods used in this study are the Chebyshev Distance, Manhattan Distance, and Euclidean Distance. Based on the Silhouette Coefficient test results for the three distance calculation methods, it was found that Manhattan Distance is the best distance calculation method for K-Means and K-Medoids. Furthermore, the results of testing the Sum Squared Error (SSE), Silhouette Coefficient (SC) and Davies Index Bouldin (DBI) methods for the resulting clusters show that the value generated by the K-Means algorithm is higher in the SC and DBI methods. This result is evidenced by the SC value of 0.838; 0.838; and 0.925 in positive cases, recovered and died. While the DBI value is 0.305 for positive cases, 0.295 for recovered cases and 1.569 for dead cases. Based on these values, it proves that K-Means is superior in grouping and placing clusters compared to K-Medoids.
Penerapan Metode MABAC dengan Pembobotan ROC Dalam Sistem Pendukung Keputusan Pemilihan Pengajar dengan Kinerja Terbaik Kraugusteeliana, Kraugusteeliana; Zen, Agustian; Suryani, Suryani; Alam, Sitti Nur; Winarno, Edy
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3000

Abstract

Selection of the best teacher is one of the problems in a decision making according to semi-structured criteria. In this research, the decision support system explains how the process of selecting alternative teachers who have the best performance in elementary schools applies the MABAC method and weights it using the Rank Order Centroid (ROC) method. The use of this method can solve the problem of determining the best teacher that occurs in elementary schools. The final result of completing the method will produce the highest ranking value which will be determined to be the highest alternative. The data used in this study were 10 alternative teacher data using 5 criteria, resulting in 1 highest ranking of the 10 alternatives obtained by alternative 1 named Servin Manullang with a value of 0.6086 as a recommendation for the teacher with the best performance
Healthy Food Recommender System for Obesity Using Ontology and Semantic Web Rule Language Aditya, Naufal; Baizal, Z. K. A.; Dharayani, Ramanti
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3005

Abstract

Today's lifestyle and eating patterns tend to be irregular due to busyness. People prefer eating foods that are fast and easy to obtain, but often lack knowledge of the nutritional content in them. These eating patterns lead to unbalanced nutrition and can cause various health problems and diseases, such as overweight and obesity. Due to a lack of information, people often turn to drugs instead of learning about healthy diets, making it difficult for them to determine what menu to choose or what type of food to consume. While there have been many studies to recommend healthy food based on user preferences, there is currently no recommender system that includes serving size and budget for each daily food recommendation that is implemented in a chatbot framework. This study proposes using ontology and the Semantic Web Rule Language (SWRL) to store knowledge in the ontology and then process it using SWRL to produce food recommendations based on user preferences. From a sample of user data which obtained 170 recommended meal menus. System performance is pretty good. Based on the validation results from nutritionists, the precision value was 0.852941, the recall was 1, and F-score of 0.920634 So that a healthy food recommendation system can be used to help the user follows a diet that meets his nutritional needs and is within his budget needed
Clickbait Classification Model on Online News with Semantic Similarity Calculation Between News Title and Content Ahmadi, Hero Akbar; Chowanda, Andry
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3030

Abstract

Clickbait is a sensational title that makes us click internet links to an article, image, or video. Online content providers use clickbait to gain user traffic, that leads to increasing income from the placed ads in their page. To attract more and more traffic, online content providers write sensational and hyperbolic titles, and even misleading and not telling the whole story. This can give us, the internet consumer, wrong perspective, and half-truth. And nowadays, clickbait titles are worse than ever. Modern clickbait titles are not hyperbolic nor ambiguous enough, and sometimes very hard to identify. This paper aims to classify clickbait titles, to help humans identify clickbait and stop sharing more online content that contains clickbait and misleading titles. This model classifies clickbait by calculating semantic similarity between the article title and the summary of the article content. The article content is summarized by T5 (Text-to-text Transfer Transformer) model. IndoBERT is then used to calculate semantic similarity score between generated summary and the article title. The article title, content, summary, and semantic similarity score are used for clickbait classification with various algorithms. The result shows that by adding article content alongside article title in the classification process improves F1-score by 7% when classified with IndoBERT. In another future research, this model can be integrated with another application such as twitter or telegram bot to send us warning every time a user consumes online content with clickbait title. Thus, it can prevent online communities from sharing misleading information caused by clickbait
Penerapan Metode Teorema Bayes Dalam Mendiagnosa Penyakit Tubercolosis Nyipto Wibowo, Gentur Wahyu; Widiastuti, Sri; Muratno, Muratno; Lolang, Enos; Soraya, Soraya
Building of Informatics, Technology and Science (BITS) Vol 4 No 4 (2023): March 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i4.3035

Abstract

In human life, health is the most valuable asset. a healthy body and soul gives happiness to everyone. But sometimes circumstances make human health decline. One of the causes of declining human health is due to viruses or bacteria that spread without people knowing it. One of the infectious diseases is tuberculosis (TB). Tuberculosis is a disease that occurs due to an infection caused by a bacterium called Mycobacterium tuberculosis. TB is a disease that spreads very easily, namely by air. The patient will spread germs through the air if the patient coughs or sneezes. Death is not uncommon because those with symptoms are afraid to go to the hospital for medical check-ups because they are hampered by a lack of funds. This situation is very concerning. Because of these circumstances, technological developments can be used as an alternative solution in replacing or assisting in diagnosing diseases experienced by people based on their symptoms. One of the technologies that develop about diagnosing disease is by using an expert system. An expert system is a system created by experts by developing technology based on science. In expert systems, their use must be combined with a method so that they can work properly and optimally. In this study the method used is the Bayes Theorem method. The Bayes Theorem method was developed by a priest named Thomas Bayes. The pastor comes from England who is a Presbyterian pastor. The Rev. Thomas Bayes developed Bayes' Theorem in 1763 and was perfected and redeveloped by an expert named Laplace. Based on the results of the above process, information was obtained that 87% of patients who consulted had pulmonary tuberculosis

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