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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.
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Articles 38 Documents
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Search results for , issue "Vol 5 No 1 (2023): Juni 2023" : 38 Documents clear
Penerapan Metode Principal Component Analysis (PCA) dan Long Short-Term Memory (LSTM) dalam Memprediksi Prediksi Curah Hujan Harian Musfiroh Musfiroh; Dian Candra Rini Novitasari; Putroue Keumala Intan; Gede Gangga Wisnawa
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3114

Abstract

Since the last three years North Luwu has experienced frequent hydrological disasters in the form of floods and landslides. The disaster had a negative impact on the availability of clean water, failed to plant and even tended to reduce the quality of the harvest. Cocoa is one of the leading commodities of North Luwu Regency whose productivity has decreased due to the impact of climate change so that it will affect the sustainability of the local population's income. Therefore, the purpose of this research is to anticipate rainfall that will occur to prevent or reduce the risk of failure and loss. Principal Component Analysis (PCA) Method is used as feature extraction to find out the most influential variables and the Long Short-Term Memory (LSTM) method is used as a prediction method. Future rainfall is predicted using meteorological variables such as pressure, evaporation, maximum temperature, average humidity, and sunshine duration from 1 January 2017 to 30 September 2022. Based on the PCA results, 4 variables are obtained that have the most influence on rainfall, namely: variable evaporation, maximum temperature, average humidity, and length of sunlight. These variables are used as input to predict rainfall using LSTM. In this study using trial parameters, namely the number of hidden, batch size, and learn rate drop period. The best prediction results were obtained for MAPE of 0.0018 with the number of hidden, batch size and learn rate drop periods of 100, 32, and 50 respectively. The prediction results show very heavy rainfall occurring on August 28, 2021 of 101.9734 mm, 21 September 2021 of 108.6528 mm, and 5 April 2022 of 116.5510 mm. In this study PCA was able to increase accuracy in considering all parameters and choosing the most effective.
Penerapan Metode Teorema Bayes Dalam Mendiagnosa Penyakit Autoimun Abdul Karim; Shinta Esabella; Kusmanto Kusmanto; Sudi Suryadi; Elvitrianim Purba
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3407

Abstract

Autoimmune diseases are caused by the failure of the immune system to attack the body itself. According to data from the US Department of Health and Human Services, more than 23.5 million Americans have an autoimmune disease, which is difficult to diagnose because of the variety of symptoms it presents. Therefore, the development of mechanisms to identify autoimmune disorders is essential. One of the developing technologies in this field is the use of expert systems in diagnosing diseases. An expert system is a system developed by experts using science-based technology. In order to use it effectively, proper methods are needed, such as the Bayes Theorem approach, described by Thomas Bayes, a priest. The Bayes Theorem approach explains the relationship between the probability of event A and event B based on available information. This study attempts to facilitate the diagnosis of autoimmune diseases by using an expert system and Bayes' Theorem technique. With a confidence level of 0.57 or 57%, the examination results show that the patient suffers from an autoimmune disease of the type Hemolytic Anemia (HA) based on the patient's input
Penerapan Data Mining Dengan Mengimplementasikan Algoritma K-Means Dalam Proses Clustering Untuk Pengelompokan Mahasiswa Calon Penerima Beasiswa KIP Usanto S
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3411

Abstract

This research is about the grouping of prospective students who will receive KIP scholarships. Data mining is a conception or design made with the aim of finding an added value contained in a database that will be able to identify a useful knowledge information. In this study, the concept of data mining was applied to assist campuses in predicting students who will get KIP scholarships by implementing the K-MeansClustering Algorithm, where the K-MeansClustering algorithm can later group each data into clusters so that data that has the same characteristics will be grouped in the same cluster and vice versa if the data has different characteristics then it is grouped into another cluster. The results of this study are 3 cluster results which will be the final result, namely data received as scholarship recipients as many as 52 data, 32 data are grouped as recipient data which will be recommended to the next stage. while the remaining 16 data are grouped as data that is not accepted
Implementasi Metode Learning Vector Quantization (LVQ) Untuk Klasifikasi Keluarga Beresiko Stunting Abdul Aziz; Fitri Insani; Jasril Jasril; Fadhilah Syafria
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3478

Abstract

Stunting is a condition where a child's height is too short compared to children of the same age. This condition affects the health of toddlers in the short and long term, such as suboptimal body posture in adulthood, decreased reproductive health, and decreased learning capacity, resulting in suboptimal performance in school. One of the causes of stunting is a lack of nutrition, basic health facilities, and poor parenting practices. However, the current data collection and classification of families at risk of stunting still use Microsoft Excel, which is ineffective in processing large data. Therefore, the LVQ method, which is an improvement of the Vector Quantization method, is used to accelerate the classification process. In this study, 5 parameters were tested, and the optimal result was achieved by using 7 input neurons, Chebychev distance as the distance measure, a learning rate of 0.1, 7 epochs, and 30% of training data. With these parameters, an accuracy of 99.38% was obtained. Based on these results, the LVQ method can help improve accuracy in classifying families at risk of stunting
Data Mining Clustering Korban Kejahatan Pelecehan Seksual dengan Kekerasan Berdasarkan Provinsi Menggunakan Metode AHC Mitha Amelia Sundari; Rahmadhani Pane; Rohani Rohani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3499

Abstract

Sexual harassment is one of the most common crimes in Indonesia recently. Acts of sexual harassment can occur in everyday life regardless of time, whether at work, on the street, or at home. Women are often the victims of sexual harassment, although men can experience the same. Perpetrators of sexual harassment can come from people we don't know, people who have hatred, even people we care about. Lack of religious and moral education, and technological developments that allow easy access to pornographic content are contributing factors to sexual harassment. To overcome this problem, fast action is needed in places where sexual harassment often occurs through socialization so that people are more vigilant when they are in these places. Apart from that, it is necessary to improve security in the area and provide consultation places such as psychologists. To identify places that are prone to sexual harassment in Indonesia, a data mining method is applied by utilizing previous data. The clustering method used is AHC using the complete linkage mode (longest distance) between the initial clusters. The final results of this research involve a manual process and the appropriate RapidMiner application, so that new clusters can be formed using RapidMiner. There are 5 provinces included in cluster 0, then there are 17 provinces in cluster 1, and 12 provinces in cluster 2
Analisa Penerapan Metode MABAC dengan Pembobotan Entropy dalam Penilaian Kinerja Dosen di Era Society 5.0 Ahyuna Ahyuna; Ben Rahman; Fifto Nugroho; I Wayan Sugianta Nirawana; Abdul Karim
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3511

Abstract

In the era of society 5.0, it is very influential on the education sector which is experiencing increasingly sophisticated technological changes, so that lecturers are expected to be able to combine learning with technology so that students' insight into technology is growing. However, in the case of a performance appraisal process, several problems often occur because the large number of lecturers will affect the time of the performance appraisal process. In assessing the performance of lecturers in the era of society 5.0, there are several criteria including Dynamic, Innovative, Number of Scientific Publishes, Discipline and Digital Skills. Therefore, the author applies a MABAC and ENTROPY method in order to find accurate and logical results. So with that, the author adopted a method and produced the highest ranking, namely on behalf of Dito Putro Utomo with a total value of 0.39925
Implementation of Complex Proportional Assessment and Rank Order Centroid Methods for Selecting Delivery Services Joko Trianto; Dartono Dartono; Rini Nuraini; Hengki Rusdianto
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3512

Abstract

Choosing the right delivery service partner is an important thing for companies to consider. This is because the selection of the right delivery service partner can minimize the risks involved. Generally, choosing a delivery partner service is done by looking at the profile of the freight forwarder's partner. It takes time to determine the right delivery service partner. This study aims to apply the Complex Proportional Assessment (COPRAS) and Rank Order Centroid (ROC) methods in a decision support system for selecting delivery service partners to make it easier to make the right decisions and meet needs. The ROC weighting method is used to determine the value of the criteria based on priority. Meanwhile, the COPRAS approach is used to determine the best solution based on an analysis of the existing options through alternative assessments by providing interval-based utility judgments. In the case study conducted, the best alternative was obtained, namely J&T Express with a score of 100, followed by JNE Express with a value of 92.09, SiCepat with a value of 91.89, Ninja Express with a value of 91.42. The COPRAS calculation results on the system developed with the manual calculation results show the same value, this means that the calculations on the system are valid. The usability scores, on the other hand, have an average value of 88.33% and are considered good
Optimal Number Data Trains in Hoax News Detection of Indonesian using SVM and Word2Vec Muhammad Sulthon Asramanggala; Sri Suryani Prasetyowati; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3516

Abstract

Along with the development of the era of technological development also has an increase. Information dissemination occurs very quickly on social media, especially Twitter. On Twitter, only some news circulating is necessarily accurate information. Lots of information that is spread is hoax news that irresponsible individuals apply. In this research, the author will build a system to determine the optimal amount of data trained in the hoax news classification process. In this study, the authors will use the support vector machine and word2vec algorithms to classify hoax and non-hoax news on the system to be created. In this study, five experiments were carried out with the number of train data used as many as 5000, 10000, 15000, 20000, and 25000. 5000 data train results in an accuracy of 77.28%, 10000 data train produce an accuracy of 79.68%, data 15,000 trains produce an accuracy of 79.892%, 20,000 data trains produce an accuracy of 80,416%, and 25,000 data trains produce an accuracy of 81,184%, by using a combination of unigram with token full token selection. This research aims to build a hoax detection system that can determine the optimal amount of data training to use. Also, this research is used to see the performance of the Support Vector Machine algorithm with Word2Vec in detecting hoax news
Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization Vina Vamilina; Rice Novita
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3526

Abstract

E-Wallet applications in Indonesia have started to be in demand since the Covid-19 pandemic. The object being analyzed is an e-wallet application that is widely used in Indonesia and can be downloaded on the Google Playstore. The applications analyzed are Dana, Ovo, PayPal Link Aja and Doku. The advantages of these five applications are that Dana is user friendly or easy to use, while using Ovo is superior in terms of benefits, and Doku is superior in terms of security, Link Aja tends to be perceived by consumers in a neutral condition between security and user convenience because it is an e-wallet. It is still considered new in Indonesia, and PayPal has become a successful online payment system in C2C field. The focus of this research is to compare the comments of the users of the five applications. The method used in this study is the Support Vector Machine (SVM) algorithm. To produce high accuracy it is optimized using the Particle Swarm Optimization (PSO) algorithm. This was taken based on previous studies which stated that SVM-PSO has the highest percentage of accuracy compared to other algorithms. The data used is a thousand (1000) per application. So, the total amount of data is five thousand (5000) data. The results of the research show that the Ovo e-wallet is superior because it has the most positive comments, namely 579 and 421 negative comments, while the lowest position is occupied by Link Aja which only has 579 positive comments and 421 negatuve comments. In the process of sentiment analysis, the accuracy percentage of the SVM-PSO algorithm was also obtained, which was 91.10% in the Link Aja application. This means that SVM-PSO is very suitable to be combined to get the highest accuracy
Klasifikasi Sentimen Masyarakat di Twitter terhadap Ganjar Pranowo dengan Metode Naïve Bayes Classifier Sinta Wahyuni Ritonga; Yusra .; Muhammad Fikry; Eka Pandu Cynthia
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3535

Abstract

Indonesia is a country with a Democratic political system. The public is given freedom of speech, collaboration and public criticism. In the modern era, the use of social media is growing rapidly at the community level. One of the social media trends in Indonesia is Twitter which is used to convey aspirations to the government and as a means to convey daily activities, opinions, culture and get the latest information or news from Indonesia and abroad. Public opinion taken from Twitter can be positive, negative and neutral. The number of tweets on Twitter one of the trend topics in Indonesia is Ganjar Pranowo, can be used as a source of data in the assessment of sentiment classification which is processed to produce accuracy values. This study aims to classify public opinion on social media Twitter about Ganjar Pranowo using Naïve Bayes Classifier method. In the classification processing using a dataset of 4000 tweet data with two labeling classes, positive and negative to determine the efficiency of NBC performance combined with TF-IDF weighting, feature selection using supervised learning approach techniques. The results of the test on the classification of public sentiment research on Twitter about Ganjar Pranowo using NBC method using 10% of the test data from the dataset used to produce an accuracy value of 83.0%.

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