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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 889 Documents
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Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Warga Penerima Bantuan Sosial Pahrudin, Pajar; Harianto, Kusno
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2276

Abstract

Social Assistance (BanSos) is a government program intended for lower-middle families. Social assistance is assistance given to the community, especially the lower middle class, which is not continuous and selective. Many types of social assistance are provided by the government with the aim of prospering and helping the community's economy. However, the problem that occurs is that there are still many people who receive social assistance that are not people who deserve to receive social assistance, while the lower middle class who should receive social assistance are neglected and do not receive the social assistance. It should be for the distributor or the kelurahan to form groups for residents who are entitled to receive social assistance. The process of grouping the recipients of social assistance can be done by processing the data of residents who have the right to receive the social assistance. The data processing can be done by using data mining. One of the algorithms that can be used to solve problems in data mining is the K-Nearest Neighbor algorithm. After carrying out the overall process with a value of K = 5, it was found that the new data from residents was declared eligible to receive social assistance
Penerapan Metode MOORA pada Sistem Pendukung Keputusan Pemilihan Kepala Laboran Harianto, Kusno; Arfyanti, Ita; Yusika, Andi
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2288

Abstract

In the process of carrying out academic activities in every university, it is inseparable from the existence of tendik. In college, the head of the laboratory is in charge of ensuring the implementation of the use of the laboratory in supporting the ongoing learning process. The head of the laboratory is in charge of regulating work mechanisms and procedures in the laboratory unit. The importance of the role of the head of laboratory for tertiary institutions requires universities to have a head of laboratory in accordance with the implementation of the tasks and responsibilities given. The selection of the head of the laboratory is not only done based on the length of work at the tertiary institution, but also must be seen from the knowledge, abilities, expertise, decision making and competency certificates possessed. Therefore, we need a way to help solve problems, especially by using a computerized system. Decision support system is a computerized information system. Decision support systems are widely used for corporate organizations to solve problems in the process of making or supporting decisions. The results obtained from the application of the MOORA Method are that alternative A1 was chosen to be the head of the laboratory with a final score of 0.48
Analisis Sentimen Kenaikan Harga BBM Pertamax Pada Media Sosial Menggunakan Metode Naïve Bayes Classifier Sitio, Sartika Lina Mulani; Nadiyanti, Ria
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2311

Abstract

Fuel Oil (BBM) is a very vital commodity. Fuel has an important role in people's lives. Because of the importance of fuel in people's lives, fuel is one of the basic needs of the community. The policy of increasing the price of fuel has always been a phenomenon in various media which causes pros and cons in society. The policy of increasing fuel prices has a big impact on society, both direct and indirect consumption. This study aims to explore public opinion, whether it shows negative or positive sentiment in the policy of increasing fuel prices. The increase in Pertamax fuel prices has drawn several opinions from citizens on Facebook social media. Sentiment analysis research was conducted to determine the response to Facebook comments on Brilio.Net accounts in 2022 related to the increase in Pertamax fuel prices with a dataset of 799 data, as well as a comparison of the number of positive, negative, and neutral comments. In addition, in this study to be able to determine the level of performance generated by the nave Bayes classifier method in the test. The author uses 80% of the comment dataset to be used as training data and 20% to be used as test data to be used as machine learning and test data. Then the data is classified by the system using orange data mining tools so as to produce a percentage of positive sentiment as much as 19%, negative sentiment as much as 22% and neutral sentiment as much as 59%. testing with the nave Bayes classifier method obtained the highest percentage accuracy rate of 99% from all datasets.
Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian Zulastri, Zulastri; Afrianty, Iis; Budianita, Elvia; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2437

Abstract

The high divorce rate has a negative impact on couples who will file for divorce and also has an extreme impact on children such as psychological disorders of children. The magnitude of the impact of divorce, it is necessary to predict the divorce decision. In this study, the application of the backpropagation method to predict divorce decisions was carried out. The data used is data on divorce decisions from the Pekanbaru Religious Court from 2020 - 2021 totaling 779. The dataset obtained is not balanced with 724 accepted classes and 55 rejected classes, balancing is done by reducing excess classes. The parameters used in this study build 3 architectural models [6-7-1], [6-9-1], [6-12-1], learning rate (0.01, 0.03, 0.09), max epoch and data sharing (70:30), (80:20), (90:10). The results of this study indicate that the best architectural model is in the network architecture [6-9-1] learning rate 0.09 epoch 300 dataset distribution 80% training data and 20% test data the accuracy value is 80% and the Mean Squared Error (MSE) is 0.1402. In this study, the backpropagation method was successful in predicting divorce decisions.
Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index Zarkasyi, Muhammad Imam; Mawengkang, Herman; Sitompul, Opim Salim
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2458

Abstract

In K-Means Clustering, the number of attributes of a data can affect the number of iterations generated in the data grouping process. One of the solutions to overcome these problems is by using a reduction technique on the dimensions of the dataset. In this study, the authors apply the Gini Index to perform attribute reduction on the data set to reduce attributes that have no effect on the dataset before clustering with K-Means Clustering. The dataset used to be tested as a testing instrument in this research is Absenteeism at work obtained from the UCI Machine Learning Repository, with 20 attributes, 740 data records and 4 attribute classes. The results of the tests in this research indicate that the number of iterations obtained from the comparison of tests using the K-Means in a Conversional (Without Attribute Reduction) is obtained by the number of 9 iterations, while the K-Means with attribute reduction with the Gini Index obtains the number of iterations totaling 6 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering in a Conversional (Without Attribute Reduction) is 1391.613, while in K-Means Clustering with attribute reduction with a Gini Index, it is 440.912. From the results of the proposed method, it is able to reduce the percentage of errors and minimize the number of iterations in K-Means Clustering by reducing the dimensions of the dataset using the Gini Index
Sistem Penentuan Lokasi Menara Base Transceiver Station dengan Algoritma AHP-TOPSIS Amar, Muh. Ikhsan; Ramdana, Ramdana; DA, Alvian Tri Putra
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2466

Abstract

Cellular telecommunication technology has now become an inseparable part of the increasingly mobile pattern of human life, this of course has an impact on the number of uses of cellular telecommunication technology which is increasing every day. This increase has encouraged cellular telecommunications technology vendors to continuously improve the quantity and quality of their telecommunications networks by establishing telecommunications equipment such as Base Transceiver Station (BTS) towers at strategic points. This study aims to build a system for determining the location of BTS towers that can provide visualization of information on the distribution of existing BTS towers and their coverage area radius. Analysis and location determination using a combination of AHP and TOPSIS methods. The AHP method is used to determine the objectivity of the weight and importance of the BTS tower location criteria. Furthermore, the results of the comparison of criteria will be used in the TOPSIS method to assess the rating of each candidate location. The results of the study obtained that the criteria for the location of BTS towers were population density with a weight of 0.42, the distance of the existing tower was 0.2, location access was 0.31, and the installation cost was 0.08. Meanwhile, the candidate locations were measured using linguistic variables with a weight of 0.67 for very good criteria, 0.23 for good, and 0.10 for good enough. System testing was carried out on determining the location of the BTS tower in Sinjai Borong District by analyzing three candidate locations and the results obtained that the Samaenre location was the best candidate with a rating value of 0.74.
Multi Kelas Speaker Recognition Menggunakan Deep Learning dengan CN-Celeb Dataset Martulandi, Adipta; Zahra, Amalia
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2467

Abstract

Speaker recognition has been widely applied in various fields of human life such as Siri from Apple, Cortana from Microsoft, and Voice Assistant by Google. One of the problems when creating speaker recognition is related to the dataset used for the modeling process. The dataset used for creating the speaker recognition model is mostly data that cannot represent real-world conditions. The result is when implemented in the real-world conditions are not optimal. This study develops a speaker recognition model using deep learning (LSTM) with the CN-Celeb dataset. The CN-Celeb dataset is data taken directly from the real world so there is a lot of noise. The hope of using this dataset is that it can represent real world conditions. Model development uses 2 stacked LSTM for multi-class speaker recognition tasks. In addition, this study performs tuning hyperparameters with a grid search method to obtain the most optimal model configuration. The results showed that the EER value of the LSTM model was 10.13% better than the reference baseline paper of 15.52%. In addition, when compared with other studies that also used the CN-Celeb dataset but using different models, it was found that the LSTM model had promising results. From the results of study that has been carried out and also compared with other people's research, it was found that the LSTM model gave promising performance. The LSTM model is compared with the x-vectors, PLDA, TDNN, and transformers models
Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier Singgalen, Yerik Afrianto
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2486

Abstract

Sentiment analysis of visitors to the tourist destinations of Borobudur Temple in Indonesia needs to be done to determine the expected product and service preferences. In addition, sentiment analysis is also helpful for managers to adjust the needs of tourists to the infrastructure provided in the tourist destination area. The classification method used in the sentiment analysis is the Naïve Bayes Classifier (NBC) against 3850 visitor reviews at Borobudur Temple. Review data is pulled from Tripadvisor web pages filtered by language, review time, and travel characteristics to analyze foreign traveler preferences comprehensively. This research stage is divided into three parts: data preparation, data processing, sentiment analysis, and algorithm performance evaluation. In addition, SMOTE Upsampling is used to balance data. The results of implementing the Naïve Bayes Classifier (NBC) classification method obtained an accuracy value of 96.36%, a precision value of 93.23%, and a recall value of 100% with an Area Under Curve (AUC) value of 0.714. In addition, the results of ranking five famous words from the review data show that there are highlights of the physical condition of the temple, scenery, and tourist visit activities at Borobudur Temple, where the four most famous words in visitor reviews are the “temple,” “visit,” “Borobudur,” “sunrise” and “place.”
Kmeans Clustering Segmentation on Water Microbial Image with Color and Texture Feature Extraction Kristanto, Sepyan Purnama; Hakim, Lutfi; Yusuf, Dianni
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2490

Abstract

Image segmentation is one of the analytical processes for digital image recognition, where this process divides the digital image into several unique regions based on homogeneous pixels. The process of homogeneous grouping images is based on several colour, texture and shape features. Colour in digital image processing is very important because colour has many information humans can easily understand. Colour has various features, combining colour intensity and grey (grayscale) and binary (black and white) values. However, the colour feature extraction process has many weaknesses. If the object used has a very small si[1]ze and range area, the use of colour features needs to be combined with extraction, and the segmentation process can be maximized. This study uses colour and texture features in the extraction process. It uses bacterial objects (microbes) from water, with limited image quality and objects that tend to be difficult to identify. The colour space feature extraction process is combined with a Gabor filter so that the segmentation process produces high-quality accuracy. Good. The Gabor filter used in this study is combined with the L*a*b space vector to increase accuracy in the segmentation process. The results showed that the use of texture features resulted in an increase in accuracy of 17.5% by testing the cluster value of 1.2.
Sistem Pendukung Keputusan Aplikasi Bantu Pembelajaran Matematika Menggunakan Metode EDAS Karim, Abdul; Esabella, Shinta; Hidayatullah, Muhammad; Andriani, Titi
Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): December 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i3.2494

Abstract

Technological developments have occurred at this time. The rapid development of technology forces students to be wiser in using it. One of the functions of technology is to become a learning medium like a cell phone. HP, which has become an obligation for students, has given rise to ideas for experts to bring up many new applications, especially in the field of mathematics. The emergence of these various applications makes students confused to choose which one is feasible to choose. To solve this problem, a Decision Support System is needed. SPK is a computer-based system whose function is to assist parties or individuals who need it, especially in making a decision. SPK can function properly, if you use the method. The method used in Ono's research is the EDAS method. The EDAS method is a method that uses a formula or formula, where the decision is generated from positive and negative distances. Based on this study, the results were found, namely alternative A1 with the name of the application, namely QANDA with a value of 0.0767 as the highest score

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