Building of Informatics, Technology and Science
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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<![CDATA[Hotel Selection Decision Support System with the Simple Additive Weighting (SAW) Method ]]>
<![CDATA[Annisaa Utami]]>;
<![CDATA[Muhammad Lulu Latif Usman]]>;
<![CDATA[Ike Fitria Ramadhani]]>;
<![CDATA[Siti Nur Fadilah Syam]]>;
<![CDATA[Fikrian Akmal Fauzan]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2262
<![CDATA[Purwokerto as the city center in southwestern Central Java which is one of the tourism places in Central Java with a fairly large number of enthusiasts. Purwokerto City has a wide selection of tourist attraction destinations that can be visited by tourists. There are not a few tourists who come from outside the city and do tours for more than one day. If you look at these conditions, a temporary stopover place is needed, namely a hotel. Purwokerto City provides so many choices of hotels spread across various locations with lodging classes, rental prices, facilities and services that are diverse. With the existence of many and different hotel facilities, of course, visitors will find it difficult to find and determine a hotel that matches the desired criteria. In addition, they will also find it difficult in finding the location of the desired hotel. The calculation results using the SAW (Simple Additive Weighting) Method found that Java Heritage Hotel got a value of 0.9, Resort and Hotel Atrium by 0.77, Surya Yudha Hotel by 0.65 and Trisno Hotel by 0.55. And get the same result between manual calculations and calculations from the system.. Based on the description above, in this study a decision support system was developed that can help tourists to determine hotels according to the wishes and needs of tourists using the SAW Method (Simple Additive Weighting)]]>
<![CDATA[Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah ]]>
<![CDATA[Ita Arfyanti]]>;
<![CDATA[Muhammad Fahmi]]>;
<![CDATA[Pitrasacha Adytia]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2275
<![CDATA[The Indonesian Smart College Card (KIP Lecture) is a government program that has been implemented from 2020 until now. KIP Lectures are distributed by the Ministry of Education, Culture, Research and Technology through universities in each region. Where each university gets a different quota - based on the level of progress of the college. The provision of quotas for each university based on the accreditation at each university raises its own problems for these universities. The problem faced is that the number of new prospective students who register to take the KIP Lecture program exceeds the quota set for each university. The provision of KIP Lecture assistance to the wrong person will lead to misuse of assistance and also inappropriate targets. The acceptance of the selection process for new prospective students can be seen from the previous process that has been carried out. Data mining is a technique used to solve problems in large data processing. Decision Tree is an algorithm that is included in the classification technique in data mining. The process in the decision tree aims to group or classify data against their respective classes. The results of the Decision Tree algorithm are in the form of decision trees and rules, the results obtained are in the form of rules that can be used for future decision-making processes]]>
<![CDATA[Penerapan Algoritma K-Nearest Neighbor Untuk Klasifikasi Warga Penerima Bantuan Sosial ]]>
<![CDATA[Pajar Pahrudin]]>;
<![CDATA[Kusno Harianto]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2276
<![CDATA[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]]>
<![CDATA[Penerapan Metode MOORA pada Sistem Pendukung Keputusan Pemilihan Kepala Laboran ]]>
<![CDATA[Kusno Harianto]]>;
<![CDATA[Ita Arfyanti]]>;
<![CDATA[Andi Yusika]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2288
<![CDATA[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]]>
<![CDATA[Analisis Sentimen Kenaikan Harga BBM Pertamax Pada Media Sosial Menggunakan Metode Naïve Bayes Classifier ]]>
<![CDATA[Sartika Lina Mulani Sitio]]>;
<![CDATA[Ria Nadiyanti]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2311
<![CDATA[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.]]>
<![CDATA[Penerapan Neural Network dengan Menggunakan Algoritma Backpropagation pada Prediksi Putusan Perceraian ]]>
<![CDATA[Zulastri Zulastri]]>;
<![CDATA[Iis Afrianty]]>;
<![CDATA[Elvia Budianita]]>;
<![CDATA[Fadhilah Syafria]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2437
<![CDATA[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.]]>
<![CDATA[Optimasi Cluster Pada K-Means Clustering Dengan Teknik Reduksi Dimensi Dataset Menggunakan Gini Index ]]>
<![CDATA[Muhammad Imam Zarkasyi]]>;
<![CDATA[Herman Mawengkang]]>;
<![CDATA[Opim Salim Sitompul]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2458
<![CDATA[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]]>
<![CDATA[Sistem Penentuan Lokasi Menara Base Transceiver Station dengan Algoritma AHP-TOPSIS ]]>
<![CDATA[Muh. Ikhsan Amar]]>;
<![CDATA[Ramdana Ramdana]]>;
<![CDATA[Alvian Tri Putra DA]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2466
<![CDATA[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.]]>
<![CDATA[Multi Kelas Speaker Recognition Menggunakan Deep Learning dengan CN-Celeb Dataset ]]>
<![CDATA[Adipta Martulandi]]>;
<![CDATA[Amalia Zahra]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2467
<![CDATA[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]]>
<![CDATA[Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier ]]>
<![CDATA[Yerik Afrianto Singgalen]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 3 (2022): Desember 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i3.2486
<![CDATA[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.”]]>
