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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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Analisis Algoritma K-Means dan Davies Bouldin Index dalam Mencari Cluster Terbaik Kasus Perceraian di Kabupaten Kuningan Sopyan, Yayan; Lesmana, Agrian Dwi; Juliane, Christina
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.v4i3.2697

Abstract

In marriage, the thing that is most avoided is a divorce. Divorce is the termination of the husband and wife relationship which is carried out legally at the time of trial. From year to year, there is an increase in the number of divorces in Indonesia, including the number of divorces in Kuningan Regency. This study analyzes divorce cases in villages in Kuningan Regency, the analysis is carried out by using data mining clustering methods using the K-Means algorithm. The clustering method is grouping data based on the same characteristics. In determining the number of clusters by using the value of the smallest Davies Bouldin Index, it is hoped that the number of clusters formed can be more optimal. The results of this study are that there are 4 clusters consisting of villages or sub-districts with different divorce rates, namely the highest divorce rate, high divorce rate, medium divorce rate, low divorce rate, and lowest divorce rate
Prediksi Hasil Produksi Tanaman Tomat di Indonesia Menurut Provinsi Menggunakan Algoritma Fletcher-Reeves Fajri, Surya; Gunawan, Heru; Batubara, Lokot Ridwan; Sitorus, Zunaida
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.v4i3.2704

Abstract

Tomatoes are essential for Indonesians because they have high economic and nutritional value. In addition, as population growth increases, the demand for tomatoes also increases. Based on this, it is essential to research to predict the future development of tomato crop production. The research in this paper uses a dataset of tomato plant production in Indonesia, which is spread across 34 provinces in the last seven years, namely from 2015 to 2021), which is sourced from the Indonesian Central Bureau of Statistics and the District/City Agriculture Service of each Province. The algorithm proposed in this study is the Fletcher-Reeves Conjugate Gradient Algorithm which will be processed with the help of Matlab2011b. Research analysis with three network architectural models: 5-7-1, 5-13-1, and 5-17-1. Based on a network comparison of the three architectural models, the best result is the 5-17-1 model because the MSE value is the smallest compared to the other two models, namely 0.0009915 compared to 0.0010851 and 0.0049764, as well as the highest level of accuracy, namely by 94% versus 91% and 88%. Therefore the 5-17-1 model is used to predict the yield of tomato production in Indonesia for the future (2022 and 2023). Based on the prediction results at the end of 2022 and 2023, there are 18 provinces where tomato crop production has the potential to increase, although not too significantly. The prediction of tomato production using the Fletcher-Reeves algorithm is quite good because it produces a small error rate and high accuracy.
Analysis of Academic and Administration Information Systems Using Servqual and Kano Methods Sari, Cahya Metta; Hamzah, Muhammad Luthfi; Angraini, Angraini; Saputra, Eki; Fronita, Mona
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.v4i3.2713

Abstract

Academic and Administrative Information System (SIAKAdm) is an online-based information system service for students of Hangtuah University Pekanbaru. With the development of information systems in the academic field, we must also test information systems, there are several problems that users feel that the quality of service of the Academic Information System (SIAKAdm) has not run effectively and efficiently, such as, there are often delays when filling in KRS, color contrast in the system is too disturbing to the user's eyes, there is no edit menu on the student profile, and finally there is no complaint lyanan menu or C3 servicedesk menu. This research was conducted using the ServQual method and the Kano method. The ServQual Method can be said to be a method used to measure the quality of service attributes of a dimension, while the Kano Method can be interpreted as a model built to understand how well their product or service meets the needs of users. This data collection process is by conducting interviews and distributing questionnaires of 98 respondents using the Simple Random Sampling technique. The data was obtained using IBM SPSS 26 and calculated the GAP value using Microsoft Excel. The results of this study The highest gap value was in the Assurance variable, with a GAP value of -4.54. While the lowest gap value is in the Responsivennes variable of -2.51.
Hate Speech Detection on Twitter through Natural Language Processing using LSTM Model Arbaatun, Cepthari Ningtyas; Nurjanah, Dade; Nurrahmi, Hani
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.v4i3.2718

Abstract

Currently, social media is a place to express opinions. This opinion can be positive or negative. However, lately, the opinion that often appears is a negative opinion, such as hate speech. Hate speech is often found on social media, such as malicious comments intended to insult individuals or groups. Based on WeAreSocial data in 2021, one of the most used social media platforms in Indonesia is Twitter, with 63.6% of users. According to the Indonesia National Police, hate speech cases were more dominant during the period from April 2020 to July 2021. Therefore, efforts are needed to identify hate speech on the Twitter platform. One way to detect hate speech is by using deep learning. In this research, we use a deep learning model of Long Short-Term Memory (LSTM) with word embedding. FastText and Global Vector (GloVe) is the word embeddings that we use as input for word representation and classification. FastText embeddings make use of subword information to create word embeddings and GloVe embeddings using an unsupervised learning method trained on a corpus to generate distributional feature vectors. From the evaluation results on the experimental model, LSTM-FastText using random oversampling has an advantage with an F1-score of 89.91% compared to LSTM-GloVe to obtain an F1-score of 82.14%.
Analisis Performa Algoritma NBC, DT, SVM dalam Klasifikasi Data Ulasan Pengunjung Candi Borobudur Berbasis CRISP-DM 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.v4i3.2766

Abstract

The approach of visitor sentiment analysis to Borobudur Temple tourist destinations in Indonesia can be classified using various algorithms to get optimal results. Good algorithm performance can be seen from the confusion matrix (accuracy, precision, recall) value, Area Under Curve (AUC) value, and Receiver Operating Characteristic (ROC). This study used the Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM) algorithms against 3850 text data obtained from the Tripadvisor website, especially reviews of Borobudur Temple visitors. The method refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for optimizing tourist destination products and services by paying attention to six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study show that the results of NBC's algorithm performance evaluation can be seen to have a change in the confusion matrix value at the accuracy value from 98.73% to 95.6%, the precision value changed from 98.72% to 98.97%, the recall value also changed from 100% to 96.54%. In addition, the Area Under Curve (AUC) of NBC also changed from 0.500 (50%) to 0.693 (69.35%). In addition, the results of the DT algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 97.55% to 94.40%, the precision value increased from 97.63% to 91.86%, the recall value also changed from 99.90% to 99.47%. The Area Under Curve (AUC) of DT value also changed from 0.591 (59.1%) to 0.932 (93.2%). The results of the SVM algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 98.73% to 99.41%; the precision value changed from 98.72% to 100%, and the recall value also changed from 100% to 99.01%. The Area Under Curve (AUC) of the SVM value also changed from 0.961 (96.1%) to 1.00 (100%). In addition, the T-test results show that the SVM algorithm is more dominant compared to other algorithms, where the SVM algorithm T-test value is 0.994 compared to the DT algorithm T-test value of 0.944 and the NBC algorithm T-test value of 0.98. Based on the Receiver Operating Characteristic (ROC) value, it can be seen that the DT algorithm also shows good performance in addition to SVM. It indicates that in analyzing the sentiment of visitors to Borobudur Temple, the best-recommended algorithm is the Support Vector Machine
Penerapan Metode Topsis Pada Sistem Pendukung Keputusan Kelayakan Penerima Dana Bantuan Operasional Sekolah Azahari, Azahari; Pahrudin, Pajar; Yunita, Yunita
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.v4i3.2290

Abstract

One of the ways used to fulfill education. The Indonesian government implements a 12-year compulsory education program. Although there is a 12-year compulsory education program by the government, there are still some students who cannot continue their education due to factors from the family economy who are unable to meet the needs or costs of the education they take. The School Operational Assistance Fund (BOS) is a financial aid given to underprivileged students/I to be able to meet learning needs such as tuition fees, book fees or personal needs that support the implementation of education for students/I. For private schools, the School Operational Assistance Fund (BOS) has its own quota to be given to students. The organizing committee for the recipients of the School Operational Assistance Fund (BOS) is required to be fair and honest in the selection process. The error is because there is still no special provision used for the selection process or the assessment process carried out by the school. Decision Support System (DSS) is a system that has been integrated with a computer, where the decision support system is used to provide certain provisions that can be used to assist in providing recommendations in the decision-making process. TOPSIS uses the principle that the chosen alternative must have the closest distance from the positive ideal solution and the farthest from the negative ideal solution from a geometric point of view by using Euclidean distance to determine the relative proximity of an alternative to the optimal solution. By applying the TOPSIS method, Alternative 4 (A4) was selected as the beneficiary with a final score of 0.7251
Implementation of Profile Matching in the Decision Support System for Best Student Selection Rumandan, Rhaishudin Jafar; Nuraini, Rini; Sari, Marliana
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.v4i3.2587

Abstract

The determination of outstanding students is carried out by schools, which provide scholarships to their students in order to provide motivation to improve their academic achievement. The selection of the best students is usually done by looking at the student's report card scores, followed by the homeroom meeting. This is considered not objective and requires a long time to select the best students. Through a decision-support system, the best students can be selected based on the best criteria and alternatives. In a decision support system, a method is needed to determine the best alternative. The profile matching method is considered to have a better level of objectivity because, to measure the value of each indicator, the valuation variable is derived again with sub-indicators and is weighted using assessment parameters. This study aims to solve the problem of selecting the best student through a decision-support system with the output in the form of a ranking using the profile matching method. Based on the calculation of profile matching, it shows that the results of calculating the final score of the best student 1 are a final score of 3.000, the best student 2 gets a final score of 2.955, and the best student 3 gets a final score of 2.693. From the final score, first place in the selection of the best student was Putri Nurlandari with a final score of 3.000.
Method comparison of Naïve Bayes, Logistic Regression, and SVM for Analyzing Movie Reviews Aziz, Muhammad Maulidan; Purbalaksono, Mahendra Dwifebri; 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.v4i4.2644

Abstract

A film can be categorized as a successful film based on the reviews given by the critics. The reviews can range from professional critics to public reviews from the general audience. Due to a large number of reviews and opinions on a film, this study aims to create a sentiment analysis model and compare the methods used to analyze datasets from a movie review. Sentiment Analysis is a method for studying and analyzing opinions, then classifying these opinions into several classes. This research will use the Naïve Bayes method, Logistic Regression, and Support Vector Machine (SVM) to analyze film review data. The film review dataset used is a collection of film reviews taken from the Rotten Tomatoes website and will be pre-processed before implementing the Naïve Bayes, Logistic Regression, and SVM methods. The SVM classifier with 80:20 data splitting has the best performance, with a result of 99.4% accuracy score and 93.5% F1 score.
Optimization in Time and Score using IID Algorithm for K-Modes Clustering Yulianti, Farah; Sen, Tjong Wan
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.v4i4.2791

Abstract

Nowadays, there are numerous methods for analyzing data, one of which is cluster analysis. Because most practical data in today's analysis contains categorical attributes, categorical data clustering has recently received a lot of attention. To cluster categorical data, unsupervised machine learning techniques, which used frequency-based method, such as K-Mode’s clustering are used. The K-Modes algorithm takes advantage of the differences between the data points (total mis-matches or dissimilarities). The lower the dissimilarities, the more similar the data points, and thus the better the cluster. This paper aims to improve K-Mode’s clustering performance by incorporating the intercluster and intracluster dissimilari-ty measure, or IID measure, into the K-Modes algorithm rather than just using the standard simple-matching method to increase the algorithm's accuracy and execution time. This combined algorithm improves accuracy and execution time of the K-Modes algorithm. As a result, this algorithm can be used as an alternative to better cluster categorical data.
Penerapan Forecasting Menggunakan Metode Time Series Untuk Menentukan Proyeksi Sales di Perusahaan Manufacturing Furniture Prasakti, Lukito Angga; Juliane, Christina
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.v4i4.2802

Abstract

The large population certainly encourages companies, including manufacturing companies, to continue to develop their production both in terms of quality and quantity, especially since the number of companies with the same focus is quite large. This is because, every certain company wants to get a lot of profit and minimal consumer or customer complaints. One way that is considered to be able to overcome this is by carrying out company policy referring to forecasting product sales in the future. Therefore, researchers want to find out more about the application of forecasting to determine monthly sales projections for the following year at a Furniture Manufacturing Company. The aim is to determine the role of forecasting in making policies on the company's production at a later time by considering sales projections based on the company's forecasting results. The method used is time series by collecting data through documentation at the regular local market in 2022 to be precise 12 months. After the data is collected, it will be analyzed in depth so that it is known from the research results that careful forecasting will produce forecasts that are not far from reality and can help in calculating sales projections for furniture manufacturing companies at a later time, with a MAPE value of 0.06

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