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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 926 Documents
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Kombinasi Metode Pembobotan Entropy dan MARCOS Dalam Seleksi Penerimaan Karyawan Divisi Keuangan Wahyuni, Dita Septia; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5835

Abstract

The selection of employees for the Finance Division is a crucial process to ensure that the selected individuals have the appropriate skills and qualifications to handle complex financial responsibilities. The main problems in the selection of Finance Division employees often revolve around the difficulty in accurately assessing the candidate's technical skills and analytical abilities. The experience and qualifications listed on a resume do not necessarily reflect the candidate's apparent ability to handle complex financial situations or in the face of stringent regulatory challenges. This study aims to apply a combination of entropy and MARCOS weighting methods in the selection of employees of the Finance Division, in order to improve the objectivity and accuracy of the decision-making process. Through this approach, to identify candidates who best suit the company's needs and requirements based on a comprehensive multi-criteria analysis. The combination of Entropy and MARCOS weighting methods in the selection of financial division employees provides a comprehensive and objective approach in decision-making. The Entropy method is used to objectively determine the weight of the criteria based on the degree of uncertainty of the information provided by each criterion, the MARCOS method is used to evaluate and rank candidates based on their proximity to the ideal solution and the distance from the anti-ideal solution. The results of the financial division employee acceptance selection ranking show that Budi Santoso occupies the top position with the highest score of 4.8848. These results provide a clear picture of each candidate's relative position in terms of final assessment, and can serve as a basis for more targeted and objective hiring decisions.
Penerapan Metode K-Nearest Neighbors dan Naïve Bayes pada Analisis Sentimen Pengguna Aplikasi Bstation melalui Platform Playstore Amrillah, Sigit Fathu; Krisbiantoro, Dwi; Prasetyo, Agung
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5863

Abstract

Streaming is a method of distributing digital content directly over the internet, which allows users to access media without the need to download files. Bstation is a streaming platform that combines (OGV) and User-Generated Content (UGC). This research assesses the effectiveness of the K-Nearest Neighbors (KNN) and Naïve Bayes algorithms in analyzing sentiment in user reviews of the Bstation application, using a data sample of 5,000 reviews. The problem faced is the large number of users of the Bstation application, so sentiment analysis is needed to measure and understand the public's assessment of the application more accurately. This research aims to analyze the sentiment of Bstation users on Playstore and compare the performance of K-Nearest Neighbors (KNN) and Naïve Bayes to determine the best method for classifying reviews and user sentiment patterns. The findings showed that Naïve Bayes achieved 84% accuracy, surpassing KNN which only achieved 68%. Naïve Bayes showed 86% precision and 88% recall for negative sentiment, while achieving 78% precision and 76% recall for positive sentiment. recall for positive sentiment. In contrast, KNN achieved 80% precision and 66% recall for negative sentiments, and 54% recall for positive sentiments. recall for negative sentiments, and 54% precision and 71% recall for positive sentiments. The F1-Score for Naïve Bayes is also higher, reflecting a better balance overall. better balance overall. The macro average and weighted average weighted average for precision, recall, and F1-score with Naïve Bayes were 82% and 83%, respectively, while KNN recorded a macro average of 0.67. In conclusion, Naïve Bayes is more effective in sentiment analysis than KNN, providing more consistent and accurate performance
Comparative Analysis of LSTM, FB Prophet, and Moving Average Methods for Fuel Sales Prediction: A Time Series Forecasting Approach Fadhilah, Ahmad Rizky; Nasution, Arbi Haza; Monika, Winda
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5877

Abstract

Fuel is an important part of vehicles and machinery where sales demand is very high and has various fluctuations. The uncertainty in these fuel sales patterns poses serious problems in inventory management and fuel distribution planning in Indonesia, which can result in excess stock or fuel scarcity in various regions. Additionally, changing trends in vehicle usage and the impact of the COVID-19 pandemic have made accurate sales predictions increasingly difficult. Therefore, this research aims to understand the current and future sales patterns and trends of fuel sales in Indonesia. Careful analysis of prices and other factors such as data processing and other variables is required. This study uses time series analysis methods and compares four models, namely Long Short-Term Memory (LSTM), FB Prophet, Simple Moving Average (SMA), and Exponential Moving Average (EMA). By comparing the results using statistics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) over various prediction time frames, we assessed the patterns of each model. The results of the analysis show that the LSTM model outperformed all other methods with the lowest MAPE for the prediction of gasoline in the next 31 days, which is 17.11%, while the FB Prophet outperformed all other methods with the lowest MAPE for the prediction of diesel in the next 31 days, which is 18.32%. Although the LSTM model generally outperformed all other algorithms, the FB Prophet model can be used to predict future trends, such as increased use of diesel and decreased use of gasoline which are expected to last within one year. This analysis also provides insights for choosing the right model for a time series problem, including the characteristics of the data to be predicted and analyzed, as well as the assumptions of stationarity and normality of the data. The results of this study indicate that machine learning algorithms can improve the accuracy of time series predictions significantly compared to traditional statistical methods.
Sistem Pakar Diagnosis Penyakit Tanaman Jagung dengan Metode Certainty Factor untuk Meningkatkan Produktivitas Petani Abdilah, Surya; Widyanto, R Arri; Artha, Emilya Ully
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5881

Abstract

Corn is an important commodity in Indonesia after rice and has great potential to be developed as a primary food source. However, corn productivity is often disrupted by pests and diseases. Farmers often have difficulty in identifying and overcoming these problems due to limited knowledge and access to appropriate solutions. This study develops an Expert System for Diagnosing Corn Plant Diseases using the Certainty Factor (CF) method to assist farmers in diagnosing and overcoming pest and disease problems in corn plants. The Certainty Factor method is used to measure the level of expert confidence in the relationship between observed symptoms and the likelihood of disease occurring. This system is designed to provide a diagnosis based on symptom input provided by the user, with a confidence level calculated using a combination of CF values from the inputted symptoms. In testing this system, several cases of corn disease diagnosis were tested using the collected symptom data. The results of system testing on users through the User Acceptance Test (UAT) showed a very good level of acceptance, with a percentage of 94.75%. This system is expected to be an effective tool for corn farmers in increasing their crop productivity in a more efficient and accurate way. Thus, this system has proven to be effective as a tool for farmers to identify and overcome corn plant disease problems more precisely and efficiently.
Classification of Rice Plant Disease Image Using Convolutional Neural Network (CNN) Algorithm based on Amazon Web Service (AWS) Anggraini, Nova; Kusuma, Bagus Adhi; Subarkah, Pungkas; Utomo, Fandy Setyo; Hermanto, Nandang
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5883

Abstract

− In agriculture, rice plays an important role in the Indonesian economy. Rice produces rice, one of the most widely consumed staple food sources in Indonesia. Many factors can cause rice production failure, one of which is leaf pests and diseases. Therefore, early identification and management of plant diseases is an important step in an effort to increase crop yields and ensure food safety. One way to detect rice leaf images early is to perform an image classification process and create a web-based application. The method that has the ability in image processing is deep learning technique with convolutional neural network (CNN) method. The Convolutional Neural Network (CNN) method works to perform and predict diseases in plants by using image categorization or object images. This research aims to apply the web application of image classification of rice plant diseases to the Amazon Web Service (AWS) by identifying and classifying various types of rice leaf diseases using the CNN algorithm, so that farmers can detect rice plant diseases quickly and accurately through image analysis. This application was created using Convolutional Neural Network (CNN) methodology and Software Development Life Cycle (SDLC). The result of this study is that researchers created a web application for the classification of rice plant diseases through leaf images which are divided into 4 categories, namely Healthy, Leaf Blight, Brown Leaf Blight and Hispa, which is made a classification model using CNN with an accuracy value of 0. 8608, then using the streamlit framework to build a website, and utilizing AWS services in the form of Amazon Elastic Compute Cloud (Amazon EC2) as a hosting service, Amazon Simple Storage Service (Amazon S3) as a service for storing rice plant disease classification models and for storing web files, and Amazon Identity and Access Management Role (Amazon IAM) as a service to create a role that gives permission to connect between AWS S3 and AWS EC2. Testing the disease classification model in rice plants implemented on the web in EC2 shows quite good results with an accuracy of 78.5%. This can affect the model's ability to recognize specific disease patterns
Penerapan Kombinasi Metode Pembobotan Entropy dan Technique for Order of Preference by Similarity to Ideal Solution Dalam Pemilihan Karyawan Terbaik Ningsih, Ristia; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5896

Abstract

The process of selecting the best employees often faces various challenges that can affect the objectivity and fairness of the results. One of the main issues is the objectivity of selecting the best employees, where appraisers may have personal preferences or prejudices that influence their decisions in making the best employee selection. This study aims to apply a more objective and systematic approach in assessing employee criteria and integrate these factors into a more structured decision-making process. By using the entropy weighting method to objectively determine the weight of the criteria and TOPSIS to rank employees based on their proximity to the ideal solution, this study is expected to provide a solid foundation for more accurate and reliable decision-making in human resource management. The application of a combination of entropy weighting and TOPSIS methods in the selection of the best employees offers a comprehensive and structured approach in overcoming the complexity of human resource evaluation. The entropy weighting method is used to objectively determine the weight of the criteria based on data variation, thereby reducing subjectivity in assessment. Meanwhile, TOPSIS is used to rank employees based on their proximity to the positive ideal solution and their distance from the negative ideal solution. The combination of these two methods allows decision-makers to integrate different aspects of employee criteria. The results of the ranking of the best employees gave the results of the first best employee with a final preference score of 0.97858 obtained by Aisyah, the best second employee with a final preference score of 0.79125 obtained by Misri, and the third best employee with a final preference score of 0.69712 obtained by Rudi Setiawan.
Sistem Pendukung Keputusan Pemilihan Pelanggan Terbaik Menggunakan Kombinasi Pembobotan Logarithmic Least Square dan MOORA Rifaldo, Setiawan; Priandika, Adhie Thyo
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5897

Abstract

The best customers are individuals or groups who not only transact frequently but also provide more value to the business through loyalty, positive feedback, and referrals to others. They typically show a high level of satisfaction with the product or service offered, potentially bringing in new customers and improving the company's reputation. Selecting the best customers is often faced with a variety of issues that can affect the accuracy and effectiveness of the process. One of the main problems that occurs is the lack of a model used in determining the best customers. The purpose of this research is to implement a system that is able to effectively and accurately identify the best customers by integrating the LLS weighting technique and the MOORA method. In addition, this study also aims to overcome the shortcomings of existing weighting and evaluation methods by integrating the two techniques, providing a more robust and adaptive solution in the context of data-based decision-making. The ranking results in determining the best customers obtained the result, namely Sabtoni occupies the first position with the highest score of 0.47344. Furthermore, Zahra is in second place with a score of 0.39815, followed by Tuty with a value of 0.39498 in third place.
Implementasi Algoritma Convolutional Neural Network dan YOLOV8 Untuk Klasifikasi Ras Kucing Adinata, Abdul Rohim; Rohana, Tatang; Baihaqi, Kiki Ahmad; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5913

Abstract

The cat with the scientific name Felis catus is a very popular pet and is often kept in various parts of the world. There are many types or breeds of cats, each of which has its own characteristics and characteristics, such as style, body shape, fur and color. However, because of the many breeds and the uniqueness of each breed, it is often difficult for ordinary people to differentiate between the types of cat breeds that exist. Therefore, technology is needed to identify and differentiate cat breeds. By comparing the Convolutional Neural Network (CNN) and YOLOV8 methods, this research aims to develop a cat breed classification model. This research uses data from six different cat breeds, namely Bengal, Bombay, Himalayan, Local, Persian and Sphynx. There are 1,200 images in all, with 200 images for each race. Before the data is used for training with the CNN and YOLOV8 methods, a preprocessing stage is carried out which includes resize and rescale for the CNN method, while for YOLOV8 a data labeling process is carried out. There are two parts to the dataset: 20% validation data and 80% training data. The training process is carried out with the same parameters for each model, namely a learning rate of 0.001, batch size of 15, and 100 epochs. From the test results with the confusion matrix, the YOLOV8 model shows the best performance with an accuracy value of 99%, precision 96.1%, recall 98.4%, and F1-score 97.2%.
Evaluation and Comparison of K-Nearest Neighbors Algorithm Models for Heart Failure Prediction Masitha, Alya; Huda, Nurul; Istiawan, Deden; Firdaus, Lucky Nur Rohman
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5925

Abstract

Heart failure is a disease that is one of the most crucial in the world. Researchers have used several machine learning techniques to assist health professionals in the diagnosis of heart failure. K-NN is a technique of supervised learning algorithm that has been successfully used in terms of classification. However, using the K-NN algorithm has stages in terms of data analysis. The data used must also be processed in such a way that it becomes data that is easier to analyse and that the results obtained are also more accurate. Data pre-processing involves transforming raw data into a format that is appropriate for the model. The normalization technique is one of the techniques contained in pre-processing. This research uses two normalization techniques, namely the simple feature scale and min-max. The purpose of this study is to compare the performance of the KNN model to obtain an optimal prediction model. This study contributes to producing a heart failure prediction model based on the K-Nearest Neighbors (KNN) algorithm that can be optimized to improve the accuracy of early detection, so that it can help medical personnel in making more appropriate clinical decisions. The results obtained from this research show that the dataset that uses the min-max normalization method is better than data that is not normalized and data that uses simple feature scale normalization. The highest level of accuracy was achieved by employing the min-max normalisation technique, with a value of K=9, resulting in an accuracy rate of 85.05%.
Analisa Perbandingan Latent Semantic Indexing (LSI) dan Latent Dirichlet Allocation (LDA) untuk Topic Modelling Aplikasi Identitas Kependudukan Digital (IKD) Cahyono, Nuri; Nurcahyo, Narwanto; Restu Agung, Akmal Fauzan
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5970

Abstract

This study aims to analyze and compare two topic modeling methods, Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA), in understanding user reviews of the Digital Population Identity (IKD) Application obtained from the Google Play Store. The main problem addressed is the large number of user reviews with diverse topics that are difficult to categorize manually, necessitating an automated method to identify the main themes in the data. The research process began with scraping 5,000 recent reviews, followed by data preprocessing (Remove Punctuation, Lowercase, and Tokenization) and vectorization using Bag of Words and DOC2BOW. Subsequently, topic modeling was performed using LSI and LDA, and the results were evaluated using the Coherence Score metric. The findings indicated that Latent Dirichlet Allocation (LDA) outperformed LSI, achieving a Coherence Score of 0.4163 compared to LSI's 0.3512, indicating that Latent Dirichlet Allocation (LDA) is more effective in identifying hidden topics within user reviews. Latent Dirichlet Allocation (LDA) is a superior method for topic modeling in IKD application reviews and can assist developers in understanding user needs and issues, thereby enhancing the application's service quality.

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