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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 21 Documents
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Search results for , issue "Vol 5 No 2 (2023): September 2023" : 21 Documents clear
Fashion Recommendation System using Collaborative Filtering Muhammad Khiyarus Syiam; Agung Toto Wibowo; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.3690

Abstract

Collaborative Filtering is an method used to build a recommendation system with the concept that conclusions from different clients are used to anticipate things that may be of interest to users. This research uses data from Rent the Runway and the method used is Item-based Collaborative filtering, where the system will look for similarities in products that have been purchased by customers and then look for predictive values. Fashion requires recommendations because it plays a crucial role in helping individuals express their identity, personal style, and personality through clothing choices, accessories, and dressing styles.The recommendation system uses the item method based on analyzing the number of purchases or sales and grouping according to each product category so that it can help consumers in choosing fashion products. It was found that the use of Adjusted Cosine Similarity produces better recommendations with an average MAE value of 0.2750, while Cosine Similarity with an average MAE difference of 0.3989. This proves that the use of adjusted cosine similarity can produce better recommendations because the adjustment algorithm not only considers user behavior, but also produces lower performance errors.
Penerapan Algoritma Adaptive Response Rate Exponential Smoothing Terhadap Business Intelligence System Romindo Romindo; Jefri Junifer Pangaribuan; Okky Putra Barus
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.3955

Abstract

PT. XYZ is one of the companies in the field of furniture sales by offering its flagship product, namely spring bed. The company's business continues to grow every year, of course, the company must be able to complete its work quickly and precisely. One of the main problems of the company is that the increase in company sales is still not able to cover the company's expenses and sometimes the company still suffers losses. This happens because companies often make mistakes in purchasing product inventory stock. Not all types of spring beds sell well, so sometimes purchases are made of the type of spring bed that is not selling well, which results in stock accumulation and instability of the company's cash inflow and outflow. In this study, a Business Intelligence System was built, which is a form of information technology implementation to store, collect and analyze data into knowledge so that it can be used as prediction results. The prediction algorithm used in this research is the Adaptive Response Rate Exponential algorithm. The expected goal of this research is to build a Business Intelligence System that can calculate product sales predictions in the following month using the Adaptive Response Rate Exponential Smoothing (ARRES) algorithm. Based on the results of the MAPE test, it can be concluded that the percentage of prediction accuracy from the ARRES algorithm on the sales transaction data of PT. XYZ is 53.33% which is categorized as quite accurate and the percentage of prediction error from the ARRES method is 46.67% which is categorized as reasonable
Penerapan Data Mining Untuk Klasifikasi Penerima Dana Bantuan Sosial Dengan Menggunakan Algoritma K-Nearest Neighbor Agung Triayudi
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.3972

Abstract

The Social Assistance Fund (Bansos) is a government program carried out to assist in eradicating community poverty in Indonesia and improving the welfare of families in Indonesia. Social Assistance Funds (Bansos) are distributed from the central ministry, then forwarded to local social services and then distributed to the community through each sub-district office. After data collection is carried out, the process of determining and selecting the families who receive Social Assistance Funds (Bansos) is carried out. However, in the implementation process there were several obstacles, one of which was that the provision of Social Assistance Funds (Bansos) was still not on target for families who deserved to receive Social Assistance Funds (Bansos). This problem is an important matter that must be resolved, this is because the main aim of the Social Assistance Fund (Bansos) program is to help eradicate poverty in Indonesia. Reviewing and processing data again based on previous data can be completed using one of the computer techniques. Data mining is a technique used to reprocess data. Data processing returns to data mining based on data previously stored in a data collection or data warehouse. Classification is part of data mining which aims to find out certain models of data so that they can be divided into several classes or groups. The K-Nearest Neighbor (K-NN) algorithm is part of a data mining technique which aims to divide data into certain groups. The results obtained in the research are the K value used in the research, namely K=7, the result of the family data grouping process which has just determined that the family received Social Assistance Funds (Bansos).
Topic Detection on Twitter using GloVe with Convolutional Neural Network and Gated Recurrent Unit Moh Adi Ikfini M; Erwin Budi Setiawan
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4057

Abstract

Twitter is a social media platform that allows users to share thoughts or information with others for all to see. However, twitters often use abbreviations, slang, and incorrect grammar because tweets are limited to 280 characters. Topic detection often has problems with low accuracy, one method that can be used to overcome this problem is feature expansion. Feature expansion on Twitter is a semantic addition to the process of expanding the original text syllables to make it look like a large Document. That way, feature expansion is used to reduce word mismatches. This study uses the expansion of the GloVe feature with the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) classification methods. The results show that the topic detection system with the GloVe feature extension and CNN-GRU hybrid classification has an accuracy of 94.41%
Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network Ar'rafi Akram; Kun Fayakun; Harry Ramza
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4063

Abstract

Insect pest attacks pose a serious threat that can potentially cause significant losses in agricultural production. Therefore, the effective recognition and control of insect pests are crucial for maintaining agricultural productivity and quality of yields. With the advancement of computer technology and artificial intelligence, computer technology can be utilized to automatically recognize images in object recognition, particularly for insect pest classification using the Convolutional Neural Network (CNN) method with the Xception architecture. CNN is one of the types of deep feed-forward artificial neural networks widely used in digital image analysis and can process data in the form of grid patterns. CNN consists of three types of layers: convolutional layer, pooling layer, and fully connected layer. The use of CNN in this research aims to facilitate the classification of insect pests. The CNN process involves stages of training, testing, and validation on insect pests to determine the classification of images of various insect pest species. This research utilizes 1363 image samples with 13 classes of insect pests. The training process of CNN involves several parameters such as batch size, number of epochs, learning rate, and optimizer. The experiment's results indicate that the best accuracy achieved by this model is 93.81% during the training phase and 81.75% during the validation phase. This demonstrates that the model successfully performs insect pest classification using the CNN method.
Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search Rizky Ahmad Saputra; Jondri Jondri; Kemas Muslim Lhaksmana
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4079

Abstract

Online social networking services allow users to post content in the form of text, images or videos. Twitter is a microblogging social networking service that enables its users to send and read text-based messages of up to 140 characters. Retweet is one of the features in Twitter that is important in disseminating information, popular tweets reflect the latest trends on Twitter, the main mechanism that encourages information dissemination is the possibility for users to re-share content posted by their social connections, then it can flow throughout the system. Retweets happen when someone republishes or forwards a post to their homepage and personal profile. Most retweets are credited to the original author of the original post. The retweet prediction system uses an Artificial neural network optimized for Harmony search with tweets about the Jakarta-Bandung Fast Train, which shows the best results when the oversampling method has been carried out with an f1 score of 96.8%.
Rekomendasi Content Creator Terbaik sebagai Pendukung Keputusan Penilaian pada Agensi Menggunakan Metode TOPSIS Eugenius Kau Suni; Stephen Aprius Sutresno
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4082

Abstract

As an agency, it is necessary to evaluate and assign value to the content outcomes that have been published by content creators periodically. Aurora News Agency, which is one of the content creator agencies for Snack Video, has over 700 member content creators. This requires specialized techniques to facilitate and expedite the assessment of the performance of these content creators. Therefore, research was conducted on a decision support system using the TOPSIS method as a decision-making tool for evaluating the best content creators within the agency. Data was collected from a total of 630 content creators, and after undergoing data cleansing processes, a total of 10,916 content items were obtained. The research results present a ranking of the top 10 content creators based on their preference scores, ranging from the highest to the lowest. Anemz Tv is the content creator with the highest preference score of 0.4368, securing the first rank. On the other hand, Talenta.TV is the content creator with a preference score of 0.3203, earning the second rank. Upon analysis, differences in strategies for each content creator became apparent, with some focusing on quantity and others placing emphasis on the quality of content. In conclusion, the application of the TOPSIS method can be implemented relatively simply, with sufficiently fast computations, and it yields a diverse range of preference scores
Polyp Identification from a Colonoscopy Image Using Semantic Segmentation Approach Wahyu Hauzan Rafi; Mahmud Dwi Sulistiyo; Sugondo Hadiyoso; Untari Novia Wisesty
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4083

Abstract

Colorectal Cancer (CRC) is a major contributor to cancer-related mortality worldwide, necessitating early detection and treatment of polyps to prevent cancer progression. A colonoscopy is a critical diagnostic procedure for identifying colon abnormalities and removing premalignant polyps. However, accurately segmenting polyps in colonoscopy images poses challenges due to their diverse appearance and indistinct boundaries. In this study, we investigate augmentation techniques to enhance polyp semantic segmentation using the U-Net model. Our analysis reveals that the most effective technique is found in sub-scenario 2.6.c with an input size of 320×320, striking a favorable balance between accuracy and efficiency. Additionally, we explore the benefits of larger input sizes, taking into account resource considerations. Moreover, we conduct further testing of the best augmentation technique identified in previous experiments with the SegNet model. The results show a 3.5% improvement in the dice coefficient and slightly better qualitative outcomes. However, it is important to note that this enhancement comes with a nearly fivefold increase in training time. Moving forward, our objective is to develop a unified model for segmenting diverse medical images, pushing the boundaries of polyp detection and medical imaging. This research provides valuable insights and lays the foundation for more advanced applications in polyp detection and medical image analysis.
Implementasi Metode Simple Multi Attribute Rating Technique (SMART) dalam Pemilihan Zona Prioritas dan Alternatif Berbasis Data Klasifikasi Indeks Vegetasi Yerik Afrianto Singgalen
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4085

Abstract

Vegetation index analysis using the Normalized Difference Vegetation Index (NDVI) model needs to be processed using a decision support model to follow up on the Landsat 8/9 Operational Land Imaginer (OLI) satellite image data interpretation results. However, studies using the Simple Multi-Attribute Rating Technique (SMART) method to determine priority zones based on vegetation index classification data are still limited. This study uses the SMART decision support model to process NDVI classification data in mangrove areas. The stages in this study consist of four parts: the data collection stage, the data processing stage; the data analysis stage; and the data interpretation stage. At the data collection stage, the raster data used was sourced from the United States Geology Survey (USGS) platform, namely Landsat 8/9 OLI with coordinate raster data (Lat 01o43'18" N, Lon: 128o04'15" E) in 2013, 2018, and 2023. In addition, video and aerial photographs at the study site were taken using drones (Phantom 4 Version 2). At the data processing stage, the model used in calculating raster data is NDVI using the QGIS 3.30.1 application. This research data analysis and interpretation stage uses the SMART decision support model. The SMART decision support model is used to produce recommendations for priority zones for mangrove ecotourism development based on the results of the NDVI classification (minimum value, average value, maximum value) adjusted to the Decree of the State Minister of Environment Number 201 of 2004 concerning standard criteria and guidelines for mangrove forest destruction (rare, medium, and dense). Based on the calculation of the utility value of criterion C1 as a cost with a weight of 0.50 in the NDVI classification data for 2023, the second observation station is recommended as a priority zone with a total value of 0.50. Meanwhile, the calculation of the utility value of criterion C3 as a cost with a weight of 0.50 in the NDVI classification data in 2023 recommended the third observation station as a priority zone with a total value of 0.88. This means that the SMART method can be used to identify and analyze priority and alternative zones for the sustainable development of mangrove ecotourism areas.
Implementation of Hyperparameters to the Ensemble Learning Method for Lung Cancer Classification Ridlo Yanuar; Siti Sa’adah; Prasti Eko Yunanto
Building of Informatics, Technology and Science (BITS) Vol 5 No 2 (2023): September 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i2.4096

Abstract

Lung cancer is the most common cause of death in someone who has cancer. This happens because of remembering the importance of lung function as a breathing apparatus and oxygen distribution throughout the body. Early identification of lung cancer is crucial to reduce its mortality rate. Accuracy is crucial since it indicates how accurately the model or system makes the right predictions. High levels of accuracy show that the model can produce trustworthy and accurate findings, essential for making effective decisions based on available data. In this research, ensemble learning approaches, namely bagging and boosting methods, were employed for classifying lung cancer. Hyperparameters, a class of parameters, are crucial to this model's effectiveness. In order to increase the lung cancer classification model's accuracy, a thorough investigation was conducted to identify the best hyperparameter combination. In this study, the dataset used is a medical dataset that contains a history of patients who have been diagnosed with lung cancer or not. The dataset is taken from Kaggle mysarahmadbhat and cancerdatahp from data world. To evaluate the model's accuracy, this study used the confusion matrix method which compares the model's prediction results with the ground truth. the study findings revealed that employing a dataset split ratio of 70:30 produced the best results, with the Random Forest, CatBoost, and XGBoost models achieving an impressive 98% accuracy, 0.98 precision, 0.98 recall, and 0.98 f1-score. but for AdaBoost, the best results were obtained on a dataset with a ratio of 80:20 with an accuracy of 96%, 0.97 precision, 0.96 recall, and 0.96 f1-score

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