Building of Informatics, Technology and Science (BITS)最新文献

{"title":"Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam","authors":"Ahmad Rifqi, Rima Tamara Aldisa","doi":"10.47065/bits.v5i2.4278","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4278","url":null,"abstract":"Apart from that, there are efforts to provide for the needs of its members as well as financial assistance for education, health and there are also concessions needed by the members. By conducting this customer cluster, it will help the company determine its potential customers so that it can implement the right marketing strategy for each type of existing customer, and will certainly provide benefits for the company in increasing the quality and loyalty of customers towards the company. Data mining has functions, namely prediction, description, classification and clustering functions. Data mining also has many methods for its application, one of these methods is K-Means. The K-Means Clustering algorithm can be implemented in grouping potential customers, especially in savings and loan cooperatives. Based on the data sampling used, the data can be grouped into 2 (two) clusterings.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Hama Serangga pada Pertanian Menggunakan Metode Convolutional Neural Network","authors":"Ar'rafi Akram, Kun Fayakun, Harry Ramza","doi":"10.47065/bits.v5i2.4063","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4063","url":null,"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.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Hasil Implementasi Multi-Attribute Utility Theory (MAUT) dalam Pengemasan Paket Wisata Tematik","authors":"Yerik Afrianto Singgalen","doi":"10.47065/bits.v5i2.4144","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4144","url":null,"abstract":"The travel industry plays a vital role in maximizing the marketing of tourist destinations, but the process of determining tour packages must consider consumer purchasing power concerning destination ticket prices, distance and travel time, availability of accommodations and amenities services, and regulations. This study seeks to use the Multi-Attribute Utility Theory (MAUT) decision support model to tourist case studies from Ternate City to determine superior tour packages. In the meantime, the context of destinations, accommodation services, and transportation services is incorporated into the use of the MAUT decision support model. The following criteria are established based on the category of the location: entrance fee; facilities and infrastructure; local tour guides; type of activity at the destination; Security, and Hygiene. The following criteria are established based on the category of lodging services: standard room rate, property amenities, room features, room type, and services. In addition, the criteria established based on the category of transportation services are as follows: rental pricing; car type; vehicle amenities; driver experience. The findings of this study indicate that A5 tourist destinations are recommended, with a total value of 0.90, based on destination category, criteria and criteria values related to ticket prices (10), facilities and infrastructure (20), availability of local tour guides (10), diversity of activities (20), safety (20), and cleanliness (20). In addition, based on the criteria and weights related to standard room rental costs (20), property amenities (20), room features (20), room type (10), and services (30), we propose A1 with a total value of 0.85 in the accommodation services category. In the field of transportation services, we offer A2 with a total score of 0.83 based on criteria and weights relating to rental price (25), vehicle type (25), car amenities (25), and driver experience (25). Using the MAUT decision support model, it is evident that the packaging of tour bundling becomes more effective and efficient","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression","authors":"Sephia Pratista, Alwis Nazir, Iwan Iskandar, Elvia Budianita, Iis Afrianty","doi":"10.47065/bits.v5i2.4260","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4260","url":null,"abstract":"Public Health Centers (Puskesmas) had a crucial role in furnishing society essential healthcare services and medication management. To preempt errors in stock management, a predictive approach is employed. This prediction methodology involves comparing Data Mining techniques utilizing the Simple Linear Regression algorithm and Machine Learning methodologies harnessing the Support Vector Regression algorithm. This research uses Paracetamol 500 mg and Cetirizine drug data from January 2020 to June 2023. The selection of these algorithms is motivated by the continuous nature of the data variables and their temporal span, spanning 42 months (period). The core aim of this study is to evaluate the magnitude of predictive errors using the Mean Absolute Percentage Error (MAPE) methodology. Implementing these methods was effectuated through the programming language Python with an 80%:20% partitioning of training and testing data. Drawing from experimental endeavors conducted concerning Paracetamol 500 mg, the utilization of the Simple Linear Regression algorithm, yields a MAPE score of 20.85%, categorized as 'Moderate,' whereas the application of the Support Vector Regression algorithm generates a MAPE of 18.39%, classified as 'Good.' Otherwise, experimentation on Cetirizine employing the Simple Linear Regression algorithm, employing an identical division of training and testing data, results in a MAPE of 18.39%, also classified as 'Good.' Meanwhile, resorting to the Support Vector Regression algorithm leads to a MAPE of 17.14%, falling under the 'Good' category. Based on the MAPE obtained, the Support Vector Regression algorithm has better prediction results than the Simple Linear Regression algorithm","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fashion Recommendation System using Collaborative Filtering","authors":"Muhammad Khiyarus Syiam, Agung Toto Wibowo, Erwin Budi Setiawan","doi":"10.47065/bits.v5i2.3690","DOIUrl":"https://doi.org/10.47065/bits.v5i2.3690","url":null,"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.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Topic Detection on Twitter using GloVe with Convolutional Neural Network and Gated Recurrent Unit","authors":"Moh Adi Ikfini M, Erwin Budi Setiawan","doi":"10.47065/bits.v5i2.4057","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4057","url":null,"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%","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm","authors":"Putri Kurniawati, Riska Yanu Fa'rifah, Deden Witarsyah","doi":"10.47065/bits.v5i2.4265","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4265","url":null,"abstract":"The continuous emergence of online transportation service platforms is one of the effects of the ever-increasing technological advancements. One such online transportation service application, Maxim, has recently been slowly gaining ground in the ride-hailing market in Indonesia. According to data collected by one media outlet in 2022, Maxim ranks third as the most preferred online transportation platform by the public, following Gojek and Grab. This suggests that there are factors causing users to lack interest in or hesitate to use the Maxim application. On the Google Play Store, user ratings (in numerical values) and written reviews serve as reasons for the potential users lack of interest. Analyzing ratings alone is less accurate and does not provide in-depth information and meaning regarding users experiences. To understand user opinions about Maxim's service and functionality, an analysis of user reviews is crucial. Therefore, this research conducts sentiment analysis on Maxim user reviews using the Support Vector Machine (SVM) algorithm to classify reviews quickly. The reviews are categorized into two classes: positive and negative sentiment. The classification process is carried out in three scenarios with different data training and testing ratios: 60:40, 70:30, and 80:20, using a Linear kernel and hyperparameter optimization with GridSearch. The best accuracy is achieved with a 70:30 ratio, which is 89.82%. Evaluation using the confusion matrix also yields a precision of 92.66%, recall of 94.09%, and an F1 score of 93.38%. The ROC-AUC curve evaluation results in an AUC value of 0.8505. The sentiment analysis results tend to lean towards positive sentiment, indicating a high level of user satisfaction with the Maxim application. Based on these sentiment results, developers can identify what aspects of the Maxim application need to be maintained and improved.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementasi Metode Simple Multi Attribute Rating Technique (SMART) dalam Pemilihan Zona Prioritas dan Alternatif Berbasis Data Klasifikasi Indeks Vegetasi","authors":"Yerik Afrianto Singgalen","doi":"10.47065/bits.v5i2.4085","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4085","url":null,"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.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rekomendasi Content Creator Terbaik sebagai Pendukung Keputusan Penilaian pada Agensi Menggunakan Metode TOPSIS","authors":"Eugenius Kau Suni, Stephen Aprius Sutresno","doi":"10.47065/bits.v5i2.4082","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4082","url":null,"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","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search","authors":"Rizky Ahmad Saputra, Jondri Jondri, Kemas Muslim Lhaksmana","doi":"10.47065/bits.v5i2.4079","DOIUrl":"https://doi.org/10.47065/bits.v5i2.4079","url":null,"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%.","PeriodicalId":474248,"journal":{"name":"Building of Informatics, Technology and Science (BITS)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134903211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}