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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 889 Documents
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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
Classification of Fruits High in Vitamin C Using Self-Organizing Map and the K-Means Clustering Algorithm Nuke L Chusna; Nurhasan Nugroho; Umbar Riyanto; Ahmad Ari Aldino
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.v5i2.4104

Abstract

Vitamin C-rich fruits not only taste fresh and delicious but also have the potential to increase the body's resistance to various diseases and maintain a proper nutritional balance. Information about fruits high in vitamin C is very important in order to increase public knowledge about which fruits contain high levels of vitamin C. However, to classify fruits high in vitamin C based on their image, a model is needed that is able to analyze the characteristics present in the image of the fruit. The purpose of this study is to build a classification model for high-vitamin C fruits with a combination of the Self-Organizing Map (SOM) artificial neural network algorithm and K-Means Clustering. Prior to classification, an image segmentation process is carried out using the K-Means Clustering algorithm, which will separate the image into parts that have similar visual characteristics. After the segmented image, the features of the object are extracted based on shape and texture. After the features of the image have been obtained, proceed with classifying images using the SOM algorithm by mapping multidimensional data into a lower-dimensional spatial representation to obtain the appropriate group or class. The accuracy test results for the built model produce an accuracy value of 93.33% and are included in the good category
Analisis Hasil Implementasi Multi-Attribute Utility Theory (MAUT) dalam Pengemasan Paket Wisata Tematik 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.v5i2.4144

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
Komparasi Metode Maut dan Moora dalam Pemilihan Sunscreen untuk Kulit Menggunakan Pembobotan ROC Gusti Tarisa Mareti; Afifah Trista Ayunda
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.v5i2.4153

Abstract

The skin is the outer layer of the human body that has various important functions. However, high sun exposure in Indonesia can cause damage to the skin due to ultraviolet rays. The use of sunscreen becomes important in preventing sunburn and skin cancer. Women with combination skin types often have difficulty choosing the right sunscreen. This study applies the Multi Attribute Utility Theory (MAUT) and Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) methods with the weighting of the Centroid Rank Order (ROC) method and the level of accuracy of MSE aims to produce decisions in choosing the right sunscreen for combination skin. At the methodology stage, data on criteria, sub-criteria, and alternatives are collected through observation and interviews. The criteria consist of 6 namely Benefit, Composition, Price, Vitamins, Side Effects, and Size. The ROC is used for weighting, while the MAUT and MOORA methods are used in the assessment and comparison of alternatives and MSE is used for the level of accuracy. The discoveries from this study hold the possibility to offer recommendations for choosing the best sunscreen for combination skin, namely A7 with the brand sunscreen Somethinc Comfort Correct for the MAUT method is 0.7134 and the MOORA method is 0.102. The results of the MSE calculation obtained a deviation value, namely the MOORA method with a value of 215.0091 better than the MAUT method of 207.4922. So that the MOORA method is the best method in choosing sunscreen for combination skin and aims to help people who are still having difficulty choosing the right sunscreen, so as to avoid mistakes in choosing inappropriate sunscreens that can have a negative impact on the skin
Tomato Ripeness Detection Using Linear Discriminant Analysis Algorithm with CIELAB and HSV Color Spaces Rini Nuraini; Teotino Gomes Soares; Popi Dayurni; Mulyadi Mulyadi
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.v5i2.4192

Abstract

Tomatoes have a relatively short ripening period, making it essential to identify their ripeness level before distribution. The ripeness level of tomatoes can be detected based on their color. Therefore, the color of tomatoes serves as a crucial indicator in determining whether they are ripe and of good quality. However, classifying tomato ripeness levels manually has several drawbacks, namely requiring a long process, a low level of accuracy, and being inconsistent. The research aimed at developing a detection model for the ripeness level of tomatoes using the LDA algorithm based on color feature extraction, namely CIELAB (L*a*b) and HSV. The L*a*b and HSV color spaces are applied to obtain information about the color of the object being detected. Furthermore, the information obtained from feature extraction is then grouped by class using the LDA algorithm, which separates information for each class and limits the spread between classes through linear projection searches to maximize the covariance matrix between classes so that members within the class can be identified. This research produces a model that can detect the level of ripeness of tomatoes with an accuracy of 88.194%.
Perbandingan Teknik Prediksi Pemakaian Obat Menggunakan Algoritma Simple Linear Regression dan Support Vector Regression Sephia Pratista; Alwis Nazir; Iwan Iskandar; Elvia Budianita; Iis Afrianty
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.v5i2.4260

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
Sentiment Analysis of Maxim Online Transportation App Reviews using Support Vector Machine (SVM) Algorithm Putri Kurniawati; Riska Yanu Fa'rifah; Deden Witarsyah
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.v5i2.4265

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.
Penerapan Data Mining dalam Implementasi Algoritma K-Means Clustering untuk Pelanggan Potensial pada Koperasi Simpan Pinjam Ahmad Rifqi; Rima Tamara Aldisa
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.v5i2.4278

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.
Penerapan Metode K-Means Clustering Untuk Mengelompokkan Data Kelayakan Penerima Bantuan Renovasi Rumah Gina Sonia; Raissa Amanda Putri
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.v5i2.4298

Abstract

The government provided one form of assistance, namely the renovation of houses in the Kuala Bangka Village area in Kualuh Hilir District, North Labuhan Batu Regency from 2015 until now. However, with this assistance, Kuala Bangka Village sometimes has problems in determining the feasibility of receiving assistance from the government, therefore the author will conduct research by categorizing the eligibility of recipients of house renovation assistance by applying the K-Means algorithm. The K-Means Clustering algorithm is an algorithm that can classify data accurately according to the previous problem. The grouping aims to determine cluster 0 and cluster 1 as recipients of house renovation assistance, cluster 0 is feasible and cluster 1 is not feasible. The attributes used in this research are number of family members, employment, housing conditions, and income. The results obtained from 170 data were cluster 0 with 91 data and cluster 1 with 79 data. From these results, 91 people were eligible to receive home renovation assistance and 79 were not eligible to receive home renovation assistance
Analisa Perbandingan Complate Linkage AHC dan K-Medoids Dalam Pengelompokkan Data Kemiskinan di Indonesia Rifqi Habibi Sachrrial; Agus Iskandar
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.v5i2.4310

Abstract

The poverty rate in Indonesia has increased from 9.54 percent in March 2022 to 9.57 percent in September 2022 due to inflation and low wages and people's incomes. To overcome this problem, steps such as providing social assistance, creating decent jobs, and increasing wage standards are needed to increase people's purchasing power and reduce poverty in the future. The government needs to pay special attention to provinces with high poverty rates through special programs and efforts to increase income and the economy in these areas. Data Mining is a solution in solving this problem by utilizing the clustering method which is known as the clustering method. The clustering method used in this study is the AHC method and the K-Medoids method. In order to determine the provinces with the highest number of poor people, the AHC and K-Medoids clustering methods will be applied separately so that the final results of each will be analyzed. The results of the analysis show the formation of three clusters with different cluster locations. The application of the AHC method resulted in cluster 2 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 1 with only 3 provinces. While the application of the K-Medoids method resulted in cluster 1 with the largest number of provinces, namely 22 provinces, followed by cluster 0 with 9 provinces, and cluster 2 with only 3 provinces. Although the location of the clusters is different between the two methods, the number of provinces in the cluster is the same so that a cluster with a total of 3 provinces is declared the province with the largest number of poor people.

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