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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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Sistem Klasifikasi Penyakit Jantung Menggunakan Teknik Pendekatan SMOTE Pada Algoritma Modified K-Nearest Neighbor Novitasari, Fitria; Haerani, Elin; Nazir, Alwis; Jasril, Jasril; Insani, Fitri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3610

Abstract

The heart is a vital organ that plays a crucial role in pumping oxygenated blood and nutrients throughout the body. Heart disease refers to damage to the heart that can occur in various forms, caused by infections or congenital abnormalities. The World Health Organization (WHO) reports nearly 17.9 million deaths each year due to heart disease. In Indonesia, the prevalence of heart disease is around 1.5%, meaning that in 2018, approximately 15 out of 1,000 people, or nearly 2,784,060 individuals, were affected by this disease, according to the Basic Health Research data (Riskesdas) 2018. Many people have limited knowledge about heart health, leading to a lack of awareness of their heart conditions. This can be attributed to a lack of understanding regarding the importance of medical checkups related to heart health. Modified K-Nearest Neighbors (MKNN) is one of the data mining methods applied for classifying the risk of heart disease. The research utilized data obtained from the UCI dataset repository, which consists of 918 records with 12 attributes. To balance the imbalanced dataset with minority classes, the Synthetic Minority Over-sampling Technique (SMOTE) approach was used to generate new synthetic samples from the minority class. The objective of developing a web-based system for heart disease classification is to assist the public in assessing their risk of heart disease as early as possible, enabling them to take preventive actions sooner. The accuracy results of the MKNN algorithm with a 90:10 ratio are 80.37%, while with the MKNN+SMOTE approach, the accuracy increased to 84.00%. The use of the SMOTE approach improved the accuracy of low-performing data.
Analisis Perbandingan Certainty Factor dan Dempster Shafer Dalam Diagnosis Penyakit Porfiria Menggunakan Metode Perbandingan Eksponensial Rizky, Firahmi; Zulham, Zulham; Nasyuha, Asyahri Hadi; Elyas, Ananda Hadi; Kartadie, Rikie
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3611

Abstract

Porphyria refers to a group of genetic disorders that affect the metabolism of porphyrins in the body. Porphyrin itself is a molecule that plays a role in the synthesis of hemoglobin, which functions as a carrier of oxygen in the blood. Disturbances in porphyrin metabolism can lead to the accumulation of porphyrins or their precursors, which in turn can produce a variety of symptoms. Porphyria comes in many forms, including acute porphyria and skin porphyria, each with its own unique symptoms. Nervous system and skin problems are common in porphyria patients, and if not treated properly, their health condition can deteriorate. A unique approach is needed to provide answers to this problem because it is still challenging to develop practical ways to address the problems experienced by patients. Certainty Factor and Dempster Shafer methods are two techniques that are widely used in the field of disease diagnosis today, where expert systems are often applied. But building expert systems in multiple disciplines is difficult due to uncertainty. Therefore, it is important to research and distinguish the many ways that these systems can be built. The exponential comparison approach is one of the most straightforward comparison techniques and helps minimize bias in the analysis process. To identify candidiasis in humans, this study attempted to apply, evaluate, and compare the two methodologies, as well as compare the results with the exponential comparison method. Comparative findings suggest that the Dempster Shafer technique provides a more precise diagnosis of porphyria
Twitter Sentiment Analysis of Kanjuruhan Disaster using Word2Vec and Support Vector Machine Rizky, Fariz Muhammad; Jondri, Jondri; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3612

Abstract

The Kanjuruhan disaster on 1 October 2022, gained the peoples attention. People share their thoughts on social media. Their posts contain a variety of perspectives. Sentiment analysis is possible to use on a dataset of people's posts. This final project applies the supervised learning Support Vector Machine (SVM) method with feature expansion using Word2Vec and TF-IDF as weighting. Three SVM kernels—rbf, linear, and polynomial—are applied. Three split data techniques and two different types of training data are used to train each kernel. Training data with oversampling and training data without oversampling are the two types of training data. The best result gained from using rbf kernel, split ratio 70:30, and oversampling. From it, oversampling trained model have relatively stable in every split rasio and kernel without having significant difference.
Sistem Pendukung Keputusan Manajemen Pemilihan Aplikasi Jasa Transportasi Online Menerapkan Metode ROC dan WASPAS Marsono, Marsono; Sudarmanto, Sudarmanto; Wasiati, Hera; Nasyuha, Asyahri Hadi
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3613

Abstract

Transportation in Indonesia has various types of vehicles, ranging from two-wheeled to six-wheeled, which are adjusted according to user needs. The ease of ordering transportation services is also increasing with the application that can be downloaded on a smartphone. This application provides convenience and security, as well as setting tariff rates according to the intended destination. Even though using an online transportation service ordering application has many advantages, such as convenience and security, there are some drawbacks that users often face. One of them is the discrepancy between the travel time stated in the application and reality, which can result in delays if there is an urgent need. In addition, sometimes the application also provides a longer travel route even though there is a road that is closer to the destination, so the driver must use an alternative road outside the specified route. Therefore, evaluation of the applications used needs to be done by comparing one application with another. This study aims to use a Decision Support System (SPK) to assist online transportation users in choosing the best application, taking into account factors such as the accuracy of travel time and the efficiency of the route taken by the application. There are many DSS methods that can be applied in the process of selecting or selecting the object under study, in this study the method used is the ROC method as a weighting of the level of importance of each criterion and the WASPAS method for the ranking process for each alternative. The final result obtained is that the Grab application (A3) is declared the best application in online transportation services with an acquisition value of 0.9331Decision Support System
Klasifikasi Penerima Bantuan Beras Miskin Menggunakan Algoritma K-NN, NBC dan C4.5 Pristiawati, Andani Putri; Permana, Inggih; Zarnelly, Zarnelly; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3617

Abstract

One of the tasks of the Dumai City Social Service is to provide poor rice assistance to people in need. The problem that often occurs in the distribution of rice to the poor is that the target recipients of poor rice often occur. In overcoming the existing problems, this research has carried out classification models using the K-Nearest Neighbor (K-NN) algorithm, Naïve Bayes Classifier (NBC), and C4.5 Algorithm. Based on the experimental results, it was found that the best classification model was produced by the K-NN Algorithm with a value of K equal to 21. Besides that, the C4.5 algorithm succeeded in making a decision tree for the classification model with the lowest complexity because it succeeded in reducing the number of attributes from 33 to 5 attributes. The decision tree can be used as material for consideration to the Social Service in making decisions on Raskin beneficiaries.
Clustering Performance Between K-means and Bisecting K-means for Students Interest in Senior High School Seniwati, Erni; Sidauruk, Acihmah; Haryoko, Haryoko; Lukman, Achmad
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3624

Abstract

The interest of high school students is an important thing to do to see the talents of each student based on the academic scores obtained in the first and second semesters. There are two majors of interest in this case study, namely natural and social studies with criteria for natural studies scores including mathematics, chemistry, biology and physics. Meanwhile, the social studies criteria include history, economics, geography and sociology. This research propose comparing of clustering time and accuracy based on manual data from school as a reference of clustering in SMAN 1 Wonosari for 2011/2012 academic year using two clustering methods namely K-means and Bisecting K-Means. The results of this research compare to manual results interest from class teacher, so this work can demonstrate the run time comparison and accuracy of this study. The accuracy result shows 87.5% for both methods but different run times. For bisecting k-means got 0.0229849 seconds to complete the clustering process faster than k-means only got 0.0929448 seconds
Performance Analysis of LVQ 1 Using Feature Selection Gain Ratio for Sex Classification in Forensic Anthropology Harni, Yulia; Afrianty, Iis; Sanjaya, Suwanto; Abdillah, Rahmad; Yanto, Febi; Syafria, Fadhilah
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3625

Abstract

One approach to handling large of data dimensions is feature selection. Effective feature selection techniques produce the essential features and can improve classification algorithms. The accuracy performance results can measure the accuracy of the method used in the classification process. This research uses the Learning Vector Quantization (LVQ) 1 method combined with Gain Ratio feature selection. The data used is male and female skull bone measurement data totaling 2524. The highest accuracy results are obtained by LVQ 1, which uses a Gain Ratio with a threshold of 0.01 with a learning rate = 0.1, which is 92.01%, and the default threshold weka(-1.7976931348623157E308) with a learning rate = 0.1, which is 92.19%. In comparison, previous research that did not use gain ratio or that did not use GR only had the best results of 91.39% with a learning rate = 0.1, 0.4, 0.7, 0.9. This shows that LVQ 1 using the Gain Ratio can be recommended to improve the performance of the Skull dataset compared to LVQ 1 without Gain Ratio.
K-Means and AHC Methods for Classifying Crime Victims by Indonesian Provinces: A Comparative Analysis Lubis, Ridha Maya Faza; Huang, Jen-Peng; Wang, Pai-Chou; Damanik, Nurafni; Sitepu, Ade Clinton; Simanullang, Ceria D
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3630

Abstract

Crime is a common phenomenon that often occurs in society and has a negative impact both individually and collectively. Gaining a deeper understanding of crime can help us tackle the problem more efficiently. In an era that is increasingly complex and globally connected as it is now, crime has undergone significant developments and changes. Crime remains a serious threat to our security, integrity, and well-being. Some common types of crime include theft, robbery, fraud, physical abuse, and murder. Crime can happen anytime and anywhere. To tackle crime, data mining techniques can be used to analyze the surrounding situation and gain new knowledge. One approach is to classify provinces based on crime data from previous years so that crime-prone areas can be identified and security measures can be increased. In this study, two grouping methods were used, namely K-Means and AHC using the complete linkage mode. There are 34 provinces in Indonesia which are grouped based on the number of victims of crime from 2019 to 2021. The grouping results using the K-Means method yield two groups with 17 provinces each. However, using the AHC complete linkage method, there is a difference in the number of provinces between cluster 0 and cluster 1 compared to the K-Means results. In addition, there are differences in the location of the province in the cluster between the two methods. In the K-Means method, provincial data is located in cluster 0, while in the AHC method, the province's data is in cluster 1. Thus, this study provides insight into crime in Indonesia and provides information about the grouping of provinces based on crime rates using the K-Means method. Means and AHC
Analyze News Effect on Trend Stock Price in Indonesia Based on Bidirectional-Long Short Term Memory Satriaman, Muhammad Azriel; Atastina, Imelda
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3631

Abstract

News platforms such as BBC, UN News, and CNN are news sites that are very global, both globally and nationally. With this site, someone can find information in other countries or their country. The news contained on the BBC website can be analyzed using sentiment analysis. Sentiment analysis is carried out to see whether the news tends to be positive, negative, or neutral so that researchers or institutions can find out how the response of the news is to other sectors such as stocks in Indonesia. With the IDX website as a list of company shares in Indonesia, sentiment analysis can be carried out on news on the BBC website that can affect the rise or fall of stock prices in Indonesia using a combination of Word2Vec and the Bidirectional- Long Short Term Memory (BiLSTM) method. The BiLSTM method is an algorithm that has a function to process text data to predict the value of stock price trends by utilizing Word2Vec for word embedding of news. In this study, the dataset used is international news on the BBC website and historical stock prices of several companies on the IDX website. This study utilizes both methods to be able to predict stock price trends. By using 15.674 data, this study shows that the BiLSTM method has an average accuracy rate of 80.03%.
Handling Imbalanced Data Sets Using SMOTE and ADASYN to Improve Classification Performance of Ecoli Data Sets Halim, Anthony Mas; Dwifebri, Mahendra; Nhita, Fhira
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3647

Abstract

In this digital era, machine learning is a technology that is in demand by organizations and individuals. In the age of data and digital information, the ability to process data efficiently is needed. As the amount of data grows, there are various problems in machine learning. One of them is that with the increasing amount of data, class imbalance is also often found. Class imbalance is a condition where a class dominates another class, in one example case is when the positive value class has less number than the negative class. The class that is less in number is categorized as the minority class, while the class that dominates the dataset is called the majority class. Class imbalance can affect classification performance in a bad way, so handling imbalanced classes is needed to improve classification results. Classification of imbalanced data using Random Forest has satisfactory results, as well as by implementing SMOTE and ADASYN as sampling methods because they are highly popular and easy to implement. The best model produced in this study is the model that applies SMOTE oversampling on a dataset with 10% IR with a balanced accuracy of 98.75%, and the best result when applying ADASYN oversampling is on a dataset with 13% IR and a balanced accuracy of 99.03%.

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