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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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Perbandingan Metode K-Means dan K-Medoids Untuk Clustering Jenis Kriminalitas Azizah, Nurul; Fauzi, Ahmad; Rohana, Tatang; Faisal, Sutan
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5723

Abstract

Crime in Indonesia includes acts that violate the law, social norms and religion which cause economic and psychological losses as well as social tensions in society. Crimes such as theft, violence, fraud and drugs are often triggered by factors such as poverty and environmental conditions that support criminal behavior. This research needs to be carried out to overcome the complex and far-reaching crime problem in Indonesia, especially in Karawang Regency. With crimes such as theft, violence, fraud and drugs on the rise, often fueled by factors such as poverty and environmental conditions, a more effective approach is needed to understand and address these problems. This research uses data mining techniques, especially cluster analysis, to group types of crime. The aim is to identify existing crime patterns and understand the factors that influence their spread. Thus, the results of this research can help the authorities in developing more targeted crime prevention and handling strategies, so as to minimize the negative impact of crime in the area. Apart from that, this research also contributes to increasing knowledge regarding the most effective methods for analyzing crime data, which can be applied in other areas with similar problems. The results of the research show that the K-Means algorithm is more effective than K-Medoids in handling data variability, with a Silhouette Coefficient value of 0.482 and a Davies Bouldin Index of 0.915. It is hoped that the implementation of this algorithm will make it easier to identify and handle crimes in the area.
Dissatisfaction of a Mobile-Based Application from Different Platforms Using Naïve Bayes for Sentiment Analysis and LDA for Topic Modelling Ohoilulin, Anastasya; Inan, Dedi I; Juita, Ratna; Sanglise, Marlinda
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5729

Abstract

A mobile application that is built and runs on different platforms, such as iOS (Apple App Store) and Android (Google Play Store), may not necessarily have the same user satisfaction (dissatisfaction) reviews understood by both user segments. This is due to, for example, the differences in the technology used, which ultimately result in different user behaviors. This can be observed from the average ratings on each platform, even though it is the same application. Therefore, this research aims to provide a foundation for the assumptions made. The case study used is the Satu Sehat mobile application, a widely utilized health service application. Text mining methods: sentiment analysis using Naive Bayes and topic modeling using Latent Dirichlet Allocation (LDA) were chosen due to their relevance to the research objectives. A total of 21,750 reviews from the Google Play Store and 7,350 reviews from the Apple App Store were collected using scraping techniques. The results showed that sentiment analysis model on negative sentiment in the Apple App Store excelled with a precision of 93%, recall of 93%, and F1-score of 95%, while in the Google Play Store it had a precision of 82%, recall of 87%, and F1-score of 85%. However, the performance of the positive sentiment model in the Apple App Store was very low, with a precision of 63%, recall of 33%, and F1-score of 43%, compared to the Google Play Store which had a precision of 78%, recall of 71%, and F1-score of 74%. This indicates that a higher level of dissatisfaction is observed in the Apple App Store compared to Android. These results are consistent with the average ratings of the application on both platforms. Topic modeling results, which presented 15 topics from each platform, showed similar common issues such as login, OTP verification, and data input errors on both platforms. However, reviews of the Satu Sehat running on the Apple tend to be more negative compared to the one of Android. Therefore, improving the application quality of the Apple platform is more expected to meet user expectations and enhancing the overall rating as in the Andrond one.
Stock Industry Sector Prediction Based on Financial Reports Using Random Forest Zhafran, Kamil Elian; Saepudin, Deni
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5743

Abstract

This study aims to predict the stock industry sector on the Indonesia Stock Exchange (IDX) based on financial reports using the Random Forest method. Implementing this machine learning approach is crucial due to the complexity of financial data, which demands robust and adaptive methods for accurate predictions. The dataset comprises financial data from companies across 10 industrial sectors on the IDX, spanning 2010-2022, and includes 17 features from each financial report. Notably, there is an imbalance in the number of companies per sector, with sector B representing 14.76% and sector G only 1.98%. This imbalance introduces bias in data analysis, thus necessitating the application of the SMOTE oversampling method to address it. The research process involves data cleaning, splitting the data into 80% training and 20% testing sets, applying the SMOTE oversampling technique, and comparing predictions from imbalanced and balanced datasets. The Random Forest method is chosen for its capability to handle complex datasets for industrial sector classification. Evaluation results indicate that without oversampling, the model achieves an accuracy of 73.57%, precision of 74.29%, recall of 73.57%, and an F1-score of 73.51%. With oversampling, these metrics improve to an accuracy of 80.21%, precision of 81.34%, recall of 80.21%, and an F1-score of 80.45%.
Multi-Aspect Sentiment Analysis Using Elman Recurrent Neural Network (ERNN) Method for TripAdvisor App User Reviews Ridho, Fahrul Raykhan; Sibaroni, Yuliant; Puspandari, Dyas
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5746

Abstract

TripAdvisor is the world's largest travel platform that assists 463 million travelers each month in making their trips the best they can be. Users of TripAdvisor can provide reviews, comments, and ratings of travel destinations. However, reviews on TripAdvisor are considered insufficient in helping prospective travelers understand the strengths and weaknesses of a hotel. Therefore, a multiaspect sentiment analysis of TripAdvisor reviews on hotels was conducted to identify commonly discussed rating aspects among visitors and to determine specific evaluations. In this study, the Elman Recurrent Neural Network (ERNN) method was employed to build a classification system for multiaspect sentiment analysis of user reviews on the TripAdvisor application. The aspects examined in this research include Service, Cleanliness, Location, Value, Rooms, and Overall Experience, aiming to provide insights into the hotels under consideration. The results indicate that the ERNN method can deliver superior outcomes in multiaspect sentiment analysis of TripAdvisor hotel reviews. The ERNN model's performance in multiaspect sentiment analysis shows optimal accuracies: 81.35% for Service, 98.71% for Cleanliness, 74.87% for Location, 93.84% for Value and 71.52% for Rooms. These findings can assist travelers in better understanding the strengths and weaknesses of accommodations.
Sistem Keamanan Dua Lapis Dengan RFID dan Pendeteksi Objek Dengan Machine Learning Jhonatan, Jhonatan; Sekarsari, Kartika
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5747

Abstract

Conventional locking systems with physical keys are still widely used to secure the house door. Additionally, marketed security devices often come with various features but only have one security method used as the access key for the security system. This research designs a two-layer security system by sequentially applying two security methods: Radio Frequency Identification (RFID) and object detection, with Arduino UNO as the main microcontroller. In designing the two-layer security device for the room door, the RFID MFRCC22 module and OV2640 camera are used on the ESP32-CAM microcontroller. Testing results show that this device can function well using an RFID card in the first layer and small objects with maximum dimensions of 20 cm in length, width, and height in the second layer. With an operating voltage of 5Vdc and a current requirement of 150mA to 250mA, this system has high efficiency with low power consumption. The response time required to access this two-layer security system is 5.71 seconds to 6.57 seconds. The maximum distance between the RFID card and the RFID Reader is 5 cm, and between the ESP32-CAM camera and the object is between 5 cm and 40 cm. Additionally, the minimum number of image samples required for each object with different positions and angles to be applied to the ESP32-CAM microcontroller is 75 image samples with RGB color parameter configuration and 48x48 pixel image size, resulting in an F1-Score percentage of 100% so that the ESP32-CAM microcontroller can recognize objects between different object models. The F1-Score value in the Background column is 1.00, the Charger Hp column is 1.00, the Motorcycle Key column is 1.00, and the Leagoo column is 1.00.
User-Centric Diet Recommender Systems with Human-Recommender System Interaction (HRI) based Serendipity Aspect Rakhman, Raihan Romzi; Kusumo, Dana Sulistyo
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5754

Abstract

Currently, obesity is on the rise globally with predictions to continue rising until 2030. Adopting a healthy diet and increasing physical activity are key strategies to reduce the risk of obesity. However, there are significant challenges in adhering to a diet, including the monotony of food choices and difficulty in maintaining motivation. This research aims to develop a user-centered dietary recommendation system that addresses these challenges by introducing serendipity into the diet planning process. Serendipity in this context refers to generating unexpected yet relevant food recommendations, thereby enhancing user engagement and satisfaction. The system uses content-based recommendation techniques, including TF-IDF, Cosine Similarity, and K-Means clustering, to provide personalized dietary suggestions based on individual health profiles, calorie needs, and food preferences. The evaluation of the system demonstrated that incorporating serendipity into recommendations significantly improves user experience and adherence to dietary plans. The findings highlight the potential of serendipity to transform dietary adherence, making the dieting process more enjoyable and sustainable.
Application of Support Vector Machine and Kriging Interpolation for Rainfall Prediction in Java Island Purwanto, Brian Dimas; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rainfall is one of the crucial meteorological elements that can significantly impact human life. Accurate rainfall prediction is essential for effective natural resource planning and management across various regions, especially in Java Island, which is one of the most densely populated areas in Indonesia. This study aims to develop a rainfall distribution prediction model for Java Island using Support Vector Machine (SVM). The scenario developed involves time-based feature expansion implemented in SVM. This method is combined with Kriging interpolation to obtain the rainfall distribution classification on Java Island. The results show that the model's performance, exceeding 90%, is effective in predicting future rainfall distribution classifications on Java Island. The contribution of this research lies in providing insights into feature expansion techniques in machine learning to refine predictive models applied in meteorology and environmental management.
Content-Based Music Recommender System Using Deep Neural Network Baizal, Z. K. A.; Andiety, Rich
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5762

Abstract

Music is one of the most popular forms of entertainment. Along with the development of information technology, music streaming platforms such as Spotify, Apple Music, and Deezer are increasingly popular among users. However, with thousands of songs available on these music streaming platforms, users often have difficulty finding songs that suit their tastes. Therefore, we design a music recommender system that can assist users in finding songs that are more in line with user preferences. In this research, we propose the development of a content-based music recommender system using a combination of Content-Based Filtering and Deep Neural Network (DNN) methods. The DNN used is Convolutional Neural Network (CNN) which serves to increase the percentage of accuracy to provide results that match user needs. This research aims to develop a music recommender system that can provide personalized recommendations to users according to the preferences of users. This research provides an accuracy result of 73.5%. From these results, it has been proven that the resulting music recommendations can be an alternative to the existing Collaborative Filtering-based recommender system.
Analysis Of Indonesian People's Sentiment Towards 2024 Presidential Candidates On Social Media Using Naïve Bayes Classifier and Support Vector Machine Mardiah, Nia; Marlina, Leni; Khairul, Khairul; Sitorus, Zulham; Iqbal, Muhammad
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5766

Abstract

This research aims to analyze the sentiment of the Indonesian public towards the 2024 presidential candidates on social media platforms X and Instagram. The main issue addressed is how to determine public opinion as disseminated on social media regarding the presidential candidates. To address this issue, two classification methods are used: Naïve Bayes Classifier and Support Vector Machine (SVM). The objective of this research is to measure public sentiment, both positive and negative, towards the 2024 presidential candidates using these two methods. The research findings indicate that the implementation of the Naïve Bayes method with manual labeling achieved the highest accuracy of 86% for X data and 85% for Instagram comments data. Meanwhile, with lexicon-based labeling, the highest accuracy was 60% for both X and Instagram data. The SVM method with manual labeling also achieved the highest accuracy of 86% for X data and 85% for Instagram data. With lexicon-based labeling, the highest accuracy was 60% for X data and 70% for Instagram data. This research concludes that both Naïve Bayes and SVM demonstrate strong performance in sentiment analysis on social media, with SVM slightly outperforming in some scenarios. The implementation of these two methods provides valuable insights into public opinion towards the 2024 presidential candidates on social media.
The Comparison RNN and Maximum Entropy on Aspect-Based Sentiment Analysis of Gojek Application Umulhoir, Nida; Sibaroni, Yuliant; Fitriyani, Fitriyani
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5767

Abstract

Nowadays, mobile applications can help a person to carry out daily activities. The use of mobile applications is also increasingly in demand by the public. One of the most popular online transportation applications in Indonesia is Gojek, with the top level of the most downloads in Indonesia. However, Gojek also experienced a significant decline from the previous download results. This is used as sentiment analysis by the author to find out how users rated Gojek application reviews from various points of view. This research compares two methods, namely Maximum Entropy and Recurrent Neural Network (RNN) using Chi-Square as feature selection and TF-IDF as feature extraction for each aspect of Availability, System, Comfort, and Transaction. As for the results of user analysis of four aspects with positive and negative sentiment, it is carried out with a 70:30 comparison ratio because it gets a better accuracy result value. The results show that the RNN method gets a better accuracy value than the Maximum Entropy method, with an accuracy value in the accessibility aspect of 90%, system aspect of 89%, comfort aspect of 80%, and comfort aspect of 80%.

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