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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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Item-Based Collaborative Filtering Bandung Café Recommender System Using Recurrent Neural Network Baizal, Z. K. A.; Sitorus, Angela Tiara Maharani
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.v6i2.5772

Abstract

The aim of this study is to develop a dependable Cafe Recommender System for the Bandung area by employing a fusion of Item-Based Collaborative Filtering (IBCF) and Recurrent Neural Network (RNN) methodologies. The motivation behind this study stems from the growing need for more accurate and relevant café recommendations in Bandung, a city renowned for its diverse selection of cafes. Previous research has primarily focused on using either collaborative filtering or natural language processing approaches independently, leading to frequent limitations in understanding the entire context of user preferences and judgments. To address these shortcomings, we utilize the IBCF technique to analyze user rating data, identifying similarities amongst cafes to generate personalized recommendations. Concurrently, we employ the Recurrent Neural Network (RNN) method to examine and understand user reviews, facilitating a more advanced and contextually sensitive suggestion procedure. Our hypothesis posits that the amalgamation of IBCF (Item-Based Collaborative Filtering) and RNN (Recurrent Neural Network) will enhance the precision and pertinence of recommendations in the Bandung region. The assessment of the recommendations is conducted using measures such as Precision, Recall, and F1-score. The model demonstrates a precision of 89.04%, a recall of 88.75%, and an F1-score of 88.62%, which suggests that it is a suitable alternative to commonly used strategies for recommending cafes.
Sistem Kontrol Air dan Pencahayaan pada Akuarium Berbasis Internet of Things (IoT) Hidayat, Dody; Ramli, Ramli
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.v6i2.5775

Abstract

Decorative fish are popular pets that are often kept in pools or aquariums. Aquariums are an effective choice for decorative fish lovers because they make it easy to monitor fish development and settlement arrangements. Important aspects in keeping ornamental fish include feeding, water clarity, and lighting systems. Continuously lit light can cause the growth of molluscs in the aquarium, which reduces the clarity of the water and affects its quality. The occupation of the owners often makes them forget to control the water condition and lighting. Water that is not regularly replaced can cause pH instability, which has a negative impact on fish health. Remaining feed that sits at the bottom of the aquarium and is not filtered by the filter also reduces the clarity of the water. Therefore, it is important to control the water clarity and lighting periodically so that the fish can grow well. The solution for owners who don't have time for water replacement and lighting control is to use an Internet of Things (IoT) based system. IoT allows remote control and monitoring over the Internet. The research developed an IoT system to control water and aquarium lighting using the NodeMCU ESP8266 module connected to the Blynk Cloud. The system is equipped with an ultrasonic sensor to measure water height, a solenoid valve to regulate water flow, LEDs as lighting, and a buzzer as warning, all of which can be controlled via a smartphone.
Penerapan Sistem Pendukung Keputusan Pemilihan Cleaning Servis Terbaik Menggunakan Kombinasi Metode Pembobotan Entropy dan COPRAS Nuari, Reflan; Setiawansyah, Setiawansyah; Mesran, Mesran
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.v6i2.5796

Abstract

The main problem in choosing the best cleaning service is often a challenge because it involves a variety of complex and subjective criteria. Cleaning service performance assessments are not only based on factors such as speed and efficiency in work, but also include other aspects such as cleanliness of work results, interpersonal skills, and the ability to comply with safety procedures. The purpose of this research is to implement a system that is able to evaluate and select cleaning service providers objectively and effectively. The Entropy method to measure and assign weights to relevant criteria in the assessment of cleaning service providers, based on the information contribution of each criterion. The COPRAS method to assess and compare various alternative cleaning service providers based on the weight of predetermined criteria, so as to identify the service provider that best meets the desired needs and standards. Based on the results of the ranking that has been carried out by applying the entropy and COPRAS weighting methods, Hadi Santoso occupies the top position with a perfect score of 100, showing that he is the best cleaning service employee among other candidates. Dewi Lestari is ranked second with a score of 96.07, which also shows a very good performance but slightly below Hadi. Meanwhile, Haryani occupies third place with a score of 92.46. Even though it is in last place, this score still reflects a fairly satisfactory performance. This difference in scores indicates a variation in the performance aspects assessed, so that it can be used as a basis for decision-making for awards or service quality improvement.
Optimasi Rekomendasi Sustainable Development Goals (SDGs) di Indonesia menggunakan Content-Based Filtering dan Algoritma Machine Learning Hulvi, Alfajri; Kusrini, Kusrini
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.v6i2.5807

Abstract

Abstrak−Lahirnya program tentang Tujuan Pembangunan Berkelanjutan atau Sustainable Development Goals (SDGs) pada tahun 2015 membuat masyarakat di semua negara mulai memandang penting pembangunan berkelanjutan untuk diimplementasikan. Indonesia, sebagai bagian dari komunitas global, juga telah mengadopsi SDGs ini sebagai kerangka kerja dalam upaya mencapai Indonesia Emas 2045. Dengan visi ini, Indonesia bercita-cita menjadi negara maju yang berdaulat, adil, dan makmur tepat pada peringatan 100 tahun kemerdekaannya. Untuk mencapai tujuan secara efektif, penting untuk menerapkan sistem rekomendasi berbasis Artificial Intelligence (AI) yang mempertimbangkan tantangan sosial, ekonomi, dan lingkungan hidup yang dihadapi oleh negara Indonesia di masa mendatang. Content-Based Filtering (CBF) adalah teknik yang populer untuk membangun sistem tersebut. Penelitian ini membahas teknik untuk optimasi CBF menggunakan beberapa algoritma machine learning tradisional yaitu SVM, KNN, DT dan algoritma Deep Learning yaitu MLP. Teknik pengambilan sample dan penyetelan hiperparameter juga diperhatikan dalam penelitian ini. Algoritma Deep Learning MLP menghasilkan akurasi tertinggi yaitu 84%.
The Role of Sentiment and Toxicity in Digital Narratives Surrounding Sulawesi's Wildlife Tourism: A Content Analysis for Enhancing Conservation Strategies Singgalen, Yerik Afrianto
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.v6i2.5821

Abstract

This research explores the intersection of wildlife tourism and digital narratives, focusing on Sulawesi's endemic species. Utilizing the Digital Content Reviews and Analysis framework, the study combines content analysis, sentiment classification, and toxicity assessment to uncover critical insights. The findings highlight digital narratives' significant role in shaping public perceptions and behaviors toward conservation and ecotourism. Through systematic content analysis, themes such as biodiversity, conservation, and local community involvement emerged as effectively communicated, resonating with audiences and promoting sustainable tourism practices. The framework's structured approach enabled a thorough examination of digital content's impact on wildlife tourism narratives, identifying critical patterns and themes. The study also employed advanced machine learning techniques, specifically the SVM algorithm enhanced by SMOTE, which achieved a sentiment classification accuracy of 88.76% ± 3.11% and an AUC of 0.977, demonstrating its effectiveness. However, toxicity assessment revealed that while most interactions were civil, specific posts contained significant levels of toxicity, with a peak score of 0.64912, underscoring the need for better moderation and engagement strategies. The research emphasizes integrating conservation-focused elements into digital narratives to foster positive engagement and support for wildlife preservation. The study provides practical recommendations for enhancing the positive influence of digital narratives on conservation and sustainable tourism, offering a foundation for future initiatives to optimize digital communication strategies in ecotourism
Exploring Toxicity and Sentiment in Cultural Heritage Documentation: Content Analysis of Sabu Island's Portrayal in KOMPASTV's Expedition Singgalen, Yerik Afrianto
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.v6i2.5838

Abstract

This study explores the dual role of media in preserving and potentially distorting cultural heritage, focusing on the portrayal of Sabu Island in KOMPASTV's expedition documentary. Utilizing the Digital Content Reviews and Analysis Framework, the research comprehensively dissection the documentary’s content, uncovering critical insights into the intricate relationship between tourism, cultural preservation, and media representation. By integrating sentiment and toxicity analysis, the study identifies the emotional tone and harmful language present within digital narratives, with the toxicity analysis revealing an average score of 0.09886 and a peak score of 0.83647, indicating the potential influence of negative discourse on cultural heritage. The sentiment classification, conducted through a Support Vector Machine (SVM) model enhanced by SMOTE, demonstrated robust performance metrics, including an accuracy of 66.43%, precision of 60.51%, recall of 94.98%, and an F-measure of 73.90%, with an AUC ranging from 0.728 to 0.904. Additionally, content analysis centered on key themes such as Economic Impact, Sacred Rituals, Tourist Experience, and Weaving Traditions, revealing the complex dynamics where cultural preservation must be balanced with economic development and tourism demands. The findings emphasize the need for responsible and authentic media portrayals to safeguard cultural identities, as media holds the power to uphold or undermine cultural narratives' integrity. This research contributes to the broader discourse on cultural heritage documentation by offering a comprehensive framework for evaluating the impact of digital narratives on the preservation of cultural identities, ensuring the accurate and respectful portrayal of cultural heritage.
An Analysis of User Engagement in the Reviews of The Guardian of Nusantara Official Music Video: Toxicity and Sentiment Analysis Singgalen, Yerik Afrianto
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.v6i2.5846

Abstract

This study investigates user engagement within digital environments, explicitly focusing on creative content like music videos, and examines how sentiment and toxicity levels in user interactions influence engagement dynamics. Employing the Digital Content Reviews and Analysis Framework, the study reveals that 95.8% of user interactions exhibit positive or neutral sentiments. In comparison, a notable 4.2% are toxic, reflecting underlying societal tensions and potentially perpetuating negative feedback loops. Analysis of 23,112 posts using the Perspective API shows an average toxicity score of 0.03972, with severe cases reaching up to 0.87787. Scores for severe toxicity, identity attacks, insults, profanity, and threats, although generally low, indicate maximum values of concern, highlighting the need for vigilant monitoring. Sentiment classification results using the VADER model and multiple algorithms demonstrate that the Support Vector Machine (SVM) model achieved the highest accuracy (68.74%) and Area Under Curve (AUC) score (0.686), outperforming other models in distinguishing sentiment. The study's discussion on user engagement suggests that high levels of participation, such as comments, likes, and shares, are indicators of user interest and community identity but are susceptible to being undermined by toxic interactions. These findings emphasize the importance of fostering positive engagement through effective moderation strategies and advanced sentiment analysis tools, ensuring digital platforms remain conducive to constructive dialogue and community building. The research underscores the necessity for sophisticated analytical approaches to navigate the complexities of user behavior in digital spaces, providing critical insights into the interplay between sentiment, engagement, and toxicity in shaping online communities.
Optimasi LSTM Mengurangi Overfitting untuk Klasifikasi Teks Menggunakan Kumpulan Data Ulasan Film Kaggle IMDB Alkhairi, Putrama; Windarto, Agus Perdana; Efendi, Muhamad Masjun
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.v6i2.5850

Abstract

This study aims to develop and optimize a Long Short-Term Memory (LSTM) model to reduce overfitting in text classification using the Kaggle IMDB movie review dataset. Overfitting is a common problem in machine learning that causes the model to overfit to the training data, thus degrading its performance on the test data. In this study, various optimization techniques such as regularization, dropout, and careful training methods are applied to improve the generalization of the LSTM model. This study shows that overfitting reduction techniques, such as dropout and the use of the RMSProp optimizer, significantly improve the performance of the Long Short-Term Memory (LSTM) model in IMDB movie review text classification. The optimized LSTM model achieves an accuracy of 83.45%, an increase of 2.07% compared to the standard model which has an accuracy of 81.38%. The precision of the optimized model increases to 89.65%, compared to 84.46% in the standard model, although the recall is slightly lower (75.69% compared to 76.91%). The F1-score of the optimized model is also higher, which is 82.07% compared to 80.53% in the standard model. The experimental results show that the techniques successfully improve the accuracy and reliability of the text classification model, with better performance on the test data. This research makes a significant contribution to understanding and overfitting in deep learning models in the context of natural language processing, and offers insights into best practices in applying LSTM models to text classification.
Pengelompokan Algoritma K-Means dan K-Medoid Berdasarkan Lokasi Daerah Rawan Bencana di Indonesia dengan Optimasi Elbow, DBI, dan Silhouette Hartama, Dedy; Wanayumini, W; Damanik, Irfan Sudahri
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.v6i2.5851

Abstract

The study examines the use of K-Means and K-Medoids algorithms in the grouping of disaster area locations in Indonesia, with the aim of identifying patterns and optimizing disaster re-sponse strategies. The data used includes geographical and historical information of various disaster events in Indonesia, such as Aceh Besar, Asahan, Badung, Bangkalan, Bekasi, and others. In the clustering process, optimization techniques such as the Elbow Method, the Da-vies-Bouldin Index (DBI), and the Silhouette Score are used to determine the optimal number of clusters. Research results show that the K-Means algorithm tends to be more stable in deal-ing with outliers than K- Means, with the results of the DBI (Davis-Booldin Index) 0.3737248981 and the cluster 7, resulting in the silhouette score of 0.868728638 and cluster 2, resulting at the elbow 98106477130.371 and claster 2. The Silhouette Score and Elbow index-es also provide a strong indication that the clustering algorithm used is capable of forming significant and meaningful clusters. The study has made important contributions to the opti-mization of clustering with three methods used so that it can be the basis for authorities in planning and implementing more effective disaster mitigation policies.
Penerapan Data Mining dengan Algoritma Regresi Linear Berganda Untuk Memprediksi Omset Penjualan Minyak Goreng Fadianty, Ramadita; Sriani, Sriani
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.v6i2.5951

Abstract

In a competitive business world, companies must be able to develop effective strategies to increase sales and revenue. One approach that can be taken is to analyze sales transaction data to support more accurate decision making. PT. Siantar Bintang Perkasa is a company engaged in the field of basic necessities that sells various products, especially cooking oil. In facing the challenge of predicting revenue due to price fluctuations and market demand, data mining is used to find relevant patterns from historical sales data. Multiple Linear Regression techniques are implemented to predict company revenue and identify influential variables. The results of the study show that sales of Sania oil with 1 liter packaging and Sovia with the same packaging tend to get a slightly different sales prediction graph from the original value. And sales of oil other than Sania and Sovia with 1 liter packaging tend to get a fairly fluctuating prediction graph. The significance value in some calculations is still quite high on average. With this approach, accuracy in business strategy planning can be increased. This study also identified significant differences with previous studies, especially in terms of the research objects and variables used, as well as the analysis methods applied. These results are expected to help companies in making better decisions to improve their financial performance in the future.

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