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INDONESIA
JURNAL MEDIA INFORMATIKA BUDIDARMA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Decission Support System, Expert System, Informatics tecnique, Information System, Cryptography, Networking, Security, Computer Science, Image Processing, Artificial Inteligence, Steganography etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,182 Documents
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Implementation of CRISP-DM for Social Network Analysis (SNA) of Tourism and Travel Vlog Content Reviews Singgalen, Yerik Afrianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7323

Abstract

Technological developments have facilitated the process of creating and publishing digital content on various platforms while influencing application user behavior in terms of consumption. In the context of tourism and travel vlog content, the publication of travel content influences perceptions and triggers the intention of visiting the destination in which the video is taken. In addition, content reviews can be seen in the comment column, which shows the response from content creators to tourists related to the topics discussed in the video content. This study aims to analyze content reviewers' social network patterns and sentiments using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach. Meanwhile, the model used in this study is Social Network Analysis (SNA) and Sentiment Classification based on analyzing network patterns of "whom, mention whom," and "who replies who," based on diameter, density, reciprocity, centralization, and modularity. The stages in the CRISP-DM method consist of business understanding, data understanding, modeling, evaluation, and deployment. The results of this study show that at the business understanding stage, tourism and travel vlog content reviews with Waseda Boys Indonesia Trip video comment datasets and Waseda Boys Trip to Manado and Likupang video and Waseda Boys Trip to Labuan Bajo video. Scraping content review data from the YouTube platform is carried out at the data understanding stage based on author, description, Global Unique Identification (GUID), like_count, link, pub_date, and author channel URL. In the modeling stage, the interaction pattern between authors in the form of networks is visualized and analyzed based on clusters. At the evaluation stage, an evaluation is carried out based on sentiment related to content related to tourism activities. At the deployment stage, recommendations for digital tourism marketing strategies based on tourism and travel vlogs can be known. Thus, tourism and travel vlog content play an important role in triggering tourism intentions so that it is effectively used in destination marketing strategies in the digital era.
Prediksi Banjir Berdasarkan Indeks Curah Hujan Menggunakan Deep Neural Network (DNN) Fafaza, Safira Alya; Rohman, Muhammad Syaifur; Pramunendar, Ricardus Anggi; Sri Winarsih, Nurul Anisa; Saraswati, Galuh Wilujeng; Saputra, Filmada Ocky; Ratmana, Danny Oka; Shidik, Guruh Fajar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7098

Abstract

Floods are natural disasters that often occur and are among the most destructive because they have significant economic and social impacts. Accurate flood predictions are essential to manage risk and organize emergency response planning effectively. This research uses Deep Neural Network (DNN) to build a flood forecasting model that relies on rainfall index indicators and captures complex and ever-changing patterns obtained from rainfall index data. Using historical information from flood disaster events in Kerala, India, an analysis was conducted to assess the impact of various factors, particularly in learning rate and optimizer type, on model performance. The experimental results show that the type of optimizer is a crucial factor in determining the model's effectiveness, as shown in the ANOVA statistics with a P-value of 0.008493, much lower than the general threshold of 0.05. This is because this type of optimizer can significantly improve prediction accuracy. With the Adam optimizer type, the learning rate range is between 0.1 and 0.4, showing an accuracy level of up to 100%. However, the choice of learning rate does not significantly impact, indicating that the main emphasis on parameter adjustment should be determined accurately. Therefore, by carrying out appropriate parameter adjustments and thorough validation to find the optimal configuration that can increase accuracy in predicting flood disasters based on rainfall indices, the DNN model has the potential to become a tool that can assist in flood risk planning and management.
Pemilihan Fitur Menggunakan Algoritma Chi-Square Dan Particle Swarm Optimization (PSO) Untuk Meningkatkan Kinerja Deep Neural Network Pada Deteksi Penyakit Diabetes Santosa, Wahyu Budi; Syukur, Abdul; Purwanto, Purwanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7277

Abstract

Diabetes is a chronic disease caused by impaired glucose metabolism in the body. In type 1 diabetes, the body's immune system attacks and destroys the insulin-producing cells in the pancreas, so that the body is unable to produce sufficient amounts of insulin. In type 2 diabetes, the body is still able to produce insulin, but is unable to use it effectively. Insulin is a hormone that is useful for controlling glucose in blood cells. If glucose in the blood is not controlled it can cause a number of problems, such as heart disease, stroke, blindness, nerve damage and so on. So the research in this study formulated an increase in accuracy in the diabetes detection process using the Deep Neural Network (DNN) method which was enhanced with the chi-square and PSO methods through the attribute selection process. The results of testing the PIMA dataset with DNN obtained an accuracy value of 76.62% with an AUC value of 0.772. Meanwhile, testing using the DNN method for attribute selection using the Chi-square method and optimization with PSO obtained an accuracy value of 85.71% with an AUC value of 0.818. So it can be concluded from testing diabetes data using the Deep Neural Network method which adds Chi-square as a selection attribute and is optimized using PSO which is better when compared to the DNN method.
Komparasi Teknik Feature Selection Dalam Klasifikasi Serangan IoT Menggunakan Algoritma Decision Tree Setiawan, Dicky; Nugraha, Adhitya; Luthfiarta, Ardytha
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6987

Abstract

Presence of Internet of Things (IoT) has revolutionized how we interact with the world on our daily life by enabling various devices to connect the internet and transmit data. However, the increasingly widespread use of IoT technology also brings serious threats to cyber security and increases the number of IoT attacks. The need for robust classification models is becoming increasingly clear to anticipate these problems. This research focuses on developing an IoT attack classification model by comparing feature selection techniques that utilize data from the CIC IoT Dataset 2023. This research faces challenges such as data imbalance and the complexity of handling various features. To overcome these challenges, this research uses random undersampling techniques to balance the data and utilizes various feature selection methods, including filter based, wrapper based, and embedded based. Apart from that, this research also tries to use a decision tree algorithm. The findings reveal that the application of wrapper based techniques as feature selection together with a decision tree algorithm produces the highest accuracy of 87.32% in classifying IoT attack types. This emphasizes that the use of techniques and algorithms that are still rarely used can provide fairly good accuracy results.
Sentiment Analysis on Twitter(X) Related to Relocating the National Capital using the IndoBERT Method using Extraction Features of Chi-Square Arista, Dufha; Sibaroni, Yuliant; Prasetyo, Sri Suryani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7198

Abstract

Sentiment analysis or commonly referred to as opinion mining is a field of science that can be used to get the percentage of positive sentiment and negative sentiment towards a person, company, institution, product, or even an issue or topic. Various topics are discussed on social media, one of which is Twitter (X). Starting from the economy, politics, social, culture, law and others. One of the most discussed topics on Twitter (X) is the transfer of Indonesia's capital city to East Kalimantan Province, which has drawn various opinions from netizens on Twitter (X). In this study, data regarding the transfer of the national capital taken by the author was taken from social media, namely from the social media Twitter (X) with a date range of January 1, 2022 to February 28, 2022. The method used in this research is IndoBERT using Chi-Square. Based on the experiments that have been carried out, the performance of IndoBERT with Chi-square selection features shows good results with an overall accuracy value of 94%, a precision value of 85%, a recall value of 91%, and an f1 value of 88.4% for all datasets.
One-Star Hotel Selection for Staycation using Simple Additive Weighting and Rank Order Centroid Singgalen, Yerik Afrianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7380

Abstract

The development of the hospitality industry provides many choices to staycation enthusiasts in choosing the right accommodation service. Accommodation service providers also increase competitiveness through excellent service to the addition of facilities to adjust guest preferences and increase sales. The primary objective of this study is to analyze the decision-making behavior associated with choosing one-star hotels for a staycation. The research investigates the decision-making patterns in the context of one-star hotel stays for recreational purposes. The study utilizes the SAW method to evaluate the criteria of facilities, services, rating, cleanliness, value for money, and location, employing Rank Order Centroid (ROC) weighting. In light of the computations using Rank Order Centroid, it is discerned that the weights assigned to the criteria are as follows: W1 with a weight of 0.4083, w2 with a weight of 0.2416, w3 with a weight of 0.1583, w4 with a weight of 0.1027, w5 with a weight of 0.0611, and w6 with a weight of 0.0277. The research findings reveal the aggregation of weighted scores and ranking of alternatives, delineating that A4 attains the first rank with a value of 0.97395, followed by A3, securing the second rank with a value of 0.96511, and A1 acquires the third rank with a value of 0.95191. Thus, the significance of the identified alternative is that it sheds light on its superior performance according to the specified criteria. In conclusion, this research contributes valuable insights into the decision-making dynamics of staycation enthusiasts, pinpointing alternative A4 as the most favorable choice within one-star hotel accommodations.
Kontrol Lampu Pada Pendeteksian Kantuk dengan Parameter Posisi Tidur Menggunakan Object Detection Effiecientdet Lite Nurul Humaera Baharudin; Abdul Latief Arda; Andani Achmad
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6466

Abstract

Drowsiness is a sign that the body is entering a phase towards rest. Integrated detection of sleepy activity light control where light control automation is carried out based on the results of object detection, namely humans when they are sleepy, will be needed to efficiently use electrical energy. Analysis of the sleep detection scheme and the effectiveness of the object detection method in detecting sleepiness with body and eye position parameters was carried out using the Efficientdet Lite object detection method as the architecture used in making sleep detection models where the model created is implemented on a Raspberry Pi which is integrated with light control resulting in detection drowsiness with a comparison of 3 sleepiness parameters (body position, eyes, facial expressions) is able to control lights in the form of automatic lights turning off and on based on the labels that appear in each drowsiness detection parameter class. The results obtained were that the metric evaluation produced a different Average Precision for 3 parameters but in the appearance of the bounding box the most precise in detecting objects was the sleeping position, for the eye parameter the bounding box was not appropriate in detecting drowsiness while in facial expressions the bounding box was difficult to appear. Accuracy testing was carried out on the sleep position parameter resulting in a detection accuracy of 80%.
Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images Windarto, Agus Perdana; Rahadjeng, Indra Riyana; Siregar, Muhammad Noor Hasan; Alkhairi, Putrama
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7199

Abstract

This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.
Implementasi Teknologi Web Geospasial dan Decision Tree untuk Klasifikasi Sebaran Pengumpulan Zakat Susanti, Pratiwi; Asyhari, Moch Yusuf; Juwari, Juwari; Ahmad, Khairul Adila; Shamsuddin, Norin Rahayu; Tajuddin, Taniza
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7027

Abstract

As one of the five pillars of Islam, zakat significantly contributes to socio-economic development and growth in a region. Zakat collection in the city of Madiun has various challenges, including the motivation of muzakki as zakat payers. Zakat is an obligation for someone with assets or income exceeding basic needs to fulfill their living needs. One way to increase motivation to pay zakat is using geospatial web technology to map muzakki in the city of Madiun. Muzakki report data, which previously had the form of a graphic diagram, was changed to a more interactive geospatial form. Several essential advantages in its implementation include embedding precise coordinate points to facilitate the geospatial-based monitoring process, important notes and information about muzakki, and the classification of zakat collection, including collecting zakat in high, medium, and low amounts. This method can represent muzakki mapping more easily and clearly so that zakat institutions (BAZNAS) can quickly determine their policies to increase zakat collection.
YouTube Viewership Increation Analysis and Prediction using Facebook Prophet Model Pratama, Rezqie Hardi; Gunawan, Putu Harry
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7240

Abstract

YouTube, a widely accessed video-sharing platform available through both mobile applications and web interfaces, serves as a medium for content creators, commonly referred to as YouTubers, to engage with their audience. The success of a YouTuber is intricately tied to their audience engagement, encompassing metrics such as total views, comments, and likes garnered by their videos. This study involves the analysis of 7,600 English-language videos uploaded on YouTube between August and September 2020. To assess the predictive success value of a video, the study employs the Facebook Prophet method. Focusing on the upload time as a primary parameter, this method forecasts the growth in the number of YouTube viewers using datasets obtained from the YouTube API. Leveraging Time Series modeling, Facebook Prophet processes data by considering audience interactions throughout a video broadcast. The results derived from the Facebook Prophet model indicate a predictive trend of increasing viewership on YouTube in the coming months. The evaluation of model linearity, measured using the R² score to gauge data reliability, reveals a score of 0.39 or 39% which indicates a positive linearity score. And using Pearson correlation it gives 75 accuracy score. This signifies the model's capability to reasonably predict the growth in the number of viewers, contributing valuable insights into the dynamics of YouTube audience engagement over time.

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