Article Per Year (5 Year)
p-Index From 2020 - 2025
6.185
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Agri-Informatika Indonesian Journal of Electrical Engineering and Informatics (IJEEI) JUITA : Jurnal Informatika AL ISHLAH Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknoinfo ILKOM Jurnal Ilmiah Jurnal ULTIMATICS Petir IJISTECH (International Journal Of Information System & Technology) Jurnal Ilmiah Sinus Indonesian Journal of Business Intelligence (IJUBI) JITU : Journal Informatic Technology And Communication Automata Journal of Education Informatic Technology and Science Cakrawala Repositori Imwi Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences IJISTECH Journal Research of Social Science, Economics, and Management
Dhomas Hatta Fudholi
Jurusan Informatika, Universitas Islam Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

Published : 49 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search
Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Analisis Faktor Penerimaan Layanan e-Government dengan Menggunakan Model UTAUT2 dan GAM di Kabupaten Gunungkidul Eko Setiawan; Wing Wahyu Winarno; Dhomas Hatta Fudholi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2565

Abstract

Government through Presidential Regulation No. 95 of 2018 concerning SPBE supports and realizes clean, effective and transparent government governance so that it has quality and trusted public services. To improve society using e-government based services, it is necessary what factors influence a person to use e-government services. This study uses the UTAUT2 research model and the Government Adoption Model (GAM) to determine the factors that influence a person using e-government in Gunungkidul. This study used the PLS SEM measurement method and found that the factors that influence e-government acceptance are effort expectation, facilitating conditions, and computer self-efficacy.
Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA Siti Mutmainah; Dhomas Hatta Fudholi; Syarif Hidayat
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5486

Abstract

The pandemic caused by the 2019 coronavirus has revitalized telemedicine as information and communication technology-based health services and as a medium for doctors' services in diagnosing, treating, preventing and evaluating health conditions. One of the telemedicine service applications in Indonesia is Alodokter, Halodoc, KlikDokter, SehatQ and YesDok. Previous research on the same domain, namely applications telemedicine uses machine learning to perform sentiment modeling. This research performs sentiment analysis using the BiLSTM method (Bidirectional Long Short-Term Memory) which can better represent contextual information and can read user feedback information in both directions. Then sentiment analysis is described explicitly to identify topics from user sentiment using LDA (Latent Dirichlet Allocation). User feedback was collected on August 14, 2022 which was obtained in the five applications totaling 244,098. The results of the analysis on feedback obtained were 112,013 positive sentiments, 34,853 neutral sentiments and 97,228 negative sentiments. The BiLSTM and Word2Vec models used have a good performance in classifying sentiments, namely 95%, while the topic modeling for each sentiment has a coherence value of 0.6437 on positive topics, 0.6296 neutral sentiments and 0.6132 negative sentiments.
COVID-19 Misinformation Detection in Indonesian Tweets using BERT Fahmi Adi Nugraha; Dhomas Hatta Fudholi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5668

Abstract

The COVID-19 pandemic has seen a marked increase in the spread of misinformation throughout various media channels, most notably social media. This is particularly true of Indonesia where a combination of middling digital literacy and the slow speed of fact-checking contributes to the continued spread of misinformation. Many of the solutions proposed by other researchers to address this problem do not use transformers despite the existence of Indonesian language BERT models. Thus, in order to both provide a potential solution to the problem of misinformation as well as a baseline for future research we propose an IndoBERT-based model for detecting misinformation in Indonesian language Tweets. For model training, we use the "small" version of the MuMiN dataset which is a comprehensive multi-lingual dataset containing fact checked Tweets. The authors of MuMiN provide a baseline LaBSE model which achieves a macro average F1-score of 54.5% when trained on the MuMiN "small" dataset. We train and evaluate our proposed model on this dataset in order to compare it to the LaBSE model. We also train and evaluate our model on a subset of the dataset containing only Tweets related to COVID-19 that we first translate into Indonesian. Our model achieves a best macro average F1-score of 59.5% on the MuMiN dataset and 79.04% on the subset.
Implementasi Arsitektur EfficientNetV2 Untuk Klasifikasi Gambar Makanan Tradisional Indonesia Erin Eka Citra; Dhomas Hatta Fudholi; Chandra Kusuma Dewa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5881

Abstract

Indonesia has many variations of traditional food and interesting tourist destinations. The large number of tourist destinations make people like traveling and try to enjoy their traditional food. However, when trying traditional foods, especially foods that are new to them, they must be more careful, because the various ingredients contained in them have an impact on health. This research will try to make an application that can recognize Indonesian traditional food. The hope is that it can provide complete information, so that it can be used to develop calorie counter applications in the future. This study aims to design a system that can classify Indonesian traditional food images to help recognize food names with a certain level of accuracy using the EfficientNetV2 architecture. EfficientNetV2 is a new family of deep learning that excels in training as well as parameter efficiency. Deep Learning is a method often used to classify complex images. The EfficientNetV2 used in this study consists of four different architectures namely EfficientNetV2_S_21k, EfficientNetV2_M_21k, EfficientNetV2_L_21k, and EfficientNetV2_XL_21k. The dataset used comes from three types of data source categories, namely from Google Images, direct image capture using a Smartphone camera, and a combination of both. Each dataset category consists of 18 classes with a total of 1,800 images from Google Images, 1,800 images from Smartphone cameras, and 3,600 images from a combination of Google Images and Smartphone cameras. The dataset is taken from three categories to compare the level of accuracy and get the best accuracy value. The results of this study indicate that EfficientNetV2 can classify images of Indonesian traditional food with the highest test accuracy value of 99.4% from the EfficientNetV2-L(21k) model and the results obtained do not occur overfitting.
Prediksi Retensi Pengguna Baru Shopee Menggunakan Machine Learning Wahyu Fajrin Mustafa; Syarif Hidayat; Dhomas Hatta Fudholi
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.v8i1.7074

Abstract

Shopee has evolved into one of the leading e-commerce platforms connecting sellers with consumers. However, the challenge of keeping users active and engaged on the platform has become increasingly complex. User retention, the ability of a platform to sustain and enhance user presence, is a key factor in the long-term success of an e-commerce platform. Understanding the factors influencing users' decisions to remain active or cease interactions with the platform involves analyzing various variables, including user behavior, preferences, shopping experiences, and interactions with the platform. This research is designed to develop an effective user retention prediction model using data from new Shopee users. By analyzing the data and applying machine learning techniques using Logistic Regression, Decision Tree, Gaussian Naive Bayes, Random Forest, KNN (K-Nearest Neighbors), MLP (Multi-Layer Perceptron), AdaBoost, and XGBoost methods, this study predicts user retention within a 14-day period after registration on Shopee. The results of this research indicate that the Random Forest model performs the best with an Accuracy value of 0.733677, Precision of 0.702161, Recall of 0.811626, and F1-Score of 0.752936. Cross-validation values demonstrate the model's consistency with an Accuracy of 0.727626, Precision of 0.698143, Recall of 0.801884, and F1-Score of 0.746328. The Random Forest model becomes a model with a high recall value, indicating good sensitivity in identifying users who retain. Consequently, the results of this research provide valuable insights for Shopee in developing retention strategies for new users, which is an important aspect in the growth and sustainability of the e-commerce business.
Co-Authors Abdullah Aziz Sembada Abdullah Aziz Sembada Abyan Fadilla Noor Aditya Perwira Joan Dwitama Affan Taufiqur Afrianto, Nurdi Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Luthfi Ahmad Rafie Pratama Altesa Yunistira Andi Wafda Andri Heru Saputra Annisa Zahra Ari Farhan Nurihsan Ari Sujarwo Arief Rahman Arrie Kurniawardhani Arrie Kurniawardhani Chandra Kusuma Dewa Dendy Surya Darmawan Deny Rahmalianto Dimas Adi Wibowo Dimas Danu Budi Pratikto Dimas Pamilih Epin Andrian Dimas Panji Eka Jalaputra Dirgahayu, Raden Teduh Dziky ridhwanulah Eko Prasetio Widhi Eko Setiawan Erin Eka Citra Fahmi Adi Nugraha Ferdian Nursulistio Fery Luvita Sari Gilang Persada Bhagawadita Gunanto Gunanto Harry Akbar Al Hakim Ibnu Fajar Arrochman Insanur Hanifuddin Iqbal Syauqi Mubarak Izzan Yattaqi Nugraha Izzati Muhimmah Jaka Nugraha LAILA KUSUMA WARDANI Lizda Iswari M. Ulil Albab Surya Negara Malik Abdul Aziz Mawar Hardiyanti Meilita . Moch Bagoes Pakarti Moch Yusuf Asyhari Muhammad Abyanda Tamaza Muhammad Habib Izdhihar Muhammad Rizhan Ridha Muhammad Sulthon Alif Novian Mahardika Putra Purwoko, Agus Raden Teduh Dirgahayu Rahadian Kurniawan Rakhmat Syarifudin Rendy Ressa Sutrisno Ridho Iman Tiyar Risca Naquitasia Royan Abida N. Nayoan Sabar Aritonang Rajagukguk Safira Yuniar Putri Buana Salma Aufa Azaliarahma Salsabila Zahirah Pranida Septia Rani Septia Rani Sigit Nugroho Siti Mutmainah Siwi Cahyaningtyas Sri Mulyati Teduh Dirgahayu Tri Handayani Umar Abdul Aziz Al-Faruq Wahyu Fajrin Mustafa Wahyuzi, Zikri windi astriningsih Yasmin Aulia Ramadhini Yoga Sahria Yudi prayudi