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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

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Implementation Of Deep Learning For Fake News Classification In Bahasa Indonesia Eko Prasetio Widhi; Dhomas Hatta Fudholi; Syarif Hidayat
Journal Research of Social Science, Economics, and Management Vol. 3 No. 2 (2023): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v3i02.546

Abstract

Fake news has become a serious threat in the digital information era. This research aims to develop a model for detecting fake news in Bahasa Indonesia using a deep learning approach, combining the Long Short-Term Memory (LSTM) method with word representations from Word2vec Continuous Bag of Words (CBOW) to achieve optimal results. Our main model is LSTM, optimized through hyperparameter tuning. This model can process information sequentially from both directions, allowing for a better understanding of the news context. The integration of Word2vec CBOW enriches the model's understanding of word relationships in news text, enabling the identification of important patterns for news classification. The evaluation results show that our model performs very well in detecting fake news. After the tuning process, we achieved an F1-Score of 97.30% and an Accuracy of 98.38%. 10-fold cross-validation yielded even better results, with an F1-Score and Accuracy reaching 99%.
Multi Aspek Sentimen Analisis pada Review Hotel Menggunakan Deep learning windi astriningsih; Dhomas Hatta Fudholi
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 3 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i3.5321

Abstract

Hotel reviews not only provide useful information for business owners but also shape the image of the hotel in the eyes of customers. Reviews generally cover various aspects expressed honestly by customers. In the era of technological development, the number of hotel reviews online is increasing, making the processing of hotel assessments discussed in reviews a challenge for many parties. To overcome this, a multi-aspect sentiment analysis has been developed to help extract more specific information about hotel evaluations from each review sentence. The aspects evaluated in the reviews include price, location, service, food, facilities, and rooms. In developing the multi-aspect sentiment analysis model, a deep learning method based on LSTM is used. The LSTM model architecture is built using a sequential model with four layers: embedding, SpatialDropout1D, LSTM, and dense. The model is trained with 10 epochs and a batch size of 32. The model is evaluated through three scenarios, including testing sentences with one aspect, testing sentences with a combination of two aspects, and testing sentences with a combination of three aspects. Top-1 and Top-2 accuracy are applied to test sentences with a combination of two and three aspects. Meanwhile, F1_score is used for testing sentences with one aspect and sentiment analysis. The obtained accuracy results are 79% for Top-2 accuracy in sentences with a combination of two and three aspects, 85.7% for F1_score in sentences with one aspect, and 83% for F1_score accuracy in sentiment analysis. These results indicate that the developed model is capable of performing multi-aspect sentiment analysis on hotel reviews.
Deteksi Kesegaran Daging Ikan Bersifat Non-Destructive Pada Aplikasi Mobile Menggunakan YOLOv4 dan YOLOv4-Tiny Malik Abdul Aziz; Dhomas Hatta Fudholi; Arrie Kurniawardhani
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 4 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i4.5885

Abstract

Fish is one of the ingredients for consumption that provides high-quality protein and can help form a healthy lifestyle. The perishable quality of fish meat demands that consumers be smart in sorting out the fish to be consumed. Therefore, knowledge about the freshness condition of fish meat is important for consumers. This study tries to build a mobile-based application that applies the Deep Learning model, using architecture You Look Only Once (YOLOv4) and YOLOv4-Tiny to detect the freshness level of fish based on the eyes and skin of the fish. The level of freshness used is fresh, medium, and spoil. The dataset used by the model are images of Deho tuna fish (Euthynnus Affinis), Manglah fish (Priacanthus Tayenus), Solok fish (Rastrelliger Brachysoma), Mackerel fish (Scomber Australasicus), Kuwe Lilin fish (Caranx Melanophygus), Teribang fish (Nemipterus virgatus), Banyar fish (Restrelliger Kanagurta), and Kolong fish (Atule mate). The mAP achieved by YOLOv4 is 99.17% and YOLOv4-Tiny is 97.25%. The fastest processing time, the freshness level of fish meat in the application reaches 2.5 seconds per image for YOLOv4 and 0.15 seconds for YOLOv4-Tiny.
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.
Context-Aware Untuk Natural Language Processing Services Menggunakan Arsitektur Microservices Abdullah Aziz Sembada; Dhomas Hatta Fudholi; Raden Teduh Dirgahayu
Cakrawala Repositori IMWI Vol. 6 No. 1 (2023): Cakrawala Repositori IMWI
Publisher : Institut Manajemen Wiyata Indonesia & Asosiasi Peneliti Manajemen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52851/cakrawala.v6i1.163

Abstract

Berkembangnya era industri 4.0 dan big data membuat Natural Language Processing banyak dibutuhkan terutama saat melakukan preprocessing data. Dengan adanya Natural Language Processing service agar bisa mempermudah peneliti dalam melakukan penelitian karena beberapa kebutuhannya sudah disediakan. Sebelum menyediakan service-service Natural Language Processing terlebih dahulu melakukan perancangan sistem. Sistem di buat menggunakan microservice architecture, microservice dipilih karena memiliki karakteristik flexible, aman, error terisolasi sangat memudahkan dalam melakukan pengembangan sistem. Dalam sistem ini ditambahkan fitur Context-Aware untuk memudahkan pengguna dalam mengolah data. Tujuan dari penelitian adalah mampu mengintegrasikan Context-Aware ke dalam sistem Natural Language Processing service sehingga sistem mampu memberikan rekomendasi algoritma yang paling tempat dari data yang dimiliki pengguna. Hasil pengujian sistem menunjukan bahwa Natural Language Processing service dapat mempersingkat penelitian tentang natural language processing. Hasil tersebut tidak lepas dari fitur context-aware yang dapat memnentukan jenis file atau data yang di-input pengguna, dengan demikian pengguna langsung diarahkan oleh sistem untuk memproses file atau data tersebut dengan algoritma clustering atau classification. Implementasi microservices juga sangat membantu dalam pengembangan terutama penambah service atau algoritma tidak akan mengganggu service yang sudah ada.
Mental Health Prediction Model on Social Media Data Using CNN-BiLSTM Abdurrahim; Dhomas Hatta Fudholi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 1, February 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i1.1849

Abstract

Social media has transformed into a global platform for expression and interaction where users can share photos, images, and videos. The rapid development and widespread use of social media afford the opportunity to analyze the construction of social life in societies and communities. As a result of alterations in lifestyle during the COVID-19 pandemic, mental health disorders increased. Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Natural language processing and analysis methods are required to address this complexity. The classification of mental health-related texts, which can serve as early warnings and early diagnoses, is facilitated by analytical and natural language processing techniques. In this investigation, a CNN-BiLSTM model was utilized, which was aided by a FastText-based word weighting method. The utilized data set consists of texts on mental health with labels such as borderline personality disorder (BPD), anxiety, depression, bipolar, mentalillness, schizophrenia, and poison. There are 35000 training records and 6108 test records. The data will undergo a data cleansing procedure, which will include lower text stages, number removal, reading mark removal, and stopword removal. Modeling with CNN-BiLSTM and FastText weighting yielded an F1-Score and accuracy of 85% and 85%, respectively. In comparison to the Bi-LSTM model, the F1-Score and accuracy were both 83%.
BASIS DATA GRAF NEO4J PADA SILSILAH KELUARGA LAILA KUSUMA WARDANI; DHOMAS HATTA FUDHOLI
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 1 (2024): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i1.46397

Abstract

Perubahan sosial masyarakat Indonesia dipengaruhi oleh peran media sosial di era globalisasi saat ini. Fenomena yang terjadi, terutama di kalangan remaja, menunjukkan bahwa kebersamaan keluarga dan pengetahuan informasi semakin berkurang. Ini berarti bahwa seseorang tidak tahu sejarah keluarganya. Untuk menyelesaikan masalah tersebut, perancangan basis data graf diperlukan untuk menyimpan data keluarga. Dalam penelitian ini, teknologi basis data graf Neo4j dianggap sebagai alat yang efektif untuk menampilkan dan menyimpan banyak data kompleks. Untuk membuat properti dalam basis data, informasi perlu dikumpulkan. Informasi ini berasal dari buku, jurnal, dan wawancara dengan orang-orang di bidang yang relevan. Selanjutnya, data diekstrak dan digabungkan dengan data anggota keluarga. Pada graf, satu anggota keluarga merepresentasikan simpul dan hubungan antar anggota keluarganya merepresentasikan sisi. Sehingga, implementasi dan visualisasi graf yang menggambarkan simpul dari tiap-tiap anggota keluarga dan ragam relasi antar anggota keluarga akan didapatkan sebagai hasil akhir dari penelitian ini.
ASPECT-BASED SENTIMENT ANALYSIS ON TWITTER TWEETS ABOUT THE MERDEKA CURRICULUM USING INDOBERT Andi Wafda; Dhomas Hatta Fudholi; Jaka Nugraha
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5692

Abstract

The curriculum has changed once again with the introduction of the Merdeka Curriculum to address learning loss in the education sector. Its implementation has elicited various responses, such as support for granting teachers the freedom to innovate, focusing on essential materials, offering diverse learning methods, and fostering student creativity. However, criticism has also arisen, including issues related to teachers’ lack of understanding, parents' concerns, and the increased workload on students due to numerous projects. To improve educational policies, an in-depth analysis of these responses is essential. This study aims to analyze public sentiment toward the Merdeka Curriculum by applying Aspect-Based Sentiment Analysis (ABSA) using data from Twitter. The research focuses on four main aspects: Teaching Modules (MA), Education Reports (RP), the Merdeka Teaching Platform (PMM), and the Strengthening of the Pancasila Student Profile Projects (P5). Data were collected using specific and relevant keywords for each aspect, followed by preprocessing, labeling, and filtering based on sentiment and aspect. The final dataset comprised 2,359 valid tweets. The ABSA model was developed using IndoBERT with fine-tuning, then tested and evaluated. The results showed that the aspect classification model achieved an accuracy of 97%, F1 score of 97%, recall of 97%, and precision of 97%. Meanwhile, the sentiment classification model achieved an accuracy of 85%, F1 score of 85%, recall of 85%, and precision of 85%. This ABSA model is expected to assist in monitoring public responses and provide valuable insights for policy development, particularly within the context of the Merdeka Curriculum.
Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning Wahyuzi, Zikri; Ahmad Luthfi; Dhomas Hatta Fudholi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5530

Abstract

This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Using a data set that encompasses electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set, particularly when comparing electricity usage during work hours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broader applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improving energy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability.
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 Chatarina Umbul Wahyuni 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 windi astriningsih Yasmin Aulia Ramadhini Yoga Sahria Yudi prayudi Zikri Wahyuzi