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Penerapan Ontologi Bidang Medis dalam Sistem Pelayanan Kesehatan Fudholi, Dhomas Hatta
Seminar Nasional Informatika Medis (SNIMed) 2017
Publisher : Magister Teknik Informatika, Universitas Islam Indonesia

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

Ontologi merepresentasikan pengetahuan dengan mendeskripsikan dengan tepat, formal, dan kaya, konsep-konsep dalam sebuah bidang beserta relasi-relasinya. Tujuan dari memodelkan pengetahuan dalam bentuk ontologi adalah untuk mendapatkan pengetahuan umum yang dapat dibagikan dan dimengerti oleh manusia dan mesin. Di dalam dunia medis, kamus istilah-istilah khusus medis dibangun untuk menyimpan dan mengkomunikasikan pengetahuan medis serta informasi pasien. Sistem informasi medis harus dapat mengkomunikasikan data medis yang kompleks dan mendetail secara efisien. Oleh karena itu, ontologi hadir untuk merepresentasikan terminologi dalam dunia medis. Makalah ini bertujuan untuk memberikan pembahasan akan gambaran pendekatan penerapan ontologi dalam sistem layanan kesehatan yang berupa model informasi cerdas untuk manajemen pengetahuan medis, dan ontologi sebagai basis pengetahuan dalam sistem pengayaan pengetahuan, rekomendasi, dan pendukung keputusan. Hal ini diharapkan mampu mengakselerasi pertumbuhan sistem layanan kesehatan dengan kecerdasan dan kepakaran serta meningkatkan kemandirian masyarakat dalam penjagaan kesehatan.

PENGGUNAAN METODE STATIS DAN LIVE FORENSIK PADA UAV UNTUK MENDAPATKAN BUKTI DIGITAL Arrochman, Ibnu Fajar; Fudholi, Dhomas Hatta; Prayudi, Yudi
ILKOM Jurnal Ilmiah Vol 11, No 2 (2019)
Publisher : Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

In recent years, the use of drones by civilians is increasing rapidly by the presentation of total sales continued to increase rapidly every year. With the increasing possibility of Unmanned Aerial Vehicle (UAV) abuse, crime in the use of UAVs to be larger. Through forensic analysis of data using static forensic and live forensic to obtain data that allows it to be used as digital evidence. To dig up information that could be used as digital evidence in the UAV and controllers, as well as to know the characteristics of digital evidence on a UAV. The results showed that digital evidence on a UAV, the smartphone is used as a controller UAV has a very important role in the investigation. The findings in aircraft has a percentage of 50% and a camera memory card with 16.6%. DJI Phantom 3 Advanced GPS coordinates always store data in flight LOG; the data is always stored even when the flight mode is used does not use GPS signals to stability. Due to DJI Phantom 3 Advanced always use GPS on flights, file, image or video captured by the camera has the best GPS location coordinates to the metadata therein.

Pemodelan Pengetahuan Graph Database Untuk Jejaring Penelitian Kesehatan di Indonesia Sahria, Yoga; Fudholi, Dhomas Hatta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 4, No 3 (2020): Juli 2020
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v4i3.2183

Health research is research approved in the medical field. Health research in Indonesia is increasingly being approved by researchers in Indonesia, therefore research is stored in repositories. Many health research journal repositories are available, but there is minimal research for analysis and modeling in the health research network in Indonesia. This research proposes a knowledge modeling with Neo4j graphic database to implement it. In this study, data obtained from the SINTA Journal with web scraping techniques. The purpose of this research is to produce network knowledge using CQL (Chyper Query Language) which is useful in the medical field. The results of this study are expected to be useful for the government, academics, researchers, or the public for health research network researchers in Indonesia. The results of research testing modeling of health research network knowledge using a graph database of 95.2% said very good

Topic Modelling pada Sentimen Terhadap Headline Berita Online Berbahasa Indonesia Menggunakan LDA dan LSTM Chairullah Naury; Naury, Chairullah; Fudholi, Dhomas Hatta; Hidayatullah, Ahmad Fathan
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.v5i1.2556

The online mass media is the source of the fastest and up-to-date information. A model that can provide mapping will help in sorting out information more precisely. In this study, the authors applied topic modeling to the results of sentiment analysis on online news headlines in Indonesian. Sources of data in this study were obtained from online mass media in Indonesian. The data collected were analyzed for sentiment using the Long Short-term Memory (LSTM) method, in order to obtain news headlines with positive, negative, and neutral sentiments. The classification obtained from the results of the sentiment analysis process is continued with the topic modeling process using the Latent Dirichlet Allocation (LDA) method and visualized in the form of wordcloud and intertopic distance map (pyLDAVis) to determine the relationship between one topic and another. The result of sentiment analysis is a model with 71.13% of accuracy level and the results of topic modeling are in the form of some topics that are easy to interpret.

Deep Learning-based Mobile Tourism Recommender System Fudholi, Dhomas Hatta; Rani, Septia; Arifin, Dimastyo Muhaimin; Satyatama, Mochamad Rezky
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29262

A tourism recommendation system is a crucial solution to help tourists discover more diverse tourism destinations. A content-based approach in a recommender system can be an effective way of recommending items because it looks at the user's preference histories. For a cold-start problem in the tourism domain, where rating data or past access may not be found, we can treat the user's past-travel-photos as the histories data. Besides, the use of photos as an input makes the user experience seamless and more effortless. The current development in Artificial Intelligence-based services enable the possibilities to implement such experience. This research developed a Deep Learning-based mobile tourism recommender system that gives recommendations on local tourism destinations based on the user's favorite traveling photos. To provide a recommendation, we use cosine similarity to measure the similarity score between one's pictures and tourism destination's galleries through their label tag vectors. The label tag is inferred using an image classifier model that runs from a mobile user device through Tensorflow Lite. There are 40 label tags, which refer to local tourism destination categories, activities, and objects. The model is trained using state-of-the-art mobile deep learning architecture EfficientNet-Lite.Â We did several experiments and got an accuracy result of more than 85% on average, using EfficientNet-Lite as the base architecture. The implementation of the system as an Android application has been proved to give an excellent recommendation with Mean Absolute Percentage Error (MAPE) equals to 5%.

Towards an Effective Tuberculosis Surveillance in Indonesia through Google Trends Fudholi, Dhomas Hatta; Fikri, Khairul
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 4, November 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i4.1114

Background. The search digital footprint, such as in Google Trend (GT), forms a large dataset that is suitable to be used as surveillance data and supports early warning systems. These advantages become great opportunities for disease surveillance agencies in Indonesia to get rapid early disease monitoring. Objective. Due to limited research in this area and the increasing level of internet penetration in Indonesia, a further study is needed in disease monitoring by utilizing Google Trends. In this research, we explore, analyze and create a set of the best search terms to be used in utilizing GT for disease surveillance in Indonesia, especially Tuberculosis. Method. We use correlation as the technique to define the relatedness between the real case data and GT results. We collect data from the Ministry of Health of Indonesia. From the data, we design a set of new search terms to take GT trend data. The collected data is analyzed using the Pearson correlation. Result. The analysis shows that the studied search terms give strong positive relationships between GT trend data and Tuberculosis cases number in Indonesia. From the correlation analysis, we get a set of proposed effective search terms with the highest score equals to 0.907. Conclusion. Finally, it is possible to monitor and make quick surveillance in tuberculosis in Indonesia through Google Trend and we have created a novel set of search terms that can be used as the basis in monitoring other diseases in Indonesia

Deep Learning-based Mobile Tourism Recommender System Fudholi, Dhomas Hatta; Rani, Septia; Arifin, Dimastyo Muhaimin; Satyatama, Mochamad Rezky
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.29262

Purpose: This study developed a deep learning-based mobile travel recommendation system that provides recommendations for local tourist destinations based on users' favorite travel photos. To provide recommendations, use cosine similarity to measure the similarity score between a person's image and a tourism destination gallery through the tag label vector. Label tags are inferred using an image classifier model run from a mobile user device via Tensorflow Lite. There are 40 tag labels that refer to categories, activities and objects of local tourism destinations.Â Methods: The model is trained using state-of-the-art mobile deep learning architecture EfficientNet-Lite, which is new in the domain of tourism recommender system.Â Result: This research has conducted several experiments and obtained an average model accuracy of more than 85%, using EfficientNet-Lite as its basic architecture. The implementation of the system as an Android application is proven to provide excellent recommendations with a Mean Absolute Percentage Error (MAPE) of 5.2%.Â Novelty: A tourism recommendation system is a crucial solution to help tourists discover more diverse tourism destinations. A content-based approach in a recommender system can be an effective way of recommending items because it looks at the user's preference histories. For a cold-start problem in the tourism domain, where rating data or past access may not be found, we can treat the user's past-travel-photos as the histories data. Besides, the use of photos as an input makes the user experience seamless and more effortless.Â

Model Pengetahuan Berbasis Ontologi pada Domain Big Data di Perguruan Tinggi Fahmi, Yunizar; Fudholi, Dhomas Hatta
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3424

The big data phenomenon is also having an impact on the higher education sector. The readiness of education sector is non-negotiable for the face of the big data era so that organizations are able to adapt. Thus, knowledge to utilize big data needs to be owned by higher education institutions in order to innovate. Basically, there are quite a lot of studies that produce models as knowledge to take advantage of big data. However, reviewing the research that has been done previously, it is also necessary to conduct research that discusses the development of models to represent knowledge about big data analytics in higher education institutions. Knowledge that has been represented as a knowledge model can then be interpreted and stored as a knowledge base in a knowledge management system so that knowledge can be managed and utilized better. Based on these problems, ontology can be used to model knowledge. This research will develop an ontology-based knowledge model related to the use of big data in the education sector, especially higher education based on the concepts of analytics, data sources, and platforms. The ontology modeling process in this study is divided into 3 (three) phases: 1) Conceptualization; 2) Implementation; and 3) Evaluation. Based on the evaluation results of ontology measurements using schema metrics, the RR (Relationship Richness) value is 0.62, meaning that the ontology model contains a variety of information; the IR (Inheritance Richness) value is 1.78, meaning that the ontology model belongs to a fairly specific category, and the AR (Attribute Richness) value is 0.06, meaning that the information contained can be improved further. Based on the results of the query test analysis, the ontology is considered capable of retrieving the knowledge stored in it accurately based on validation that adjusts the query results with the ontology.

Klasifikasi Emosi pada Teks dengan Menggunakan Metode Deep Learning Bagus W, Alan Tusa; Fudholi, Dhomas Hatta
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : CV. Ridwan Publisher

Emosi merupakan reaksi dari tubuh manusia ketika mendapatkan reaksi terhadap sesuatu yang dapat disampaikan secara nonverbal yaitu dengan menunjukan ekspresi wajah ataupun perilaku. Dalam dunia digital, lebih tepatnya media sosial yang berbasiskan teks, manusia mengekspresikan emosinya melalui teks dan perlu dilakukan analisa untuk mengetahui emosi yang disampaikan oleh orang tersebut. Untuk mengatasi masalah diatas, perlu melakukan metode klasifikasi pada kalimat-kalimat opini untuk mengetahui jenis emosi yang ada dalam teks tersebut, lalu emosi tersebut dikelompokan menjadi sembilan kelas emosi yaitu marah, takut, sedih, netral, bahagia, tertarik, percaya, kaget, dan jijik. Semua jenis emosi tersebut dapat mewakilkan emosi pada manusia. Metode yang diterapkan pada penelitian ini adalah metode klasifikasi menggunakan BERT (Bidirectional Encoder Represantions from Tranformers). Dataset yang digunakan adalah data dari opini-opini seseorang di twitter yang dikumpulkan menjadi satu kedalam file csv. Klasifikasi emosi menggunakan BERT menghasilkan nilai akurasi sebesar 89.2% dengan sembilan kelas emosi pada data training, dan menghasilkan akurasi 76 % pada metode indoBERT

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