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Case Based Reasioning (CBR) for Medical Question Answering System Basuki, Setio; Rizky, Alfira; Wicaksono, Galih Wasis
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 2, May-2018
Publisher : Universitas Muhammadiyah Malang

In this research, the researchers implement a medical Question Answering System (QAS), a complaint system in the form of sentences or paragraphs of questions about the complaint (illness) suffered by a person. Afterwards, the system will give answer to the questions with answers in the form of diagnosis based on the system knowledge. The system in this study has knowledge of the system obtained based on Case Based Reasoning (CBR) method from the previous cases stored in the database. When there is a new case, the system will perform a matching process using CBR and Sorenson Coefficient calculations to find out which the previous cases have the highest percentage of matches with the new case. Then the selected previous cases will be taken and given to the new case. Testing is processed by using 2 types of testing, expert validation testing with result of 28 data of appropriate test from 30 test data and accuracy testing resulting of 93,33% from the appropriate test data.

Hand Hygiene Compliance Behavior and Glove Use in the Pediatric Intensive Care Unit During COVID-19 Pandemic Bangkit Putrawan; Dominicus Husada; Parwati Setiono Basuki; Risa Etika; Ismoedijanto; Dwiyanti Puspitasari; Leny Kartina
Indian Journal of Forensic Medicine & Toxicology Vol. 15 No. 4 (2021): Indian Journal of Forensic Medicine & Toxicology
Publisher : Institute of Medico-legal Publications Pvt Ltd

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37506/ijfmt.v15i4.16811

Background: The condition of Covid-19 pandemic potentially influences hand hygiene compliance as aresult of workload changes, increased awareness of healthcare workers (HCWs) and personal protectiveequipment procedures, especially in terms of hand glove use,Objective to know the adherence of healthcare workers at the pediatric intensive care unit (PICU) to complywith hand hygiene and glove use during the pandemic.Methods An observational prospective study was carried out including all HCWs stationed at the PICU DrSoetomo General Hospital during January 2021. All participants were to sign an informed consent beforethe study took place. A target of 500 opportunities was estimated during the observation, and recorded usinginfra-red cameras placed at ten points. Hand hygiene compliances were evaluated according to the videosurveillance records by an independent auditor. Compliance was measured by dividing total number ofobserved appropriate hand hygiene by the sum of opportunities. Data were analysed using Chi Square testat a significance of p<0.05.Results: A total of 28 HCWs were eligible for the study; 9 were excluded. The majority were female(21; 75%), the mean age was 37.9 (SD 5.2) years. During 72 hours’ observation among 526 glove-useopportunities 104 (19.7%) actual glove-use episodes were evident. The hand hygiene compliance was lower(41.3%) when wearing gloves as compared to those with no glove use (68.2%) (p<0.001).

Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning Muhammad Irfan; Setio Basuki; Yufis Azhar
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.2344

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.

Diabetes prediction based on discrete and continuous mean amplitude of glycemic excursions using machine learning Lailis Syafaah; Setio Basuki; Fauzi Dwi Setiawan Sumadi; Amrul Faruq; Mauridhi Hery Purnomo
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2387

Chronic hyperglycemia and acute glucose fluctuations are the two main factors that trigger complications in diabetes mellitus (DM). Continuous and sustainable observation of these factors is significant to be done to reduce the potential of cardiovascular problems in the future by minimizing the occurrence of glycemic variability (GV). At present, observations on GV are based on the mean amplitude of glycemic excursion (MAGE), which is measured based on continuous blood glucose data from patients using particular devices. This study aims to calculate the value of MAGE based on discrete blood glucose observations from 43 volunteer patients to predict the diabetes status of patients. Experiments were carried out by calculating MAGE values from original discrete data and continuous data obtained using Spline Interpolation. This study utilizes the machine learning algorithm, especially k-Nearest Neighbor with dynamic time wrapping (DTW) to measure the distance between time series data. From the classification test, discrete data and continuous data from the interpolation results show precisely the same accuracy value that is equal to 92.85%. Furthermore, there are variations in the MAGE value for each patient where the diabetes class has the most significant difference, followed by the pre-diabetes class, and the typical class.

Implementasi Teknik Seleksi Fitur Pada Klasifikasi Malware Android Menggunakan Support Vector Machine Hendra Saputra; Setio Basuki; Mahar Faiqurahman
Fountain of Informatics Journal Vol 3, No 1 (2018): Mei
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v3i1.1875

Android Malware has grown significantly along with the advance of the times and the increasing variety of technique in the development of Android. Machine Learning technique is a method that now we can use in the modeling the pattern of a static and dynamic feature of Android Malware. In the level of accuracy of the Malware type classification, the researcher connect between the application feature with the feature required by each type of Malware category. The category of malware used is a type of Malware that many circulating today, to classify the type of Malware in this study used Support Vector Machine (SVM). The SVM type will be used is class SVM one against one using the RBF Kernel. The feature will be used in this classification are the Permission and Broadcast Receiver. To improve the accuracy of the classification result in this study used Feature Selection method. Selection of feature used is Correlation-based Feature Selection (CFS), Gain Ratio (GR) and Chi-Square (CHI). A result from Feature Selection will be evaluated together with result that not use Feature Selection. Accuracy Classification Feature Selection CFS result accuracy of 90.83%, GR and CHI of 91.25% and data that not use Feature Selection of 91.67%. The result of testing indicates that permission and broadcast receiver can be used in classifying type of Malware, but the Feature Selection method that used have accuracy is a little below the data that are not using Feature Selection.

Transient Blindness in A Child with Dengue Shock Syndrome Irwanto Irwanto; Soegeng Soegiyanto; Parwati Setiono Basuki; Wisnujono Soewono; Diany Yogiantoro
Paediatrica Indonesiana Vol 37 No 5-6 (1997): May - June 1997
Publisher : Indonesian Pediatric Society

Dengue hemorrhagic fever (DHF) is characterized by acute fever associated With haemorrhagic diathesis and tendency to develop fatal shock (dengue shock syndrome). Signs and symptoms of DHF are generally secondary to plasma leakage and hemorrhage. Dengue shock syndrome (DSS) is a severe clinical manifestation of DHF usually as a consequence of severe plasma leakage. During tire last 5 years, there have been reports of DHF patients with unusual manifestations, including some patients with central nervous system (CNS) involvement We report a 5 year old DHF patient who experienced transient blindness, which was the first case found in Soetomo Hospital, Surabaya.

Case Based Reasioning (CBR) for Medical Question Answering System Setio Basuki; Alfira Rizky; Galih Wasis Wicaksono
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 3, No 2, May-2018
Publisher : Universitas Muhammadiyah Malang

In this research, the researchers implement a medical Question Answering System (QAS), a complaint system in the form of sentences or paragraphs of questions about the complaint (illness) suffered by a person. Afterwards, the system will give answer to the questions with answers in the form of diagnosis based on the system knowledge. The system in this study has knowledge of the system obtained based on Case Based Reasoning (CBR) method from the previous cases stored in the database. When there is a new case, the system will perform a matching process using CBR and Sorenson Coefficient calculations to find out which the previous cases have the highest percentage of matches with the new case. Then the selected previous cases will be taken and given to the new case. Testing is processed by using 2 types of testing, expert validation testing with result of 28 data of appropriate test from 30 test data and accuracy testing resulting of 93,33% from the appropriate test data.

Analisis Sentimen Data Kritik Dan Saran Pelatihan Aplikasi Teknologi Informasi Menggunakan Algoritma Support Vector Machine Alimuddin Hasan Al Kabir; Setio Basuki; Galih Wasis Wicaksono
Jurnal Repositor Vol 1 No 1 (2019): November 2019
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v1i1.11

Opini publik merupakan salah satu instrumen yang bisa digunakan untuk melakukan evaluasi terhadap suatu kegiatan. Penelitian ini dilandasi oleh beberapa masalah diantaranya (1) perlunya peningkatan kualitas pelaksanaan kegiatan Pelatihan Aplikasi Teknologi Informasi, (2) opini peserta yang sudah ditampung belum dimanfaatkan secara maksimal karena terkendala banyaknya data dan tidak mungkin di analisis secara manual. Kritik dan saran diambil dari data periode pelaksanaan 2016/2017 sebanyak 1050 data. Support Vector Machine digunakan sebagai metode dalam analisis sentimen. Proses latih data akan menghasilkan hyperplane terbaik yang dijadikan acuan untuk menentukan kelas sentimen mana sesuai untuk suatu kalimat. Pengujian dilakukan dengan membagi dataset ke dalam data uji sebanyak 20% dan data latih sebanyak 80% sehingga bisa dilakukan proses analisis hingga 5 kali iterasi dengan susunan data yang berbeda. Hasil pengujian menunjukkan perhitungan Akurasi, Precision, Recall, dan F-Measure yang dihasilkan oleh sistem adalah sebesar 82,08%, 83,42%, 81,16%, dan 81,82%.

Prediksi Diagnosa Berdasarkan Data Rekam Medis Pasien Menggunakan Support Vector Regression Muhammad Nasrul Tsalatsa Putra; Agus Eko Minarno; Setio Basuki
Jurnal Repositor Vol 2 No 4 (2020): April 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v2i4.46

Rekam medis merupakan suatu berkas dari hasil pemeriksaan kesehatan, pengobatan yang diberikan, tindakan, dan pelayanan lain yang telah diberikan kepada pasien. Penelitian ini dilandasi oleh beberapa permasalahan, diantaranya (1) kurangnya pengawasan, informasi, dan tidak meratanya pemberian layanan kesehatan, (2) terhambatnya perencanaan puskesmas dalam menangulangi kasus yang sudah ada atau yang sering terjadi karena tingginya jumlah dan keberagaman kasus/diagnosa yang ditemukan di masyarakat. Dari permasalahan tersebut dapat diterapkan sistem prediksi diagnosa dengan menerapkan metode Support Vector Regression (SVR). Model SVR yang diterapkan yaitu kernel Linear, kernel Polynomial, serta kernel Radial Basis Function. Pengujian dilakukan dengan membagi dataset ke dalam data uji dan data latih, kumudian dilakukan proses pengujian hingga 9-fold untuk masing-masing model dengan susunan data yang berbeda. Hasil pengujian menunjukkan fungsi kernel RBF memiliki kinerja terbaik dibanding dengan fungsi lainnya dimana nilai NRMSE tertinggi 0.0797 dan nilai akurasi terendah sebesar 0.4826. Hasil prediksi tersebut dapat memberikan sebuah gambaran dan trend mengenai diagnosa yang akan datang berdasarkan data rekam medis pasien.

Deteksi Topik Tentang Tokoh Publik Politik Menggunakan Latent Dirichlet Allocation (LDA) Faizun Nuril Hikmah; Setio Basuki; Yufis Azhar
Jurnal Repositor Vol 2 No 4 (2020): April 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v2i4.52

Twitter merupakan salah satu Social Networking yang memperbolehkan pengguna untuk mengirim dan membaca sebanyak 140 karakter. Berdasarkan survey sekitar 500 juta tweet tiap harinya yang dikirim melalui twitter. Data-data tersebut dapat berupa opini-opini publik mengenai politik, tokoh publik, makanan, dan lain sebagainya. Data tersebut akan diolah dengan teknik Topic Detection untuk menghasilkan suatu topik yang sedang marak dibicarakan masyarakat tentang tokoh publik politik. Permasalahan dalam penulisan ini yaitu, bagaimana mengekstraksi suatu tweet tentang tokoh publik politik dari pengguna Twitter. Data tweet yang diambil tentang tokoh publik politik diantaranya yaitu mengenai Joko Widodo, Basuki Tjahaja Purnama (Ahok), Anies Baswedan, Sandiaga Uno, dan Habib Rizieq Shihab. Dengan adanya data atau tweet tentang tokoh publik politik dapat diolah menggunakan metode Agglomerative untuk mengcluster tiap data yang akan digunakan sebagai topik acuan, LDA (Latent Dirichlet Allocation) yang akan berfungsi sebagai pemodelan topik dari tweet-tweet yang telah tercluster, serta TF-IDF untuk mengetahui tweet mana saja yang mengandung kata-kata dalam LDA yang akan dijadikan sebagai topik acuan. Sehingga akan menghasilkan deteksi topik yang relevan berdasarkan tweet mengenai tokoh publik politik.

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