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All Journal Ahkam: Jurnal Ilmu Syariah SMATIKA BAREKENG: Jurnal Ilmu Matematika dan Terapan Petir IJISTECH (International Journal Of Information System & Technology) Journal of Information System, Applied, Management, Accounting and Research EVOLUSI : Jurnal Sains dan Manajemen Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Ilmu Teknik dan Komputer ICIT (Innovative Creative and Information Technology) Journal REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Inovasi Informatika IJISTECH Jurnal Manajemen Informatika Jayakarta Jurnal Interkom : Jurnal Publikasi Ilmiah Bidang Teknologi Informasi dan Komunikasi ABDINE Jurnal Pengabdian Masyarakat IIJSE Jurnal Inovasi Global Jurnal Maisyatuna
Budi Sudrajat
Universitas Bina Sarana Informatika
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Journal : IJISTECH

 1 
Application of Naive Bayes Method For Diagnosis of Pregnancy Disease Embun Fajar Wati; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 1 (2022): June
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v6i1.216

Abstract

The risk of pregnancy can be known by early detection of pregnancy with risk factors, so that health workers can know more about treatment. In diagnosing a disease in the field of medicine requires tools such as the application of artificial intelligence, one of which is an expert system. One method that can be applied in expert systems is naive bayes. In this study, naive bayes for the process of diagnosing the disease during pregnancy was done by including symptoms that appear in pregnant women. The stage of research is the collection of data from previous research journal articles with the same theme, but different methods and other journal articles with the same theme and different from the naive bayes method. The next stage is data analysis with naive bayes calculations of patient symptoms and validation, namely comparing the results of naive bayes calculations with expert calculations. The results obtained were 14 patients out of 20 patients, which is 70% have the same results between experts with calculations with naive bayes. The results showed that the calculation of symptoms with naive bayes was sufficient to give valid and feasible results to use
Pregnancy Disease Diagnostic Expert System With Certainty Factor Method Embun Fajar Wati; Elvi Sunita Perangin-Angin; Budi Sudrajat
IJISTECH (International Journal of Information System and Technology) Vol 6, No 6 (2023): April
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/.v6i6.292

Abstract

Lack of knowledge and information about diseases in pregnancy can delay pregnant women from knowing there are diseases in their pregnancy. Diseases that attack a woman's womb need to be examined by an expert, while experts for this disease are still rare and require a lot of money. In order for the initial diagnosis to be carried out by pregnant women, a solution is proposed in the form of an expert system for diagnosing pregnant women's diseases using the Certainty Factor (CF) method based on the symptoms felt by pregnant women. The research stages used in this study used 4 steps, namely data collection consisting of disease data and symptom data, disease data and symptoms, as well as patient data, symptoms and weights, data analysis using the certainty factor method, validation and evaluation. Diagnostic results that are not in accordance with the CF calculation of around 37.5%, while the results in accordance with the CF calculation were 11 patients or 62.5%.
Modelling of C4.5 Algorithm for Graduation Classification Wati, Embun Fajar; Sudrajat, Budi; Nasution, Raudah
IJISTECH (International Journal of Information System and Technology) Vol 8, No 1 (2024): The June edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i1.345

Abstract

Student admissions in universities every year become a routine thing to do, some even do student admissions every semester. That way, the number of students will continue to grow. Especially if there are students who graduate late, it will increase the number of students in the university. There are many things that can affect graduation, namely personal data (gender, age, marital status, job status) and academic data (grade). Before making a decision, universities must analyze the number of students and the factors that most influence student graduation. Analysis by classifying graduation using C4.5 algorithms. The research method used consists of selection to ensure the data used in the KDD process is appropriate and quality data. Then preprocessing by means of data cleaning, data reduction, and data normalization. The next method is transformation for age attributes to young and old, grade attributes to large and small. The last method is C4.5 algorithm modeling with rapid miner and evaluation. Through the calculation process using the classification method and C4.5 algorithm with the attributes described earlier, the results were obtained that the accuracy of the graduation classification reached 97.00%, the precision value was 91.79%, and the recall value was 100.00%, and the AUC value was 0.978. This means that the model has a very high level of accuracy and has an excellent ability to separate samples from both target classes.
Co-Authors Ade Mahmud Adytya, I Gusti Ngurah Afit Muhammad Lukman - STMIK Nusa Mandiri Jakarta, Afit Muhammad Lukman Agus Yulianto Arief Fadholi Asep Saefullah Azim, Muhamad Fadli Catur Eko Purnomo Darrusalam, Muhammad Dede Burhanudin, Dede Didin Nurul Rosidin Eko Budianto Elvi Sunita Perangin-angin Embun Fajar Wati Fahlepi Roma Doni Febri Putriana Firmansyah Firmansyah Hamsyiah, Nurmaita Hasta Herlan Asymar Herlan Asymar, Hasta Irawan, Wahri Jajuli, M. Sulaeman Jaya, Tri Ika Mareanus Lase Masmedia Pinem Maulana, Dika Mike Indarsih, Mike Muhammad Darrusalam Muhammad Lukman, Afit Mulia Rahmayu Munahar Muktar Nurman Kholis, Nurman Purwoko, Agus Puspita, Cici Ramdhan Fazrianto Suwarman Raudah Nasution Roma Doni, Fahlepi Salsabila, Sayyid Saraswati, Henny Syibromalisi, Arif Syifa, Dhe Salsabila Tatu Zakiyatun Nufus Tri Ika Jaya Wahyu Hidayat Wida Prima Mustika Wigandi, Dikdik Permana Zebua, Elnita