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All Journal TEKNIK INFORMATIKA JUITA : Jurnal Informatika JOIV : International Journal on Informatics Visualization Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JSAI (Journal Scientific and Applied Informatics) Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Jurnal Teknik Informatika (JUTIF) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT JPMTT (Jurnal Pengabdian Masyarakat Teknologi Terbarukan)
Sudianto
Institut Teknologi Telkom Purwokerto
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering Public Health Other

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Journal : Journal of Dinda : Data Science, Information Technology, and Data Analytics

 1 
Prediction of Covid-19 Cases in Central Java using the Autoregressive (AR) Method Tangguh Widodo; Siti Maghfiroh; Surya Haganta Brema Ginting; Alif Aryaputra; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.740

Abstract

Since the beginning of the Covid-19 case in Indonesia in March 2020, more than 6 million confirmed cases had been confirmed. The rapid development of this case can be accessed through the covid19.go.id page. In Central Java province, confirmed cases as of July 6, 2022, reached 628,393 people, with the number of recovered patients reaching 594,783 people and the number of patients dying as many as 33,215 people. With this data, a prediction is needed to help the government anticipate an increase in Covid-19 cases in Central Java Province. This study aims to create a forecasting model using the Autoregressive (AR) method by optimizing the function parameters. Then Mean Squared Error (MSE) to analyze the results of forecasting data errors. The results are the best parameter functions on AR (30) with the smallest MSE. Furthermore, predictions are made from July 1 to August 30, 2022, showing an increase in cases
Diabetes Diagnostic Expert System using Website-Based Forward Chaining Method Tiara Khumaira Putri; Mahda Laina Arnumukti; Khusnul Khatimah; Egidya Zalsabila; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.752

Abstract

Diabetes is a chronic disease. The World Health Organization predicts that Indonesia's number of diabetic patients will continue to increase significantly to 16.7 million in 2045. As early prevention, early diagnosis is needed to anticipate more severe diabetes. This study aims to build an expert system for detecting diabetes using a web-based forward chaining method. The expert system is built by collecting indications from experts by collecting facts using the forward chaining method. Furthermore, judging by the unhealthy lifestyle of many people who consult with hospitals or health workers. From the results obtained, the system can work well based on knowledge from experts
An Expert System for Diagnosing the Impact of Traffic Accidents using the Forward Chaining Method Akbar Maulana Yusuf; Jonathan Indra Chelidivano; Tavany Amalia Rizky; Yanuar Sabikhi; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 1 (2023): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i1.767

Abstract

Unexpected events that we often hear about are traffic accidents caused by many factors. Accidents also cause impacts in terms of health. This study aims to provide information regarding the effects of traffic accidents in terms of health based on some visible symptoms that emerged from the victim's body at the scene using an expert system. The Expert System is designed on a website-based application. The forward chaining method is used to get a conclusion based on the facts. The results of this research users gain knowledge about the impact of traffic accidents and the diagnosis on the victim's body that is close to the knowledge of experts with accuracy 87.5%. The website is designed to be used as a guide for users to be able to provide appropriate first aid to accident victims.
Dominant Requirements for Student Graduation in the Faculty of Informatics using the C4.5 Algorithm Alvina Tahta Indal Karim; Sudianto Sudianto
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 3 No 2 (2023): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v3i2.1040

Abstract

Graduating on time is one of the indicators in the achievement and ranking of educational institutions. The achievement of graduating on time in educational institutions is essential to balance incoming and graduating students. The problem that occurs, the attributes for graduating on time have varying weightings, so the determinants of the attributes for passing on time need to be known so that the anticipation of achieving graduation on time can be met. The purpose of this study is to find out the dominant attributes in the prediction of graduating on time for students. The attributes used are credit scores (Semester Credit Units), GPA scores (Grade Point Average), and English scores (TOEFL). The method used is the C4.5 Algorithm which is one of the classification methods in data mining. The data used was 262 data, split randomly with a composition of training and testing data of 80:20. Data is processed using the data mining process by creating decision trees. The decision tree results using the C4.5 Algorithm show that the GPA value is the most influential attribute in predicting a student's graduation time. In addition, predictions based on the decision tree of the C4.5 Algorithm with criterion = 'gini' and max_depth = 5 showed an accuracy result of 77%.
Co-Authors Abdul Aziz Akbar Maulana Yusuf Al Fachri, Moh Aminullah Aldean, Muhammad Yusril Alif Aryaputra Alvina Tahta Indal Karim Andini Mulia Kusumaningrum Asti Dwi Sripamuji Ayuningtyas, Puji Bagas Prihatnowo Cahyo Prihantoro Chandra Ayunda Apta Soemedhy Dedy Agung Prabowo Dwi Puspa Martiyaningsih Egidya Zalsabila Hapsari Warih Utami Hasna Nur Hanifah Hermanto, Agyl Restu Hikari Adriansyah Hilmawan, Muhammad David Imada Riski Ramadhanti Jonathan Indra Chelidivano Juan Arton Arton Masheli Julian Saputra Khusnul Khatimah Lasama, Jerry LILIK BUDIPRASETYO M Yoka Fathoni Mahda Laina Arnumukti Martin Parhusip Muhammad Hilmi Kafabi Muhammad Rahaji Jhaerol Naden, Yoga Naufal, Adzan Bari Nawang Anggita Winanti Nicolaus Euclides Wahyu Nugroho Nora Trivetisia Nursatio Nugroho Puspa Wahyuningtias Rafi Wika Ananda Rumpoko Ramadhani, Rafian Reni Dyah Wahyuningrum Reni Dyah Wahyuningrum Risa Riski Amalia Rizki Mugi Setya Adi Sandhy Fernandes Satria Nur Saputro Satria, Muhammad Adhan Hady Siti Khomsah, Siti Siti Maghfiroh Surya Haganta Brema Ginting Tangguh Widodo Tavany Amalia Rizky Tiara Khumaira Putri Tri Ginanjar Laksana Tri Wulandari Utami Uli Ahda Raihan Ummi Athiyah Widi Afandi Yanuar Sabikhi Yehezkiel Nicholas Adanson Yeni Herdiyeni Zarkasih Akhmad