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All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Universitas Pamulang Jurnal ICT : Information Communication & Technology Technologia: Jurnal Ilmiah Jurnal Sistem Komputer dan Informatika (JSON) SKANIKA: Sistem Komputer dan Teknik Informatika Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Sangkabira Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Academic Journal of Computer Science Research (AJCSR) Jurnal Ticom: Technology of Information and Communication Prosiding Seminar Nasional Pengabdian Kepada Masyarakat JAMAIKA: Jurnal Abdi Masyarakat Telematika MKOM Artinara
Kusumaningsih, Dewi
Teknik Informatika, Fakultas Teknologi Informasi, Universitas Budi Luhur
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
ACCREDITATION PREDICTION OF EARLY CHILDHOOD EDUCATION INSTITUTIONS USING MACHINE LEARNING TECHNIQUES Noripansyah Noripansyah; Abdul Kadir; Dewi Kusumaningsih; Haderiansyah Haderiansyah
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 3 (2023): JUTIF Volume 4, Number 3, June 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.3.999

Abstract

Accreditation is an acknowledgement of an educational institution regarding the feasibility of carrying out the educational process. Making predictions can save time for early childhood education institutions in compiling accreditation forms that will be submitted. Prediction in determining accreditation becomes an important lesson for an institution in self-assessing the quality of its services. Choosing which method to use in the accreditation prediction process becomes a serious problem, so the prediction results can be the closest or most accurate. Machine Learning is an application that is part of Artificial Intelligence which is widely used in prediction research. In this experiment, three algorithms in machine learning are tested, namely SVM, KNN and ANN. This study uses data from the accreditation results of early childhood education institutions in South Kalimantan; the sample data is 75%, and the remaining data is 25%. The results of the KNN algorithm with Euclidean distance and the number of neighbours 5 have the best performance in predicting the value of the accreditation predicate compared to other methods. The results of calculations using the KNN method produce Area Under Curve values of 1,000, CA 1,000, F1 1,000, precision 1,000 and Recall 1,000.
Enhancing Prediction of Treatment Duration in New Tuberculosis Cases: A Comprehensive Approach with Ensemble Methods and Medication Adherence Rusdah, Rusdah; Painem, Painem; Kusumaningsih, Dewi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4263

Abstract

Tuberculosis (TB) remains a significant global health problem, with treatment duration varying among patients. TB patients have difficulty following a long-term treatment regimen. After the final diagnosis is determined, it is necessary to know the predicted duration of treatment for a patient. By increasing patient compliance with taking medication, the percentage of TB patients will increase, and this can reduce cases of multi-drug resistant patients and dropouts. This study aims to build a prediction model for the duration of treatment for new cases of Pulmonary TB patients by adding medication compliance parameters using the ensemble method. The research methodology uses CRISP-DM. This study begins with identifying problems and objectives, collecting data, preprocessing and analyzing data, modeling, evaluating, and validating models. The results showed that adding medication compliance parameters can improve model performance. However, the results of model exploration with feature selection techniques and various ensemble methods have not shown good performance. The medication adherence parameters used in this study are the number of medications swallowed in Phase I and Anti-Tuberculosis drug compliance in Phase I. These parameters had never been used in previous studies. The prediction model can be used as an early warning for a patient. If a patient is predicted to have a treatment duration of more than six months, then the patient will receive stricter drug intake supervision. Thus, this proposed model is expected to help achieve the target of eliminating Tuberculosis in 2030 to reduce the death rate by 90% compared to 2019.
Co-Authors Abdul Kadir Ahmad Galih Pramudito Amri Wicahyo Anita Fauziah Ardiansyah Arya Panji Nugroho Asep Ahmadi Binangkit, Anindito Denis Arya Saputra Desnita Desnita Devi Anisah Devit Setiono Elizabeth Yohanes Ferdiansyah Ferdiansyah Gunawan Pria Utama Haderiansyah Haderiansyah Hanifudin, Rizky Indra Nugroho Joko Christian Chandra Juliasari, Noni Martin Hasiholan Natanael Melisa Dwi Wulandari Mohammad Syafrullah Muhammad Bagus Bintang Timur Nadiah Safitri Nia Rahma Kurnianda Noripansyah . Noripansyah Noripansyah Noripansyah, Noripansyah Novia Rana Fadhila Nugraha Abdullah, Indra Nugroho, Radityo Painem, Painem Pandu Yuli Santoso Pratama, Rivan Dwi Pudoli, Ahmad Purba, Ade Rizki Sariaman Raharja, Rian Umbara Maulana Ratna Ujiandari Roeswidiah, Ririt Rofid Aljabar Royansyah, Royansyah Rusdah Rusdah Ruwirohi, Jan Everhard Ryanda Rachman Sani Akazar Pebresega Sari, Yunita Sartika Sekti Pambudi Septian Apriyanto Stefanus Adhie Putra Pramudita Triana Anggraini Tritera Erlangga Wahyu Dwi Widodo Wahyu Pramusinto Wicahyo, Amri Yulianawati Yulianawati Yulianawati Yulianawati