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All Journal Jurnal Riset Informatika Abditeknika - Jurnal Pengabdian Kepada Masyarakat Computer Science (CO-SCIENCE) SENTRI: Jurnal Riset Ilmiah Justika : Jurnal Sistem Informasi Akuntansi Journal of Accounting Information System The Indonesian Journal of Computer Science Jurnal Komtika (Komputasi dan Informatika)
Ita Yulianti
STMIK AMIKOM Purwokerto
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Implementasi Metode Forward Chaining, Certainty Factor dan Dempster Shafer pada Sistem Pakar Diagnosis Penyakit Gigi dan Mulut Nurajizah, Siti; Yulianti, Ita; Saputra, Elin Panca; Dewi, Rani Kurnia
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.5995

Abstract

Dental and oral disease is one of the diseases that has been felt by most of the people. Insufficient information and the limited level of public awareness of the prevention of dental and oral diseases make the impact quite dangerous if not handled properly. An appropriate information system is needed in overcoming and providing solutions for handling a disease as early as possible. Expert systems can be used as a means of information on the treatment of dental and oral diseases. The manufacture of the expert system in this study initially used the forward chaining method, which is a method that searches based on information that is made into a set of rules so as to get a conclusion. However, after re-analysis, two other methods, namely certainty factor and dempster shafer, were also applied in this study with the aim of overcoming the shortcomings of the forward chaining method, one of which is uncertainty in producing a conclusion or diagnosis of disease. Determining the type of dental and oral disease can be known by looking at the symptoms experienced by the patient. The use of an expert system for diagnosing dental and oral diseases can be used as an initial solution in helping someone to treat the disease. The existence of this expert system can be used as consideration in making decisions to determine the type of dental and oral disease quickly, precisely and accurately.
Relaksasi Autogenik Terhadap Hemodinamik Pasien di ICU Supriyanti, Endang; Wahyuningsih, Wahyuningsih; Selowarni, Dati; Yulianti, Ita
SENTRI: Jurnal Riset Ilmiah Vol. 4 No. 11 (2025): SENTRI : Jurnal Riset Ilmiah, November 2025
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/sentri.v4i11.4691

Abstract

Critically ill patients experiencing dysfunction in one or more organs are highly dependent on hemodynamic monitoring equipment and Intensive Care Unit (ICU) therapy. Autogenic relaxation is one of the non-pharmacological therapy options that can help stabilize the hemodynamic status of patients because it can provide a relaxing effect, reduce mild to moderate physical and psychological stress and tension, and provide comfort so that hemodynamics become stable. The purpose of this study was to determine the effect of autogenic relaxation on the hemodynamics of patients in the ICU at Permata Medika Hospital in Semarang. This study was a quasi-experimental study with a pre-test and post-test non-equivalent control group design, providing a 10-minute autogenic relaxation intervention twice a day for 2 days to the intervention group. Hemodynamics were measured on the first day before the intervention and on the last day after the intervention. Meanwhile, in the control group, hemodynamic measurements were only taken on the first day and the last day using a bedside monitor and observation sheet. The research population consisted of patients treated in the ICU of Permata Medika Hospital in Semarang, with a sample size of 30 people divided into intervention and control groups. The sampling method was accidental sampling. The data were analyzed using the Mann-Whitney test because the results of the normality test showed that the data were not normally distributed. The results of the study showed p-values for systolic blood pressure of 0.340 and diastolic blood pressure of 0.693, pulse of 0.803, respiration of 0.835, body temperature of 0.815, and oxygen saturation of 0.963, indicating no significant difference in hemodynamic status between the intervention group and the control group. Therefore, it can be concluded that autogenic relaxation has no effect on the hemodynamics of patients in the ICU at Permata Medika Hospital in Semarang. These results are likely due to the small and non-heterogeneous sample size. Therefore, further research with a larger and more heterogeneous sample size is needed.
ENHANCING SLEEP QUALITY PREDICTION THROUGH SMOTE-BASED DATA BALANCING AND HYBRID MACHINE LEARNING MODELS Rahmawati, Ami; Yulianti, Ita; Oktarini Sari, Ani; Nurajizah, Siti; Hikmatulloh
Jurnal Riset Informatika Vol. 8 No. 1 (2025): Desember 2025
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i1.456

Abstract

Sleep is a vital aspect in maintaining a person's physical and psychological balance. Poor sleep quality can reduce physical and cognitive performance, increasing the risk of various health problems. This study aims to develop a predictive model for sleep quality based on factors such as lifestyle, stress, daily activities, and caffeine consumption, using XGBoost combined with Recursive Feature Elimination (RFE). XGBoost was chosen for its ability to handle imbalanced datasets and heterogeneous features, while RFE helps simplify the model without losing important information. In the data pre-processing stage, a class imbalance was found, so the Synthetic Minority Over-sampling Technique (SMOTE) process was carried out to balance the proportion of the minority class. The dataset in this study was divided into two parts, namely 80% as training data and 20% as testing data, and validated using cross-validation to ensure generalization. The results show very high model performance with an accuracy of 99.79% on training data, 99.63% on cross-validation, and 99.10% on testing data. This model was then developed into a web application for practical use in analyzing sleep quality prediction. This study emphasizes the methodological contribution of a SMOTE-based hybrid machine learning model and its ready-to-use application implementation, while also opening opportunities for further testing on more diverse datasets and evaluating biases caused by synthetic data.
Co-Authors A Gunawan Adli, Idham Abdi Al Amegia Saputra, Rizal Ami Rahmawati Anggi Oktaviani ANI OKTARINI SARI, ANI OKTARINI Dahlia Sarkawi Dewi, Rani Kurnia Dwi Nur Aini, Dwi Nur Dwi Retnaningsih, Dwi Endang Supriyanti, Endang Fitrika, Sonia Hikmatulloh Hikmatulloh, Hikmatulloh Ibhammurrival, M. Napitupulu, Golda TM Oktarini Sari, Ani Panca Saputra, Elin Pribadi, Denny Rahmawati Maulida, Nurul Rahmawati, Ami Ramdiani Azzahra, Dinda Rifa Nurafifah Syabaniah Rizal Amegia Saputra Rusda Wajhillah, Rusda Sahrul, Nizar Saumi Ramdhani, Lis Selowarni, Dati Septiani Nurfauzia K, Tya Siti Nurajizah Sri Melati Putri, Rahma Tati Mardiana, Tati Taufik Hidayatulloh Wahyuni, Ayu Dewi Sri Wahyuni Wahyuningsih Wahyuningsih Yuri Rahayu Yusti Farlina