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All Journal International Journal of Health, Engineering and Technology Sevaka: Hasil Kegiatan Layanan Masyarakat
Dinda Salsabila Ritonga
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Analysis of Factors Causing Students' Failure to Complete Their Thesis on Time Using the Random Forest Algorithm Riszki Fadillah; Intan Nur Fitriyani; Nur Indah Nasution; Rahadatul 'Aisy Riadi; Dinda Salsabila Ritonga
International Journal of Health Engineering and Technology (IJHET) Vol. 3 No. 1 (2024): IJHET May 2024
Publisher : CV. AFDIFAL MAJU BERKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55227/ijhet.v3i1.281

Abstract

This research aims to analyze the factors that influence students' delays in completing final assignments using the Random Forest algorithm. The data used includes variables such as GPA, number of credits, employment status, frequency of guidance, organizational activities, and personal motivation. These variables were analyzed to determine their effect on students' ability to complete their final assignments on time. The Random Forest model is applied to predict whether students complete their final assignments on time or not. The model results show an accuracy of 63.33%, with the frequency of guidance and personal motivation being the most influential factors in completing the final assignment on time. Followed by the number of credits and GPA, which also have a significant but smaller influence. Organizational activity factors and employment status have a lower contribution to tardiness, but are still relevant in the context of student time management. Based on these results, research suggests the importance of academic guidance support and motivation management to help students overcome obstacles in completing their final assignments on time. This research, which uses the case of ITKES Ika Bina students, is expected to provide recommendations for universities in improving the academic mentoring process to support student graduation.
Sosialisasi Penerapan Aplikasi Mobile Dalam Pembelajaran Bahasa Inggris Untuk Mahasiswa Sistem Informasi Suerni; Eka Ramadhani Putra; Dinda Salsabila Ritonga
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 1 No. 4 (2023): November: Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i4.522

Abstract

This study aims to analyze the socialization process of implementing a mobile application for English language learning among Information Systems students and to evaluate the effectiveness of the socialization. The research employs a descriptive qualitative method, collecting data through observation, interviews, questionnaires, and documentation. The subjects consist of 30 students purposively selected based on their interest and readiness to participate in the socialization. The socialization process includes material delivery, application demonstration, and brief training. Results show an increase in students’ positive perceptions regarding the ease of use and benefits of the application in supporting English learning. Additionally, students’ learning interest significantly improved after the socialization. The effectiveness evaluation reveals that the delivered material is easy to understand, and the interactive methods successfully engage student enthusiasm. The usage of the mobile application among students notably increased, both in terms of downloads and routine usage frequency. However, some challenges remain related to motivation for consistent application use, which should be addressed in future development and socialization efforts. This study recommends enhancing technical support and motivation strategies to encourage sustainable adoption. Thus, implementing mobile applications in English learning can be an effective alternative to improve learning quality in the digital era.
Penyuluhan Penerapan Metode Naive Bayes Untuk Kalsifikasi Data Pasien Tipus Di RSUD Rantauprapat Intan Nur Fitriyani; Quratih Adawiyah; Rika Handayani; Fitriyani Nasution; Dinda Salsabila Ritonga
Sevaka : Hasil Kegiatan Layanan Masyarakat Vol. 2 No. 4 (2024): November: Sevaka : Hasil Kegiatan Layanan Masyarakat
Publisher : STIKES Columbia Asia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62027/sevaka.v2i4.524

Abstract

Typhoid fever is an infectious disease caused by the bacterium Salmonella typhi, commonly found in developing countries, including Indonesia. Prompt and accurate treatment is crucial to prevent serious complications in patients. One way to assist in diagnosing typhoid fever is by applying machine learning methods to classify patient data. The Naive Bayes method is one of the machine learning algorithms frequently used in medical data classification due to its strong ability to handle large and complex datasets. This article discusses the application of the Naive Bayes method for classifying typhoid patient data at Rantauprapat General Hospital (RSUD Rantauprapat). By utilizing medical data that includes clinical symptoms, laboratory test results, and patients’ medical histories, the Naive Bayes model can provide fairly accurate predictions regarding the likelihood of a person having typhoid fever. The research findings indicate that Naive Bayes is reliable in predicting typhoid diagnoses with adequate accuracy, thereby supporting healthcare professionals in making faster and more precise decisions. It is expected that the implementation of this method can accelerate the diagnostic process and improve the quality of healthcare services at RSUD Rantauprapat, as well as in other regions.
Co-Authors Eka Ramadhani Putra Fitriyani Nasution Intan nur Fitriyani Nur Indah Nasution Quratih Adawiyah Rahadatul 'Aisy Riadi Rika Handayani Riszki Fadillah Suerni