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Student Perceptions Analysis of Online Learning: A Machine Learning Approach Suparwito, Hari; Polina, Agnes Maria; Budiraharjo, Markus
Indonesian Journal of Information Systems Vol 4, No 1 (2021): August 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v4i1.4594

The covid-19 pandemic is currently occurring affects almost all aspects of life, including education. School From Home (SFH) is one of the ways to prevent the spread of Covid-19. The face-to-face learning method in class turns into online learning using information technology facilities. Even though there are many barriers to implementing classes online, online learning provides a new perspective for students' learning process. One of the factors for the online learning process's success is the interaction between the two main actors in the learning process, i.e., lecturers and students. The study's purpose was to analyze students' perceptions of the online learning process. The research data were obtained from a student questionnaire, which included five main criteria in the learning process: 1) self-management aspects, 2) personal efforts, 3) technology utilization, 4) perceptions of self-roles, and 5) perceptions of the role of the lecturer. Students provide an assessment through a questionnaire about the online learning methods they experience during the Covid-19 pandemic. The random forest algorithm was applied to examine data. The study results were focused on three main criteria (variable importance) that affect students' perceptions of the online learning process. The results described that the students' satisfaction in online learning is influenced by 1) The relationship between students and lecturers. 2) The learning materials need to be changed and adapted to the online learning method; 3) The use of technology to access online learning. The study contributes to improving the online learning method for the student.

Pembangunan dan Implementasi Sistem Informasi Bank Darah di PMI dan RSUD Muntilan Kabupaten Magelang Agnes Maria Polina; Fenty Fenty
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 1, No 1 (2018): April 2018
Publisher : Universitas Sanata Dharma

Blood bank administration in PMI, Magelang Regeency is still managed manually, which prevents hospitals from accessing information on blood stock quickly. Hospitals can access information about blood stock by phone only. Such a manual system could be a problem in delivering an excellent service. Therefore, a solution is required to improve the quality of services delivered by PMI. The use of information technology called Blood Bank Information System (SIBD/Sistem Informasi Bank Darah) is promising to improve the quality of services of the PMI. The SIBD helps the hospital to improve time efficiency in the blood demand process. There were several activities which have been completed, i.e.: 1) Developing SIBD at PMI of Magelang Regency; 2) Improving hardware facilities at PMI of Magelang Regency and RSUD Muntilan, including the procurement of computer hardware, modem, and Internet access for each institution; 3) Training on the use of SIBD for both PMI and hospital staff. The results wrer: 1). SIBD at PMI which is linked to RSUD Muntilan and other hospitals for blood supply information and online order; 2) Blood Bank Administration Database which can improve quality of service; 3) capability of PMI and RSUD Muntilan, Magelang Regency staff in utilizing SIBD to improve services to patients.

Student Perceptions Analysis of Online Learning: A Machine Learning Approach Hari Suparwito; Agnes Maria Polina; Markus Budiraharjo
Indonesian Journal of Information Systems Vol. 4 No. 1 (2021): August 2021
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v4i1.4594

The covid-19 pandemic is currently occurring affects almost all aspects of life, including education. School From Home (SFH) is one of the ways to prevent the spread of Covid-19. The face-to-face learning method in class turns into online learning using information technology facilities. Even though there are many barriers to implementing classes online, online learning provides a new perspective for students' learning process. One of the factors for the online learning process's success is the interaction between the two main actors in the learning process, i.e., lecturers and students. The study's purpose was to analyze students' perceptions of the online learning process. The research data were obtained from a student questionnaire, which included five main criteria in the learning process: 1) self-management aspects, 2) personal efforts, 3) technology utilization, 4) perceptions of self-roles, and 5) perceptions of the role of the lecturer. Students provide an assessment through a questionnaire about the online learning methods they experience during the Covid-19 pandemic. The random forest algorithm was applied to examine data. The study results were focused on three main criteria (variable importance) that affect students' perceptions of the online learning process. The results described that the students' satisfaction in online learning is influenced by 1) The relationship between students and lecturers. 2) The learning materials need to be changed and adapted to the online learning method; 3) The use of technology to access online learning. The study contributes to improving the online learning method for the student.

Tourism Site Recommender System Using Item-Based Collaborative Filtering Approach Robertus Adi Nugroho; Agnes Maria Polina; Yohanes Dicky Mahendra
International Journal of Applied Sciences and Smart Technologies Volume 02, Issue 02, December 2020
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v2i2.2987

Many people like traveling. But, often they are difficult to find a tourism site that they like much. Too many information about tourism is the problem. To overcome this problem, we need to filter the information. Recommender System could filter the information. By considering the advantages, the system used item-based collaborative filtering approach to give recommendation. Some tourism site around Yogyakarta province were used in this research. The system is able to give recommendation to users. The accuracy of the rating prediction is 0,6293 and the average time consumption is 1693,33 millisecond.

ASESMEN KESIAPAN SEKOLAH SD DAN PEMBUATAN SISTEM INFORMASI UNTUK PELAPORAN HASIL ASESMEN Astuti, Ratri Sunar; Polina, Agnes Maria; Etikawati, Agnes Indar; Suprawati, MM.Nimas Eki
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2023): April 2023
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/aa.v6i1.5307

As affected by Covid-19 pandemic, most of children experienced online learning. This online learning could affect children school readiness when they entering school. They showed various problems, such as math, writing, emotion, attention and in following instructions. To assist these children, teachers need to assess their school readiness using NST, and their intellectual potential using CPM. As the assessment process often takes time, we developed an information system integrated in this assessment. This system enables the psychologists to reduce time in doing the assessment and teachers may get the result faster. Based on the assessment using NST and CPM, it was found that in general children are ready to enter the school.

CLASTERIZATION OF LECTURER'S PROFILE IN ONLINE LEARNING DURING THE COVID-19 PANDEMIC Polina, Agnes Maria; Aprinastuti, Christiyanti; Suparwito, Hari
IJIET (International Journal of Indonesian Education and Teaching) Vol 7, No 2 (2023): July 2023
Publisher : Sanata Dharma University Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijiet.v7i2.6495

The learning process changed from classroom to online learning during the COVID-19 pandemic. One of the things that must be done is to analyze the readiness of lecturers in facing online learning. The purpose of this study is to cluster the profiles of lecturers dealing with online learning. The clustering method uses a Machine Learning approach with the K-means algorithm. Data were taken from 274 lecturers who returned questionnaires during April–June 2022. The questionnaire consisted of 27 questions on a Likert scale (1–4). The Boruta technique is used to determine the five most significant variables (Variable Importance) in the clustering. The results of the clustering show that the lecturers are divided into 2 large groups with the following criteria: focus on learning methods, learning materials, student independence, exploration of new knowledge, and online learning evaluation tools.

Real-Time Vehicle Detection and Air Pollution Estimation Using YOLOv9 Suparwito, Hari; Prakoso, Bernardus Hersa Galih; Kumalasanti, Rosalia Arum; Polina, Agnes Maria
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2329

Pollution of air, particularly in cities, is becoming an issue to be taken seriously owing to the health and environmental risks associated with it, and the major contributor to air pollution is car emissions. The objective of the study is to identify and classify vehicles such as motorbikes, cars, buses, trucks in order to monitor live traffic and potentially determine the extent to which the pollution level elevates, utilizing the YOLOv9 model. Traffic CCTV camera footage was gathered under a wide range of circumstances including different lighting and varying traffic intensity. Folders were particularly structured and images annotated, in the manner, which served the purpose of meeting the requirements of the YOLO structure. Once it was trained with a labeled dataset, the vehicle identification by YOLOv9 model was found to be quite satisfactory. Overall vehicle identification accuracy was calculated to be mAP50:95 of 0.826. In contrast, it had a harder time with smaller items like motorcycles, with a mAP50:95 of 0.682. Findings indicate that larger items were detected more than smaller items. Camera angles and the small size of the objects often make small objects appear to blend in to the background. This research indicates that AI can be of help when dealing with the urban structure. It offers a way of measuring traffic volume to predict the amount of CO emissions that can be avoided or controlled. The rest are keen in enhancing the effectiveness of recognizing small objects within the system and deploying it in multiple settings.

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