Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal Society: Jurnal Pengabdian Masyarakat
Afkarinah, Afni Izzah
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Society: Jurnal Pengabdian Masyarakat

 1 
Utilization of Machine Learning for Property Price Segmentation and Prediction Andriansyah, Akbar; Dzulkarnain, Mulki Djenfik; Afkarinah, Afni Izzah; Amili, Fadel; Ramadhika, Gilang; rosanti, Nurvelly; Ambo, Siti Nurbaya; Andharani, Yana; Sutrisno, Mirza
Society : Jurnal Pengabdian Masyarakat Vol 4, No 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.537

Abstract

Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39 participants from various backgrounds participated in this activity. The evaluation showed that 38.7% of participants were satisfied, while 51.6% were very satisfied with the program. In addition, the post-test results showed a significant increase in understanding compared to the pre-test results. Based on these results, this activity proved to be successful in providing new insights into the application of machine learning in property price prediction and equipping participants with practical skills that can be applied in the real world.
Design Faster, Code Smarter: Web Design With SASS Hidayat, Taufik; Anjani, Meisya Putri; Afkarinah, Afni Izzah; Aullia, Mochammad Rizqi; Mujiastuti, Rully; Nurbaya Ambo, Sitti; Sutrisno, Mirza; Rosanti, Nurvelly
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 5 (2025): September
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i5.630

Abstract

The "Design Faster, Code Smarter: Web Design With SASS" webinar and workshop, held on July 16, 2025, successfully educated participants on modern web design using SASS. This activity was implemented to address the challenges of managing traditional CSS code, which tends to be repetitive, difficult to maintain, and prone to errors in large-scale projects. SASS was chosen as the solution due to its dynamic, structured, and modular approach to writing CSS, offering features such as variables, functions, and mixins that significantly improve development efficiency. This community service activity employed a descriptive quantitative method, utilizing a survey approach through pre- and post-test questionnaires, as well as feedback forms, to measure participants' understanding. The 21 participants were students from the Informatics Engineering program at the University of Muhammadiyah Jakarta. A pre-test assessed initial knowledge, followed by a post-test and feedback questionnaire after the workshop. The workshop also provided hands-on coding experience, guiding participants in creating landing pages using Visual Studio Code and Figma designs. The post-test and feedback results revealed high participant satisfaction and a significant increase in understanding of SASS concepts. The percentage of correct answers to post-test questions ranged from 78.9% to 89.5%. Furthermore, 89.5% of participants stated that the material was clear (21.1% strongly agreed, 68.4% agreed), and 89.5% were satisfied with the overall event (21.1% felt very good, 68.4% good). This initiative successfully demonstrated that hands-on training effectively improves skills in modern web development and underscores the importance of continuing education programs to further enhance participants' SASS skills.
Utilization of Machine Learning for Property Price Segmentation and Prediction Andriansyah, Akbar; Dzulkarnain, Mulki Djenfik; Afkarinah, Afni Izzah; Amili, Fadel; Ramadhika, Gilang; rosanti, Nurvelly; Ambo, Siti Nurbaya; Andharani, Yana; Sutrisno, Mirza
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.537

Abstract

Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39 participants from various backgrounds participated in this activity. The evaluation showed that 38.7% of participants were satisfied, while 51.6% were very satisfied with the program. In addition, the post-test results showed a significant increase in understanding compared to the pre-test results. Based on these results, this activity proved to be successful in providing new insights into the application of machine learning in property price prediction and equipping participants with practical skills that can be applied in the real world.
Co-Authors Ambo, Siti Nurbaya Amili, Fadel Andharani, Yana Andriansyah, Akbar Anjani, Meisya Putri Aullia, Mochammad Rizqi Dzulkarnain, Mulki Djenfik Nurbaya Ambo, Sitti Nurvelly Rosanti, Nurvelly Ramadhika, Gilang Rully Mujiastuti Sutrisno, Mirza Taufik Hidayat