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All Journal JURNAL PENDIDIKAN TAMBUSAI Society: Jurnal Pengabdian Masyarakat
Ambo, Siti Nurbaya
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Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences

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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.
Webinar dan Workshop : From Zero to Hero with KNIME: Introduction to Machine Learning Workflows & Storytelling with Data Using Looker Studio Pratama, Renaldi; Ramadhan, Atthilla Sulthan; Putri, Khansa Aqilla; Nugraha, Wahyu; Fahmi, Sonny; Meilina, Poppy; Jumail, Jumail; Adharani, Yana; Ambo, Siti Nurbaya; Mujiastuti, Rully
Jurnal Pendidikan Tambusai Vol. 8 No. 2 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan analisis data dan kecerdasan buatan membuka peluang baru dalam pengolahan informasi dan pengambilan keputusan berbasis data. Namun, kompleksitas teknologi ini menimbulkan tantangan bagi banyak profesional dan mahasiswa dalam menguasainya. Untuk mengatasi hal tersebut, kami mengadakan webinar dan workshop berjudul "From Zero to Hero with KNIME: Introduction to Machine Learning Workflows & Storytelling with Data Using Looker Studio". Kegiatan ini dilaksanakan dengan metode pemaparan materi melalui Webinar dan dilanjutkan dengan Workshop praktis. Evaluasi kepuasan peserta dilakukan melalui kuisioner yang menunjukkan respon positif terhadap pemateri dan materi yang disampaikan. Hasil menunjukkan bahwa kegiatan berjalan sukses, dibuktikan dengan partisipasi dari 40 peserta dari berbagai latar belakang. Peserta mengisi Post-Test yang menunjukkan pemahaman yang baik terhadap materi, dengan rata-rata skor 85%. Feedback yang didapatkan dari peserta menunjukkan 61,5% merasa puas dan 38.5% merasa sangat puas dengan kegiatan ini. Kombinasi ML, KNIME, dan Google Looker Studio terbukti menawarkan pendekatan yang efektif untuk menganalisis data kompleks dan menyajikannya secara mudah dipahami.
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 Adharani, Yana Afkarinah, Afni Izzah Amili, Fadel Andharani, Yana Andriansyah, Akbar Dzulkarnain, Mulki Djenfik Fahmi, Sonny Jumail, Jumail Meilina, Poppy Nurvelly Rosanti, Nurvelly Pratama, Renaldi Putri, Khansa Aqilla Ramadhan, Atthilla Sulthan Ramadhika, Gilang Rully Mujiastuti Sutrisno, Mirza Wahyu Nugraha