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All Journal Society: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Ramadhika, Gilang
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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PENGOLAHAN DATA: PEMAHAMAN GEMPA BUMI DI INDONESIA MELALUI PENDEKATAN DATA MINING Faridzi, Salman Al; Faza Shafa Azizah; Mustafa, Faizal; Nindya Putri, Azzahra; Ramadhika, Gilang; Rizky Aditya, Fauzan; Sherli Fadilah, Ridha; Habibi, Yusuf; Sutrisno, Mirza; Jumail, Jumail; Dewi Risanty, Rita; Rosanti, Nurvelly
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 1 (2024): Februari
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i1.506

Abstract

Gempa bumi merupakan bencana alam yang sering terjadi di Indonesia akibat interaksi lempeng tektonik. Indonesia terletak pada pertemuan empat lempeng tektonik dunia, yang menyebabkan aktivitas zona tumbukan dan patahan yang berpotensi memicu gempa bumi. Meskipun telah terjadi sejumlah peristiwa gempa bumi besar di Indonesia, prediksi gempa secara tepat waktu masih sulit karena kompleksitas geologi dan dinamika kerak bumi. Peningkatan pemahaman tentang perilaku geologi dan sistem peringatan dini menjadi kunci dalam mempersiapkan diri menghadapi ancaman gempa bumi di masa mendatang. Data mining adalah proses yang berguna untuk mengeksplorasi dan mencari nilai informasi kompleks yang tersimpan dalam basis data. Dengan menggunakan data mining, dampak atau akibat dari gempa bumi yang terjadi di Indonesia dapat dipelajari berdasarkan data gempa bumi yang telah terjadi sebelumnya. Maka, dilakukanlah webinar dan workshop tentang penggunaan data mining untuk memahami pola gempa bumi di Indonesia selama 10 tahun terakhir. Webinar membahas dasar-dasar data mining dan fakta gempa yang terjadi di Indonesia, sementara workshop membahas pengolahan dan visualisasi data gempa bumi menggunakan bahasa Python dan Google Colab. Workshop ini terbatas pada pengolahan dan visualisasi data csv gempa bumi saja. Kegiatan webinar dan workshop dilaksanakan pada tanggal 29 Januari 2024 pukul 13.00 WIB. Hasil evaluasi menunjukkan bahwa peserta menyatakan kepuasan mereka terhadap acara tersebut, dengan sebagian besar peserta memberikan nilai positif terhadap penyampaian materi, kesesuaian materi dengan tema, kejelasan informasi, serta kualitas audio visual selama acara berlangsung.
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 Afkarinah, Afni Izzah Ambo, Siti Nurbaya Amili, Fadel Andharani, Yana Andriansyah, Akbar Dewi Risanty, Rita Dzulkarnain, Mulki Djenfik Faridzi, Salman Al Faza Shafa Azizah Habibi, Yusuf Jumail, Jumail Mustafa, Faizal Nindya Putri, Azzahra Nurvelly Rosanti, Nurvelly Rizky Aditya, Fauzan Sherli Fadilah, Ridha Sutrisno, Mirza