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All Journal Jurnal Informatika Universitas Pamulang OKTAL : Jurnal Ilmu Komputer dan Sains Buletin Ilmiah Ilmu Komputer dan Multimedia (BIIKMA) APPA : Jurnal Pengabdian Kepada Masyarakat International Journal of Educational Technology and Society Riau Jurnal Teknik Informatika
Kecitaan Harefa
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Astronomy Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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Application of Multiple Linear Regression Algorithm for House Price Estimation Based on Building Location and Area to Improve Predictive Accuracy in Real Estate Valuation Kecitaan Harefa
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.3993

Abstract

Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R² value indicates that the location and area variables of the building are not sufficient to explain the overall variation in house prices, so the addition of other variables—such as the number of rooms, facilities, and environmental conditions—is needed to improve the accuracy of the prediction and produce a more representative price estimate.
Socio Technical Perspectives on Implementing Artificial Intelligence for Formative Assessment in Culturally Diverse Educational Institutions Rinna Rachmatika; Kecitaan Harefa
International Journal of Educational Technology and Society Vol. 2 No. 4 (2025): December: International Journal of Educational Technology and Society
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijets.v2i4.468

Abstract

The integration of Artificial Intelligence (AI) into educational settings, particularly in formative assessments, offers significant benefits in terms of personalized learning, real time feedback, and increased efficiency. However, the successful implementation of AI driven formative assessments depends not only on technological capabilities but also on socio cultural and organizational factors that shape its adoption. This study explores the socio technical factors influencing the use of AI in formative assessments, emphasizing the importance of considering cultural diversity, institutional culture, and educators' beliefs. AI technologies, while powerful in automating grading and providing personalized assessments, often face limitations in addressing complex student responses that require human judgment. Furthermore, cultural factors, such as students' prior exposure to technology and different cultural attitudes towards AI, play a critical role in the acceptance and effectiveness of these tools. Organizational factors, including leadership support, digital literacy, and the readiness of institutions to adopt AI, are also key determinants in the successful implementation of AI systems in education. Teachers’ beliefs about assessment influence their acceptance and use of AI tools, highlighting the need for professional development and training to ensure that AI enhances pedagogical goals rather than replacing human expertise. The study concludes that the alignment of technology, culture, and assessment beliefs is essential for the effective use of AI driven formative assessments in educational settings. Recommendations for educational institutions include adopting a socio technical approach to AI integration, with a focus on providing resources, training, and fostering a culture of innovation. Future research directions should focus on expanding studies to diverse educational contexts, conducting longitudinal research on AI’s impact on learning outcomes, and exploring additional socio technical frameworks to guide AI adoption in education.
Co-Authors Aditya Rangga Prasetya Aditya Tri Herdiansyah Afrizal Nur Perdana Aqidatul Izzah Chairul Ari Fachri Zadiq Arisman laia Dony Sapta Nurhayadi Dzaky Azizan Haidar Adlia Hernanda Faris Khoiri Jason Ikbal Khairul Umam Kristiani Apriyanti Ndraha Kurnianto Octian Junaidi Muhamad Fariz Rivai Muhammad Ridwan Ramadhan Oktafpianus Gea Pras Parman Wau Priska Diniati Rebeka Ndaki Rinna Rachmatika Sigit Dwiantoro Tian Aldi Kurniawan Wahyu Candra Wijaya Yosef Ibrahim