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All Journal Gaung Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Varidika CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Abdihaz: Jurnal Ilmiah Pengabdian pada Masyarakat Jurnal Tekinkom (Teknik Informasi dan Komputer) TEPIAN Jurnal Pengabdian Teknologi Tepat Guna Amarasi : Jurnal Desain Komunikasi Visual prosiding seminar nasional JOMLAI: Journal of Machine Learning and Artificial Intelligence Engineering and Technology International Journal (EATIJ) Scientica: Jurnal Ilmiah Sains dan Teknologi ADIMA Journal of Information System and Application Development Journal of Engineering and Science Application
Dahlan Susilo
Universitas Sahid Surakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Other

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COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND RANDOM FOREST ALGORITHM FOR PREDICTING HOUSING PRICES Susilo, Dahlan; Diyah Ruswanti; Supriyanta; Wawan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7256

Abstract

House price predictions are an important thing in the property industry and are useful for buyers in making decisions. Principal Component Analysis (PCA) and Random Forest (RF) methods were used for accuracy analysis in predicting housing prices. Purpose of this research is to measure the accuracy of both methods also to compare RF method optimized with PCA and the one that has not been optimized. The data used is house prices in Karanganyar city based on data scraping results on the rumah123.com site. The analysis reveals that Jaten has the highest number of house sales, and sales of houses with land ownership certificates are also the highest. Of the 10 variables used, land area and buildings have the most influence on selling prices. The model training results show that the RF and PCA methods combination has more optimal value than only using the RF method. The error rate of the PCA method is smaller, averaging 0.0257, making its value more consistent than using only the RF method, which has a larger error value with an average of 0.0332. The model training time using PCA is faster (5005.75) than only using the RF method (6099.25)
Multi Criteria Decision Making Method For Developing Smart Indonesia Program Scholarship Recipient Candidate System Supriyanta, Supriyanta; Sutanto, Yusuf; Susilo, Dahlan; Setyadi, Heribertus Ary; Syukron, Akhmad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3706

Abstract

The Government of Indonesia is continuously striving to improve its education quality with the provision of scholarship programs, one of which is the Smart Indonesia Program (SIP). Students' interest in obtaining SIPs is increasing, but the selection process still relies on conventional methods. Without adequate IT support, the selection process for SIP scholarship candidates will be complex, less objective, and somewhat unfair. State Vocational High School (SVHS) 5 Surakarta was selected as a case study for this research to establish the selection process and the data collection methods used in previous years. The research aims to develop a Decision Support System (DSS) to assist in nominating students deemed eligible for SIP scholarship recommendations. The applied methods include Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA). Four criteria have been set in this DSS: card ownership status, total parental income, household income, and number of siblings. Each of which is further broken down into several sub-criteria and assigned a value for use in the AHP process. Upon comparing data from 2021 to 2023, it was found that the accuracy in 2021 was 92.9%, in 2022 it reached 94.7%, and in 2023 it recorded 92.3%. Based on the results of this system accuracy test, it can be concluded that the AHP and MOORA methods can be used to objectively produce recommendations for students eligible for SIP scholarships, based on the input criteria.
Co-Authors ., palgunadi A.A. Ketut Agung Cahyawan W Abdush Shomad, Abdush Achmad Syah, Elvandec Rizky Aditya Dwi Jatmiko, Aditya Dwi Agus Purwo Handoko Akhmad Syukron Al Haris, Firdhaus Hari Saputra Amri Eka Widayanto Angga Kusuma Dawami Angky Sudarmono, Angky Anwariningsih , Sri Huning Ari Wibowo Ari Wibowo Arif Yulianto Arif Yuliyanto Ariwibowo, Heru Ashsidiq, Muhammad Fadhlan Ravi Astri Charolina Astri Charolina Budi Prabowo Cristya, Arkha Destina Paningrum Dwi Retnoningsih Dwi Retnoningsih Erwin Kartinawati Firdhaus Hari Saputro Al Haris Fiska Kusuwawati Fitriani, Nisa Fitriyadi, Farid Hadi Pratikno Hardika Khusnuliawati, Hardika Harry Abri Anto Henny Tri Hastuti Hasana Heri Kusharyanto Heribertus Ary Setyadi Indah Puspita Mutiara Indriastiningsih, Erna Iqbal Aditya Ferryanto Joko Cahyono Keristiawan, Subakti Khoirul Huda Kristiawan, Ade Kuncoro, Isnu Kusuwawati, Fiska Lina Sulistyani Mochtar Yunianto Muhammad Arya Priasatama Putra Muhammad Dwi Nugroho Muhammad Dwi Nugroho Murni Murni Mutiara, Indah Puspita nisa fitriani Nugroho, Moh Andi Setyo Nurul Pradana Ratmawati Omega, Yuseva Kristine Palgunadi . Permana, Tatok Andri Pranowo Setiaji Pratikno, Hadi Prawesti, Nindia Fika Rizky Purnama, Christian Roy Putra, Adi Wijaya Putra, Muhammad Arya Priasatama Ratmawati, Nurul Pradana Retnoningsih , Dwi Riskyanto Pangemanan Role, Vinsensius Sony rudi pranoto, rudi Ruswanti, Diyah Salsabila, Afrida Nur Salsabila, Unik Hanifah Setiawan, Gigih Setiawan, Sukawati Novy Siti Zubaidah Sri Ernawati Sri Huning Anwariningsih Sukawati Novy Setiawan Sulistyani, Lina Supriyanta Supriyanta Supriyanta Vinsensius Sony Role Wahyudi . Wahyuningsih, Dwi Prastiwi Wawan Nugroho Widayanto, Amri Eka Yunitasari, Veronica Sri Yusuf Sutanto