Pemodelan Prediksi Nilai IQ Menggunakan Algoritma Machine Learning
Abstract
IQ stands for Intelligence Quotient. It is a numerical score obtained from various psychometric tests designed to measure a person's general intelligence or cognitive ability. IQ is often used as an indicator of academic potential, success in the workplace, and adaptability to new environments. However, it is important to remember that IQ is only one aspect of human intelligence. IQ assessments are widely used in fields as diverse as education, psychology, and employment. Manually administered IQ tests are often time-consuming, require human intervention, and are prone to error. On the other hand, the development of data-driven technology allows for faster and more accurate information processing. Machine learning is a system that can learn to make its own decisions without being reprogrammed by humans, allowing computers to become smarter and learn from their experience with data. That's why the author conducted research to develop an IQ prediction model using machine learning algorithms.
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A. Saputra, I. Satiri, and L. Erlina, Intelligence Quetiont (IQ), Emotional Quetiont (EQ), dan Spiritual Quetiont (SQ) Qur`ani Ulul Albab, Zad Al-Mufassirin, vol. 3, no. 2, pp. 250267, Dec. 2021, doi: 10.55759/zam.v3i2.47.
L. Herlina and Suwatno, Kecerdasan intelektual dan minat belajar sebagai determinanprestasi belajar siswa, Jurnal Pendidikan Manajemen Perkantoran, vol. 3, no. 2, p. 99, Jul. 2018, doi: 10.17509/jpm.v3i2.11770.
I. Permata, M. Aprilia, and M. Asbari, Pengaruh Kecerdasan Intelektual dan Kecerdasan Emosional dalam Perspektif Neurosains di Dunia Pendidikan, JOURNAL OF INFORMATION SYSTEMS AND MANAGEMENT, vol. 03, no. 02, 2024, [Online]. Available: https://jisma.org
A. Rahmawati, U. Pmi, and K. Malang, PENGARUH KECERDASAN INTELEKTUAL, KECERDASAN EMOSIONAL DAN KECERDASAN SPIRITUAL TERHADAP KINERJA KARYAWAN DENGAN KEPUASAN KERJA SEBAGAI VARIABEL INTERVENING DI UTD PMI KOTA MALANG, JUBIS, vol. 3, no. 1, 2022.
A. Salfa Nabila and Z. A. Chaniago, MACAM KECERDASAN MENURUT HOWARD GARDNER, SERTA MACAM INTELEGENSI.
K. Richardson and S. H. Norgate, Does IQ Really Predict Job Performance?, Jul. 03, 2015, Psychology Press. doi: 10.1080/10888691.2014.983635.
A. Wijoyo, A. Y. Saputra, S. Ristanti, R. Syaban, M. Amalia, and R. Febriansyah, Pembelajaran Machine Learning.
J. T. Santoso, S. Kom, M. Kom, and A. Machine, P Y YAYASAN PRIMA AGUS TEKNIK Learning Dengan Python.
A. F. A. Naibaho and A. Zahra, PREDIKSI KELULUSAN SISWA SEKOLAH MENENGAH PERTAMA MENGGUNAKAN MACHINE LEARNING, Jurnal Informatika dan Teknik Elektro Terapan, vol. 11, no. 3, Jul. 2023, doi: 10.23960/jitet.v11i3.3056.
M. Riziq sirfatullah Alfarizi, M. Zidan Al-farish, M. Taufiqurrahman, G. Ardiansah, and M. Elgar, PENGGUNAAN PYTHON SEBAGAI BAHASA PEMROGRAMAN UNTUK MACHINE LEARNING DAN DEEP LEARNING, 2023.
A. Devia and B. Soewito, Analisis Perbandingan Metode Seleksi Fitur untuk Mendeteksi Anomali pada Dataset CIC-IDS-2018, Jurnal Teknologi Dan Sistem Informasi Bisnis-JTEKSIS, vol. 5, no. 4, p. 572, 2023, doi: 10.47233/jteksis.v5i4.1069.
Z. A. Fikriya, M. I. Irawan, and Soetrisno, Implementasi Extreme Learning Machine untuk Pengenalan Objek Citra Digital.
R. Martha and D. E. Herwindiati, Prediksi Hujan Menggunakan Metode Artificial Neural Network, K-Nearest Neighbors, dan Nave Bayes, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, pp. 859865, Nov. 2024, doi: 10.47233/jteksis.v6i4.1650.
M. I. A. Guno Wibowo and I. Pratama, Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine, Jurnal Teknologi Dan Sistem Informasi Bisnis, vol. 6, no. 4, pp. 715722, Oct. 2024, doi: 10.47233/jteksis.v6i4.1552.
I. S. Aisah, B. Irawan, and T. Suprapti, ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK ANALISIS SENTIMEN ULASAN APLIKASI AL QURAN DIGITAL, 2023.
W. Indrasari and H. Suhendar, ANALISIS MODEL PREDIKSI CUACA MENGGUNAKAN SUPPORT VECTOR MACHINE, GRADIENT BOOSTING, RANDOM FOREST, DAN DECISION TREE, Prosiding Seminar Nasional Fisika (E-Journal), vol. XII, doi: 10.21009/03.1201.FA18.
Z. Rais, ANALISIS SUPPORT VECTOR REGRESSION (SVR) DENGAN KERNEL RADIAL BASIS FUNCTION (RBF) UNTUK MEMPREDIKSI LAJU INFLASI DI INDONESIA, VARIANSI: Journal of Statistics and Its Application on Teaching and Research, vol. 4, no. 1, pp. 3038, 2022, doi: 10.35580/variansiunm13.
R. P. Furi, M. S. Jondri, and D. Saepudin, Prediksi Financial Time Series Menggunakan Independent Component Analysis dan Support Vector Regression Studi Kasus : IHSG dan JII, 2015. Accessed: Jan. 12, 2025. [Online]. Available: https://repositori.telkomuniversity.ac.id/pustaka/102168/prediksi-financial-time-series-menggunakan-independent-component-analysis-dan-support-vector-regression-studi-kasus-ihsg-dan-jii-.html
I. Dwi Sulistyowati, S. Sunarno, and D. D. Djuniadi, PENERAPAN MACHINE LEARNING DENGAN ALGORITMA SUPPORT VECTOR MACHINE UNTUK PREDIKSI KELEMBAPAN UDARA RATA-RATA, 2024. [Online]. Available: https://jurnal.umj.ac.id/index.php/just-it/index

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