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Hadijah
Universitas Negeri Medan
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Prediksi Harga Rumah Berbasis Machine Learning dengan Explainable AI untuk Interpretabilitas Faktor Penentu Hadijah; Wiwin Handoko; Rizty Maulida Badri
Management of Information System Journal Vol 4 No 3: Juli 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/mis.v4i3.2860

Abstract

House prices are determined by numerous interrelated factors, making it essential to develop prediction methods that are not only accurate but also interpretable by property business practitioners, investors, and policymakers. This study aims to construct a house price prediction model using a machine learning approach integrated with Explainable Artificial Intelligence (XAI) to produce predictions that are more transparent and comprehensibly interpretable. The data used in this study were derived from real property listings, incorporating several key variables including building area, land area, number of bedrooms, number of bathrooms, and garage capacity. Four machine learning algorithms were evaluated and compared, namely Linear Regression, Random Forest, XGBoost, and Gradient Boosting. The performance of each model was assessed using multiple evaluation metrics, comprising Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the Random Forest algorithm achieved the best performance, yielding an R² value of 0.7636, MAE of IDR 1.305 billion, RMSE of IDR 2.037 billion, and MAPE of 25.84%. The best-performing model was subsequently analyzed using SHapley Additive exPlanations (SHAP) to provide both global and local model interpretability, as well as Local Interpretable Model-agnostic Explanations (LIME) to explain individual predictions at the instance level. The analysis reveals that building area and land area are the most influential factors in determining house prices. The proposed approach demonstrates a measurable improvement in model transparency, rendering prediction outcomes more comprehensible and trustworthy for end users.
Edukasi Model Matematika dalam Menganalisis Harga Wajar dan Estimasi Peluang Penjatahan Saham IPO sebagai Strategi Investor Pemula Muhammad Hafiz; Yuan Anisa; Nanda Novita; Desniarti; Mahliza Nasution; Hadijah; Rizty Maulida Badri
Jurnal Pengabdian kepada Masyarakat (PEMAS) Vol. 3 No. 2 (2026): Mei 2026
Publisher : Yayasan Ran Edu Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63866/pemas.v3i2.129

Abstract

Kurangnya pemahaman investor pemula mengenai analisis saham IPO menyebabkan keputusan investasi sering dilakukan tanpa pertimbangan kuantitatif yang memadai. Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan memberikan edukasi dan pelatihan mengenai penggunaan model matematika sederhana dalam menentukan harga wajar saham IPO menggunakan metode Price Earning Ratio (P/E Ratio) serta estimasi peluang penjatahan saham melalui pendekatan probabilitas. Kegiatan dilaksanakan selama satu hari secara daring menggunakan aplikasi Zoom Meeting dengan metode ceramah, demonstrasi, praktik perhitungan, dan diskusi interaktif kepada 50 peserta yang terdiri dari mahasiswa dan masyarakat umum. Contoh saham IPO yang digunakan meliputi PT Wira Bakti Sejahtera Tbk (WBSA), PT Chandra Daya Investasi Tbk (CDIA), dan PT Hartadinata Abadi Tbk (EMAS). Hasil evaluasi pre-test dan post-test menunjukkan peningkatan rata-rata pemahaman peserta sebesar 47,8%, dari nilai rata-rata pre-test 34,4% menjadi 82,2% pada post-test. Sebanyak 74% peserta mencapai kategori nilai Sangat Baik (≥80) pada post-test. Peserta juga mampu melakukan simulasi perhitungan harga wajar dan probabilitas penjatahan secara mandiri. Kegiatan ini diharapkan dapat meningkatkan literasi keuangan masyarakat serta membantu investor pemula dalam mengambil keputusan investasi yang lebih rasional dan terukur.
Co-Authors Desniarti Mahliza Nasution Muhammad Hafiz Nanda Novita Rizty Maulida Badri Wiwin Handoko Yuan Anisa