Rancang Bangun Robo-Advisor untuk Pendanaan Rumah Syariah Berbasis Aplikasi Bergerak

JAYA KUNCARA ROSA SUSILA; PUJO LAKSONO; MUHAMMAD AFIT

doi:10.26760/mindjournal.v7i1.98-110

Rancang Bangun Robo-Advisor untuk Pendanaan Rumah Syariah Berbasis Aplikasi Bergerak

JAYA KUNCARA ROSA SUSILA, PUJO LAKSONO, MUHAMMAD AFIT

Sari

ABSTRAK

Pembiayaan properti berbasis syariah saat ini meningkat seiring dengan kesadaran ummat muslim dalam menjalankan agamanya. Persetujuan pembelian properti syariah lebih diniatkan untuk membantu ummat mendapatkan rumah namun ini menjadi kendala karena ditemukan ada cicilan yang macet setelah beberapa waktu. Perlu dibangun sistem pendukung keputusan yang berfokus pada pendampingan dan rekomendasi skema pembiayaan properti syariah. Parameter masukan sebanyak 12 variabel profil calon pembeli yang diambil dari formulir surat persetujuan pembelian rumah (SPPR), sedangkan targetnya adalah persetujuan pembelian yang diperoleh dari sistem kecerdasan artifisial. Untuk mendapatkan model yang optimal, dilakukan perbandingan 3 model, yaitu logaritmic regression, decision tree, dan random forest. Random forest memiliki tingkat akurasi tertinggi, yaitu 90,831%. Framework Flask digunakan sebagai aplikasi web yang dikonversi menjadi aplikasi bergerak.

Kata kunci: kecerdasan artifisial, robo-advisor, sistem pendukung keputusan, random forest, flask

ABSTRACT

Sharia-based property financing is currently increasing along with the awareness of Muslims in practicing their religion. The approval for the purchase of sharia property is intended to help the community get a house, but this is an obstacle because it is found that there are installments that are stuck after some time. It is necessary to build a decision support system that focuses on mentoring and recommending sharia property financing schemes. The input parameters are 12 profile variables of prospective buyers taken from the house purchase approval letter form (SPPR), while the target is purchase approval obtained from an artificial intelligence system. To get the optimal model, a comparison of 3 models was made, namely logarithmic regression, decision tree, and random forest. Random forest has the highest accuracy rate, which is 90.831%. The Flask framework is used as a web application that is converted into a mobile application.

Keywords: artificial intelligence, robo-advisor, decision support system, random forest, flask

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Referensi

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DOI: https://doi.org/10.26760/mindjournal.v7i1.98-110