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All Journal JIMAT (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha Journal of Artificial Intelligence and Digital Business
Handika, Muhamad
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Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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Sistem Informasi Akuntansi Penjualan Umkm Berbasis Fintech (Studi Kasus Umkm Di Singaraja) Handika, Muhamad; Musmini, Lucy Sri
JIMAT (Jurnal Ilmiah Mahasiswa Akuntansi) Undiksha Vol 12, No 2 (2021)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jimat.v12i2.29496

Abstract

A research on the development of sales accounting information system of UMKM operation was neccesrily done in order to gain an accurate information as to the real condition of UMKM  at a certain time. This study uses a descriptive approach in researching and analyzing the phenomenon of the UMKM industry. Support the growth of the UMKM operation. The approach of this study uses uses the scope of topic covering its width and depth,which is in the form of a case study. The subject of this study was UMKM join to Grab, and the object of the study was sales accounting information system of UMKM, that is, the UMKM itself becoming the the case study.The data of this study were collected through direct observation and interview. The data that had been collected were annalysed qualitatively, which resulted in a desciption of sales accounting information systemof UMKM that had been developed. From the analysis of the data, it can be concluded that: the sales accounting information system possed by the UMKM is currently very simple and not systematic yet. Viewed from the information obtained as well as from the current documents of the UMKM, the manager does not know well about the development and the working system of his business.The sales accounting information system of the UMKM needs to be organised in a much better way, so that the business can provide information about the sale, cash input, cash output, supply, sale capital prize, and gross benefit for each period. Then, if the manualsystem has been designed with adequate information output, the computer-based system can be made. In the computer-based sales accounting information system, the staff is only concerned with entering the input, an then the program will process the data being entered. At last, automatically the output than can be used as the basis for decision making by the manager can be obtained.   
Analisis Perbandingan Kinerja Algoritma Linear Regression, Random Forest, dan XGBoost dalam Prediksi Harga Rumah Fauzi, Muhamad Rizki; Handika, Muhamad; Awinanto, Alfian; Wahidin, Ahmad Jurnaidi; Rahmatullah, Beni; Kurniawati, Ika
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.3620

Abstract

Penelitian ini bertujuan untuk mengembangkan dan membandingkan model prediksi harga rumah dengan menggunakan tiga algoritma, yaitu Linear Regression, Random Forest, dan XGBoost, yang memanfaatkan fitur fisik serta faktor lokasi. Variabel yang dianalisis mencakup berbagai karakteristik properti seperti ukuran tanah, luas bangunan, jumlah kamar tidur, kondisi bangunan, serta aspek lokasi seperti kedekatan dengan pusat kota dan akses ke fasilitas publik. Tahapan penelitian ini mencakup pembersihan data untuk mengeliminasi data yang tidak sesuai, transformasi variabel agar seragam, dan rekayasa fitur baru yang dapat meningkatkan ketepatan prediksi model. Hasil penelitian menunjukkan bahwa Linear Regression memberikan prediksi yang paling tepat dengan nilai RMSE terendah sebesar 1.150,87, lebih baik dibandingkan dengan Random Forest yang menghasilkan RMSE sebesar 1.183,11 dan XGBoost yang mencapai 1.200,33. Linear Regression menunjukkan keunggulan karena mampu menangani hubungan linier antar variabel dengan harga rumah. Walaupun Random Forest dan XGBoost lebih efektif untuk menangani hubungan non-linier, Linear Regression lebih optimal dalam penelitian ini karena hubungan antar variabel lebih sederhana. Penelitian ini diharapkan memberikan wawasan yang bermanfaat bagi pengembang properti dan lembaga keuangan dalam pengambilan keputusan yang lebih efisien dan akurat, serta memberikan perkiraan harga rumah yang lebih objektif. Model ini juga dapat digunakan untuk memperkirakan harga rumah di masa depan dengan lebih tepat, yang pada gilirannya dapat mengurangi ketidakpastian dalam pasar properti dan memfasilitasi pengambilan keputusan yang lebih berbasis data.
Co-Authors Ahmad Jurnaidi Wahidin Awinanto, Alfian Beni Rahmatullah Fauzi, Muhamad Rizki Ika Kurniawati Lucy Sri Musmini