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All Journal Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Kaunia: Integration and Interconnection of Islam and Science Journal Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Ekasakti Pareso Jurnal Akuntansi SmartComp Edu Komputika Journal
Pratama, Irfan
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Analysis of Forecasting Methods on Rice Price Data at Milling Level According to Quality Aulia, Indira Dhekawanti; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.4763

Abstract

Rice is a primary source of carbohydrates for many Indonesians, and its prices often surge due to uncontrolled demand. Therefore, the government is crucial in monitoring rice prices to maintain stability. Information technology, particularly data mining such as forecasting, is essential for providing accurate information on future rice prices. It will assist various stakeholders in making informed pricing policy decisions. This study employs Random Forest Regression and Gradient Boosting Regressor methods to predict rice prices using a dataset that includes monthly average rice prices at milling levels, categorized by quality (Premium and Medium), spanning from January 2013 to April 2024. The dataset consists of 136 rows, each representing a unique combination of year, month, and quality, and is stored in CSV format. Methodological steps include data collection, preprocessing, modeling, and model evaluation using monthly average rice prices at milling levels based on quality, including premium and medium grades. The results from Random Forest Regression indicate Root Mean Square Error (RMSE) values of 24.90 for premium rice and 25.47 for medium rice. The study reveals that Random Forest Regression outperforms Gradient Boosting Regressor in this context. Future research should explore additional prediction methods and consider other variables influencing rice prices to enhance model accuracy.
Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports Pradana, Michael Anggun Kado; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.7879

Abstract

Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.
Analisis Segmentasi Perilaku Pembeli Marketplace Shopee Menggunakan Model RFM dan Algoritma K-Means Clustering pada Toko Retail Online XYZ di Yogyakarta Amymaztura, Rezky; Pratama, Irfan
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11903

Abstract

Perkembangan Marketplace Shopee di Indonesia membuat salah satu Toko Retail Online XYZ yang bergerak di bidang fashion di Yogakarta memerlukan strategi pemasaran yang efektif agar tepat sasaran. Data transaksi shopee toko tersebut hanya berakhir menjadi arsip penjualan dan belum diolah secara optimal sehingga mengalami kesulitan dalam mengelompokkan dan memahami perilaku pelanggan. Penelitian ditujukan untuk melakukan segmentasi perilaku pelanggan menggunakan pendekatan Customer Relationship Management (CRM) analitik dengan model RFM (Recency, Frequency, Monetary) dan Algoritma K-Means Clustering. Penelitian menggunakan 85.781 data transaksi periode 2025, yang kemudian dilakukan tahap preprocessing dan pembersihan outlier dengan Interquatile Range (IQR) sehingga menjadi 43.036 data. Evaluasi gabungan dilakukan untuk menentukan klaster optimal menggunakan metode Elbow, Silhouette Score dan Davies-Bouldin Index. Penelitian menetapkan hasil k=3 sebagai jumlah klaster optimal dengan titik siku pada grafik yang ditunjukkan pada grafik Elbow serta divalidasi menggunakan Silhouette Score dengan nilai paling tinggi 0,4949 dan nilai minimum Davies-Bouldin Index 0,7429. Mayoritas pelanggan memiliki karakteristik one-time buyers, didasari oleh nilai Frequency yang relatif sama sehingga nilai variabel Recency dan Monetary lebih mempengaruhi segmentasi. Segmen yang dihasilkan adalah Klaster 0 (New Customer / Potential), Klaster 1 (At Risk Customer), Klaster 2 (Lost Customer). Penelitian berhasil mengubah ribuan data transaksi menjadi informasi yang strategis dengan menggambarkan karakteristik setiap segmen pelanggan sehingga perusahaan dapat menerapkan strategi pemasaran dengan tepat.
Co-Authors Ade Kurnia, Dian Albert Yakobus Chandra Alifudin, Arif Amymaztura, Rezky Andre Bustari Asanah, Nurul Aulia, Indira Dhekawanti Ayuningsih, Ekatri Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Manurung, Syalom Kristian Nandasari, Dayang Nugroho Budi Wibowo Odi Nurdiawan Paneo, Anto Farwanto Pradana, Michael Anggun Kado Prasetyaningrum, Putri Taqwa Pratama, George Recksy Sandy Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Wuri Handayani