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All Journal Jurnal Sarjana Teknik Informatika Jiko (Jurnal Informatika dan komputer)
Wahyu Syaifullah Jauharis Saputra
Universitas Pembangunan Nasional "Veteran" Jawa Timur
Computer Science & IT

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FORECASTING SALES USING SARIMA MODELS AT THE SINAR PAGI BUILDING MATERIALS STORE Ahmad Adiib Aminullah; Mohammad Idhom; Wahyu Syaifullah Jauharis Saputra
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8266

Abstract

Sinar Pagi Building Materials Store faces the challenge of maintaining optimal stock levels of goods to avoid excess and understock, which affects customer satisfaction and operational efficiency. This study applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) method to forecast sales in the store. Leveraging its ability to model seasonal patterns on historical sales data, various SARIMA models were analyzed and compared using the Akaike Information Criterion (AIC) and Root Mean Square Error (RMSE). The dataset is divided by a 95:5 ratio into training and testing sets for robust evaluation. The results show that the SARIMA model with SARIMA notation (p,d,q)(P,D,Q  has the best model value of (1,0,0) . This model is the most suitable model based on the lowest AIC value of 1245 and the lowest RMSE of 7,95 compared to other SARIMA models after model identification using the model looping test. For other models such as model (1,0,1)  and (0,0,1) , the AIC and RMSE values are greater, namely model (1,0,1)  with AIC 1246 and RMSE of 8,05, while model (0,0,1)  gets an AIC of 1252 and an AIC of 8,15 .The lower the AIC value, the better the model and the lower the RMSE value, the better the model. This shows a superior balance between model complexity and prediction accuracy. The model manages to capture seasonal patterns in sales data, providing a pretty good prediction framework. This study shows that the SARIMA (1,0,0)  model is effective in the accuracy of the sales forecasting process so that Sinar Pagi Building Materials Store can make more reliable sales predictions, which can help in inventory planning and marketing strategies
Prediksi Suhu Udara Kota Surabaya Menggunakan Prophet dengan Grid Search Hyperparameter Kemal Fahreza Jibran Jibran; Rizky Parlika; Wahyu Syaifullah Jauharis Saputra
Jurnal Sarjana Teknik Informatika Vol. 14 No. 2 (2026): Juni
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v14i2.32011

Abstract

Perubahan iklim meningkatkan jumlah panas yang mencapai permukaan terutama wilayah inti perkotaan seperti Surabaya yang terkena dampak urbanisasi dan fenomena Urban Heat Island. Model ini memprediksi suhu dengan cara yang seakurat mungkin, dan kondisi ini menuntut model prediksi suhu udara harian. Penelitian ini bertujuan untuk memprediksi suhu udara harian Kota Surabaya menggunakan model deret waktu Prophet yang dioptimasikan menggunakan Grid Search Hyperparameter. Sample data sebanyak 2.182 observasi setiap triwulan mulai Januari 2020 hingga akhir Desember 2025. Tahapan penenitian meliputi pengumpulan data, prapemrosesan, transformasi, pembagian data secara-kronologis, pelatihan model baseline, optimasi hyperparameter, and evaluasi kinerja. RMSE 0,868362, MAE 0,660211, dan MAPE 2,325906 adalah model prophet baseline RMSE, MAE, and MAPE. Setelah dilakukan optimasi pada parameter changepoint_prior_scale, seasonality_prior_scale, and seasonality_mode, diperoleh peningkatan kinerja dengan nilai evaluasi RMSE 0,858426, MAE 0,657965, and MAPE 2,311441. Hasil dekomposisi menunjukkan adanya tren jangka panjang serta pola musiman tahunan yang dominan. Optimasi hyperparameter terbukti secara keseluruhan meningkatkan prediksi suhu udara di Surabaya.
Co-Authors Ahmad Adiib Aminullah Kemal Fahreza Jibran Jibran Mohammad Idhom Parlika, Rizky