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All Journal Transformasi: Jurnal Pengabdian Masyarakat Jurnal Informatika dan Teknik Elektro Terapan RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Applied Technology and Computing Science Journal Journal of Electrical Engineering and Computer (JEECOM) Joutica : Journal of Informatic Unisla Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sistem Informasi Dan E-Bisnis Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Mustain Mustain
Universitas Islam Lamongan
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Social Sciences

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SISTEM PREDIKSI PERSEDIAAN STOK BARANG DI BUBIN CATERING MENGGUNAKAN METODE SES DAN WMA Istiana Nur Khasanah; Purnomo Hadi Susilo; Mustain Mustain
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

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Abstract

Raw material inventory management in catering businesses required an accurate planning system to prevent stock shortages and overstock conditions that could disrupt operational activities. This study aimed to develop a web-based inventory prediction system by implementing the Weighted Moving Average (WMA) and Single Exponential Smoothing (SES) methods and to compare the accuracy of both methods using the Mean Absolute Percentage Error (MAPE). The data used in this study were historical raw material usage records from Bubin Catering covering the period from March 12, 2025, to May 8, 2025. The system was developed using the Laravel framework and included inventory management, incoming goods, outgoing goods, prediction, and detailed calculation features. The results of Black Box Testing showed that all system functions operated as expected. Based on the evaluation of five raw material samples, the WMA method produced MAPE values of 33%, 36%, 24%, 55%, and 41%, while the SES method produced the best MAPE values of 32.53%, 36.80%, 56.70%, 61.00%, and 46.60%. Overall, the WMA method produced lower prediction errors for four of the five raw material samples, whereas the SES method performed better for only one sample. Therefore, the WMA method was more suitable for predicting inventory requirements using the historical data of Bubin Catering in this study.
Predicting Daily Goride Orders From Online Hours, Day Type, And Weather Purnomo Hadi Susilo; Mochammad Sihabudin Firdaus Rifani; Affan Bachri; Mustain Mustain; Mochammad Sholikhin
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 2 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i2.16937

Abstract

Daily order fluctuations make it difficult for motorcycle ride-hailing drivers to plan online working time. This study develops an interpretable multiple linear regression model to predict the number of daily GoRide orders from online hours, day type, and weather. The dataset contains 100 driver-day observations collected from eight GoRide driver-partners operating in central Tulungagung. To preserve observation order, the first 70 records were used for training and the last 30 for testing. Day type and weather were expressed as binary indicators, yielding the operational equation ŷ = −1.9887 + 1.0036X₁ + 1.4187Dweekend + 2.3968Dclear. The model obtained R² = 0.8440 and MAPE = 15.62% on training data. On the held-out test set, it achieved R² = 0.8341, MAPE = 13.48%, MAE = 0.989 orders, and RMSE = 1.322 orders. Twenty of 30 predictions (66.7%) were within one order of the actual value, and 26 (86.7%) were within two orders. However, predictions were positively biased by 0.784 orders on average, with 23 of 30 observations overpredicted and a maximum absolute error of 4.458 orders. The model was integrated into a Flask–MySQL web application for data management and interactive prediction. The results show that a compact linear model can provide useful and explainable driver-level estimates, but its reliability remains conditional on the small, locally collected sample. Rolling validation, driver-aware evaluation, richer temporal variables, and count-regression benchmarks are required before operational deployment at scale.
Co-Authors Affan Bachri Ahmad Fathoni Danang Bagus Reknadi Istiana Nur Khasanah Kurnia Yahya Kurnia yahya M Munif Mauladi , Kemal Farouq Mochammad Fajrin Mochammad Sholikhin Mochammad Sihabudin Firdaus Rifani Munif Munif MUNIF ROCHMAWANTO Munif, Munif Purnomo Hadi Susilo, Purnomo Hadi Retno Wardhani Rifki Rifki Susilo , Purnomo Hadi