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All Journal JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Aprillia Dwi Ardianti
Universitas Nahdlatul Ulama Sunan Giri, Bojonegoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Peramalan Penjualan Obat dengan Menggunakan Metode Single Moving Average Alvinatul Hidayah; Mula Agung Barata; Aprillia Dwi Ardianti
Journal of Information System Research (JOSH) Vol 7 No 1 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i1.8256

Abstract

Luas Nusa Pharmacy faces challenges in managing drug inventory due to unpredictable demand fluctuations, often leading to overstocking or shortages. This situation affects operational efficiency and customer satisfaction. Therefore, a forecasting method is needed to help predict stock requirements more accurately. Forecasting is the process of estimating future needs based on historical data analysis, aimed at supporting decision-making in inventory management. This study employs the Single Moving Average (SMA) method to forecast drug stock at Luas Nusa Pharmacy. Weekly data from 10 best-selling drugs, namely Sanmol Tab, Andalan Biru, Promag Tab, Pirocam, Voltadex, Wiros, Tolak Angin, Stanza, Kalmethasone, and Antangin, were used as the basis for calculations over the past year. The study tested three forecasting periods: 3, 4, and 6 weeks. The results indicate that the 4-week period provides the most accurate prediction with the lowest error values: MAD of 34.80986, MSE of 1797.98, and MAPE of 13.80044, achieving an accuracy rate of 86.20%. The predicted drug stock for the following week, based on the 4-week period, is 224 units. With its high accuracy, the 4-week SMA method is recommended as an effective approach to help Luas Nusa Pharmacy manage drug inventory more efficiently. The implementation of this method is expected to minimize the risk of overstocking or shortages, improve operational efficiency, and ensure optimal service to the community.
Studi Komparatif Algoritma Random Forest dan Logistic Regression dalam Analisis Sentimen Ulasan Aplikasi E-Wallet Dana Diah Fitriani; Afril Efan Pajri; Aprillia Dwi Ardianti
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9517

Abstract

The increasing use of digital wallets in Indonesia has led to a growing number of user opinions expressed, including on the DANA platform in the Play Store. These reviews reflect users' experiences and satisfaction levels, necessitating sentiment analysis to comprehend public opinions about the application’s service quality. The study conducts an analytical comparison between Random Forest and Logistic Regression methods in classification sentiments for DANA application. Data was obtained through scraping techniques, resulting in 2,068 reviews after the cleaning process. The analysis stages include text preprocessing, labeling based on review scores, weighting using TF-IDF, and modeling with both algorithms. The evaluation results demonstrate that Random Forest obtains an accuracy of 86.23%, while Logistic Regression obtains an accuracy of 84.54%. Both models are capable of classifying positive sentiments well but are less optimal in detecting negative sentiments. Random Forest shows higher performance compared to Logistic Regression within the task in sentiment analysis for DANA app reviews. Thus, we can conclude that using the random forest algorithm is able to produce accurate sentiment analysis and can act as a basis for making decisions in further research
Co-Authors Afril Efan Pajri Alvinatul Hidayah Diah Fitriani Mula Agung Barata