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JURIKOM (Jurnal Riset Komputer)
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 2407389X     EISSN : 27157393     DOI : http://dx.doi.org/10.30865/jurikom.v7i1.1927
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
JURIKOM (Jurnal Riset Komputer) membahas ilmu dibidang Informatika, Sistem Informasi, Manajemen Informatika, DSS, AI, ES, Jaringan, sebagai wadah dalam menuangkan hasil penelitian baik secara konseptual maupun teknis yang berkaitan dengan Teknologi Informatika dan Komputer. Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 44 Documents
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Search results for , issue "Vol. 12 No. 4 (2025): Agustus 2025" : 44 Documents clear
Seasonal Pattern Analysis In Bolu House Sales Using Seasonal Adjustment Method Andrean, Stefhany; Sembiring, Muhammad Ardiansyah; Sena, Maulana Dwi
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8937

Abstract

Rumah Bolu is a business engaged in the sale of sponge cakes located on Jalan Perumahan Duta Mas 9, Sei Kamah II, Kec. Sei Dadap, Asahan Regency, North Sumatra 21263. The problems faced by Rumah Bolu include if the stock decreases, Rumah Bolu risks losing sales and customer trust, while if the stock is excessive but sales are low it can cause wasteful costs, decreased product quality and financial losses that hinder maximum profits. During certain seasons such as weekends or big celebrations, the demand for sponge cakes can increase drastically which risks causing shortages of raw material stock and delays in marketing. Conversely, on Monday to Friday, sales tend to decrease which can result in stock piling up and increase the risk of loss. With the need for a forecasting system, Rumah Bolu can adjust sales based on seasonal demand patterns so as to avoid the risk of shortages or excess stock, optimize operational costs and increase profits to the maximum. To predict the sale of sponge cake, a forecasting method is applied, namely the Seasonal Adjustment method, which is used when the time series data pattern obtained has a seasonal pattern. The seasonal pattern is a fairly unique sales pattern because it can be seen when there is a certain increase in a certain season. The purpose of this study is to design a sponge cake stock forecasting application with the Seasonal Adjustment method and to apply a forecasting method to predict the stock of sponge cakes at Rumah Bolu using PHP and its MySQL database. The research method used in this study is a quantitative research method. The results of the Seasonal Adjustment calculation prediction on banana sponge cakes for the January 2025 period were 424 with a MAPE of 5.53%. Then the results of the Seasonal Adjustment calculation prediction on pandan sponge cakes for the January 2025 period were 73.67 with a MAPE of 3.84% and the results of the Seasonal Adjustment calculation prediction on birthday sponge cakes for the January 2025 period were 142.67 with a MAPE of 3.03%.
Implementation of the Single Exponential Smoothing Forecasting Method for Product Sales Prediction at UD XYZ Wiguna, Deny Arya; Manurung, Nuriadi; Latiffani, Chitra
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8939

Abstract

In the competitive world of business, demand forecasting is a critical aspect of inventory management. Errors in estimating demand can result in various negative impacts, such as excess inventory that increases storage costs or stock shortages that can lead to lost sales opportunities. UD. Haili often faces challenges in managing inventory due to unpredictable fluctuations in demand. In some cases, demand for certain products experiences sudden spikes, while at other times demand drops sharply. This creates the risk of inventory imbalances, leading to increased operational costs and the potential loss of customers. The objective of this study is to apply the single exponential smoothing (SES) method in forecasting demand for goods so that the forecasting results can provide strategic recommendations for UD. Haili in managing inventory based on the forecasting results. The forecasting results using the developed forecasting application indicate that round shrimp paste has the smallest MAPE, using an alpha of 0.4 with 1.26%, square shrimp paste uses an alpha of 0.1 with 1.98%, and triangular shrimp paste uses an alpha of 0.1 with 4.53%.
Clustering Analysis Of Toddler Nutritional Status Using The K-Means Method On Posyandu Data Nanda, Yurizka Sri; Rahmadani, Nurul; Muhazir, Ahmad
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.8947

Abstract

The issue of toddler nutritional status remains a serious concern because it can affect children's health and development, including the risk of stunting and cognitive impairment. At the Tanjung Asri Village Health Center, nutritional status is still recorded manually, which is inefficient and prone to classification errors. This study aims to develop a system for classifying the nutritional status of infants using the K-Means Clustering method based on desktop software to simplify the classification of nutritional status into three categories: malnourished, moderately nourished, and well-nourished. This study uses a quantitative approach with primary data from 100 infants collected through observation and interviews in May and June 2025. The clustering process was performed using RapidMiner with the parameter k = 3. The test results showed that the K-Means method was able to produce accurate centroid centers consistent with manual results. In May 2025, there were 22 infants with poor nutrition, 21 infants with moderate nutrition, and 7 infants with good nutrition, while in June 2025, there were 27 infants with poor nutrition, 8 infants with moderate nutrition, and 15 infants with good nutrition. The developed system has proven effective in supporting the classification and monitoring of infant nutritional status in a more objective and efficient manner.
Analisis Sentimen Terhadap Kinerja Wakil Presiden Pada Tahun 2025 Menggunakan Metode Support Vector Machine Fani, Try; Nasution, Yusuf Ramadhan
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 4 (2025): Agustus 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i4.9021

Abstract

This study examines public perception of the performance of Indonesia’s Vice President in 2025 by utilizing opinion data from social media X/Twitter. The research addresses the lack of up-to-date quantitative insights into public sentiment polarity following the inauguration, particularly regarding Gibran Rakabuming Raka, whose appointment has sparked mixed reactions. The objective of this study is to classify sentiments as positive or negative and to evaluate the performance of the classification model on a corpus of user posts. The dataset consists of 898 tweets collected using the hashtags #wapres, #Gibran, and #WapresGibran. Data processing involved cleaning the text, converting all characters to lowercase (case folding), tokenization, normalization, removal of stopwords, and stemming. Feature representation was carried out using Term Frequency–Inverse Document Frequency (TF-IDF), while modeling was performed with the Support Vector Machine (SVM) algorithm. Results show 647 tweets with positive sentiment and 251 tweets with negative sentiment, indicating a generally positive tendency while maintaining some diversity of opinion. The SVM model achieved an accuracy of 80.68%, demonstrating reliable performance on high-dimensional textual data. These findings provide a concise overview of public opinion that can serve as a reference for policymakers and government communication strategies. The study’s main contribution lies in offering empirical evidence from social media on sentiment dynamics toward the Vice President’s performance, while also highlighting the effectiveness of combining TF-IDF and SVM in contemporary political sentiment analysis.

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