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All Journal OKTAL : Jurnal Ilmu Komputer dan Sains Jurnal Teknik Informatika dan Teknologi Informasi
Rangga Febri Kasih
Universitas Indo Global Mandiri
Astronomy Chemistry Computer Science & IT Electrical & Electronics Engineering Social Sciences

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Analisa Akurasi Penggunaan Metode Single Eksponential Smoothing Untuk Perkiraan Penjualan Minyak Solar (HSD) Putri Octaria; Rangga Febri Kasih; Terttiavini
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 2 No 05 (2023): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

This study analyzes the accuracy of the Single Exponential Smoothing (SES) method in predicting diesel oil (HSD) sales. The SES method is used in the oil and gas industry for inventory management, strategic decision making, financial planning, and market analysis. Historical diesel sales data is used to train and test the SES model in predicting future sales. The accuracy of the method is measured using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The forecasting method used is Single Exponential Smoothing, which uses the average value of past data to estimate future values. Three constant values of α (0.1, 0.6, and 0.9) were used, and the prediction results were evaluated using MAD, MSE, and MAPE. The results show that α = 0.1 gives the smallest MAPE, signifying higher accuracy in predicting HSD oil sales.
Analisis Sentimen Kepuasan Pengguna Lintas Rel Terpadu (LRT) menggunakan Metode Support Vector Machine Rangga Febri Kasih; Rendra Gustriansyah; Zaid Romegar Mair
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 2 (2025): Agustus: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i2.5832

Abstract

This study aims to analyze public sentiment toward the Palembang LRT service by utilizing user reviews available on the Google Maps platform. Sentiment analysis was conducted to understand public perceptions of service quality, which can serve as a basis for decision-making in improving public transportation services. The method employed in this research is the Support Vector Machine (SVM) algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) for word weighting, which classifies reviews into two sentiment categories: positive and negative. A total of 500 reviews were randomly selected as the dataset and processed through a text preprocessing stage, including data cleaning, tokenization, and stopword removal to enhance data quality. The SVM model was then evaluated using an 80:20 split for training and testing, achieving an accuracy of 91%, which indicates excellent performance in identifying sentiment patterns in the Indonesian language. The findings of this study confirm that SVM-based approaches are effective and reliable for sentiment analysis in the context of public transportation. These results provide practical contributions for Palembang LRT management, as insights into public sentiment can be used as a strategic reference for decision-making, reputation management, and improving service quality based on user needs. Future research is recommended to expand the dataset, include neutral sentiment categories, and compare SVM performance with other machine learning algorithms to achieve more comprehensive and robust results.
Co-Authors Putri Octaria Rendra Gustriansyah Terttiavini