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All Journal Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Restikom : Riset Teknik Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) SATIN - Sains dan Teknologi Informasi
Elin Haerani
Jurusan Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Klasifikasi sentimen terhadap larangan pernikahan beda agama menggunakan metode Naive Bayes Classifier Muhammad Rizki Syafapri; Elin Haerani; Iwan Iskandar; Liza Afriyanti
Computer Science and Information Technology Vol 5 No 1 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i1.6889

Abstract

Interfaith marriage is still a controversial issue in multicultural Indonesian society. The provisions of Law Number 1 of 1974 which prohibit interfaith marriages have triggered various responses in society. In 2023, the Supreme Court (MA) decided to prohibit religious courts from registering interfaith marriages. This further strengthened the controversy and sparked various reactions from the public. Platforms like Instagram have become a forum for people to express their various sentiments regarding this issue, ranging from support, rejection, to questions and doubts. This research classifies 1000 Instagram comments collected from five news social media accounts. These comments were labeled manually by an expert who works as an Indonesian language lecturer, so they were divided into 500 positive comments and 500 negative comments. After going through the text preprocessing process and TF-IDF weighting, the Naïve Bayes Classifier method succeeded in achieving the highest level of accuracy of 76% by using 10% of test data from the dataset to classify public sentiment towards the ban on interfaith marriage in Instagram comments.
Model Prediksi Jumlah Penjualan Pelumas Mesin Di PT. X Dengan Algoritma Naïve Bayes Purnama, Nilam; Fitri Insani; Elin Haerani; Iis Afrianty
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i3.8250

Abstract

Machine lubricants are essential materials used to reduce friction between two moving surfaces, improve machine efficiency, and extend the lifespan of components. This study aims to predict the sales volume of machine lubricants at PT. X using the Naïve Bayes algorithm. The data used includes attributes such as year, month, material description, total allocation, realization, and remaining allocation, with a total of 3,006 data points obtained from PT. X's Warehouse Management System (WMS). The model was tested using the 10-Fold Cross Validation method and testsing without such validation. The test results show an accuracy of 71% with 10-Fold Cross Validation, compared to 14% without validation. Additional testing showed an accuracy of 5%, with RMSE of 124.71 and MAPE of 0.95. Based on these results, it is recommended to optimize data preprocessing, such as handling data imbalance and feature normalization, to improve prediction accuracy. Furthermore, using more diverse validation techniques, such as stratified cross-validation, can provide more stable evaluations. Given that predictions are influenced solely by historical data, it is recommended to periodically update the data to keep the model relevant and accurate. This research is expected to assist PT. X in planning sales strategies and managing lubricant stock more effectively.
Co-Authors Alwis Nazir Fadhilah Syafria Febi Nur Salisah febi nursalisah Fitri Insani Fitri Wulandari Iis Afrianty Iwan Iskandar Liza Afriyanti Lola Oktavia Muhammad Affandes Muhammad Fikry Muhammad Hanif Abdurrohman Muhammad Ichsanul Bukhari Muhammad Rizki Syafapri Nailatul Fadhilah Novrianto novrianto Pizaini Purnama, Nilam Putri Mardatillah Rice Novita Rice Novita Rometdo Muzawi, Rometdo Teddie Darmizal Zuriati Ardila Safitri