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All Journal Jurnal Manarang Manajemen dan Bisnis SAGA: Journal of Technology and Information Systems
Muhammad Ashdaq
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science Other

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Analisis Pengendalian Persediaan Bahan Baku Pada Pabrik Tahu UD. Sumber Rezeki Majene Ervyna, Ervyna Sari; Muhammad Ashdaq; Sri Utami Permata; Novia Sandra Dewi; Muhammad Nadir; Muhammad Fauzan
Jurnal Manarang Manajemen dan Bisnis Vol 2 No 2 (2024): Manarang : Manajemen & Bisnis - April
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jurnal manarang.v2i2.3787

Abstract

Penelitian ini bertujuan untuk mengetahui bagaimana Analisis Pengendalian Persediaan Bahan Baku Pada Pabrik Tahu UD Sumber Rezeki Majene pendekatan penelitian adalah pendekatan kuantitatif dengan menggunakan metode eoconomic order quantity dan re order point lokasi penelitian dilakukan di pabrik tahu UD Sumber Rezeki Majene sebanyak 8 orang karyawan. Pengambilan data pembelian dan pemakaian pabrik menggunakan metode wawancara yang dimana semua data yang diambil akan dihitung menggunakan microsof exel dan kalkulator adapun sumber data yang diambil dari data sekunder teknik pengumpulan data yang dilakukan dengan cara wawancara,observasi,dan dokumentasi yang dilakukan oleh peneliti. Hasil penelitian ini menunjukkan bahwa pengendalian persediaan bahan baku dengan menggunakan metode economic order quantity dan re order point pada pabrik tahu UD Sumber Rezeki dapat meminimumkan total biaya persediaan pada pabrik UD Sumber Rezeki adalah benar.
Sentiment Analysis on Erspo Jersey in X Using Machine Learning Algorithms Andi Asrida Reskinah. D; Najib, Marhawati; Muhammad Ashdaq
SAGA: Journal of Technology and Information System Vol. 2 No. 3 (2024): August 2024
Publisher : CV. Media Digital Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58905/saga.v2i3.334

Abstract

This research conducts a sentiment analysis on Erspo jerseys using machine learning algorithms on the X platform. The objective is to identify the public's sentiment and compare the performance of three algorithms: Naïve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Data was collected through web scraping of tweets between January and September 2024, containing keywords related to Erspo. Using a lexicon-based approach, the preprocessing steps involved cleaning, tokenizing, normalizing, and labeling data into positive, negative, and neutral sentiments. Results show that the Naïve Bayes algorithm provided the highest accuracy in sentiment classification, followed by SVM and KNN. Positive sentiment primarily centered on product loyalty, while negative sentiment largely criticized jersey design and quality. The findings offer important insights for Erspo stakeholders to refine marketing strategies and product improvements. This study highlights the potential of machine learning in analyzing consumer opinions at scale, making it a valuable tool for real-time consumer feedback analysis.
Co-Authors Andi Asrida Reskinah. D Ervyna, Ervyna Sari Muhammad Fauzan Muhammad Nadir Najib, Marhawati Novia Sandra Dewi Sri Utami Permata