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All Journal Telcomatics Jurnal Logic: Logistics & Supply Chain Center
Nurulita, Salsabila
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Transportation

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Analisis Penerapan Lean Warehouse untuk Minimasi Waste pada PT Pos Logistik Indonesia Nurulita, Salsabila
Jurnal Logic: Logistics & Supply Chain Center Vol. 3 No. 1 (2024): Jurnal Logic: Logistics & Supply Chain Center
Publisher : Widyatama University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jlscc.v3i1.2276

Abstract

Divisi Value Added Service (VAS) PT Pos Logistik Indonesia seringkali tidak dapat mencapai target produksi yang ditetapkan. Identifikasi awal menunjukkan faktor penyebab keterlambatan yaitu menunggu datangnya material pendukung. Metode Lean Warehouse berdasarkan konsep Lean Manufacturing digunakan untuk mengatasi permasalahan tersebut. Tujuan dari penelitian ini adalah untuk menemukan akar dari jenis pemborosan dan mendapatkan solusi yang tepat untuk mengurangi waktu tunggu stiker dan pelabelan Bedtime Lotion. produk 100ml. Hasil penelitian menunjukkan urutan persentase waste yaitu defect waste 22,40%, overproduction waste 16,76%, motions waste 16,32%, inventory waste 14,47%, transportation waste 10,70%, process waste 10,59% dan waiting waste 8,77 %. Terdapat peningkatan pada aktivitas Non-Value Added (NVA) yang semula berjumlah 430 detik menjadi 395 detik, sedangkan pada aktivitas Value Added (VA) yang semula berjumlah 700 detik menjadi 632,8 detik setelah dilakukan perbaikan atau penurunan waktu sebesar 0,35% pada aktivitas Non-Value Added (NVA), sedangkan pada aktivitas Value Added mengalami penurunan sebesar 0,672%.
Analisis Sentimen Review Aplikasi Chat GPT dengan Memanfaatkan Algoritma Support Vector Machine Prabadaru, Alit Damar; Zahrawati, Ashifa; Jibran, Muhammad Agmal; Nurulita, Salsabila; Dewana, Nadya Cantika Apriani
Telcomatics Vol. 10 No. 2 (2025)
Publisher : Universitas Internasional Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37253/telcomatics.v10i2.11759

Abstract

This study analyses user sentiment toward the ChatGPT application based on reviews collected from the Google Play Store. The goal of this research is to classify user opinions into positive and negative categories using the Support Vector Machine (SVM) algorithm. The dataset was obtained through web scraping and processed using several text preprocessing steps, including case folding, tokenization, stopword removal, and stemming. The TF-IDF method was applied to convert the text into numerical feature vectors suitable for machine learning models. A linear SVM model was used to perform sentiment classification due to its effectiveness in handling high-dimensional text data. The results of the evaluation show that the linear SVM provides stable and accurate performance when identifying sentiment in user reviews. The findings also indicate that TF-IDF features contribute significantly to improving model accuracy. Overall, this research concludes that SVM is a suitable and reliable method for sentiment analysis of application reviews. The outcomes can help developers understand user perceptions and improve the quality of the ChatGPT application based on the insights obtained
Co-Authors Dewana, Nadya Cantika Apriani Jibran, Muhammad Agmal Prabadaru, Alit Damar Zahrawati, Ashifa