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All Journal Journal of Industrial and Engineering System J-Intech (Journal of Information and Technology)
Raya, Dimas
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Industrial & Manufacturing Engineering Library & Information Science Transportation

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Analisis Upaya Menurunkan Cacat Produk Crank Case LH pada Proses Die Casting dengan Metode PDCA dan FMEA di PT. Suzuki Indo Mobil/Motor Yunan, Agustinus; Raya, Dimas; Rosihan, Rifda Ilahy
Journal of Industrial and Engineering System Vol. 1 No. 1 (2020): Juni 2020
Publisher : Program Studi Teknik Industri, Fakultas Teknik, Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jies.v1i1.160

Abstract

Suzuki Indo Mobil / Motorcycle is the largest motorcycle manufacturing industry in Indonesia today. Die Casting is one of the departments that is important in the manufacture of motorcycle parts. In the Die Cating section, there are a number of types of defects that exceed the company tolerance standards. During the February to July period, 228 pcs of defect products were found. For this reason, it is necessary to determine the most dominant factor in the occurrence of defects and determine the proposed improvement of the root problem. PDCA is a useful tool for continuous improvement and FMEA or Failure Mode Analysis is a tool that is often used in quality improvement methods. FMEA serves to determine the consequences of failure associated with failure in the Crank Case LH. There are three types of defects found, namely Chipped, Cracked, Wrinkled. With pareto diagram, it is known that there are three types of defect Crank Case LH which are the most dominant, namely: 9.9% chipped, Crack 6.75%, Wrinkles 4.72%. aluminum & mold is too low, engine filling time is too long, Crank Case LH Cracks the surface of the rough molding machine, engine pressure is too large, Crank Case LH Machine wrinkles Less pressure. Improve made is to make a standard number of Crank Case LH setting parameters of the engine and required SOP.
Application of K-Nearest Neighbor Algorithm For Sentiment Analysis On Free Fire Online Game Based On Google Play Store Reviews Raya, Dimas; Yusnita, Amelia; Haristyawan, Ivan
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1882

Abstract

The swift expansion of the digital gaming sector, especially online games like Free Fire, has produced extensive user feedback via platforms like the Google Play Store. This research utilizes the K-Nearest Neighbor (KNN) algorithm to conduct sentiment analysis on 5,000 user reviews, with the goal of assessing its classification effectiveness. Following preprocessing (case folding, Text Cleaning, tokenization, stopword Removal, stemming), the data was converted using TF-IDF and balanced through SMOTE. Experimental findings indicate that KNN attained a peak accuracy of merely 36.53% (at k = 14), reflecting weak performance with high-dimensional textual data. In contrast, Logistic Regression attained a notably higher accuracy of 88%, showcasing its dominance for this task. The results offer perspectives for game developers to assess user feelings and emphasize the significance of selecting suitable machine learning models. Future research should investigate advanced classifiers like SVM, Random Forest, or deep learning methods to enhance accuracy.
Co-Authors Amelia Yusnita Haristyawan, Ivan Rifda Ilahy Rosihan Yunan, Agustinus