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Dani Leonidas
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Humanities Languange, Linguistic, Communication & Media

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ANALISIS FAKTOR PENGENDALIAN KUALITAS TEH HITAM PADA PT. ABC DENGAN MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) Anri Yosafat Sitompul; Dani Leonidas; Ekra Sanggala
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 3 (2023): Jurnal Cahaya Mandalika
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i3.2074

Abstract

Indonesia is currently the seventh-largest producer in the world. One that produces tea in Indonesia is PT ABC. PT ABC is a State-Owned Enterprise engaged in the management, processing, and marketing of plantation products. The commodities produced by PT ABC are tea, rubber, palm oil, and cocoa. However, the author will focus on discussing black tea production at PT ABC The method used in this study is a qualitative method. The method used in this research is the Principal Component Analysis (PCA) method. Principal Component Analysis (PCA) is one-way identifying patterns in correlated data and expressing the information in a way that emphasizes discrepancies and distinctions. This method will analyze the priority factors for improvement that cause black products not to pass QC from several variables that have been setInterpretation of the results of factor analysis based on the value or magnitude of the loading factor previously described. Thus the 12 variables have been reduced to 2 factors that can affect black tea production does not pass QC at PT ABC, where factor 1 is a process factor then factor 2 is a raw material factor. factor analysis there are two factors that are priorities for improvement factor 1 with variables picking tea shoots exposed to sunlight (0.884) and room temperature regulation at the time of oxidation/fermentation is erratic (0.895). factor 2 with variable coarse tea shoots (0.957), Aroma of smoke in tea (0.952).
Peramalan Jumlah Persediaan Sparepart Carburetor Assy dengan Metode Exponential smoothing Brown dengan Genetic Algorithm (order cross over dan one point mutation) di PT Hitachi Astemo Bekasi Powertrain System Dian Miftahul Jannah; Dani Leonidas; Ekra Sanggala
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 5 No. 1 (2024): Jurnal Cahaya Mandalika
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v5i1.2378

Abstract

Abstrac: One of the business people in the automotive sector who continues to move as a manufacturing company in the production of vehicle spare parts is PT ABC which is the largest manufacturer of spare parts for several vehicle companies such as Astra Honda Motor (AHM), Yamaha and so on. One type of production from PT ABC is the carburator assy which functions to mix fuel and air to produce propulsion for vehicles. Seeing its very important function, PT ABC places the carburator assy as a type of spare part that must be produced every month In research, a method is needed to solve the problems that occur. Research methods are scientific ways to obtain data / information as it is and not as it should be, with certain purposes and uses. This research is based on descriptive research. This study used the double exponential smoothing Brown method for forecasting carburator assy 052A with Alpha assessment using Genetic Algorithm order Cross over PT ABC.Calculations carried out for forecasting the Double exponential smoothing Brown method using Alpha values of 0.36 and 0.32 obtained randomly resulted in RMSE values that were greater than Alpha values obtained using Genetic Algorithms, namely 103.7234 for Alpha values of 0.36 and 117.6129 for Alpha values of 0.32. Therefore, it can be interpreted that determining the Alpha value using the Genetic Algorithm method can be a solution to reduce the RMSE error value in Double exponential smoothing Brown forecasting. In research conducted at PT ABC by analyzing the influence of Genetic Algorithms in determining α values, several conclusions can be obtained, namely as follows: Determination of α value for the calculation of Double exponential smoothing Brown using the Genetic Algorithm obtained an Alpha value of 0.43 with the smallest error result of 95.29633. Calculations MADe on Genetic Algorithms can determine α value that can minimize errors in forecasting compared to α values without Genetic Algorithms.
Co-Authors Anri Yosafat Sitompul Dian Miftahul Jannah Ekra Sanggala