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All Journal AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Nugroho, Muhammad Rivan
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance

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K-Means Algorithm for Clustering Students Based on Areas of Expertise (A Case Study) Yuyun, Yuyun Yusnida Lase; Sinaga, Christian Roi Tua; Nugroho, Muhammad Rivan; Ridha, Muhammad Rasyid
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 1 No. 1 (2024): VOLUME 1, NO 1: JUNE 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v1i1.23

Abstract

Every student of the Software Engineering Technology study program, the Computer and Informatics Department of the Politeknik Negeri Medan in the lecture process is required to take all the courses in the curriculum. From the courses in the curriculum, there are several subject groups that shape student expertise, such as Software Engineering, System Analyst, Database Administrator, and IT Entrepreneur. It is hoped that this expertise will later be used as a reference by students in carrying out their thesis at the end of lectures. The purpose of this study is to group students based on their respective expertise based on data processing of student course scores related to each skill. The data used is data on student scores for batches of 2020 and 2021 with a range of courses from semester 1 to semester 3. The data is tested by implementing the K-Means algorithm. The results of the tests that have been carried out show the grouping of students based on their expertise, with 7 times the number of iterations. Then, data testing was carried out with the RapidMiner application to get the results of the distribution of cluster members obtained, including 12 students occupying Software Engineer skills, 21 students with System Analyst skills, 5 students with Database Administrator skills, and 31 students with IT Entrepreneur skills, along with the distribution chart. Thus, the K-Means algorithm is quite good at grouping students based on their expertise
Tree Triple Exponential Smoothing Analysis in Forecasting of Fertilizer Sales Prayudani, Santi; Banjarnahor, Wiwin Sry Adinda; Nugroho, Muhammad Rivan; Tazkiyatun Nisa
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: DECEMBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v1i2.49

Abstract

The majority of Indonesia's population relies on the agricultural sector, making fertilizer an essential raw material to increase productivity. PT. Pupuk Iskandar Muda (PIM), faces challenges in maintaining the balance of urea fertilizer production and demand. In 2021, PIM's urea fertilizer production was unable to meet demand, while in 2019, 2020, and 2022 there was overproduction. This inventory non-optimization can lead to productivity bottlenecks and increased storage costs. One solution to this problem is forecasting. This research uses the Triple Exponential Smoothing (TES) forecasting method in forecasting urea fertilizer sales for the next period. The data used is fertilizer sales data from PT PIM for the 2019-2023 period. Evaluation of the accuracy value is done using the MAD, MSE, and MAPE matrices. The results of this study indicate that the TES method with a smoothing weight value of Alpha = 0.4, Beta = 0.2, and Gamma = 0.4 produces a MAD value of 22,017.75, MSE of 990,752,983.08, and MAPE of 22.3% which can be categorized as quite feasible to use in forecasting the demand for urea fertilizer at PT PIM seen from the MAPE value.
Co-Authors Banjarnahor, Wiwin Sry Adinda Muhammad Rasyid Ridha Prayudani, Santi Sinaga, Christian Roi Tua Tazkiyatun Nisa Yuyun, Yuyun Yusnida Lase