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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Scientific Contribution Oil and Gas
Irfan Rasyid
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Energy Engineering Environmental Science

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Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16 Irfan Rasyid; Yudianto, Muhammad Resa Arif; Maimunah; Tuessi Ari Purnomo
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12936

Abstract

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Best Practices to Achieve Optimal Geothermal Drilling Performance in A Cost-Effective Manner: Case Study of the Fastest Geothermal Well Drilling in Java and Sumatra Bambang Yudho Suranta; Irfan Rasyid; Akhmad Sofyan; Arif Rahutama
Scientific Contributions Oil and Gas Vol. 46 No. 3 (2023): SCOG
Publisher : Testing Center for Oil and Gas LEMIGAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29017/scog.46.3.331

Abstract

Indonesia, recognized for possessing substantial geothermal energy potential, is working towards harnessing the resource to achieve numerous objectives. Among the primary challenges encountered is the considerable expense of geothermal drilling. One of the most significant obstacles to achieving this objective is the high drilling cost, which constitutes 35-40% of the total cost of geothermal energy development. The drilling cost is mainly affected by the time needed to drill one well because the faster the time, the lower the cost. Therefore, this research analyzed drilling activities, identified the fastest and most effective methods for optimal geothermal drilling performance, and reduced costs. The research also determined the factors that contributed to the sustained status of Well X as the fastest well drilled in the past decade. The methodology comprised literature review, data collection through adequate background on well and geothermal field, and data analysis. The result showed that the fastest drilling operation of a geothermal well in Indonesia in 2012 occured in West Java (Well X) for only 9.9 days with 1736.5 meters (mMD). Meanwhile, in 2021, Well Y in Sumatra spent 21.74 days to reach a depth of 2200 mMD. The use of a single-run and clean-out Bottom Hole Assembly (BHA) throughout the entire section affected the drilling duration and significantly reduced the inner side cleaning time, respectively. The cost of Well Y drilling, achieved using the best performance of two wells, reduced drilling costs by 19.2%.
Co-Authors Akhmad Sofyan Arif Rahutama Bambang Yudho Suranta Maimunah Muhammad Resa Arif Yudianto Tuessi Ari Purnomo