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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Okta Andrica Putra
Universitas Putra Indonesia YPTK, Padang, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Social Sciences

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Performance Analysis and Model Determination for Forecasting Aluminum Imports Using the Powell-Beale Algorithm Nur Arminarahmah; Syafrika Deni Rizki; Okta Andrica Putra; Anjar Wanto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 5 (2022): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i5.186

Abstract

Aluminum is one of the most important metals for the industrial world, but currently, aluminum is scarce due to a shortage of electricity, which makes manufacturers limit their production. Therefore, to overcome this scarcity, the government imports aluminum. Imports that are carried out continuously will more or less affect the wheels of the economy in this country. Therefore, it is necessary to predict the value of aluminum imports in the future so that later the demand for aluminum in Indonesia is stable and not too excessive in importing. The prediction method used is the Powell-Beale algorithm, which is one of the most commonly used artificial neural network methods for data prediction. This paper does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on imported Aluminum datasets obtained from the Central Statistics Agency. The research data used is aluminum import data by the leading country of origin from 2013-to 2020. A network architecture model will be formed and determined based on this data, including 3-15-1, 3-20-1, and 3-25-1. From these five models, after training and testing, the results show that the best architectural model is 3-20-1 with an MSE value of 0,03010927, the lowest among the other four models. So it can be concluded that the model can be used to predict aluminum imports.
Pemilihan Deteksi Tepi Terbaik Untuk Menganalisa Citra Ultrasonografi Kehamilan Syafrika Deni Rizki; S Sumijan; Okta Andrica Putra
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.344

Abstract

In its development, image processing is very helpful for solving problems faced by humans. Imaging processing is an image processing technique of an object to distinguish the background and the object to be analyzed. This research uses a segmentation method that can distinguish between objects and backgrounds. Analyzing objects from ultrasound images requires the expertise of an experienced doctor. In addition, there are artificial factors that make automated analysis complicated. We aim to improve natural imagery. Therefore to overcome the potential difficulties in analysis, we present four Comparison of Edge Detection, namely Gradient Image, Roberts Operator, Sobel Operator and Prewitt Operator. In image processing, data accuracy and accuracy are required as well as knowledge of statistics because image processing is related to data processing. The result of this research is to determine which edge detection is more appropriate for analyzing ultrasound images. The conclusion from this research is that from the four operators that were tried, the results of the testing process showed that the Prewit operator succeeded in detecting existing objects, even objects that function as background were successfully detected. The resulting edge detection is smoother compared to other operators.
Pemilihan Deteksi Tepi Terbaik Untuk Menganalisa Citra Ultrasonografi Kehamilan Syafrika Deni Rizki; S Sumijan; Okta Andrica Putra
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.344

Abstract

In its development, image processing is very helpful for solving problems faced by humans. Imaging processing is an image processing technique of an object to distinguish the background and the object to be analyzed. This research uses a segmentation method that can distinguish between objects and backgrounds. Analyzing objects from ultrasound images requires the expertise of an experienced doctor. In addition, there are artificial factors that make automated analysis complicated. We aim to improve natural imagery. Therefore to overcome the potential difficulties in analysis, we present four Comparison of Edge Detection, namely Gradient Image, Roberts Operator, Sobel Operator and Prewitt Operator. In image processing, data accuracy and accuracy are required as well as knowledge of statistics because image processing is related to data processing. The result of this research is to determine which edge detection is more appropriate for analyzing ultrasound images. The conclusion from this research is that from the four operators that were tried, the results of the testing process showed that the Prewit operator succeeded in detecting existing objects, even objects that function as background were successfully detected. The resulting edge detection is smoother compared to other operators.
Performance Analysis and Model Determination for Forecasting Aluminum Imports Using the Powell-Beale Algorithm Nur Arminarahmah; Syafrika Deni Rizki; Okta Andrica Putra; Anjar Wanto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 5 (2022): February
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (891.15 KB) | DOI: 10.30645/ijistech.v5i5.186

Abstract

Aluminum is one of the most important metals for the industrial world, but currently, aluminum is scarce due to a shortage of electricity, which makes manufacturers limit their production. Therefore, to overcome this scarcity, the government imports aluminum. Imports that are carried out continuously will more or less affect the wheels of the economy in this country. Therefore, it is necessary to predict the value of aluminum imports in the future so that later the demand for aluminum in Indonesia is stable and not too excessive in importing. The prediction method used is the Powell-Beale algorithm, which is one of the most commonly used artificial neural network methods for data prediction. This paper does not discuss the prediction results. Still, it discusses the ability of the Powell-Beale algorithm to make predictions based on imported Aluminum datasets obtained from the Central Statistics Agency. The research data used is aluminum import data by the leading country of origin from 2013-to 2020. A network architecture model will be formed and determined based on this data, including 3-15-1, 3-20-1, and 3-25-1. From these five models, after training and testing, the results show that the best architectural model is 3-20-1 with an MSE value of 0,03010927, the lowest among the other four models. So it can be concluded that the model can be used to predict aluminum imports.
Co-Authors Nur Arminarahmah S Sumijan Syafrika Deni Rizki Wanto, Anjar