Garuda - Garba Rujukan Digital

Multi-criteria decision making using weighted aggregated sum product assessment in corn seed selection system Hendra Mayatopani
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.302.pp21-31

Corn is one of the seven national strategic commodities developed by the government. The role of corn in the national industry is so great that the process of cross-breeding is often carried out in order to obtain superior varieties. Given the important role of corn in Indonesian agriculture, it is normal for corn seeds to be scattered in the market. For this reason, corn farmers or someone who wants to grow corn must be careful to choose the right corn seeds for their needs and what they want. This study aims to implement the Multi-Criteria Decision Making (MCDM) approach with Weighted Aggregated Sum Product Assessment (WASPAS) on a corn seed selection decision support system, in order to obtain the best alternative according to the needs of several alternatives and certain criteria. The WASPAS method is able to solve multi-criteria problems by optimizing the assessment for selecting the highest and lowest values to get the best alternative. The DSS developed is based on a website, with the main features including managing criteria and weight data, alternative data, conducting alternative assessments, calculating processes using the WASPAS method and displaying the best alternative in the form of ranking. In addition, the developed system produces valid WASPAS method calculations, because the results are in accordance with manual calculations. Based on the tests carried out with the black-box testing approach, it shows that the system built has been running well.

Material requirements planning method for controlling inventory of raw materials Hendra Cipta; Rima Aprilia; Hari Kurniawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.358.pp1-8

The existence of shortages and excesses of production raw materials often occurs in the NR Brownies company where this situation greatly influences the smooth production process, especially in the supply of raw materials which results in sub-optimal expenditure costs. The aim of this research is to find solution how to prevent companies from experiencing these problems on a regular basis which results in less than optimal spending costs. In overcoming this problem, the Material Requirement Planning method is applied, this method is expected to minimize expenditure costs so that the benefits obtained are more optimal. From the research results, the Material Requirement Planning Method for EOQ calculations produces the minimum costs for sugar and oil raw materials of 18.295.130 and 19.591.230 respectively, while the POQ calculation produces the minimum costs for raw materials 14.839.500 for flour, 15.450.000 for eggs, 1.356.650 for cocoa flour, and 2.504.387 for baking powder.

Decision support system to determine the best customer using weighted aggregated sum product assessment method Saifur Rohman Cholil; Andri Roy Irawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.367.pp32-47

A business engaged in the sale of building materials and building tools. Customers are important assets that must be maintained properly, because each customer will bring profits that can keep the business running. To maintain customer loyalty, business owners provide an appreciation and appreciation for the best customers. However, determining the best customer is still difficult because the process of determining the best customer is still done manually and randomly. The number of customers is also an obstacle to the process of determining the best customer. The method used is weighted aggregated sum product assessment (WASPAS) to determine the best customer by looking at 5 criteria including: Total Spend, Visit Loyalty, Quantity of Shopping, Distance, Frequency of Complaints. This method was chosen because it can reduce errors - errors or optimize in estimation for the selection of the highest and lowest values. The best customers will be rewarded and appreciated by business owners. The result of this study is a Decision Support System to Determine the Best Customer Using the WASPAS Method. Questionnaire testing obtained user satisfaction results as much as 65% strongly agree, 25% agree, 10% sufficient and 0% disagree which shows that the system is in accordance with the needs and can be used.

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter Wina Fadia Ardianti; Sriani Sriani; Abdul Halim Hasugian
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.375.pp09-20

Computerization requires system testing and further system development, namely color feature extraction with KNN. Avocado is one that has a high protein content in it. This research uses the KNN algorithm method and feature extraction in order to get more effective results, the purpose of this research is to make it easier for people to choose the ripeness level of butter avocados because people still don't know about the maturity level of butter avocados. In this study, testing was carried out by bringing the avocado fruit closer to the cellphone camera connected to the researcher's internet, after which the application will automatically match the color of the avocado. to the system, the system will produce output based on that color with output in the form of the ripeness level of the avocado, whether it is ripe, ripe, half ripe, rotten and also generates information on how much longer the avocado will ripen. All stages of system development are carried out by analyzing data first, then taking sample data, training and testing datasets, then the results of the system will become benchmarks. The test data in this study used several types of avocado objects, namely: Raw, Half Ripe, Ripe, Ripe, Rotten. It consisted of 55 data samples consisting of 11 raw avocado samples, 11 half-ripe avocado samples, 11 ripe avocado samples, 11 ripe avocado samples and 11 rotten avocado samples. Obtained euclidean distance values for each type of avocado butter. After that, the sum is done to get the overall level of accuracy by adding up the total euclidean distance with the total euclidean distance for each type of avocado. After getting the added value multiply it by 100%. Then the overall accuracy results obtained are 98.38%.

Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique Muhathir Muhathir
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.386.pp48-57

Spice classification is a crucial task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the Histogram of Oriented Gradient (HoG) feature extraction method and bagging method. The objective of this research is to compare the performance of three different models of bagging method, including Bootstrap Aggregating (Bagging), Random Forests, and Extra Tree Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the Random Forest model achieved the best performance, with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.861, 0.8633, 0.8587, 0.8607, 0.7694, and 0.8733 respectively. On the other hand, the Extra Tree Classifier had the lowest performance with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.7034, 0.7958, 0.7037, 0.7047, 0.5635, and 0.72 respectively. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and bagging method. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods. The findings of this research may have significant implications for the food industry in ensuring the quality and safety of food products.

Multi-criteria decision making using weighted aggregated sum product assessment in corn seed selection system Hendra Mayatopani
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.302.pp21-31

Corn is one of the seven national strategic commodities developed by the government. The role of corn in the national industry is so great that the process of cross-breeding is often carried out in order to obtain superior varieties. Given the important role of corn in Indonesian agriculture, it is normal for corn seeds to be scattered in the market. For this reason, corn farmers or someone who wants to grow corn must be careful to choose the right corn seeds for their needs and what they want. This study aims to implement the Multi-Criteria Decision Making (MCDM) approach with Weighted Aggregated Sum Product Assessment (WASPAS) on a corn seed selection decision support system, in order to obtain the best alternative according to the needs of several alternatives and certain criteria. The WASPAS method is able to solve multi-criteria problems by optimizing the assessment for selecting the highest and lowest values to get the best alternative. The DSS developed is based on a website, with the main features including managing criteria and weight data, alternative data, conducting alternative assessments, calculating processes using the WASPAS method and displaying the best alternative in the form of ranking. In addition, the developed system produces valid WASPAS method calculations, because the results are in accordance with manual calculations. Based on the tests carried out with the black-box testing approach, it shows that the system built has been running well.

Material requirements planning method for controlling inventory of raw materials Hendra Cipta; Rima Aprilia; Hari Kurniawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.358.pp1-8

The existence of shortages and excesses of production raw materials often occurs in the NR Brownies company where this situation greatly influences the smooth production process, especially in the supply of raw materials which results in sub-optimal expenditure costs. The aim of this research is to find solution how to prevent companies from experiencing these problems on a regular basis which results in less than optimal spending costs. In overcoming this problem, the Material Requirement Planning method is applied, this method is expected to minimize expenditure costs so that the benefits obtained are more optimal. From the research results, the Material Requirement Planning Method for EOQ calculations produces the minimum costs for sugar and oil raw materials of 18.295.130 and 19.591.230 respectively, while the POQ calculation produces the minimum costs for raw materials 14.839.500 for flour, 15.450.000 for eggs, 1.356.650 for cocoa flour, and 2.504.387 for baking powder.

Decision support system to determine the best customer using weighted aggregated sum product assessment method Saifur Rohman Cholil; Andri Roy Irawan
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.367.pp32-47

A business engaged in the sale of building materials and building tools. Customers are important assets that must be maintained properly, because each customer will bring profits that can keep the business running. To maintain customer loyalty, business owners provide an appreciation and appreciation for the best customers. However, determining the best customer is still difficult because the process of determining the best customer is still done manually and randomly. The number of customers is also an obstacle to the process of determining the best customer. The method used is weighted aggregated sum product assessment (WASPAS) to determine the best customer by looking at 5 criteria including: Total Spend, Visit Loyalty, Quantity of Shopping, Distance, Frequency of Complaints. This method was chosen because it can reduce errors - errors or optimize in estimation for the selection of the highest and lowest values. The best customers will be rewarded and appreciated by business owners.Â The result of this study is a Decision Support System to Determine the Best Customer Using the WASPAS Method. Questionnaire testing obtained user satisfaction results as much as 65% strongly agree, 25% agree, 10% sufficient and 0% disagree which shows that the system is in accordance with the needs and can be used.

Application of color extraction methods and k-nearest neighbor to determine maturity avocado butter Wina Fadia Ardianti; Sriani Sriani; Abdul Halim Hasugian
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.375.pp09-20

Computerization requires system testing and further system development, namely color feature extraction with KNN. Avocado is one that has a high protein content in it. This research uses the KNN algorithm method and feature extraction in order to get more effective results, the purpose of this research is to make it easier for people to choose the ripeness level of butter avocados because people still don't know about the maturity level of butter avocados. In this study, testing was carried out by bringing the avocado fruit closer to the cellphone camera connected to the researcher's internet, after which the application will automatically match the color of the avocado. to the system, the system will produce output based on that color with output in the form of the ripeness level of the avocado, whether it is ripe, ripe, half ripe, rotten and also generates information on how much longer the avocado will ripen. All stages of system development are carried out by analyzing data first, then taking sample data, training and testing datasets, then the results of the system will become benchmarks. The test data in this study used several types of avocado objects, namely: Raw, Half Ripe, Ripe, Ripe, Rotten. It consisted of 55 data samples consisting of 11 raw avocado samples, 11 half-ripe avocado samples, 11 ripe avocado samples, 11 ripe avocado samples and 11 rotten avocado samples. Obtained euclidean distance values â€‹â€‹for each type of avocado butter. After that, the sum is done to get the overall level of accuracy by adding up the total euclidean distance with the total euclidean distance for each type of avocado. After getting the added value multiply it by 100%. Then the overall accuracy results obtained are 98.38%.

Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique Muhathir Muhathir
Jurnal Teknik Informatika C.I.T Medicom Vol 15 No 1 (2023): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol15.2023.386.pp48-57

Spice classification is a crucial task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the Histogram of Oriented Gradient (HoG) feature extraction method and bagging method. The objective of this research is to compare the performance of three different models of bagging method, including Bootstrap Aggregating (Bagging), Random Forests, and Extra Tree Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the Random Forest model achieved the best performance, with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.861, 0.8633, 0.8587, 0.8607, 0.7694, and 0.8733 respectively. On the other hand, the Extra Tree Classifier had the lowest performance with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.7034, 0.7958, 0.7037, 0.7047, 0.5635, and 0.72 respectively. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and bagging method. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods. The findings of this research may have significant implications for the food industry in ensuring the quality and safety of food products.

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Naety

Contact Email
jurnalmedicom@iocscience.org

Phone
+6281381251442

Journal Mail Official
jurnalmedicom@iocscience.org

Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia

Location
Unknown,

Unknown

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Naety

Contact Email jurnalmedicom@iocscience.org

Phone +6281381251442

Journal Mail Official jurnalmedicom@iocscience.org

Editorial Address Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia

Location Unknown, Unknown INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Naety

Contact Email
jurnalmedicom@iocscience.org

Phone
+6281381251442

Journal Mail Official
jurnalmedicom@iocscience.org

Editorial Address
Perumahan Romeby Lestari Blok C, No C14 Deliserdang, Sumatera Utara, Indonesia

Location
Unknown,

Unknown

INDONESIA