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Jurnal Teknik Informatika C.I.T. Medicom
Published by Institute of Computer Science
ISSN : 23378646     EISSN : 2721561X     DOI : -
Core Subject : Science,
Computer Science & IT
The Jurnal Teknik Informatika C.I.T a scientific journal of Decision support sistem , expert system and artificial inteligens which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 129 Documents
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Decision Support System SIM Card Provider Selection Using the Simple Additive Weighting Method Vanny Fitria Ramadanti
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Communication and information media is growing and continues to change over time. For example, in the past, letters were a medium for communicating, then they were replaced with pagers, public telephones, landlines, and now using mobile or cellular phones. Media information providers also continue to experience developments ranging from newspapers, magazines, television, and the internet. However, currently there are tools that can cover both functions, namely smartphones that can be used to communicate as well as access information. To support smoothness and convenience in communicating and accessing information via the internet, a Subscriber Identity Module Card or SIM card or starter pack is required. However, some consumers still find it difficult to determine which SIM card provider to use, even though this is of course very important to consider considering that the selection of this SIM card provider will determine the level of comfort and smoothness of communicating and surfing the internet without having to worry about billing costs and quotas. which is drained. This research was conducted with the aim of knowing which SIM cards are the most widely used today, providing information to users to be more familiar with the various types and brands of SIM cards issued by several providers, determining the best SIM card based on certain criteria to be a recommendation to consumers. users, and provide input to the provider company about what criteria are taken into consideration by users when choosing a SIM card. The study was conducted by using the weighted summation of the Simple Additive Weighting method to determine the best SIM card provider. The final result of the study obtained a value of 0.90 for SIM card provider Tri as a recommendation for the best SIM card provider.
Analysis of Risk Factors for Hypertension at Pustu Petuk Ketimpun Palangka Raya Hendra Budi
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Hypertension is an asymptomatic condition, where high blood pressure in the arteries causes an increased risk of cardiovascular-related diseases such as stroke, heart failure, heart attack, kidney damage. The prevalence of hypertension in Central Kalimantan Province in 2018 was 34.5% and in Palangka Raya City was 35.84%. The purpose of this study was to determine the factors associated with hypertension in Pustu Petuk Ketimpun Palangka Raya. This type of research uses a cross sectional design. The population is patients who seek treatment at Pustu Petuk Ketimpun Palangka Raya and the research sample is 218 patients. The sampling technique was chosen by simple random sampling. Data analysis using chi square statistical test. The results showed that age (p-value = 0.0003373), gender (p-value = 0.000002151), excessive sugar consumption (p-value = 0.0001199), excessive salt consumption (p-value = 0, 000001993), alcohol consumption (p-value = 0.01815) and smoking (p-value = 0.03175) are risk factors for hypertension in patients at Pustu Petuk Ketimpun Palangka Raya, Central Kalimantan.
Sentiment Analysis in Valorant Game Review Using Information Gain Rafif Milzam
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Video games are one of the entertainments that have developed rapidly compared to other forms of entertainment such as movies or books. because of the rapid development that makes many games need to be reviewed to determine whether they game is worth playing or not. In general, reviews are divided into 2 opinions, namely negative reviews, and positive reviews, but some reviews cannot be included in the positive or negative category by the computer because the review has ambiguous words. Therefore we need a method that can help the computer in determining the category of the review, one of the methods used is the sentiment analysis method. In this study, the authors researched the valorant game review dataset using the information gain method using several classifiers, namely SVM, and Multinomial Naive Bayes and produced the greatest accuracy for the Multinomial Naive Bayes classifier method with an accuracy of 85.90%.
Application of Gray Level Co-Occurrence Matrix and Histogram Feature Extraction Methods for Batik Image Classification Nani Sulistianingsih; Siti Agrippina Alodia Yusuf; Muhamad Irwan
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Abstract

Batik is the art of integrating cultural elements like symbols and techniques into cotton and silk clothes. The Indonesian population has traditionally used batik in their daily lives. Each location has distinctive designs, patterns, and colors that reflect its meanings and philosophical perspectives. There are many different motifs on batik fabric, including geometric, geometrical, animal, and other designs. Batik motifs are frequently employed to convey social rank. The variety of batik designs and motifs is challenging for machine learning-based pattern detection and classification. This research applies the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and Histogram feature extraction on batik images and the K-Nearest Neighbor (KNN) classifier. This study focuses on 4 batik patterns (motifs), namely Lereng, Nitik, Kawung, and Tambal. Dissimilarity, Correlation, Contrast, Homogeneity, and Energy from various angles and distances are the GLCM features employed, and their sum equals 1. Mean, standard deviation, smoothness, skewness, energy, and entropy are the histogram features employed. This work uses 120 batik image data—90 training data and 30 test data—. The findings indicate that at k=15 and k=17, accuracy attained using GLCM feature extraction is 77%, while Precision and Recall are 77%. Comparatively, the histogram feature extraction accuracy, Precision, and Recall are 53%, 54%, and 53%, respectively, with a value of k=27. This outcome demonstrates how feature extraction using GLCM can more accurately portray batik.
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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.
Integration of stochastic and robust optimization techniques into DEA model for more accurate and reliable efficiency estimation Hengki Tamando Sihotang; Patricius Michaud Felix; Aisyah Alesha; Joan De Mathew
Jurnal Teknik Informatika C.I.T Medicom Vol 14 No 2 (2022): September: 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.Vol14.2022.229.pp1-4

Abstract

Efficiency assessment is vital to assessing decision-making units (DMUs) in numerous sectors. DEA is a prominent non-parametric efficiency assessment tool. Traditional DEA models assume deterministic inputs and outputs, ignoring real-world uncertainties and variability. To improve efficiency estimation, stochastic and robust optimization approaches can be integrated into DEA models. To improve efficiency estimation, we present a stochastic and robust optimization framework incorporating DEA. Probabilistic inputs and outputs allow stochastic optimization to account for uncertainty. The model can capture data variability and create stochastic DMU efficiency scores by adding probability distributions. For data uncertainties and outliers, the DEA model uses robust optimization. Robust optimization considers worst-case scenarios and minimizes extreme observations on efficiency estimation. This makes efficiency scores more resilient to data outliers and noise.  DEA models benefit from stochastic and resilient optimization. First, considering data uncertainties and fluctuations improves DMU efficiency representation. Second, eliminating outliers and extreme observations improves efficiency estimation. Third, efficiency scores help decision-makers make better, more informed choices. A case study in a specific industry shows the framework's efficacy. We compare classic and integrated stochastic-robust DEA model outcomes. The integrated model provides more accurate and dependable efficiency estimates, helping decision-makers understand DMU performance. DEA models with stochastic and resilient optimization increase efficiency estimation. By considering uncertainties and outliers, this paradigm helps decision-makers evaluate DMUs in many sectors more accurately and reliably.

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