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All Journal Teknosains : Jurnal Sains,Teknologi dan Informatika
Johanes Apriadi Parlinggoman Sirait
Medan State University, Medan, North Sumatra, Indonesia 20221
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT

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Decision support system for selection of the best teacher at SD Muhammadiyah 18 Medan using the analytical hierarchy process method Josua Nainggolan; Johanes Apriadi Parlinggoman Sirait; Muhammad Fadlan Ikromi; Putri Ameliya Lubis; Debi Yandra Niska
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 1 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK-Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i1.569

Abstract

A Decision Support System can be interpreted as a system that can and can provide solutions or capabilities both the ability to provide solutions or solve problems and the ability to communicate on semi-structured problemsHierarchy is a complex problem in a multi-level structure where the first level is the goal, followed by the level of problem factors, criteria, sub-criteria, alternatives, and so on down to the last level of alternatives. With a hierarchy, a complex problem can be translated into a collection of groups which are then organized into a hierarchical form so that the problem will appear more structured and systematicDecision Support System designed can be an alternative for SD Muhammadiyah 18 Medan and other schools in determining the best teacher. And with the establishment of a computerized system with the Analytical Hierarchy Process (AHP) method, the data processing process is faster and greatly minimizes errors and shortcomings in the calculation of values and also obtains maximum results. Obtained teacher data and the results of the value will be stored in the database so that if an error is found in the data entry and calculation results, the wrong data can be replaced through the database used. The final result can be concluded that the AHP method has given the best value recommendation to the teacher Syamsul Hidayat S, Pd with a value of 0.4473 with a percentage of 45%.
Red Chili Classification Using HSV Feature Extraction and Naive Bayes Classifier Hermawan Syahputra; Josua Nainggolan; Johanes Apriadi Parlinggoman Sirait; Muhammad Fadlan Ikromi; Putri Ameliya Lubis
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 1 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK-Sekolah Tinggi Teknologi Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i1.593

Abstract

In the culinary industry, the classification of red chili pepper types is used to identify varieties that differ in terms of flavor, pungency, or other uniqueness. This enables their proper use in various recipes and meals. In the market, the classification of red chili pepper types helps in pricing, variety selection, or quality standards applied. For this reason, the purpose of this research is to classify red chili peppers using HSV Feature Extraction and Naive Bayes Clasifier. The stages carried out include: data collection, preprocessing, feature extraction and classification. Red chilies are grouped into 4 classes, namely large red chilies, cakplak red chilies, curly red chilies and chili red chilies. The red chili data used is 119 training data and 123 testing data. In the preprocessing, the image is converted to grayscale, then converted to binary image with the thresholding method. Furthermore, feature extraction is done with the HSV method. Finally, classification is done with Naive Bayes. The results of the study provide an accuracy value for training data of 92.43% and for testing data obtained an accuracy of 92.69%. This method is suitable for use in classification because it gives good results
Co-Authors Hermawan Syahputra Josua Nainggolan Muhammad Fadlan Ikromi Niska, Debi Yandra Putri Ameliya Lubis