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All Journal J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) Brahmana : Jurnal Penerapan Kecerdasan Buatan IJISTECH J-SAKTI (Jurnal Sains Komputer dan Informatika)
Sriani, S
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Social Sciences

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Face-Based Attendance Data Using Principal Component Analysis Aulia, Muhammad Arief; Furqan, Mhd.; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 7, No 5 (2024): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i5.331

Abstract

The face is one of the easiest physiological measurements and is often used to distinguish the identity of one individual from another. This facial recognition process uses raw information from image pixels produced through the camera which is then represented in the Principal Components Analysis method. The way the Principal Components Analysis method works is by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector the eigenface value of each image will be obtained and then the closest eigenface value of the image will be searched for. image of the face you want to recognize. The test results show the overall success rate of face recognition that the application can carry out face recognition using digital camera hardware for the attendance system by displaying the name of the face owner as well as the date and time of recognition. The average accuracy value of the test with the light intensity level is 96.66%, the accuracy value The average test value with changes in the distance between the camera and the face was 98.33% and the average accuracy value of the test using glasses and hat accessories was 85%.
Application of The K-Means Clustering Algorithm to Identify Strawberry Fruit Ripe Rizki, Muhammad; Furqan, Mhd; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.356

Abstract

Fruit ripeness will usually be determined by several parameters, including size, weight, color characteristics, fragrance, etc. The parameter of fruit ripeness in terms of fruit skin color is one of the important factors in identifying fruit maturity. Segmentation is a method in digital image processing to differentiate objects in an input image. The general classification process is carried out by looking directly at the fruit object. The purpose of this research is to create an analysis in identifying the ripeness of strawberry fruit and designing an application system that can identify the ripeness of strawberry fruit. This application was built with the MATLAB application. The methods used include K-Means Clustering segmentation, labeling and feature extraction. The detection of the type of fruit is carried out using feature matching at the level of shape and color. Before classifying the name of the type of fruit and the level of maturity, the fruit training must be carried out first and then continued with fruit detection and identification of maturity. Based on the results of the strawberry image maturity identification test with six test strawberry images consisting of three types of maturity levels, the results were obtained, namely mature test one and mature test two levels of ripeness and correct identification results, half ripe test one and half ripe test two levels of ripeness and results Correct understanding, raw test one and raw test two mature levels and correct recognition. Meanwhile, the accuracy test results obtained an accuracy value of 100% for identifying the maturity of strawberry images. From the results of the tests carried out, it can be concluded that identification of ripeness in strawberry fruit images was successfully applied using the K-Means Clustering method on images of ripe, half-ripe and unripe strawberries. And from testing the identification of ripeness of strawberry fruit with test data of six images and training data of twelve images, it gave an accuracy result of 100%.
Application of The K-Means Clustering Algorithm to Identify Strawberry Fruit Ripe Rizki, Muhammad; Furqan, Mhd; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 8, No 2 (2024): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v8i2.356

Abstract

Fruit ripeness will usually be determined by several parameters, including size, weight, color characteristics, fragrance, etc. The parameter of fruit ripeness in terms of fruit skin color is one of the important factors in identifying fruit maturity. Segmentation is a method in digital image processing to differentiate objects in an input image. The general classification process is carried out by looking directly at the fruit object. The purpose of this research is to create an analysis in identifying the ripeness of strawberry fruit and designing an application system that can identify the ripeness of strawberry fruit. This application was built with the MATLAB application. The methods used include K-Means Clustering segmentation, labeling and feature extraction. The detection of the type of fruit is carried out using feature matching at the level of shape and color. Before classifying the name of the type of fruit and the level of maturity, the fruit training must be carried out first and then continued with fruit detection and identification of maturity. Based on the results of the strawberry image maturity identification test with six test strawberry images consisting of three types of maturity levels, the results were obtained, namely mature test one and mature test two levels of ripeness and correct identification results, half ripe test one and half ripe test two levels of ripeness and results Correct understanding, raw test one and raw test two mature levels and correct recognition. Meanwhile, the accuracy test results obtained an accuracy value of 100% for identifying the maturity of strawberry images. From the results of the tests carried out, it can be concluded that identification of ripeness in strawberry fruit images was successfully applied using the K-Means Clustering method on images of ripe, half-ripe and unripe strawberries. And from testing the identification of ripeness of strawberry fruit with test data of six images and training data of twelve images, it gave an accuracy result of 100%.
Face-Based Attendance Data Using Principal Component Analysis Aulia, Muhammad Arief; Furqan, Mhd.; Sriani, S
IJISTECH (International Journal of Information System and Technology) Vol 7, No 5 (2024): The February edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i5.331

Abstract

The face is one of the easiest physiological measurements and is often used to distinguish the identity of one individual from another. This facial recognition process uses raw information from image pixels produced through the camera which is then represented in the Principal Components Analysis method. The way the Principal Components Analysis method works is by calculating the average flatvector pixel of images that have been stored in a database, from the average flatvector the eigenface value of each image will be obtained and then the closest eigenface value of the image will be searched for. image of the face you want to recognize. The test results show the overall success rate of face recognition that the application can carry out face recognition using digital camera hardware for the attendance system by displaying the name of the face owner as well as the date and time of recognition. The average accuracy value of the test with the light intensity level is 96.66%, the accuracy value The average test value with changes in the distance between the camera and the face was 98.33% and the average accuracy value of the test using glasses and hat accessories was 85%.
Algoritma K-Means Untuk Segmentasi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Furqan, Mhd; Sriani, S; Aulia, Atiqah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.437

Abstract

The condition of citrus fruits can be determined by looking at several parameters, one of which is color, larger pores, and even yellow skin. So far, the identification of the maturity level of citrus fruits by farmers and consumers has used manual techniques, for example paying attention to the color, pores and peel of the orange product. Such identification will be very large and fluctuating developmental days because people have visual impairments in recognizing, fatigue, and judgment on great development. Barriers to strategy guidance require innovations that can complete the development process impartially, and with clearer results. One of them is the segmentation process using yahoo k-means. The segmentation process aims to divide or separate the image into several (local) districts based on the specified attributes. The k-means algorithm will cluster data with similar attributes assembled into one set and data with various qualities assembled into different sets. From the results of taking pictures from 6 angles, namely front, back, top, bottom, and right and left using 8 datasets, it produces 48 images, and by testing the clustering results, ripe oranges produce 6 and 2 ripe.
Penentuan Kualitas Bibit Padi Menggunakan Metode Fuzzy Mamdani Furqan, Mhd.; Sriani, S; Hasugian, Abdul Halim; Hsb, Munawir Siddik
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.354

Abstract

the agriculture sector still faces fairly basic challenges, namely the quality problem and the increase in competitiveness through productivity and efficiency. This research determines the criteria for the best quality types of rice seeds and how to apply the Fuzzy Mamdani Method, to determine the quality of rice seeds in order to assist farmers in determining the quality of the best rice seeds. Mamdani fuzzy method is one example of a method that can help the optimal decision making process to solve practical problems. The problem solved is the determination of the best quality of rice seeds, based on established criteria, namely the type of rice, the shape of the rice, the color of the seeds, the age of the seeds, and roots. This is done to reinforce the output or output of each input variable membership. Then after the output input output variable is determined, the implementation of the rules for each parameter is carried out. After that do defuzzyfication with the centroid method. So that the output of one parameter is 60 with verry good information. This system was built with a website application where the application is able to help users to determine the quality of rice seeds and obtain information about the best seeds.
Algoritma K-Means Untuk Segmentasi Kematangan Buah Jeruk Berdasarkan Kemiripan Warna Furqan, Mhd; Sriani, S; Aulia, Atiqah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.437

Abstract

The condition of citrus fruits can be determined by looking at several parameters, one of which is color, larger pores, and even yellow skin. So far, the identification of the maturity level of citrus fruits by farmers and consumers has used manual techniques, for example paying attention to the color, pores and peel of the orange product. Such identification will be very large and fluctuating developmental days because people have visual impairments in recognizing, fatigue, and judgment on great development. Barriers to strategy guidance require innovations that can complete the development process impartially, and with clearer results. One of them is the segmentation process using yahoo k-means. The segmentation process aims to divide or separate the image into several (local) districts based on the specified attributes. The k-means algorithm will cluster data with similar attributes assembled into one set and data with various qualities assembled into different sets. From the results of taking pictures from 6 angles, namely front, back, top, bottom, and right and left using 8 datasets, it produces 48 images, and by testing the clustering results, ripe oranges produce 6 and 2 ripe.
Penentuan Kualitas Bibit Padi Menggunakan Metode Fuzzy Mamdani Furqan, Mhd.; Sriani, S; Hasugian, Abdul Halim; Hsb, Munawir Siddik
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.354

Abstract

the agriculture sector still faces fairly basic challenges, namely the quality problem and the increase in competitiveness through productivity and efficiency. This research determines the criteria for the best quality types of rice seeds and how to apply the Fuzzy Mamdani Method, to determine the quality of rice seeds in order to assist farmers in determining the quality of the best rice seeds. Mamdani fuzzy method is one example of a method that can help the optimal decision making process to solve practical problems. The problem solved is the determination of the best quality of rice seeds, based on established criteria, namely the type of rice, the shape of the rice, the color of the seeds, the age of the seeds, and roots. This is done to reinforce the output or output of each input variable membership. Then after the output input output variable is determined, the implementation of the rules for each parameter is carried out. After that do defuzzyfication with the centroid method. So that the output of one parameter is 60 with verry good information. This system was built with a website application where the application is able to help users to determine the quality of rice seeds and obtain information about the best seeds.
Sistem Pengendalian Water Pump Untuk Mengatur Volume Level Air Dengan Logika Fuzzy Pada Pengairan Hidroponik Kurniawan, Rakhmat; Sriani, S; Ramadhan, Alfan
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.186

Abstract

Hydroponic culture can produce optimal plant growth, the yield and quality are better than soil media, the efficiency and quality of production depends on many factors such as controlling nutrition, plant genotype, freezing method, disease control, temperature control and controlling the amount of water in the tendons. Water that is circulated through the hydroponic network is supplied through a water pump. A lot of water supply cannot be stable, which can result in a decrease in plant quality, namely the control of hydroponic water pumps can be adjusted with the Tsukamoto fuzzy system. This study used the NFT hydroponic method, which is suitable for green vegetables such as mustard greens, lettuce, spinach, pakcoy, cucumber, tomatoes, and others. The application of this pump sensor monitoring tool uses a microcontroller based on the IoT platform. Also used Blynk software to display information from programming via Arduino software. Ultrasonic circuit, the sensor will measure the distance to the air surface, which will determine at what distance the water tap will open to fill air into the tendons. After running a number of experiments the content of water tendons can be controlled by fuzzy Tsukamoto. It is hoped that for further development it can use sensors other than Ultrasonic, and for further development it can use a large number of plants.
Co-Authors Abdul Halim Hasugian Aulia, Atiqah Aulia, Muhammad Arief Hsb, Munawir Siddik Mhd Furqan Muhammad Rizki Rakhmat Kurniawan R Ramadhan, Alfan