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Digital Image Processing using Texture Features Extraction of Local Seeds in Nekbaun Village with Color Moment, Gray Level Co Occurance Matrix, and k-Nearest Neighbor Yampi R Kaesmetan; Marlinda Vasty Overbeek
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2038

The problem in determining the selection of corn seeds for replanting, especially maize in East Nusa Tenggara is still an important issue. Things that affect the quality of corn seeds are damaged seeds, dull seeds, dirty seeds, and broken seeds due to the drying and shelling process, which during the process of shelling corn with a machine, many damaged and broken seeds are found. So far, quality evaluation in the process of classification of the quality of corn seeds is still done manually through visible observations. Manual systems take a long time and produce products of inconsistent quality due to visual limitations, fatigue, and differences in the perceptions of each observer. The selection of local maize seeds in Timor Island, East Nusa Tenggara Province, especially in Nekbaun Village, West Amarasi District with feature extraction with a color moment shows that the mean, standard deviation and skewness features have an average validation of 88% and use the GLCM method which shows the neighbor relationship. Between the two pixels that form a co-occurrence matrix of the image data, namely GLCM, it shows that the features of homogeneity, correlation, contrast and energy have an average validation of 70.93%. The k-Nearest Neighbor (k-NN) algorithm is used in research to classify the image object to be studied. The results of this study were successfully carried out using k-Nearest Neighbor (k-NN) with the euclidean distance and k = 1 with the highest extraction yield of 88% and the results of GLCM feature extraction for homogeneity of 75.5%, correlation of 78.67%, contrast of 65.75 % and energy of 63.83% with an average accuracy of 70.93%.

Ant Colony Optimization for Traveling Tourism Problem on Timor Island East Nusa Tenggara Yampi R Kaesmetan; Marlinda Vasty Overbeek
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 1 (2020): March 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i1.9274

Timor island consists of five districts and one city, namely Kupang District, South Central Timor District, North Central Timor, Belu District, Malaka District, and Kupang City. On the Timor island, it has natural tourist destinations, culinary tours, cultural and historical attractions most on the island of Timor. The Ant Colony Optimization (ACO) Algorithm is very unique compared to the other nearby search algorithm, this algorithm adopted because of Ant Colony who were looking for food from the nest to food sources by leaving a footprint called Pheromone. Mapping system algorithm using ant, tourist sites can show the shortest route between two points is desired. Ants algorithm proved to be applied in determining the optimum route, but still has the disadvantage of dependence on the parameter value is not maximized. From the test results based on parameters of the cycle and the number of ants affects the simulation time, for ant algorithm parameters. From the test results based on the parameters, α and β affects, number of node, the simulation time and the shortest distance varying toward the destination even if the starting location and ending on the same location.

Selection of Superior Rice Seed Features Using Deep Learning Method Dinda Ayusma Tonael; Yampi R Kaesmetan; Marinus I. J. Lamabelawa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 4, No 2 (2021): September 2021
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v4i2.13947

Indonesia is a tropical country known as an agricultural country, where 88.57% of the population works in the agricultural sector (BPS Indonesia, 2020). Indonesia is rich in agricultural products such as rice, soybeans, corn, peanuts, cassava and sweet potatoes. Rice (Oryza sativia L) is one of the most dominant food commodities for the people of Indonesia. The carbohydrate content per 100 grams of rice reaches 79.34 grams. The main benefit of rice is as a source of carbohydrates and a source of energy for the body. Seed is one of the factors that play a role as a carrier of technology in advanced agriculture, therefore the seeds used must be of good quality. Farmers tend to equate rice seeds from previous harvests, the rice seed classification process is carried out manually through visual observation and soaking rice seeds in a container filled with water, submerged and floating rice seeds are selected for use, and those that float are discarded. But in reality it still produces less than optimal results, for example rice that is less dense and cracked. This study uses a color moment to be extracted using GLCM (gray level co-occurence matrix) then classified with k-NN to determine the class, then uses the SVM model to display the best hyperplane line to separate the two classes, namely superior and non-superior classes after that system tested with confusion matrix. With a continuous and more intense work process, the research entitled Selection of Superior Rice Seed Features Using Deep Learning Methods. The output of this research leads to a conclusion which rice seeds are superior and which are not superior, aiming to optimize the yield of rice with better quality. The research was successfully carried out using the deep learning method with the highest accuracy of 92.85%.

Data Train Reduction on Data Image With K Support Vector Nearest Neighbor (Case Study : Maize Leaf Image) Marlinda Vasty Overbeek; Yampi R Kaesmetan
Indonesian Journal of Artificial Intelligence and Data Mining Vol 3, No 2 (2020): Spetember 2020
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v3i2.10451

In this study, we applied the K Support Vector Nearest Neighbor algorithm to reduce data train on data image. The data image that we used is the maize leaves image infected with fungi and healthy maize leave. The aim of data train reduction in this study is to get faster and more accurate prediction results. This because by using the K Support Vector Nearest Neighbor algorithm, a support vector that is formed from the algorithm really characterize the objective function of the problem. The accuracy obtained from this study is 0.20 or 20% mean error for the value of nearest neighbor K = 3 and using K Nearest Neighbor as a model construction algorithm. The error value is smaller than when we compared to the construction of the model without performing data train reduction. The error value if not doing any reduction is 0.209 or 20.9%. Whereas in terms of time efficiency, working with the K Support Vector Nearest algorithm is 24 seconds faster than without performing data train reduction

PREDIKSI HASIL PANEN PADI KABUPATEN & KOTA DI PROPINSI NUSA TENGGARA TIMUR DENGAN FUZZY INFERENCE SYSTEM (FIS) Yampi R. Kaesmetan
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 1 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no1.p42-48

Rice (Oryza sativa) is a staple food source for the people of Indonesia. Most of the rice consumed is the result of national rice productivity. Often the government has difficulty in estimating the adequacy of basic food items that can be provided by domestic agriculture. Therefore a method is needed to predict rice yields accurately and precisely. The agricultural sector in East Nusa Tenggara is not a flagship of the community's economic activities. This is due to the geographical conditions of NTT which are less supportive for business activities in the agricultural sector. Even so, the prediction of agricultural products, especially rice yields, is needed to be predicted so that a forecast can be obtained in determining rice yields in 2017. Fuzzy logic method in this case Fuzzy Inference System (FIS) is widely applied for forecasting or prediction. Fuzzy logic has a slowness in predicting crop yields for the following year based on crop yields in the previous year and information taken from the fuzzy information provided. Fuzzyinformation can be made a rule or rule as a consideration in predicting yields. By using the formula of Mean Absolute Percentage Error (MAPE) or Average Absolute Error, from the Fuzzy Mamdani model The Fuzzy Inference System (FIS) with the Mamdani model that has been built can be used to estimate the amount of rice production in the City District in NTT with the truth value reaching 97.8%. To determine the amount of rice production in 2017, the data is processed by using the help of the Matlab 2012 fuzzy toolbox software using the centroid method for defuzzification.

PERBANDINGAN EKSTRAKSI TEKSTUR CITRA UNTUK PEMILIHAN BENIH KEDELAI DENGAN METODE STATISTIK ORDE I DAN STATISTIK ORDE II Yampi R. Kaesmetan
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 10 No. 2 (2019): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol10no2.p92-102

The problem in determining the selection of soybean seeds for replanting, especially in East Nusa Tenggara is still an important issue. The thing that affects the quality of soybean seeds is found broken seeds, dull seeds, dirty seeds, and broken seeds due to the process of drying and shelling. Determination of soy bean quality is usually done manually by visual observation. The manual system takes a long time and produces products with inconsistent quality due to visual limitations, fatigue, and different perceptions of each observer. This research was conducted using comparison of image texture extraction with statistical methods of order I (color moment) and order II statistics (GLCM) for soy bean selection. Order I statistics (color moment) show the probability of the appearance of the value of the gray degree of pixels in an image, while the order II statistics (GLCM) show the probability of a neighborhood relationship between two pixels that form a cohesion matrix from the image data. This research is expected to help the classification process in determining soybean seeds. The k-Nearest Neighbor (k-NN) algorithm used in previous studies to classify the image objects to be examined. The results of this study were successfully conducted using k-Nearest Neighbor (k-NN) with euclidean distance and k = 1 with the results of color moment extracts getting the highest accuracy of 88% and the results of GLCM feature extraction for homogeneity characteristics of 75.5%, correlations of 78.67% , contrast is 65.75% and energy is 63.83% with an average accuracy of 70.93%.

SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI Maria Yunita Nesi; Yampi R Kaesmetan; Meliana O. Meo
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 11 No. 2 (2020): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol11no2.p73-80

The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.

Simulasi Pengukuran Kadar Air, Ph Tanah, Kelembaban Dan Suhu Udara Menggunakan Mikrokontroler (Arduino-Uno R3) Melania Zemil; Yampi R. Kaesmetan; Edwin A. U. Malahina
(JurTI) Jurnal Teknologi Informasi Vol 6, No 2 (2022): DESEMBER 2022
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v6i2.2618

Pada kantor Instalasi Penelitian dan Pengembangan Teknologi Pertanian (IPPTP) Naibonat memiliki sebuah masalah yaitu sulit dalam mengetahui kesuburan tanah ini dikarenakan hanya memiliki 12 orang petugas lapangan yang mengontrol lahan tanam yang luas. Berdasarkan masalah yang ada maka dibuatlah sebuah alat dimana alat ini dapat membantu petugas lapangan dalam mengetahui apakah lahan yang akan ditanami subur atau tidak dengan memperhatikan faktor-faktor yang mempengaruhi kesuburn tanah yaitu kelembaban tanah, suhu dan kelembaban udara, serta pH tanah. Rangcangan alat yang akan dibuat ini menggunakan capacitive soil moisture sensor v1.2 sebagai sensor yang berfungsi mengukur kelembaban tanah, sensor pH tanah yang digunakan untuk mengukur tingkat acid (keasaman) dan alkali (kebasaan) tanah,sensor DHT11 untuk mengukur suhu dan kelembaban udara. Dengan di buatnya alat ini diharapkan dapat membantu petugas lapangan di Instalasi Penelitian dan Pengkajian Teknologi Pertanian (IPPTP) Naibonat dalam menngontrol kesuburan tanah pada lahan tanam.

Digital Image Processing to Detect Sumba Woven Fabric Contour Using Gray Level Co-occurrence Matrix and Self Organizing Map Bintang Vieshe Mone; Yampi R Kaesmetan; Meliana O. Meo
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28355

Sumba woven cloth is one of the cultural heritages of the island of Sumba. Based on its manufacture, the classification process for Sumba woven fabrics is based on the identification of colors or motifs. However, the classification process is not an easy process. In addition to the classification process, the wider community also does not get much information about Sumba woven fabrics clearly, therefore digital image processing technology is needed to build a system that can overcome the problems faced. The image of the Sumba woven fabric sample is converted to grayscale and resized, then segmented using Sobel detection. Then extracted using Gray level co-occurrence matrix (GLCM). After extraction, it will be classified using a Self Organizing Map (SOM). Based on the results of this study, it was concluded that the accuracy of the validation test was 80%, and the program was successful.

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