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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri Indonesian Journal of Artificial Intelligence and Data Mining Indonesian Journal of Electrical Engineering and Computer Science
Jasril Jasril
UIN Sultan Syarif Kasim Riau
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

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Implementasi Pengolahan Citra dan Klasifikasi K-Nearest Neighbour Untuk Membangun Aplikasi Pembeda Daging Sapi dan Babi Berbasis Web Elvia Budianita; Jasril Jasril; Lestari Handayani
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 12, No 2 (2015): Juni 2015
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v12i2.1005

Abstract

Salah satu cara untuk mengenali daging sapi dan babi di bidang informatika adalah menggunakan pengolahan citra. Pada penelitian ini akan dibuat suatu sistem pengolahan citra untuk membedakan daging sapi dan babi menggunakan metode HSV, GLCM, dan klasifikasi K-Nearest Neighbour (K-NN). Tahapan analisa yang dilakukan adalah Data acquisition dengan menggunakan kamera handphone dilakukan pemotretan terhadap data daging sapi dan babi sehingga diperoleh citra digital daging sapi dan babi dalam format jpg. Gambar (citra) diambil dari daging babi segar, daging sapi segar, daging sapi yang telah membusuk, dan daging campuran (oplosan). Preprocessing dilakukan peningkatan kualitas citra yaitu dengan melakukan pencerahan citra dan peregangan kontras. Ekstraksi fitur menggunakan histogram model warna HSV untuk fitur warna, dan metode orde dua untuk ekstraksi fitur tekstur. Klasifikasi citra daging sapi dan babi menggunakan K-NN dengan dua tahapan yaitu tahap training dan testing. Pengujian terdiri dari 4 pengujian yaitu pengujian tanpa background dengan akurasi keberhasilan 88,75%, pengujan dengan background sebesar 73,375%, pengujian campuran sebesar 88,75% dan pengujian berjarak sebesar 50% . Pengujian dengan akurasi pengujian tertinggi terdapat pada pengujian tanpa background dan pengujian campuran sebesar 88,75%. Pada pengujian campuran dinyatakan berhasil apabila hasil klasifikasinya adalah daging campuran (oplosan) dan daging babi segar. Sedangkan nilai K yang paling baik pada semua pengujian adalah K=5 dengan total akurasi keberhasilan 78,75%. Hasil klasifikasi dipengaruhi oleh nilai ciri, jarak terdekat, nilai K, dan yang paling mempengaruhi adalah background
Learning Vector Quantization 3 (LVQ3) and Spatial Fuzzy C-Means (SFCM) for Beef and Pork Image Classification Jasril Jasril; Suwanto Sanjaya
Indonesian Journal of Artificial Intelligence and Data Mining Vol 1, No 2 (2018): September 2018
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (819.086 KB) | DOI: 10.24014/ijaidm.v1i2.5024

Abstract

Base on some cases in Indonesia, meat sellers often mix beef and pork. Indonesia is a predominantly Muslim country. Pork is forbidden in Islam. In this research, the classification of beef and pork image was performed. Spatial Fuzzy C-Means is used for image segmentation. GLCM and HSV are used as a feature of segmentation results. LVQ3 is used as a method of classification. LVQ3 parameters tested were the variety of learning rate values and window values. The learning rate values used is 0.0001; 0.01; 0.1; 0.4; 0.7; 0.9 and the window values used is 0.0001; 0.4; 0.7. The training data used is 90% of the total data, and the testing data used is 10%. Maximum epoch used is 1000 iterations. Based on the test results, the highest accuracy was 91.67%.
Eigenvalue of Analytic Hierarchy Process as The Determinant for Class Target on Classification Algorithm Mustakim Mustakim; Novia Kumala Sari; Jasril Jasril; Ismu Kusumanto; Nurul Gayatri Indah Reza
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1257-1264

Abstract

Data mining has two main concepts of data distribution, namely supervised learning and unsupervised learning. The most easily recognizable concepts from data distribution is related to the dataset, with and without target class. Analytic Hierarchy Process (AHP) technique that carries the concept of pairwise comparison able to answer the problem related to the dataset, which is to change unsupervised to be supervised by determining eigenvalue value of each attribute and sub attribute in AHP method. The case study conducted in this issue is related to determining the target classes used to predict the success of a student learning in UIN Suska Riau. The three main attributes are Procrastination, Total Credits (SKS) and Number of Repeated Courses, each having eigenvalues of 0.319; 0.189 and 0.171 which become the feedback in the determination of the Target Timely Graduation (TG) or Possibility of Timely Graduation (PTG). The biggest consistency ratio generated in the AHP case is 9.4% in the GPA attribute. This research recommends that further research should use datasets that have been arranged based on experimental combinations of the three main attributes above, then applied to the classification or prediction algorithm. So that it would obtain a decision of accuracy from data used against the real result on the field.
Co-Authors Elvia Budianita Ismu Kusumanto Lestari Handayani Mustakim Mustakim Novia Kumala Sari Nurul Gayatri Indah Reza Suwanto Sanjaya