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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JPBM (Jurnal Pendidikan Bisnis dan Manajemen) EMITTER International Journal of Engineering Technology Fountain of Informatics Journal IT JOURNAL RESEARCH AND DEVELOPMENT Comvice : Journal of community service Indonesian Journal of Community Services Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Journal of Intelligent Computing and Health Informatics (JICHI) Jurnal Teknologi Informasi dan Komunikasi Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Walisongo Journal of Information Technology JAST Journal of Information Technology (JIfoTech) Jurnal Nasional Teknik Elektro dan Teknologi Informasi TECHSI - Jurnal Teknik Informatika
Waliyansyah, Rahmat Robi
UNIVERSITAS PGRI SEMARANG
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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IMPLEMENTATION SIMPLE ADDITIVE WEIGHTING METHOD IN DETERMINING FEASIBILITY SACRIFICIAL ANIMALS Saputro, Nugroho Dwi; Waliyansyah, Rahmat Robi; Novita, Mega
Jurnal Transformatika Vol. 20 No. 1 (2022): July 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i1.4542

Abstract

Many things from the life sector have used the existence of technology. Where a technology is able to help various problems in various fields such as livestock and agriculture. Computers have been included in it as a tool to do a job or identify existing problems. However, sometimes as a practitioner in the field of animal husbandry, especially qurban animals, they come to the conclusion that it is often found that sacrificial animals in the market that want to be sacrificed do not meet the requirements both in syari ah (law) and health. With the application of determining the feasibility of sacrificial animals according to the Syariah using the web-based Simple Additive Weighting (SAW) method. This system is later expected to be able to determine whether or not a sacrificial animal will be sacrificed so that the community or people who sacrifice are not harmed and the reward for the sacrifice is perfect.
Identifikasi Jenis Biji Kedelai (Glycine Max L) Menggunakan Gray Level Coocurance Matrix (GLCM) dan K-Means Clustering Waliyansyah, Rahmat Robi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kacang kedelai merupakan tanaman pangan yang dapat diolah dalam berbagai olahan, seperti tempe & tahu. Indonesia mempunyai banyak varietas kedelai, varietas lokal atau impor. Meningkatnya konsumsi kedelai tersebut sangat dipengaruhi oleh pemilihan varietas dari kedelai tersebut. Tetapi hanya beberapa varietas saja yang dapat diolah dalam industri pengolahan kedelai, khususnya industri tahu & tempe. Untuk itu perlu adanya aplikasi identifikasi kedelai yang dapat membedakan varietas biji kedelai. Aplikasi untuk identifikasi jenis biji kedelai menggunakan pengolahan citra digital, dalam proses segmentasinya menggunakan Citra L*a*b dan K-Means Clustering. Ekstraksi ciri yang digunakan ada dua yaitu tekstur dan morfologi. Ekstraksi ciri tekstur menggunakan Metode Gray Level Coocurrence Matrix (GLCM) dengan jarak spasial 2 pixel. Parameter yang digunakan ada 4 yaitu energy, contrast, homogeneity & correlation.. Ekstraksi ciri morfologi menggunakan 2 parameter yaitu Metric dan Eccentricity. Ada pun varietas biji kedelai yang digunakan adalah : Anjasmoro, Burangrang, Dering-1, Dena-1, Demas-1 dan Grobogan untuk jenis varietas kedelai emas serta Detam-1, Detam-3, Detam-4 untuk jenis varietas kedelai hitam. Berdasarkan hasil pengujian, didapatkan tingkat akurasi sebesar 47% dari total 198 sampel citra uji biji kedelai dan 0% pada pengujian biji-bijian yang lain (kacang hijau) yang secara tekstur, bentuk dan warna mirip dengan kedelai (hitam). Hasil pengujian yang kurang baik ini disebabkan oleh belum maksimalnya data yang digunakan, karena sampel biji kedelai tidak selalu tersedia dan juga tiap jenis kedelai yang dipanen memiliki ukuran yang berbeda. AbstractSoybeans are food crops that can be processed in various preparations, such as tempeh & tofu. Indonesia has many varieties of soybeans, both local and imported varieties. Increased consumption of soybeans is strongly affected by the selection of varieties of soybeans. But only a few varieties that can be processed in soybean processing industry, in particular the tofu & tempe industry. Applications made using digital image processing, while the segmentation used is the Image L * a * b and K-Means Clustering. The feature extraction used is two, i.e. texture and morphology. The extraction of Texture feature was using the Gray Level Co-occurrence Matrix Method (GLCM) with a spatial distance of 2 pixels. The parameters used were 4, i.e. energy, contrast, homogeneity & correlation. Morphological feature extraction used 2 parameters, Metric and Eccentricity. There were also soybean seed varieties that were used: Anjasmoro, Burangrang, Dering-1, Dena-1, Demas-1 and Grobogan which are grouped into the types of golden soybean varieties, and Detam-1, Detam-3, Detam-4 for black soybean varieties. Based on the test results, an accuracy rate of 47% was obtained from a total of 198 samples of soybean seed test images. This unfavorable test result is caused by the lack of data used because soybean seed samples are not always available and also each type of soybean that grows has a different size.
Rancang Bangun Sistem Informasi Perpustakaan Menggunakan Algoritma Apriori Dalam Penentuan Penempatan Buku Di SMAN 1 Warureja Kabupaten Tegal Handayanto, Agung; Waliyansyah, Rahmat Robi; Irwanto, Muhammad Riyan
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 5 No. 1 (2022): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v5i1.259

Abstract

The library is one of the facilities provided by educational institutions as a support and support for the process of teaching and learning activities for students. In the process of collecting data on book transactions at the SMA N 1 Warureja library still using a manual database so that all transactions are still written on the book, the use of manual databases in accessing data will be slow and less efficient. In addition, placing books far apart causes students to take longer to find books when borrowing books of different types. To overcome these problems, the authors design and build a library information system using the a priori algorithm in determining the placement of books at SMA N 1 Warureja, Tegal Regency. In this study, the method used is an Object Oriented Program (OOP) approach using the Unified Modeling Language (UML), system development using the waterfall method and will utilize an a priori algorithm in determining book placement. The results of system testing are obtained from black box testing with the results of the existing system functions being 100% valid and 0% invalid. and UAT (User acceptance testing) with results that have a percentage of 90.29% in terms of usefulness, 93.33% in terms of interface appearance, and 98% in terms of use so that it can be concluded that the system is running as expected. In white-box testing, it was found that the result of complexity is 3. Suggestions that can be submitted are that the a priori algorithm method can be used as an alternative in determining the placement of books in the library.
Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image Waliyansyah, Rahmat Robi; Umar Hafidz Asy'ari Hasbullah
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.536

Abstract

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.
Co-Authors Aditya Galih Prathama Agung Handayanto Aris Tri Jaka Harjanta, Aris Tri Bagus Priyatno Bambang Agus Herlambang, Bambang Bonifasius Wahyu Setyawan Edi Dwi Supratiyan Febrian Murti Dewanto Fitriyah, Citra Galih Hermawan Irfan Nur Ridwan Irwanto, Muhammad Riyan Istiyaningsih Istiyaningsih Jatmiko Endor Suseno Khoirul Junianto Khoirya Latifah Kusworo Adi Laelia Puti Aditasar Mahmud Yunus Mahmud Yunus Mega Novita Mega Novita Mega Novita Nugroho Dwi Saputro Nugroho Dwi Saputro Nugroho Dwi Saputro, Nugroho Dwi Nur Aksin Nur Aksin, Nur Qristin violinda Ridwan, Irfan Nur Septio Oggy Pradana Setyoningsih Wibowo Slamet Budirahardjo Suseno, Jatmiko Endor Suwarno Widodo Umar Hafidz Asy'ari Hasbullah Yayuk Wahyuningsih Zainal Arifin