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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sains dan Informatika Elsains : Jurnal Elektro Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Pengabdian Kepada Masyarakat (Mediteg) Aisyah Journal of Informatics and Electrical Engineering Scientific Journal of Informatics Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Computer Science and Technology (JACOST) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Brilliance: Research of Artificial Intelligence Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) As-Sakinah : Jurnal Hukum Keluarga Islam Informatika Sains Teknologi
Veri Julianto
Politeknik Negeri Tanah Laut
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Social Sciences Other

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Journal : KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal)

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ANALYSIS OF MODIFIED K-MEANS CLUSTERING IN DECISION SUPPORT OF INDUSTRIAL PARTNER GROUPING Billy Sabella; Veri Julianto; Ahmad Rusadi Arrahimi
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 9, No 1 (2022)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v9i1.429

Abstract

Internship is part of achieving the competencies expected in the educational process. Therefore, the suitability of students to companies that serve as a place for street vendors is something important to pay attention to. Weaknesses in the previous system, there are still many students who are not right in choosing companies/agencies. They are still not paying attention to the competencies expected in this internship process. This study aims to help group industrial partners according to the competency achievements of each department. The method used in this research is Modified K-Means Clustering in the grouping process. While the criteria used are the suitability of the company's field with the department, credibility, company ecosystem, company track record in the field of education, and the facilities provided. In carrying out this work, a system will be developed to process the data resulting from the questionnaire so that groups from each company are obtained. The results of the study were obtained from 86 respondents who were apprentices who had been in 37 companies or agencies. 22 questions that build 7 criteria resulted in 4 stable clusters after 8 iterations.Keywords: internship, decision support system, Modified K-Means Clustering.
Machine learning to Detect Palm Oil Diseases Based on Leaf Extraction Features and Principal Component Analysis (PCA) Arrahimi, Ahmad Rusadi; Julianto, Veri; Rahmanto, Oky
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 11, No 1 (2024)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v11i1.659

Abstract

Palm oil tree is one of the economically important crops that is the backbone of the Indonesian economy. However, palm oil production is often hampered by various diseases. The disease is difficult to detect in the early stages because infected trees often show no symptoms. Therefore, it is necessary to carry out identification and classification to determine whether this palm coconut plant is sick or infected with disease. In this study the disease was identified in palm coconut by identifying it through leaves by modifying the extraction process features using PCA and comparing it with no PCA for sick and healthy types. Subsequently, the classification will be done using SVM (Support Vector Machine) with various treatments such as variation of the features used and the amount of data to be processed in carrying out experiments or tests. The results obtained show that if the feature used for classifying a number of 4 or more then the accuracy value remains at 97%.
Co-Authors Ade Kurniawan Ade Kurniawan Adelia, Firda Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Arif Suprianto Arif Supriyanto Dewi Saadah Dhika Sinta Pratama Dwi Ratna Sari, Dwi Ratna Eka Wahyu Sholeha Fathurahmani Fathurahmani Fathurahmani, Fathurahmani Heldalina Hendrik Setyo Utomo herpendi, herpendi Jaka Permadi Jamil, Khafifah Karima Zam Zam Khairul Anwar Hafizd, Khairul Anwar Lastriani Lastriani Lastriani Lastriani Meldayanoor Meliana Mohammad Sofwat Aldi Muhammad Indra Darmawan Muhammad Muslih Amirudin Muhammad Noor Muhammad Rusyadi Arrahimi Mustofa, Jupri Norfifah Noviyanti Nur Maida Oktaviyani Oktaviyani Oktaviyani, Oktaviyani Oky Rahmanto Pratama, Dhika Sinta Rabini Sayyidati Rahman, Ramdani Rahmanto, Oky Ramdani Rahman Ramitha Ramitha Ramitha, Ramitha Rhomadona, Herfia Saadah, Dewi Sabella, Billy Wan Yuliyanti Wan Yuliyanti Wijayati, Titik Winda Aprianti Yundari, Yundari Yunita Prastyaningsih