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All Journal JUSIFO : Jurnal Sistem Informasi PROtek : Jurnal Ilmiah Teknik Elektro Jiko (Jurnal Informatika dan komputer) JURNAL PengaMAS Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Journal Of Khairun Community Services BAKTI : Jurnal Pengabdian Kepada Masyarakat Journal of Computer Engineering, Electronics and Information Technology
Abdul Mubarak
Fakultas Teknik, Program Studi Teknik Informatika Universitas Khairun Ternate
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Social Sciences Transportation Other

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SUPPORT VECTOR MACHINE (SVM) ALGORITHM FOR STUDENT SENTIMENT ANALYSIS OF ONLINE LECTURES Abdul Muis; Abdul Mubarak; Arifandy M Mamonto; Satria Dwi Surya
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 1 (2023)
Publisher : JIKO (Jurnal Informatika dan Komputer)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i1.5836

Abstract

Covid-19 was first discovered in Wuhan City, Hubei Province, China at the end of December 2019. According to the WHO (World Health Organization) as of October 13 2020, the number of positive confirmed cases of Covid-19 reached 38,103,332 cases, while in Indonesia the number of cases exposed to Covid-19 reached 268.85 cases and is likely to increase every day (Covid-19 Handling Task Force, 2020). The formulation of the problem that will be raised from this research is to measure the level of accuracy obtained from the results of classifying sentiments of distance learning during the Covid-19 pandemic using the Support Vector Machine (SVM) method and measuring the impact of implementing online lectures during the Covid-19 pandemic. The data used in this research is in the form of public responses regarding distance learning policies implemented during the Covid-19 pandemic, taken from January to March 2022. The data obtained will then be divided into training data as much as 80% of the the total data and test data is 20% of the total data. Based on the results of testing the previous Support Vector Machine classification model, the accuracy value for the entire system can be calculated at 70.8%. Based on the results of testing the previous Support Vector Machine classification model, the accuracy value for the entire system can be calculated at 70.8%.
SISTEM INFORMASI KEPENDUDUKAN KANTOR KELURAHAN JATI KOTA TERNATE Abdul Mubarak; Hairil Kurniadi Sirajuddin; Rosihan Rosihan; Arifandi Mario Mamonto
Journal Of Khairun Community Services Vol 3, No 1 (2023): JOURNAL OF KHAIRUN COMMUNITY SERVICES
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jkc.v3i1.6039

Abstract

Kelurahan merupakan salah satu instansi yang melakukan pengolahan data kependudukan seperti pembuatan Kartu Keluarga (KK), Surat Kelahiran, Surat Kematian, Surat Keterangan Pendatang, dan Surat Keterangan Pindah. Pengabdian ini berangkat dari Kantor kelurahan Jati Kota Ternate saat ini masih menggunakan pengolahan atau pelayanan administrasi kependudukan yang masih konvensional, dimana petugas kelurahan masih mengandalkan penyimpanan dan pengelolaan data kependudukan dalam laporan yang seringkali banyak terdapat masalah di dalamnya, seperti masih mencari satu per-satu data kependudukan bahkan ada data penduduk yang telah hilang atau rusak, sehingga menyebabkan beberapa permasalahan seperti lambatnya proses pelayanan terhadap masyarakat, kurang akuratnya dalam membuat laporan dan mengirim laporan yang nantinya akan diserahkan kepada kecamatan. Solusi yang bisa dilakukan untuk mengatasai permasalahan ini yaitu tim Pengabdian Kepada Masyarakat (PKM) membangun suatu Sistem Informasi Kependudukan yang bisa digunakan oleh Kantor Kelurahan Jati untuk manajemen data kependudukan secara elektronik dan juga memberikan keterampilan untuk penggunaan sistem yang telah dibangun. Hasil dari pelaksanaan PKM ini yaitu adanya Sistem Informasi Kependudukan yang bisa digunakan oleh kantor Kelurahan Jati untuk manajemen data kependudukan secara elektronik. Kegiatan ini juga menanamkan pemahaman tentang pentingnya pelaksanaan pemerintahan berbasis elektronik dan membekali pegawai Kelurahan berupa keterampilan untuk penggunaan Sistem Informasi Kependudukan kantor Kelurahan Jati.
Classification of Device Addiction to Students Using SAS-SV with K-Nearest Neighbor Algorithm Method Basyir Al Musthoqfirin Majid; Abdul Mubarak; Salkin Lutfi
Journal of Computer Engineering, Electronics and Information Technology Vol 1, No 1 (2022): COELITE: Volume 1, Issue 1, 2022
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.564 KB) | DOI: 10.17509/coelite.v1i1.51616

Abstract

A gadget is a small electronic device with a particular purpose, often thought of as an innovation of new goods. Not only to help facilitate human activities, but gadgets are also a part of the lifestyle for modern citizens. With this innovative feature, the gadget has attracted users more and more, or in other words, users have become more addicted to the gadget. This study aims to investigate how addictive gadgets are to students at the Department of Informatic Engineering, Khairun University, Ternate, Indonesia using K-Nearest Neighbor (KNN) Algorithm. In KNN, there is a Training dataset where one set of data contains the class's value and a predictor that will be used as one of the requirements for determining a suitable grade per the predictor. In contrast, the Testing dataset contains the new data that will be classified based on the model made and the accuracy of classification in the data collection process. Questionnaires were made using Google forms, then distributed through the internal groups of the Informatics Engineering department of  Khairun University. A total of 78 questionnaires were successfully collected. The results showed that the testing accuracy with k = 3 is 86% and k = 5 is 80%. This show that KNN algorithm can be applied to measure the level of addiction to students.
Co-Authors Abdul Muis Abdul Muis ACHMAD FUAD Ahmad Junaidy Mustafa Amal Khairan Arifandi Mario Mamonto Arifandy M Mamonto Aryuni, Vrita Tri Bahraen Folaiman Basyir Al Musthoqfirin Majid Faisal F Taran Firda Marasabessy Hairil Kurniadi Siradjudin Hairil Kurniadi Sirajuddin Masrudin H.Saleh Munazat Salmin Neneng Kurniasari Rosihan rosihan, rosihan Saiful Do Abdullah Salkin Lutfi Satria Dwi Surya Syarifuddin N Kapita Tempola, Firman