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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.
AN EVALUATION OF THE POWER SUPPORT INTERNET INFRASTRUCTURE OF MAKASSAR CITY IN TELEMEDICINE FRAME Figur Muhammad; Andani Achmad; Adnan Adnan; Abdul Mubarak; Abdul Muis
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 1 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i1.7785

Abstract

This research aims to find the quality of the internet in Makassar City. It uses a 10 Mbps service from Indihome to support telemedicine. The study is a case study of sending raw MRI image data to the AWS cloud. The research uses a virtual server from the AWS cloud. It stores raw MRI image data. The data will be sent via the FTP client FileZilla. The tests were carried out eight times. They used the quality of service standard formula from TIPHON. The results come from 8 tests. In the tests, MRI image data was sent to the AWS cloud. The results show that the average throughput value was 4.53 Mbps with an index of 4. This result is excellent. Packet loss is low at 0.01% with an index of 4, which is very good. The delay is 1.7 ms with an index of 3, which is good. The jitter is 1.69 ms with an index of 3, which is good. The quality of service test results are based on TIPHON standards. They show that sending Raw MRI image data to the AWS cloud at 10 Mbps from Indihome in Makassar City is good.
AN LSTM-BASED APPROACH FOR INDONESIAN NEWS CATEGORIZATION: PERFORMANCE ANALYSIS OF HYPERPARAMETER TUNING AND PREPROCESSING Iwan La Udin; Firman Tempola; Abdul Mubarak; Muhammad Sabri Ahmad; Munazat Salmin; Saiful Do Abdullah
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 3 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i3.10783

Abstract

News disseminated through internet-based systems or news portals is generally classified into specific categories, such as politics, sports, economy, entertainment, technology, health, and others. Currently, this categorization is performed manually, requiring a thorough reading of the entire news content. To address this inefficiency, an automatic classification system for Indonesian news articles is necessary to categorize them based on predetermined categories. This research employs a Natural Language Processing (NLP) approach and implements the Long Short-Term Memory (LSTM) architecture. The study was conducted using several testing scenarios, including (1) hyperparameter tuning of the learning rate to 0.01 and 0.001, (2) the application and omission of stemming, and (3) various dataset comparison ratios of 60:40, 70:30, 80:20, and 90:10. The evaluation utilized a dataset of 10,000 articles across 5 categories and was measured using accuracy, precision, recall, and f-measure metrics. From the three scenarios, seven training models were generated. The second model, with a learning rate of 0.001, without stemming, and a 90:10 dataset ratio, achieved the highest accuracy of 90.7%, with average precision, recall, and f-measure scores of 91%. The third and fourth models, which applied stemming, did not demonstrate a performance improvement, both yielding an accuracy of 89%. The fifth model, with a 60:40 dataset ratio, produced an accuracy of 90%, while the sixth and seventh models, with 70:30 and 80:20 ratios, resulted in accuracies of 79% and 88%, respectively.
Co-Authors Abdul Muis Abdul Muis Abdul Muis ACHMAD FUAD Adnan Adnan Ahmad Junaidy Mustafa Amal Khairan Andani Achmad Arifandi Mario Mamonto Arifandy M Mamonto Aryuni, Vrita Tri Bahraen Folaiman Basyir Al Musthoqfirin Majid Faisal F Taran Figur Muhammad Firda Marasabessy Hairil Kurniadi Siradjudin Hairil Kurniadi Sirajuddin Iwan La Udin Masrudin H.Saleh Muhammad Sabri Ahmad Munazat Salmin Munazat Salmin Neneng Kurniasari Rosihan rosihan, rosihan Saiful Do Abdullah Salkin Lutfi Satria Dwi Surya Syarifuddin N Kapita Tempola, Firman