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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal ULTIMA InfoSys METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Informatika : Jurnal Informatika, Manajemen dan Komputer Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA Jurnal Ilmiah Teknologi dan Komputer (JITTER)
Silalahi, Arina Prima
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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Journal : METHOMIKA: Jurnal Manajemen Informatika

 1 
SUPERVISED LEARNING METODE K-NEAREST NEIGHBOR UNTUK PREDIKSI DIABETES PADA WANITA Arina Prima Silalahi; Harlen Gilbert Simanullang; Marlyna Infryanty Hutapea
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 1 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No1.pp144-149

Abstract

Supervised learning is a technique of machine learning by doing learning that has a reference value to direct something, one of which is the K-Nearest Neighbor (KNN) method. This method is for object classification through learning data that is closest to the object (neighbor) using euclidean distance to calculate the distance. KNN can be used for data classification that already has a reference, in this case the dataset used is the diabetes mellitus dataset in women. DM is a disease that can cause complications in parts of the body that cause death. DM in women can be seen from several parameters such as glucose levels, blood pressure, skin thickness, insulin hormone, body index mass, age, number of pregnancies, and the number of family history of diabetes. In this research, KNN will be used for the classification of diabetes in women with two classes, namely DM Positive and DM Negative, in other words, a woman can be predicted to suffer from DM disease or not. This method will be implemented into a system with PHP programming language and Codeigniter Framework. KNN testing is carried out with three test scenarios, the 1st test with 150 test data gets an 82% accuracy rate, the 2nd data test with 200 test data gets an 84% accuracy rate, and the 3rd data test with 300 test data gets an 82% accuracy rate.
PENERAPAN METODE RANDOM FOREST DALAM MENDETEKSI BERITA HOAX Tambunan, Tio; Yohanna, Margaretha; Silalahi, Arina Prima
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 7 No. 2 (2023): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol7No2.pp301-306

Abstract

Hoax is information that is not true. The Ministry of Communication and Informatics (Kominfo) found that there was 2,099 hoax news that was spread thousands of times via social media. This generally impacts the community so it can lead to a crisis of confidence in the government. This arises because many message recipients have different literacy levels, which will affect how people analyze the information conveyed. This research uses the Random Forest method, which is used to classify large amounts of data to detect hoax news. The research results show that the Random Forest method is proven to be able to classify hoax news based on data that has been weighted and entered into the system. From the results of the study using 200 data sets, which were divided by 80% in the form of training data and 20% of testing data, the classification results obtained from the testing data were in the form of 28 positive sentiments and 23 negative sentiments with an accuracy rate of 98%.
EVALUASI TATA KELOLA TEKNOLOGI INFORMASI PADA PERUSAHAAN MENGGUNAKAN FRAMEWORK COBIT : Studi Kasus: PT. Telkom Gaharu Medan-Divisi Data Management Silalahi, Arina Prima; Jaya, Indra Kelana; Sartika, Dewi; Manalu, Darwis R.; Larosa, Fati G. N.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 1 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No1.pp9-19

Abstract

Teknologi informasi memegang peranan penting dalam sebuah perusahaan atau instansi lainnya, namun untuk melihat kesesuaian teknologi dengan kebutuhan perusahaan diperlukan evaluasi tata kelola teknologi informasi. Pada penelitian yang dilakukan, fokus evaluasi meliputi divisi Data Management PT Telkom Gaharu dengan menggunakan kerangka kerja COBIT 2019 dengan domain EDM04 (Ensure Resources Optimization), MEA01 (Managed Performance and Conformance Monitoring) dan DSS03 (Managed Problems) yang berfokus pada sistem informasi yaitu Unified Inventory Management. Penelitian ini menggunakan teknik pengumpulan data studi literatur, observasi, wawancara dan kuesioner yang dikelola menggunakan pengukuran skala Guttman dengan bantuan perhitungan Microsoft Excel. Evaluasi yang dilakukan berfokus pada nilai Capability Level dan Gap Analysis yang disajikan dalam bentuk Tabel dan Grafik Radar. Nilai Capability Level dari domain EDM04 sebesar 92% (Fully Achieved) dengan Gap Analysis 0.97 maka dapat dikatakan bahwa proses tata kelola teknologi informasi untuk domain EDM04 sepenuhnya sudah tercapai. Nilai Capability Level domain MEA01 sebesar 94% (Fully Achieved) dengan Gap Analysis 1.33 maka dapat dikatakan bahwa proses tata kelola teknologi informasi untuk domain MEA01 sepenuhnya sudah tercapai. Nilai Capability Level domain DSS03 sebesar 81% (Largely Achieved) dengan Gap Analysis 0.25 maka dapat dikatakan bahwa proses tata kelola teknologi informasi untuk domain DSS03 sepenuhnya sudah tercapai.
Co-Authors Aritonang, Mendarissan Darwis Robinson Manalu DEWI SARTIKA Doli Hasibuan Edward Rajagukguk Fati Gratianus Nafiri Larosa Gea, Asaziduhu Gresa Simarmata Harlen Gilbert Simanullang Harlen Gilbert Simanullang Hasibuan, Doli Humuntal Rumapea Indra Kelana Jaya Indra M. Sarkis S. Jhoni Maslan Jhoni Maslan, Jhoni Larosa, Fati G. N. Lumbanraja, Posma Lyna M. N. Hutapea Mufria J. Purba Nettina Samosir Posma Lumbanraja Purba, Nova Ferawati Putra Palipi Rumapea, Yolanda Y. P. Sarkis, Indra M. Simanullang, Harlen Gilbert Sri Agustina Rumapea Surianto Sitepu Tambunan, Tio Yohanna, Margaretha yolanda yulianti pratiwi rumapea