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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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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.
Analisis Pengaruh Variasi Nilai P Pada Metode Minkowski Distance dalam Menentukan Kemiripan Abstrak Skripsi Simanullang, Harlen Gilbert; Silalahi, Arina Prima; Duha, Nadyarni Natalis Caesarin
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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Abstract

The Computer Science Study Program of Universitas Methodist Indonesia is faced with the challenge of verifying the authenticity of student theses, which is still done manually. This study applies the Minkowski Distance method to analyze the level of similarity of thesis abstracts using one hundred samples. The preprocessing stage is carried out through five systematic steps: cleansing to remove non-alphabetic characters, case folding for letter standardization, tokenizing for text splitting, filtering for stopword elimination, and stemming to obtain root words, resulting in word vectors that are analyzed. The Minkowski Distance method is implemented with three parameter variations, P = 3, P = 5, and P = 7, where the selection of parameters is based on differences in sensitivity to vector dimensions; the higher the P value, the greater the emphasis on significant differences between dimensions. The test results show that the parameter P = 7 provides the most optimal similarity measurement with the smallest distance of 3.84 for documents with the highest similarity. These findings contribute to the development of a more effective similarity detection system to maintain academic integrity.
Penerapan Algoritma K-Nearest Neighbors dalam Mengklasifikasi Penyakit Multiple Sclerosis Sitompul, Andrew Efraim Nicholas; Margaretha Yohanna; Arina Prima Silalahi
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

The central nervous system is impacted by multiple sclerosis (MS), a chronic autoimmune disease that requires early identification for successful treatment. Because of its many symptoms and similarities to other neurological disorders, MS can be difficult to diagnose. Artificial intelligence techniques like the K-Nearest Neighbors (KNN) algorithm can be used to help with quicker and more precise classification in order to solve this problem. The goal of this study is to classify MS using the KNN technique and assess how well it performs in this regard. The Kaggle platform provided the dataset, which consists of 273 patient records with 18 clinical characteristics. With k = 3 as the number of neighbors, the data was split into 80% for training and 20% for testing. The Python programming language was used to implement the classification procedure. According to the findings, the KNN algorithm classified MS with an accuracy of 81.82%. The precision, recall, and f1-score for class 1 were 0.83, 0.76, and 0.79, respectively, according to additional analysis utilizing a classification report, whereas the scores for class 2 were 0.81, 0.87, and 0.84. These findings suggest that the KNN method has the potential to serve as a supportive tool in the diagnosis of Multiple Sclerosis.
Co-Authors Aritonang, Mendarissan Darwis Robinson Manalu DEWI SARTIKA Doli Hasibuan Duha, Nadyarni Natalis Caesarin 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 Margaretha Yohanna Mufria J. Purba Nettina Samosir Posma Lumbanraja Purba, Nova Ferawati Putra Palipi Rajagukguk, Edward Rumapea, Yolanda Y. P. Samosir, Nettina Sarkis, Indra M. Simanullang, Harlen Gilbert Simanullang, Harlen Gilbert Sitompul, Andrew Efraim Nicholas Sri Agustina Rumapea Surianto Sitepu Tambunan, Tio Yohanna, Margaretha yolanda yulianti pratiwi rumapea