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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal ULTIMA InfoSys Jurnal ULTIMATICS 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
Silalahi, Arina Prima
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health

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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

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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.
Penerapan Metode Holt-Winters untuk Memprediksi Produksi Biji Kopi Arabica Lintong Nihuta Arina Prima Silalahi; Tamado Simon Sagala; Laura Sridevi Sihombing; Harlen Gilbert Simanullang
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp143-150

Abstract

Arabica coffee bean production in Lintong Nihuta fluctuates every month, requiring a method to predict future production volumes. Accurate predictions can aid production planning and decision-making. This study aims to predict Arabica coffee bean production using the Holt-Winters multiplicative method, which can capture trends and seasonal patterns in time series data. The data used are 60 monthly production data points from January 2021 to December 2025. The analysis process begins with determining the initial values of the level, trend, and seasonal components, followed by a smoothing process using parameters α = 0.4, β = 0.45, and γ = 0.35. Model evaluation was performed using the Mean Absolute Percentage Error (MAPE) using 2025 data as the evaluation data. The evaluation results show a MAPE value of 13.12%, indicating that the model has a good level of accuracy. The prediction results show that Arabica coffee bean production in 2026 is expected to fluctuate, with the highest predicted value in December at 75,297.15 kg and the lowest in May at 36,737.38 kg. Therefore, the Holt-Winters multiplicative method can be used to predict Arabica coffee bean production in the Lintong Nihuta District in the future.
Pelatihan Desain UI/UX Menggunakan Figma pada SMA PGRI Siborongborong Samuel V. B. Manurung; Mufria J. Purba; Humuntal Rumapea; Darwis Robinson Manalu; Sri Agustina Rumapea; Indra M. Sarkis S.; Jimmy F. Naibaho; Yolanda Y. P. Rumapea; Arina Prima Silalahi; Agus Syahputra
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp13-17

Abstract

This Community Service Program (PKM) aimed to enhance students’ digital literacy and skills through UI/UX design training using the Figma application for students of SMA PGRI Siborongborong, Tapanuli Utara Regency, North Sumatra. This activity represents the implementation of the Tri Dharma of Higher Education by the Faculty of Computer Science, Universitas Methodist Indonesia Medan, in the form of knowledge and technology transfer to secondary education institutions.The training was conducted over two days using a combination of lectures, demonstrations, and hands-on practice in designing mobile-based application interfaces. The materials covered included an introduction to digital application concepts, the fundamentals of User Interface (UI) and User Experience (UX), and the utilization of key features in Figma to design simple application prototypes. The results indicated that participants were able to understand basic UI/UX concepts and independently produce application interface designs. The students’ enthusiasm and active participation throughout the program reflected a strong interest in developing digital design skills. This activity contributed positively to improving students’ information technology competencies while strengthening collaboration between the university and the school as a community service partner.
Analisis Pengaruh Variasi Nilai P Pada Metode Minkowski Distance dalam Menentukan Kemiripan Abstrak Skripsi Harlen Gilbert Simanullang; Arina Prima Silalahi; Nadyarni Natalis Caesarin Duha
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 | DOI: 10.46880/jmika.Vol9No2.pp255-263

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 Margaretha Yohanna; Andrew Efraim Nicholas Sitompul; 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 | DOI: 10.46880/jmika.Vol9No2.pp307-315

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.
Detection of AI-Generated Videos Using Local Binary Pattern and Support Vector Machine Ergy David Lundy Tumanggor; Harlen Gilbert Simanullang; Arina Prima Silalahi
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4675

Abstract

The rapid advancement of generative artificial intelligence enables the creation of highly realistic fake videos, escalating the risks of financial fraud, hoaxes, and cybersecurity threats. As conventional visual verification methods are increasingly inadequate against evolving manipulation techniques, this research proposes a robust and lightweight AI-generated video detection approach. This method combines Local Binary Pattern (LBP) for spatial texture extraction with a Support Vector Machine (SVM) for binary classification. Utilizing the SDFVD2.0 dataset of 927 video samples, the methodology extracts frames and applies rigorous preprocessing, including grayscale conversion, resizing, noise reduction, and face cropping. To capture local micro-texture characteristics, LBP features are aggregated across frames using statistical mean and standard deviation, accounting for temporal dynamics and forming a comprehensive 144-dimensional feature vector. Subsequently, a linear SVM, optimized with balanced class weights and a soft-margin penalty, classifies these vectors. The LBP-SVM model achieved an accuracy of 81.18% on the testing split. During further generalization testing on unseen data, the model correctly predicted three out of five videos with a 62.72% average confidence rate and a swift processing time of 38.13 seconds. Although the model shows margins of error with highly complex artifacts, this combination provides an efficient, interpretable, and computationally economical baseline.
Co-Authors Agus Syahputra Andrew Efraim Nicholas Sitompul Darwis Robinson Manalu DEWI SARTIKA Doli Hasibuan Duha, Nadyarni Natalis Caesarin Edward Rajagukguk Ergy David Lundy Tumanggor Fati Gratianus Nafiri Larosa Gea, Asaziduhu Gresa Simarmata Hasibuan, Doli Humuntal Rumapea Hutapea, Marlyna I. Indra Kelana Jaya Indra M. Sarkis S. Jhoni Maslan Jhoni Maslan, Jhoni Jimmy F. Naibaho Larosa, Fati G. N. Laura Sridevi Sihombing Lumbanraja, Posma Lyna M. N. Hutapea Margaretha Yohanna Mendarissan Aritonang Mufria J. Purba Nadyarni Natalis Caesarin Duha Nettina Samosir Posma Lumbanraja Purba, Nova Ferawati Putra Palipi Rajagukguk, Edward Rumapea, Yolanda Y. P. Samosir, Nettina Samuel Van Basten Halomoan Manurung Sarkis, Indra M. Simanullang, Harlen Gilbert Sitompul, Andrew Efraim Nicholas Sri Agustina Rumapea Surianto Sitepu Tamado Simon Sagala Tambunan, Tio Yohanna, Margaretha Yolanda Y. P. Rumapea yolanda yulianti pratiwi rumapea