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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Majalah Ilmiah METHODA
Sagala, Tamado Simon
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health

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Optimasi Algoritma Genetika pada Perbandingan ANN dan KNN untuk Klasifikasi Penyakit Jantung Zai, Andreas; Rambe, Lima Hartima; Putra, Reza Ananda; Rosnelly, Rika; Sagala, Tamado Simon; Jaya, Indra Kelana
Majalah Ilmiah METHODA Vol. 15 No. 1 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No1.pp10-23

Abstract

A comparative analysis of genetic algorithm optimization methods on the performance of Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN) in heart disease classification shows significant results. The research used a heart disease dataset consisting of 303 samples with 14 attributes. Genetic algorithm optimization produced substantial performance improvements in both models. The optimized ANN model achieved 94.85% accuracy, 93.00% precision, 97.00% recall, and 97.00% ROC AUC, demonstrating excellence in positive case identification. Meanwhile, the optimized KNN model achieved 93.30% accuracy, 92.00% precision, 95.00% recall, and 96.77% ROC AUC, yielding more balanced performance. The genetic algorithm optimization method proves its effectiveness in improving heart disease classification accuracy, where ANN is optimal for applications requiring high sensitivity and KNN is more stable for small datasets.
Pengaruh Kepemimpinan serta Kompensasi terhadap Motivasi Atlet Esports Pelatda Provinsi Sumatera Utara Sagala, Tamado Simon
Majalah Ilmiah METHODA Vol. 14 No. 2 (2024): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol14No2.pp235-240

Abstract

Esports is an official sports branch in Indonesia, as stated in the results of the KONI National Working Meeting (RAKERNA) in 2020, which declared esports to be part of the prestigious sports branch. This is also in line with the establishment of the Indonesian Esports Association (PBESI) as the governing body of electronic sports under the National Sports Committee of Indonesia (KONI). Research results show that the t-value of both variables is below 0.005, and F calculated is greater than F table (570.063 > 2.91). It can be explained that leadership has a significant positive influence on the achievement motivation of esports athletes in the provincial training center (Pelatda) of North Sumatra. Compensation also has a significant positive influence on the achievement motivation of esports athletes in the provincial training center (Pelatda) of North Sumatra partially. Furthermore, it is found that both leadership and compensation have a significant positive influence on the achievement motivation of esports athletes in the provincial training center (Pelatda) of North Sumatra simultaneously.
Sistem Pendukung Keputusan Penentuan Role Atlet Esports Provinsi Sumatera Utara Menggunakan Pendekatan Machine Learning Sagala, Tamado Simon; Yusuf Ijonris; Samosir, Nettina
Majalah Ilmiah METHODA Vol. 15 No. 3 (2025): Majalah Ilmiah METHODA
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methoda.Vol15No3.pp313-321

Abstract

The continuous growth of esports as a technology-driven competitive activity has increased the demand for professional team management, particularly in assigning suitable roles to athletes based on their individual skills. One of the major challenges faced by coaches is determining athlete roles objectively, as this process is often influenced by subjective judgment and lacks support from systematic data analysis. To address this issue, this study aims to develop a decision support system for determining esports athlete roles in North Sumatra Province by utilizing machine learning approaches. This research applies several classification methods, namely K-Nearest Neighbor (KNN), Naive Bayes, and Support Vector Machine (SVM). The dataset used in this study consists of performance data for esports athletes that have undergone preprocessing stages and are divided into training and testing sets. The evaluation of model performance is conducted using standard classification assessment metrics to compare the effectiveness of each algorithm. The findings show that the KNN and SVM algorithms are better at classifying esports athletes' roles than the Naive Bayes algorithm. These two methods yield more stable and dependable results, rendering them more appropriate for facilitating decision-making processes concerning athlete role assignment. This study is expected to provide practical support for coaches and relevant stakeholders in making objective and data-driven decisions regarding the determination of esports athlete roles. Furthermore, future research can enhance the proposed system by increasing the amount of data and exploring other machine learning techniques to improve overall system performance
Penerapan Metode Holt-Winters untuk Memprediksi Produksi Biji Kopi Arabica Lintong Nihuta Silalahi, Arina Prima; Sagala, Tamado Simon; Sihombing, Laura Sridevi; Simanullang, Harlen Gilbert
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.
Co-Authors Indra Kelana Jaya Putra, Reza Ananda Rambe, Lima Hartima Rika Rosnelly, Rika Samosir, Nettina Sihombing, Laura Sridevi Silalahi, Arina Prima Simanullang, Harlen Gilbert Yusuf Ijonris Zai, Andreas