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PENERAPAN METODE NAÏVE BAYES CLASSIFIER PADA SENTIMEN ANALISIS APLIKASI INVESTASI KEUANGAN DIGITAL: Studi Kasus: Bareksa Dan Bibit Girsang, Jhon Vebrianto; Jaya, Indra Kelana; Simanullang, Harlen Gilbert
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.pp225-230

Investing online is a very promising opportunity. There are many online investment enthusiasts who do not understand how to invest online correctly and be able to minimize risk. Lack of public understanding of the investment implementation process can lead to fraud by irresponsible parties. So understanding investing online is very necessary. There are many online investment applications on the Google Play Store, but these investment applications have their own advantages and disadvantages. The objects of research are the applications of Bareksa and Seeds because the news media often report on these applications at the top and selecting an application requires a collection of information obtained from previous user reviews. The method used is the Naïve Bayes Classifier. Based on the results, the classification is divided into 3 (three) sentiments, namely positive, negative and neutral. With a comparison of training data and testing data 70%:30% accuracy in the Bareksa application was obtained 54% and 44% in the seed application.

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp255-263

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 METODE NAÏVE BAYES CLASSIFIER PADA SENTIMEN ANALISIS APLIKASI INVESTASI KEUANGAN DIGITAL: Studi Kasus: Bareksa Dan Bibit Girsang, Jhon Vebrianto; Jaya, Indra Kelana; Simanullang, Harlen Gilbert
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.pp225-230

Investing online is a very promising opportunity. There are many online investment enthusiasts who do not understand how to invest online correctly and be able to minimize risk. Lack of public understanding of the investment implementation process can lead to fraud by irresponsible parties. So understanding investing online is very necessary. There are many online investment applications on the Google Play Store, but these investment applications have their own advantages and disadvantages. The objects of research are the applications of Bareksa and Seeds because the news media often report on these applications at the top and selecting an application requires a collection of information obtained from previous user reviews. The method used is the Naïve Bayes Classifier. Based on the results, the classification is divided into 3 (three) sentiments, namely positive, negative and neutral. With a comparison of training data and testing data 70%:30% accuracy in the Bareksa application was obtained 54% and 44% in the seed application.

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

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

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.

LEXICON BASED ANALISIS DAN RANDOM FOREST TERHADAP ISU POLITIK DINASTI INDONESIA PADA APLIKASI X Rumapea, Humuntal; Krisna Diva; Simanullang, Harlen Gilbert
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i1.4700

Dynastic politics in Indonesia remains a widely discussed issue, eliciting diverse public opinions ranging from support as a political right to criticism of democratic quality, with social media, particularly the X platform, serving as an important venue for public sentiment analysis. This study employs a combination of the Lexicon Based method using the InSet Lexicon and the Random Forest algorithm to analyze public sentiment on dynastic politics. The dataset consists of 1,593 tweets collected from August 1 to December 24, 2024, which underwent text preprocessing, labeling into three sentiment categories: positive, negative, and neutral, and word weighting using TF-IDF. The methodology includes splitting the data into training and testing sets with an 80:20 ratio, applying undersampling on the training data to balance class distribution, and training a Random Forest model with 100 decision trees and a maximum depth of 5 per tree, based on the entropy criterion. Evaluation results show that the model successfully classifies public sentiment with an accuracy of 89%, precision of 82%, recall of 81%, and f1-score of 81%.

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

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.

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