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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi
Simanullang, Harlen Gilbert
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Computer Science & IT Economics, Econometrics & Finance

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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

Abstract

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

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 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

Abstract

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

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.
Co-Authors Duha, Nadyarni Natalis Caesarin Girsang, Jhon Vebrianto Indra Kelana Jaya Silalahi, Arina Prima