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All Journal InComTech: Jurnal Telekomunikasi dan Komputer Paradigma Journal Medical Informatics Technology
Octaviantara, Adi
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Computer Science & IT Dentistry Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health

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Performance Comparison of Three Classification Algorithms for Non-alcoholic Fatty Liver Disease Patients Using Data Mining Tool Octaviantara, Adi; Abbas, Moch Anwar; Azhari, Ahmad; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 1, March 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i1.2

Abstract

This study aims to carry out a comparative analysis of the three classification algorithms used in research on Nonalcoholic Fatty Liver Disease (NAFLD) Patients. NAFLD is a liver condition associated with the accumulation of fat in the liver in individuals who do not consume excessive alcohol. The algorithms used in the analysis are Decision Tree, Naïve Bayes, and k-Nearest Neighbor (k-NN), with data processing using RapidMiner software. The data used is sourced from Kaggle which comes from the Rochester Epidemiology Project (REP) database with research conducted in Olmsted, Minnesota, United States. The measurement results show that the Decision Tree algorithm has an accuracy of 92.56%, a precision of 93.24%, and a recall of 99.08%. The Naïve Bayes algorithm has an accuracy of 89.93%, a precision of 95.40% and a recall of 93.56%. While the k-Nearest Neighbor algorithm has an accuracy of 91.33%, a precision of 91.94%, and a recall of 99.27%. ROC curve analysis, all algorithms show "Excellent" classification quality. However, only the k-NN algorithm reached 1.0, showing excellent classification results in solving the problem of classifying Nonalcoholic Fatty Liver Disease patients. This study concluded that the k-NN algorithm is a better choice in solving the problem of classifying Non-alcoholic Fatty Liver Disease patients compared to the Decision Tree and Naïve Bayes algorithms. This study provides valuable insights in the development of classification methods for the early diagnosis and management of NAFLD.
Sipkumhamai Application Success Analysis Using the Delone And Mclean Model Said, Fadillah; Octenta, Chintia; Octaviantara, Adi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4608

Abstract

Evidence-based policy aims to increase the efficiency and effectiveness of policy settings and increase alternative opportunities. The Legal and Human Rights Policy Strategy Agency created the SIPKUMHAMAI application to support evidence-based legal and human rights policies, support legal and human rights research with better data, and provide information to the public about legal and human rights issues. It is very important to make efforts to provide comprehensive and systematic data and information on legal and human rights issues originating from real situations on the ground. In addition to overall legal and human rights issues, this data and information can be used to find out more about the causes of legal and human rights problems, identify deficiencies in law enforcement and human rights protection, and carry out analyzes and provide various recommendations to strengthen systems and mechanisms for enforcing law and human rights in Indonesia. To achieve this goal, a system evaluation must be carried out to determine which components need to be improved. This is necessary to determine whether the system used provides significant benefits for users and the organization. Using the Delone and McLean model, from the six relationships of Information System Success Model, it was obtained that only Hypothesis 7, Hypothesis 8, and Hypothesis 9 were significantly supported and accepted by the data. These findings provide several implications for eGovernment research and practice, especially regarding how to maximize applications. This paper concludes by discussing the limitations that the proposed hypotheses are not fully supported by the research results.
Prediksi Harga Saham Berdasarkan Data Histori Menggunakan Algortima LSTM, GRU & RNN Abbas, Moch Anwar; Saputra, Heru Dwi; Octaviantara, Adi; Effendi, Ade Irfan; Yasrizal, Yasrizal
InComTech : Jurnal Telekomunikasi dan Komputer Vol 14, No 3 (2024)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v14i3.25132

Abstract

Deep LearningGruLstmRNNPrediction
Sipkumhamai Application Success Analysis Using the Delone And Mclean Model Said, Fadillah; Octenta, Chintia; Octaviantara, Adi
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 2 (2024): September 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i2.4608

Abstract

Evidence-based policy aims to increase the efficiency and effectiveness of policy settings and increase alternative opportunities. The Legal and Human Rights Policy Strategy Agency created the SIPKUMHAMAI application to support evidence-based legal and human rights policies, support legal and human rights research with better data, and provide information to the public about legal and human rights issues. It is very important to make efforts to provide comprehensive and systematic data and information on legal and human rights issues originating from real situations on the ground. In addition to overall legal and human rights issues, this data and information can be used to find out more about the causes of legal and human rights problems, identify deficiencies in law enforcement and human rights protection, and carry out analyzes and provide various recommendations to strengthen systems and mechanisms for enforcing law and human rights in Indonesia. To achieve this goal, a system evaluation must be carried out to determine which components need to be improved. This is necessary to determine whether the system used provides significant benefits for users and the organization. Using the Delone and McLean model, from the six relationships of Information System Success Model, it was obtained that only Hypothesis 7, Hypothesis 8, and Hypothesis 9 were significantly supported and accepted by the data. These findings provide several implications for eGovernment research and practice, especially regarding how to maximize applications. This paper concludes by discussing the limitations that the proposed hypotheses are not fully supported by the research results.
Co-Authors Abbas, Moch Anwar Ahmad Azhari Dwiza Riana Effendi, Ade Irfan Hewiz, Alya Shafira Octenta, Chintia Said, Fadillah Saputra, Heru Dwi Yasrizal