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All Journal International Journal of Electrical and Computer Engineering Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence JTET (Jurnal Teknik Elektro Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal SOLMA Jurnal Telematika Jurnal Teknologi Terapan Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Infotekmesin JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Madani : Indonesian Journal of Civil Society Madaniya Jurnal Teknologi Informasi dan Komunikasi Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Pengabdian Teknologi Tepat Guna Jurnal Pengabdian Kepada Masyarakat (JPKM) Langit Biru Jurnal Nasional Teknik Elektro dan Teknologi Informasi JAMAIKA: Jurnal Abdi Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Jurnal Abdimas: Pengabdian dan Pengembangan Masyarakat Journal of Applied Community Engagement (JACE) Pengabdian Jurnal Abdimas Hikmayo: Jurnal Pengabdian Masyarakat SmartComp Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Oman Somantri
Politeknik Negeri Cilacap
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Journal : Infotekmesin

 1 
Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Wildani Eko Nugroho; Ali Sofyan; Oman Somantri
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization Wildani Eko Nugroho; Teguh Prihandoyo; Oman Somantri
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1048

Abstract

The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease Linda Perdana Wanti; Nur Wachid Adi Prasetya; Oman Somantri
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1797

Abstract

This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
Metode Naive Bayes Dalam Menentukan Program Studi Bagi Calon Mahasiswa Baru Eko Nugroho, Wildani; Sofyan, Ali; Somantri, Oman
Infotekmesin Vol 12 No 1 (2021): Infotekmesin: Januari 2021
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v12i1.491

Abstract

In a university, determining a study program for prospective students is something that is often done to focus on prospective students so that they are in accordance with their competencies. This is a very important hope, because prospective students can develop self-competence according to their academic abilities. This research method uses several stages, including data cleaning, data collection, determining criteria, determining probability, and final testing. The Naïve Bayes method with a case study at the Private Madrasah Aliyah PAB 6 Helvetia and testing of 100 student data with an accuracy rate of 90% is a previous research. The purpose of this study was to make a classification of majors based on the criteria, while in this study the aim of making a classification of study programs for prospective new students. In this study, the same method was used but the number of data records was different, the test data was 1671 student data records, the data was obtained from 2256 data records.From the total data records were 2256, after data cleaning and data collection were carried out, 1671 test data were obtained. In the test data, there are several probability values that contain various criteria and attributes used to determine the classification of study programs for prospective new students. The number of data records is divided into 2 parts, the first is used for training data with 1158 data with a percentage of 70%, and testing data with 513 data records with a percentage of 30%. From the test results with the same method with different number of data records, the accuracy rate is from 90% to 96% with an accuracy value of 96.68%. From this accuracy value shows that the classification results obtained show the Pharmacy DIII study program.
Optimalisasi Metode Naive Bayes untuk Menentukan Program Studi bagi Calon Mahasiswa Baru dengan Pendekatan Unsupervised Discretization Eko Nugroho, Wildani; Prihandoyo, Teguh; Somantri, Oman
Infotekmesin Vol 13 No 1 (2022): Infotekmesin: Januari, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i1.1048

Abstract

The admission of prospective new students must consider various procedures to direct prospective new students in determining the study program they are interested in. This study will discuss the optimization of the Naive Bayes method to determine the study program or major for prospective new students with the Unsupervised Discritization method approach. There are several stages of research methods carried out in this study, including Data Cleaning, Data Collection, Criteria Determination, Probability Determination, and Data Testing. This research has been carried out using the same method, namely the Naïve Bayes method which is used to classify the interests of prospective new students in determining the study program with an accuracy value of 96.68%. Ongoing research uses the same method, namely Naive Bayes, then optimization is carried out with the Unsupervised Discretization method approach. For data testing, there are 1671 student data records. After testing with the same method and optimizing it, the accuracy value from 96.68% became 97.66% with the classification results showing the DIII Pharmacy study program. The purpose of this research is to produce a classification in determining the study program or major for prospective new students using the Naïve Bayes method by the optimization of the Unsupervised Discretization method. From the results of testing the data, the Naïve Bayes method after optimization with the Unsupervised Discretization method is very good compared to the application before optimization.
Comparison of The Dempster Shafer Method and Bayes' Theorem in The Detection of Inflammatory Bowel Disease Wanti, Linda Perdana; Adi Prasetya, Nur Wachid; Somantri, Oman
Infotekmesin Vol 15 No 1 (2024): Infotekmesin: Januari, 2024
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v15i1.1797

Abstract

This study discusses the comparison of the Dempster-Shafer method and Bayes' theorem in the process of early detection of inflammatory bowel disease. Inflammatory bowel disease, better known as intestinal inflammation, attacks the digestive tract in the form of irritation, chronic inflammation, and injuries to the digestive tract. Early signs of inflammatory bowel disease include excess abdominal pain, blood when passing stools, acute diarrhea, weight loss, and fatigue. The Dempster-Shafer method is a method that produces an accurate diagnosis of uncertainty caused by adding or reducing information about the symptoms of a disease. Meanwhile, Bayes' theorem explains the probability of an event based on the factors that may be related to the event. This study aims to measure the accuracy of disease detection using the Dempster-Shafer method compared to the probability of occurrence of the disease using Bayes' theorem. The results of calculating the level of accuracy show that the Bayes Theorem method is better at predicting inflammatory bowel disease with a probability of occurrence of disease in the tested data of 75.9%.
Co-Authors Abdul Rohman Supriyono Abdul Rohman Supriyono Agus Susanto Agus Susanto Ali Sofyan Amir Hamzah Andesita Prihantara Annisa Romadloni Ari Kristiningsih Arif Wirawan Muhammad Ayu Pramita Catur Supriyanto Dairoh Dairoh Dairoh Dairoh, Dairoh Dany Artha Widiyanto Dega Surono Wibowo Dega Surono Wibowo, Dega Surono Dodi Satriawan Dwi Wahyu Susanti Dyah Apriliani Dyah Apriliani Dyah Apriliani Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Eka Tripustikasari, Eka Eko Nugroho, Wildani Erna Alimudin Evila Purwanti Sri Rahayu, Theresia Fadillah Fadillah Fadlilah, Ilma Faulin, Muhammad Husni Ganjar Ndaru Ikhtiagung Ginanjar Wiro Sasmito, Ginanjar Wiro Hety Dwi Astuti Ida Afriliana Ika Dewi Rozaurrohmah Iyat Ratna Komala Johanna, Anne Karyati, Titin Khoeruddin Wittriansyah Laura Sari Lina Puspitasari Linda Perdana Wanti Linda Perdana Wanti Linda Perdana Wanti Linda Perdana Wanti Lutfi Syafirullah Maharrani, Ratih Hafsarah Mohammad Khambali, Mohammad Muchamad Sobri Sungkar, Muchamad Sobri Muhammad Nur Faiz Muhammad Nur Faiz Musyafa Al Farizi Nur Wachid Adi Prasetya Nurlinda Ayu Triwuri Oto Prasadi Perdana Wanti, Linda Prih Diantono Abda'u Prih Diantono Abda’u Prih Diantono Abda`u Prihandoyo, Teguh Purwaningrum, Santi Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Ratih Hafsarah Maharrani Riyadi Purwanto Riyanto Riyanto Rohayah, Siti Santi Purwaningrum Santi Purwaningrum Sari, Laura Sasmito, Ginanjar Wiro Sena Wijayanto Sofyan, Ali Taufiq Abidin Taufiq Abidin Taufiq Abidin, Taufiq Teguh Prihandoyo Titin Kartiyani Titin Kartiyani Wanti, Linda Perdana Wildani Eko Nugroho Wildani Eko Nugroho, Wildani Eko Wiyono, Slamet Yeni Priatna Sari, Yeni Priatna