Article Per Year (5 Year)
p-Index From 2020 - 2025
3.575
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Masyarakat Informatika Bulletin of Electrical Engineering and Informatics Kreano, Jurnal Matematika Kreatif-Inovatif Sainteknol : Jurnal Sains dan Teknologi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Abdimasku : Jurnal Pengabdian Masyarakat Unnes Journal of Mathematics Education Unnes Journal of Mathematics Journal of Community Empowerment Proceeding ISETH (International Summit on Science, Technology, and Humanity) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Recursive Journal of Informatics Kreano, Jurnal Matematika Kreatif Inovatif Jurnal Dharma Indonesia Jurnal Abdi Negeri
Riza Arifudin
Unknown Affiliation
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mathematics Medicine & Pharmacology Public Health Social Sciences Other

Published : 85 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 85 Documents
Search

 3   4   5   6   7 
Improve the Accuracy of Support Vector Machine Using Chi Square Statistic and Term Frequency Inverse Document Frequency on Movie Review Sentiment Analysis Larasati, Ukhti Ikhsani; Muslim, Much Aziz; Arifudin, Riza; Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.14244

Abstract

Data processing can be done with text mining techniques. To process large text data is required a machine to explore opinions, including positive or negative opinions. Sentiment analysis is a process that applies text mining methods. Sentiment analysis is a process that aims to determine the content of the dataset in the form of text is positive or negative. Support vector machine is one of the classification algorithms that can be used for sentiment analysis. However, support vector machine works less well on the large-sized data. In addition, in the text mining process there are constraints one is number of attributes used. With many attributes it will reduce the performance of the classifier so as to provide a low level of accuracy. The purpose of this research is to increase the support vector machine accuracy with implementation of feature selection and feature weighting. Feature selection will reduce a large number of irrelevant attributes. In this study the feature is selected based on the top value of K = 500. Once selected the relevant attributes are then performed feature weighting to calculate the weight of each attribute selected. The feature selection method used is chi square statistic and feature weighting using Term Frequency Inverse Document Frequency (TFIDF). Result of experiment using Matlab R2017b is integration of support vector machine with chi square statistic and TFIDF that uses 10 fold cross validation gives an increase of accuracy of 11.5% with the following explanation, the accuracy of the support vector machine without applying chi square statistic and TFIDF resulted in an accuracy of 68.7% and the accuracy of the support vector machine by applying chi square statistic and TFIDF resulted in an accuracy of 80.2%.
K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor Safri, Yofi Firdan; Arifudin, Riza; Muslim, Much Aziz
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i1.12057

Abstract

Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.
Optimization Neuro Fuzzy Using Genetic Algorithm For Diagnose Typhoid Fever Fata, Muhamad Nasrul; Arifudin, Riza; Prasetiyo, Budi
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.17097

Abstract

Neuro Fuzzy is one method in the field of information technology used in diagnosing an disease. The application of Neuro Fuzzy is to identify disease. Genetic algorithms can be used to find solutions without paying attention to the subject matter specifically, one of which is an optimization problem. Typhoid or typhoid fever is a disease caused by Salmonella enterica bacteria, especially its derivatives. The diagnosis of typhoid fever is not an easy thing to do. This is because some of the indications experienced by patients also appear in other diseases. The number of patients with typhoid fever that requires accuracy in diagnosing typhoid fever based on indications caused. Based on this background this study aims to assist in the diagnosis of typhoid fever with 11 indication variables. This study uses medical record data for typhoid fever in 2017 Tidar Magelang Hospital. The method used is Neuro Fuzzy which optimizes the value of the degree of membership with genetic Algorithms. Then the value of the degree of neuro fuzzy membership is more optimal. The results of this optimization are the diagnosis of typhoid fever based on the variable of indications entered. From the research results obtained from the neuro fuzzy method get an 80% accuracy value and neuro fuzzy optimization results with genetic algorithms with a value of pc 0.5, pm 0.2 and max generation 25 the value of accuracy increases to 90%. Suggestions from this study, need to add more specific indication variables.
The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency Devi, Feroza Rosalina; Sugiharti, Endang; Arifudin, Riza
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.15452

Abstract

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.
Decision Support Systems with AHP and SAW Method for Determination of Cattle with Superior Seeds Josaputri, Clarissa Amanda; Sugiharti, Endang; Arifudin, Riza
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i2.7908

Abstract

Department of Animal Husbandry and Fisheries of Semarang District is an institution in charge of livestock and animal health. Basically the Animal Husbandry Department has provided standardization for quality livestock cattle with superior seeds that usually can be judged or measured by various criteria.They are weight, age and value of BCS (Body Condition Score).They needed a system that could help the Department of Livestock and Fisheries of Semarang District in determining the electoral process cattle with superior seeds.  In this research, the manufacture of Decision Support Systems in the determination cattle with superior seedsis using a combination of two methods is Analytical Hierarchy Process (AHP) and the Simple Addictive Weighting (SAW). In AHP will perform an importance value calculation criteria that will be paired up with an alternative to the SAW the next process is the sum of the weight from performance rating of all the attributes to each alternative, a ranking conducted to determine the result of cattle with superior seeds. Suggestions on this system, can be developed further by combining other methods to determine the recommendation that more effective.
Use of K-Means Clustering and Analytical Methods Hierarchy Process in Determining the Type of MSME Financing in Semarang City Sukmadewanti, Irahayu; Arifudin, Riza; Sugiharti, Endang
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.16221

Abstract

The Indonesian government launched an entrepreneurial program to encourage economic growth, one of which is MSME(micro, small and medium enterprises). The constraints commonly faced by MSME are limited enterprises capital. The government has also tried to provide assistance financing for MSMEs in the form of CSR (Corporate Social Responsibility), KUR (Credit People's Enterprises) and KTA (Unsecured Credit). For this type of financing or credit determined based on the type of enterprises accompanied by criteria including number of assets, turnover annually, number of employees, current enterprises period and net income. Based on background behind this research aims to help provide recommendations on types MSME capital financing based on assets, turnover, number of employees, enterprises period and net income of a MSME. This research uses data from MSME in the Semarang City, which has been registered with the Semarang City Cooperatives and MSME Office. K-Means Clustering Method is used to cluster net profit criteria. Then the Analytical Hierarchy Process (AHP) method is used to search recommendations on the types of MSME financing based on each weighted criteria. The results of this application are recommendations for types of capital financing MSME is based on assets, turnover, number of employees, enterprises period and every net profit of MSME. For testing of the system being built, it is carried out by means of a blackbox test. From the test results obtained show that the actual results are appropriate with the expected results so that the functional system is running well. Suggestions from this research, it is necessary to develop further systems regarding grouping data to be more specific.
Information Retrieval System for Determining The Title of Journal Trends in Indonesian Language Using TF-IDF and Na?ve Bayes Classifier Trihanto, Wandha Budhi; Arifudin, Riza; Muslim, Much Aziz
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.11876

Abstract

The journal is known as one of the relevant serial literature that can support a researcher in doing his research. In it’s development journal has two formats that can be accessed by library users namely: printed format and digital format. Then from the number of published journals, not accompanied by the growing amount of information and knowledge that can be retrieved from these documents. The TF-IDF method is one of the fastest and most efficient text mining methods to extract useful words as the value of information from a document. This method combines two concepts of weight calculation that is the frequency of word appearance on a particular document and the inverse frequency of documents containing the word. Furthermore, data analysis of journal title is done by Naïve Bayes Classifier method. The purpose of the research is to build a website-based information retrieval system that can help to classify and define trends from Indonesian journal titles. This research produces a system that can be used to classify journal titles in Indonesian language, with system accuracy in determining the classification of 90,6% and 9,4% error rate. The highest percentage result that became the trend of title classification was decision support system category which was 24.7%.
Decision Making System to Determine Childbirth Process with Naïve Bayes and Forward Chaining Methods Kumalasari, Putri Laksita; Arifudin, Riza; Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25352

Abstract

Childbirth is the last stage before the infant comes into the world. There may be incidents that could cause death in the process of childbirth for mothers and infants. Lack of knowledge and attention to the labor process can increase maternal mortality rate. Maternal mortality rate in Indonesia was recorded at 190 per 100,000 live births on 2015. The figure is still far from the fifth Millennium Development Goals target of 102 per 100,000. The increasing development of technology in health informatics to provide health care more effective can be used to help overcome the problems of pregnant women. To reduce maternal mortality rates, a web-based expert system is perfect one for use. Naïve Bayes method is a simple, fast and high accuracy method. Forward Chaining method is a inference method that performs a fact or statement that starts from the condition (IF) then to the conclusion (THEN). Based on analysis of the method obtained with 233 patients data on childbirth process using expert system, the Naïve Bayes method has accuracy in diagnosing by 90,987124463519% while Forward Chaining method accuracy is 86.69527897%.
The Comparison between Bayes and Certainty Factor Method of Expert System in Early Diagnosis of Dengue Infection Rachmawati, Eka Yuni; Prasetiyo, Budi; Arifudin, Riza
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.15740

Abstract

The development of existing artificial intelligence technology has been widely applied in detecting diseases using expert systems. Dengue Infection is one of the diseases that is commonly suffered by the community and may cause in death. In this study, an expert diagnosis system for dengue infection is made by comparing between both Bayes method and Certainty Factor. The aims are to build an expert system using Bayes and Certainty Factor for early diagnosis of dengue infection and also to determine their level of accuracy. There are 80 data used in this study which are obtained from the medical records of Sekaran Health Center in Semarang City. The test results show that the level of accuracy obtained from 80 medical record data for Bayes method is 90% and the Certainty Factor method is 93,75%.
EFEKTIVITAS METODE DRILL BERBANTUAN “SMART MATHEMATICS MODULE” TERHADAP KEMAMPUAN PEMECAHAN MASALAH Ariska, Mega; Asikin, Mohammad; Arifudin, Riza
Unnes Journal of Mathematics Education Vol 2 No 1 (2013): Unnes Journal of Mathematics Education
Publisher : Department of Mathematics, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ujme.v2i1.3323

Abstract

Penelitian ini bertujuan untuk mengetahui apakah pembelajaran menggunakan metode drill berbantuan “Smart Mathematics Module” dapat mencapai ketuntasan belajar siswa pada kemampuan pemecahan masalah dan untuk mengetahui apakah rata-rata kemampuan pemecahan masalah siswa yang diajar menggunakan metode drill berbantuan “Smart Mathematics Module” lebih dari rata-rata kemampuan pemecahan masalah siswa yang diajar menggunakan metode ceramah berbantuan LKS. Populasi dalam penelitian ini adalah siswa kelas XI SMK Teuku Umar Semarang tahun pelajaran 2012/2013. Sampel dalam penelitian ini diambil dengan teknik purposive sampling. Sebagai kelas kontrol terpilih kelas XI AP 1 dan sebagai kelas eksperimen kelas XI AP 2. Data diperoleh dengan metode tes dan metode observasi. Data hasil tes dianalisis menggunakan uji proporsi dan  uji t. Hasil penelitian menyatakan pembelajaran menggunakan metode drill berbantuan “Smart Mathematics Module” mencapai ketuntasan belajar siswa pada kemampuan pemecahan masalah serta rata-rata kemampuan pemecahan masalah siswa yang diajar menggunakan metode drill berbantuan “Smart Mathematics Module” lebih dari rata-rata kemampuan pemecahan masalah siswa yang diajar dengan metode ceramah berbantuan LKS. Dapat disimpulkan pembelajaran matematika menggunakan metode drill berbantuan “Smart Mathematics Module” efektif karena mencapai ketuntasan belajar pada kemampuan pemecahan masalah.
Co-Authors Abas Setiawan Adha, Nugraha Saputra Adhitiya, Ervan Nur Adi Nur Cahyono Aditya, Rozak Ilham Aji Saputra Aji, Septiko Al Hakim, M. Faris Alamsyah - Alfatah, Abdul Muis Alfatah, Abdul Muis Amalia Fikri Utami Amin Suyitno Anggita, Anggita Anggyi Trisnawan Putra Ardhi Prabowo Arief Agoestanto Arief Broto Susilo Arif Widiyatmoko, Arif Ariska, Mega Arka Yanitama Arrohman, Ramadhan Ridho Asih, Tri Sri Noor Atikah Ari Pramesti, Atikah Ari Budi Prasetiyo, Budi Chakim, Muhamad Nur Choirunnisa, Rizkiyanti Clarissa Amanda Josaputri, Clarissa Amanda Damayanti, Angreswari Ayu Damayanti, Tiara Desy Fitria Astutianingtyas Devi, Feroza Rosalina Devi, Feroza Rosalina Dewi, Nuriana Rachmani Dian Tri Wiyanti Dwijanto Dwijanto, Dwijanto Endang Sugiharti, Endang Faozi, Faozi Farkhan, Feri Fata, Muhamad Nasrul Fata, Muhamad Nasrul Fitriana, Jevita Dwi Habaib, Taufik Nur Hakim, M. Faris Al Hani'ah, Ulfatun Hardi Suyitno Hardianti, Ririn Dwi Hariyanto, Abdul Hidayat, Kukuh Triyuliarno Hidayat, Kukuh Triyuliarno Hikmah, Al Hikmawati, Zahra Shofia Hikmawati, Zahra Shofia Ichsan, Nur Jumanto Jumanto, Jumanto Jumanto Unjung Kumalasari, Putri Laksita Kuncoro, Rizki Danang Kartiko Larasati, Ukhti Ikhsani Larasati, Ukhti Ikhsani Mashuri Mashuri Masrukan Masrukan Maulana, Bagus Surya Melissa Salma Darmawan Mohammad Asikin Much Aziz Muslim Mudzakir, Amat Muhammad Fariz Muttaqin, Irfan Fajar Nugroho, Ari Yulianto Nugroho, Muhammad Andi Nugroho, Prisma Bayu Pramadita, Anjar Aditya Putriaji Hendikawati Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahmanda, Primana Oky Rahmanda, Primana Oky Ratna Dewi, Novi Rizki Nor Amelia Rochmad - Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Safri, Yofi Firdan Safri, Yofi Firdan Sasongko, Andry Scolastika Mariani Sekartaji, Novanka Agnes Setiawan, Danang Aji Stephani Diah Pamelasari Subarkah, Agus Subhan Subhan Sukmadewanti, Irahayu Sukmadewanti, Irahayu Susanto, Febri Trihanto, Wandha Budhi Trihanto, Wandha Budhi Utami, Hamdan Dian Jaya Rozi Hyang Utami, Hamdan Dian Jaya Rozi Hyang Wibowo, Eric Adie Widyawati, Kharisa Yahya Nur Ifriza Yulianto, Muhamad Maulana Yulianto, Muhamad Maulana Zaenal Abidin Zulfikar Adi Nugroho, Zulfikar Adi