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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Simetris Jurnal Buana Informatika Journal of Telematics and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JRSI (Jurnal Rekayasa Sistem dan Industri) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika Universitas Pamulang Jurnal SITECH : Sistem Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) Joined Journal (Journal of Informatics Education) Aiti: Jurnal Teknologi Informasi E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Scientific Journal of Informatics Jurnal Pengabdian Siliwangi
Rianto, Rianto
Departement Of Informatics, Universitas Siliwangi
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Scientific Journal of Informatics

 1 
Associative Analysis Data Mining Pattern Against Traffic Accidents Using Apriori Algorithm Ruswati, Ruswati; Gufroni, Acep Irham; Rianto, Rianto
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.v5i2.16199

Abstract

Traffic accidents are one of the causes of high mortality in the community. Based on information from the World Health Organization (WHO) the number of accident victims in each year amounts to 1,300,000 fatalities, this is caused by traffic accidents that exist throughout the world. The police recorded data on accidents that occurred in several regions of East Priangan namely Ciamis and Tasikmalaya Regencies for the 2016-2017 period reaching an accident rate of ± 1500. The analysis that can be done to reduce the intensity of the occurrence of these events is to use data mining processing techniques. The right method is used by looking at the condition of the data obtained, namely the Association Rules method with the calculation of the Apriori Algorithm. This method will look for patterns of data relations that are formed from combinations of an itemset, so that knowledge will appear from large datasets. The pattern of the relationship sought is the linkages of itemset variables involved in the accident by involving 4 variables that describe the identity of the perpetrators, namely gender, age, profession and level of education and 22 attributes of the dataset. The minimum limit of support, confidence and lift ratio values used in the Apriori Algorithm calculation rules is 15%, 70% and 1.1. This value is used to get many rules that have a high level of occurrence accuracy. The results of the combination pattern calculation were 3 times iterations on each number of data in each region, the pattern of associations found in the Tasikmalaya region were the relation of the professional variables and the age of the perpetrator with the attribute of the Student profession dataset and the boundary group ages 16 to 30 years, while for the pattern associations found in the area of Ciamis Regency, namely the relation between age and education level with the attribute dataset of the 16 to 30 year age group and high school education level. The accuracy of the value obtained is calculated manually and uses one of the data mining applications as a comparison of value accuracy, namely Tanagra 1.4.
Sentiment Analysis Provider By.U on Google Play Store Reviews with TF-IDF and Support Vector Machine (SVM) Method Fransiska, Susanti; Rianto, Rianto; Gufroni, Acep Irham
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.v7i2.25596

Abstract

Provider By.U is a relatively new and attractive telecommunications service with claims to be the first digital provider in Indonesia. All services are done digitally with the By.U application that offers convenience. Even so not all users are satisfied with the service, there are criticisms and suggestions, one of which is delivered through the By.U app review feature on the Google Play Store. Sentiment analysis is performed to extract information related to provider by.U. The steps taken are scrapping review data, positive and negative labeling, preprocessing data including data cleaning, data normalization, stopword removal and negation handling, sentiment classification using Support Vector Machine (SVM) and TF-IDF as feature extraction. TF-IDF+SVM with 5-Fold Validation produces pretty good accuracy with an average accuracy of 84.7%, precision of 84.9%, recall of 84.7%, and f-measure of 84.8%. The highest accuracy results in fold 2, 86.1%. The effect of TF-IDF on the measurement of model performance is not so great, but it is better.
Comparative Analysis Performance of K-Nearest Neighbor Algorithm and Adaptive Boosting on the Prediction of Non-Cash Food Aid Recipients Yustikasari, Yusi; Mubarok, Husni; Rianto, Rianto
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.32369

Abstract

Purpose: The implementation of this manual system is considered less accurate in obtaining the results of social assistance recipients. From these problems to overcome this problem, systematic calculations are needed. In processing data, a model is needed that can explain the data with its application, so a machine learning model is made that can help process the data.Methods: This study's classification of non-cash food social assistance receipts uses the K-Nearest Neighbor and Adaptive Boosting algorithms. This study will compare the performance of the two algorithms.Result: The results obtained for Adaptive Boosting are the best classification results with a maximum accuracy of 100% and produce a high AUC value of 1.0. In comparison, the ROC curve for the K-Nearest Neighbor algorithm produces an accuracy of 96% with an AUC value of 0.94.Novelty: ROC curves in the two algorithms are good classification results because the two graphs cross above the diagonal line and produce an AUC value included in the Excellent classification.
Co-Authors Acep Irham Gufroni Alam Rahmatulloh Algi Muhkamaat Abdillah Aradea Aradea Ari Sandriana Baetulloh, Uci Desi Purnamasari Dwi Ramti Asih Fauzi Athallah, Zico Firmansah, Teguh Anugrah Firmansyah Maulana Fransiska, Susanti Galih Permana Givany Salsabila Siti Rukoyah Gunawan, Rohmat Gunawan, Rohmat Hadi Purwadi Heni Sulastri Hilmi Aziz Husni Mubarok Husni Mubarok Husni Mubarok Hutari Maulida Kurnia Iqbal Muhammad Fajar Nuralam Kurnia, Hutari Maulida Lusi Robiatul Fadilah Meidiyan, Mohamad Gibran Mohammad Rizal Ramli Muhammad Adi Khairul A Muhammad Adi Khairul Anshary Neng Ika Kurniati Nur Widiyasono Oktalia, Silvia Dwi R. Moch. Dysa Satria Anggara Rahmi Nur Shofa Rizal, Randi Ruswati, Ruswati Shihab, Muhammad Quraish Shofa, Rahmi Nur Shofa, Rahmi Nur Silvia Dwi Oktalia Siti Rukoyah, Givany Salsabila Yustikasari, Yusi