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All Journal CogITo Smart Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Sebatik Indonesian Journal of Data and Science TEPIAN Ilmu Komputer untuk Masyarakat
Khoirunnita, Aulia
Amikom University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics Other

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Improving Database Quality by Applying Consistency Aspects to Naming Fields and Tables Raissa Maringka; Aulia Khoirunnita; Rodney Maringka; Ema Utami; Kusnawi
TEPIAN Vol. 2 No. 1 (2021): March 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i1.304

Abstract

The database is one of the benchmarks that affect the quality of information systems. An effective information system certainly has a quality database. Aspects that can be measured to determine the quality of the database are aspects of truth, consistency, range, level of detail, completeness, minimalism, ability to integrate and readability. One of the mistakes that are often encountered in databases is related to the consistency aspect. Consistency aspects that are not paid much attention to its application can lead to data conflicts due to ambiguity and data duplication. This study aims to improve the quality of the database by applying consistency to the naming of fields and tables. A naming method to produce consistency in standardization was applied in this study.
Android App Rating Classification on Google Play Store Using Random Forest Algorithm with SQL Server Preprocessing Raissa Maringka; Aulia Khoirunnita; Rodney Maringka; Erna Utami; Kusnawi
TEPIAN Vol. 2 No. 2 (2021): June 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i2.404

Abstract

The increasing number of Android applications available on the Google Play Store with the benefits the developers get has attracted the attention of many Android application developers. To benefit from developing Android apps, one way is to know the characteristics of highly rated apps on the Google Play Store. This research will investigate the features of size, installs, reviews, type (free / paid), rating, category, content rating, and price on applications on the Google Play Store to determine the characteristics of high-rated applications. This study uses the Random Forest algorithm to identify the most influential features in high ranking applications on the Google Play Store. At the preprocessing stage, this research uses data cleaning methods and data reduction using SQL Server. This study uses feature important to find out the attributes that most influence the high ranking of Android apps on the Google Play Store. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy.
Analysis of Indonesian Public Opinion Sentiment on Policy on Twitter Social Media “PPKM” Using K-Nearest Neighbor Aulia Khoirunnita; Kusnawi
TEPIAN Vol. 2 No. 4 (2021): December 2021
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v2i4.508

Abstract

COVID-19 or Coronavirus disease 2019 is currently a pandemic that is spreading very quickly throughout the world, including Indonesia. Various handling and policies have been carried out, one of which is called “PPKM” policy or what can also be called the Enforcement of Restrictions on Community Activities issued by the Indonesian government. “PPKM” is currently one of the topics that is often discussed by the public, one of which is on the Twitter social media platform. The existence of opinions given by the community, it is necessary to have a sentiment analysis. Sentiment analysis is an analytical process obtained from various social media platforms and the internet. The aim is to find out how the public's sentiment towards the implementation of “PPKM” policies in Indonesia is through tweets and comments on the Twitter social media platform. In this study, the process of analyzing public opinion regarding the “PPKM” policy will be carried out by classifying opinions into 3 sentiments, namely positive, negative or neutral. Classification is done using the K-Nearest Neighbor algorithm. The K-Nearest Neighbor (K-NN) algorithm is a classification method for a set of data based on previously classified data learning. Included in supervised learning, where the results of the new query instance are classified based on the majority of the distance proximity of the categories in K-NN. The results of data preprocessing and sentiment classification, in the first test positive sentiment 37.6% of 261 data, negative sentiment 65.9% of 636 data and neutral sentiment 9.
Co-Authors Asro Nasiri Dedy Cahyadi, Dedy Ema Utami Erna Utami Febryan, Raihan Firdaus, Muhammad Bambang Indah Fitri Astuti, Indah Fitri Iwan Muhamad Ramdan Jamil, Muh Kusnawi Kusnawi Maringka, Raissa Maringka, Rodney Giovanni Nanda, Arya Nur Rahmad , Didi Nur Rohmah Prafanto, Anton Putra, Gubtha Mahendra Rasni Alex Rodney Maringka Rosmasari Rosmasari Rosmasari, Rosmasari Saputra, Muhammad Fawaz Wardhana, Reza Wing Wahyu Winarno