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All Journal Jurnal Simetris Jurnal technoscientia JURNAL MEDIA INFORMATIKA BUDIDARMA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) JURNAL TEKNOLOGI TECHNOSCIENTIA Journal of Intelligent Software Systems
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia, Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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STUDI KOMPARASI MENYIMPAN DAN MENAMPILKAN DATA HISTORI ANTARA DATABASE TERSTRUKTUR MARIADB DAN DATABASE TIDAK TERSTRUKTUR INFLUXDB -, Hendra; Andriyani, Widyastuti
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 12 No 2 Februari 2020
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The use of structured databases is still very widely used by companies in small and medium scale with the aim of processing data so that from these data conclusions can be drawn to determine a decision. But over time, of course the need for data that continues to grow can make a system run very slowly when using a structured database. That is caused by the amount of data that continues to increase every day, even for certain cases the data can increase every second. For this reason, an unstructured database is needed specifically for storing history data. From some existing unstructured databases, InfluxDB is one of the unstructured databases specifically intended for storing history data and has a very good ability to process data into a matrix for analysis. One of the key factors in an unstructured database is the database structure which is very different and supports to maximize database performance.
STUDI KOMPARASI MENYIMPAN DAN MENAMPILKAN DATA HISTORI ANTARA DATABASE TERSTRUKTUR MARIADB DAN DATABASE TIDAK TERSTRUKTUR INFLUXDB -, Hendra; Andriyani, Widyastuti
JURNAL TEKNOLOGI TECHNOSCIENTIA Technoscientia Vol 12 No 2 Februari 2020
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The use of structured databases is still very widely used by companies in small and medium scale with the aim of processing data so that from these data conclusions can be drawn to determine a decision. But over time, of course the need for data that continues to grow can make a system run very slowly when using a structured database. That is caused by the amount of data that continues to increase every day, even for certain cases the data can increase every second. For this reason, an unstructured database is needed specifically for storing history data. From some existing unstructured databases, InfluxDB is one of the unstructured databases specifically intended for storing history data and has a very good ability to process data into a matrix for analysis. One of the key factors in an unstructured database is the database structure which is very different and supports to maximize database performance.
PENERAPAN MADM DENGAN METODE SAW UNTUK MENENTUKAN TARGET PROMOSI BERDASARKAN ASAL JURUSAN DI SEKOLAH B. T. SUTRISNO; Widyastuti Andriyani
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 2 (2020): JURNAL SIMETRIS VOLUME 11 NO 2 TAHUN 2020
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i2.4784

Abstract

Penerimaan Mahasiswa Baru (PMB) adalah proses yang sangat penting untuk merekrut mahasiswa baru, di mana jumlah mahasiswa sangat penting untuk operasional dan perencanaan pengembangan Perguruan Tinggi Swasta (PTS). Sebagai bagian dari PTS di Yogyakarta, Fakultas Kesehatan Universitas Jenderal Achmad Yani Yogyakarta berusaha meningkatkan jumlah mahasiswanya, dimana salah satunya dengan mengoptimalkan penenttuan target promosi. Model Multiple Attribute Decision Making (MADM) dengan metode Simple Additive Weighting (SAW) dapat menghasilkan urutan peringkat yang dapat digunakan untuk menentukan keputusan terkait target promosi berdasarkan asal jurusan pada calon siswa di sekolah menengah, sehingga pembuat kebijakan Penerimaan Mahasiswa di Fakultas Kesehatan Universitas Jenderal Achmad Yani Yogyakarta dapat menentukan target promosi yang sesuai untuk meningkatkan jumlah mahasiswa baru.
OPTIMASI PEMROGRAMAN QUERY UNTUK ALGORITMA APRIORI BERBASIS ASOSIASI DATA MINING Femi Dwi Astuti; Widyastuti Andriyani
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 1 (2016): Edisi Juli
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (447.322 KB) | DOI: 10.30645/jurasik.v1i1.3

Abstract

Secara umum, setiap pengukuran diasosiasikan dengan sebuah nilai threshold yang nilainya dapat ditentukan sendiri oleh pengguna data mining. Aturan asosiasi yang tidak menggunakan threshold cenderung tidak menarik karena tidak merepresentasikan pengetahuan kepada pengguna data mining. Salah satu faktor yang memberikan kontribusi untuk menentukan apakah suatu pola menarik atau tidak adalah kesederhanaannya dalam pemahaman manusia. Semakin kompleks struktur sebuah aturan, maka semakin sulit untuk diinterpretasikan sehingga pola yang dibentuk semakin tidak menarik.
Query Execution Performance Analysis of Column-Oriented Database in Dashboard Bagas Triaji; Widyastuti Andriyani; Totok Suprawoto; Muhammad Agung Nugroho; Rikie Kartadie
Journal of Intelligent Software Systems Vol 1, No 2 (2022): Desember
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (649.54 KB) | DOI: 10.26798/jiss.v1i2.768

Abstract

In making reports or dashboards from operational data, problems often occur in the query process with low speed in responding to an output, causing the server to experience overload. This condition often occurs in companies or higher education organizations in managing academic data. This condition can be improved by optimizing the database server by integrating relational databases with column-oriented databases to speed up query responses and save development costs. Based on the experiments that had been carried out, column-oriented has succeeded in optimizing with a significant difference in query execution time and the server does not crash.
Data Warehouse to Support the Decision Using Vikor Method Heri Muhrial; Bambang Purnomosidi.D.P; Widyastuti Andriyani; Hamdani Hamdani
Journal of Intelligent Software Systems Vol 1, No 2 (2022): Desember
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (656.731 KB) | DOI: 10.26798/jiss.v1i2.767

Abstract

Data warehouse is a place where data compilations are stored extensively and periodically. The ability of the data warehouse to integrate data lightens CV. Visi Indonesia Mandiri companies in evaluating and making decisions on operational, strategic and tactical processes. The problem is that the company has not provided a data warehouse yet. Moreover, there is no service to give out the needs of easy, consistent, valid and accurate information on operational data, tactical data and strategic data from the decision-making process at the executive level. The data warehouse architecture was established as decision making using the Vikor method analysis.
Mushroom Image Classification Using C4.5 Algorithm Cucut Hariz Pratomo; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.930

Abstract

This study applied five types of Mushrooms, they are Button mushrooms, Wood Ear mushrooms, Straw mushrooms, Reishi mushrooms and Red Oyster mushrooms. The feature extraction used is Order 1 with the parameters of mean, skewness, variance, kurtosis, and entropy. The process carried out to identify mushroom images by preparing image objects. There were 15 images of each mushroom class were taken for each mushroom and stored in .jpg format. The image processing is carried out by a feature extraction process. Then five images for each mushroom class are chosen. They were used as test images which will be classified so that identification results are obtained. This study applies the Classification Algorithm C4.5 to build a decision tree, which will also identify the results of the accuracy of processed mushroom images. The obtained result of accuracy was 84% in the classification of feature extraction Order 1
IOT BASED SOIL MOISTURE MONITORING AND SOIL MOISTURE PREDICTION USING LINEAR REGRESSION (CASE STUDY OF VINCA PLANTS) Kuindra Iriyanta; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Intelligent Software Systems Vol 2, No 1 (2023): July
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i1.929

Abstract

Soil moisture is something that becomes important. Indonesia as an agricultural country, most of the population has a profession as a farmer. In agriculture, one of the important parts is the water composition in the soil or soil moisture. One attempt to maintain soil moisture is to provide sufficient water intake to the soil. However, in practice, it is sometimes complicated for farmers to do proper irrigation of their agricultural land. This humidity condition will ultimately determine the success of vinca plant cultivators. The accuracy of giving water both in terms of time management and volume are two things which are an important focus of vinca crop growing. This system is designed using a humidity sensor which is used to measure the moisture composition contained in the soil, and an air temperature sensor. The NodeMCU ESP2866 microcontroller acts as a link between Google spreadsheet sensors. NodeMCU ESP2866 will send humidity and temperature sensor reading data to Google spreadsheets using a RESTfull API which can connect one application to another. The sensor data is then saved to Google spreadsheet and processed using the linear regression method. The processing results will be displayed on the Google Data Studio dashboard. The output of this process is to provide information about soil moisture conditions, notification of soil moisture conditions if it is too dry or damp, thus the prevention of the death of vinca plants can be carried out. The benefit for users is that they can carry out periodic and real-time monitoring by simply using the Telegram instant messaging application, which is expected to reduce the risk of plant death due to drought or excessive watering
Clustering Analysis of Poverty Levels in North Sumatra Province Using the Application of Data Mining with the K-Means Algorithm Widyastuti Andriyani; Asyahri Hadi Nasyuha; Yohanni Syahra; Bagas Triaji
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6867

Abstract

North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention and in-depth analysis. Thus, research on poverty levels in this province becomes very relevant and urgent. Therefore, a more in-depth analysis is needed regarding poverty levels in various regions within this province using data mining methods. The data mining approach is a way to gain understanding from large amounts of data. In the context of the problem of poverty levels, data mining has the potential to help reveal patterns that may be hidden in economic and social data. One algorithm that is often applied in clustering analysis is the K-Means algorithm. The K-Means algorithm is a clustering method that is widely used in data analysis and allows grouping data based on similar characteristics, so that it can be used to classify areas with similar levels of poverty. The results of this research show that data mining with the application of the K-Means algorithm can help produce more effective decisions in analyzing clustering of poverty levels in North Sumatra Province involving the use of data over a ten-year period, namely from 2013 to 2022, which records the number of poor people based on District and city. 3 groups were produced, namely 3 levels of poverty, including relatively stable, very vulnerable and vulnerable. Data from 33 districts or cities in North Sumatra resulted in a poverty level clustering of 1 city that was very vulnerable, 4 cities that were vulnerable and 27 cities that were relatively stable.
Co-Authors Asyahri Hadi Nasyuha B. T. SUTRISNO Bagas Triaji Bambang Purnomosidi Dwi Putranto Bambang Purnomosidi.D.P Cucut Hariz Pratomo Femi Dwi Astuti Hamdani Hamdani Hendra Heri Muhrial Kuindra Iriyanta Nugroho, Muhammad Agung Rikie Kartadie Totok Suprawoto Yohanni Syahra