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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris Jurnal technoscientia Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informatika dan Rekayasa Perangkat Lunak JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Intelligent Software Systems Eduvest - Journal of Universal Studies Journal Of Artificial Intelligence And Software Engineering
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia, Yogyakarta
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Neuroscience Social Sciences Other

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Journal : Journal of Intelligent Software Systems

 1 
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
TEMPERATURE SENSOR DATA QUALITY ASSESSMENT IN MANUFACTURING ENVIRONMENT USING HAMPEL FILTER AND QSD P.D.P., Bambang; Andriyani, Widyastuti; Dahlan, Akhmad
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2003

Abstract

In the Industry 4.0 era, integrated temperature sensors in system production become source main data for taking decisions. However, the quality of the data produced often influenced by noise, missing values, and disturbing anomalies accuracy of analytical processes. Research This proposes a monitoring pipeline designed data quality For environment manufacturing based on the Internet of Things (IoT), with focus on usage Hampel Filter and Quality Score Delta (QSD) methods. Hampel Filter is used for detecting and handling outliers in temperature data in a way adaptive, while QSD is used for measure dynamics change data quality from time to time. Architecture system built with using Apache Kafka for data ingestion, InfluxDB For time-series storage, and Grafana for real-time visualization. Case study performed on temperature sensor data from the conveyor motor, and the results show that method. This capable detect degradation data quality in general proactive. Findings show potential big in increase reliability industrial monitoring system as well as support maintenance predictive data- based. Research This give contribution significant in developing modular and adaptive approach for management data quality in the manufacturing sector.
DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE Laksono, Triyan Agung; Andriyani, Widyastuti; Putra, Fadhlih Girindra; Ruas da silva, Ivonia Fatima; widayani, Wiwi
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2005

Abstract

This research develops an approach for clustering digital activity locations based on Twitter geospatial data with the aim of supporting business intelligence spatiotemporal . By utilizing the Twitter Geospatial Data dataset containing more than 14 million tweets geo-tagged from the United States, this study implements and compares the DBSCAN and K- Means algorithms to identify spatial and temporal patterns of Twitter user activity. The research process begins with the data pre -processing stage using the Knowledge Discovery Database (KDD), followed by the implementation of the clustering algorithm , and ending with the integration of the results into the dashboard.business intelligence using Power BI . The results show that DBSCAN is able to detect irregular clusters that follow geographic patterns and population density, while K- Means produces a division of the region into three main clusters (West Coast, Central Region, and East Coast) with different temporal activity patterns. Integration of clustering results into a BI dashboard produces actionable business insights , such as identification of digital activity hotspots , optimal time for content delivery, geographic segmentation for marketing strategies, and temporal activity patterns for campaign scheduling. This research contributes to the development of an integrated spatiotemporal analysis pipeline to support data-driven decision making.
Rule Based System to Support Decisions on Determining Employee Status (Lecturers) for Scholarship Student Graduates Sipayung, Hotma Sadariahta; Andriyani, Widyastuti; Purnomosidi Dwi Putranto, Bambang; Kriestanto, Danny
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1337

Abstract

Salah satu permasalahan yang terjadi di Universitas Teknologi Digital Indonesia (UTDI) adalah proses seleksi yayasan Dosen Tetap yang disebut-sebut baru untuk diterapkan kepada mahasiswa penerima calon beasiswa S2 di Magister Teknologi Informasi (MTI). UTDI Yogyakarta. Kriteria yang digunakan dalam aturan tersebut adalah Indeks Prestasi (IP) Semester 1, IP Semester 2, IP Semester 3, Indeks Prestasi Kumulatif (IPK), Makalah (karya ilmiah), Kerjasama, Disiplin, Komunikasi, Pra Tesis, Tesis, Nilai C. , dan Durasi Studi yang diperoleh dari MTI UTDI, selanjutnya akan menggunakan Algoritma C4.5 untuk menghasilkan pohon keputusan yang akan dipelajari aturan dalam sistem. Penelitian ini menggunakan kaidah yang diperoleh dari MTI UTDI oleh Ketua Program Studi (Kaprodi) yaitu 41 data latih dan 8 data uji. Menggunakan forward chaining sebagai metode dalam sistem pakar yang mencari solusi melalui permasalahan, kemudian menggunakan Algoritma C4.5 yang merupakan algoritma yang digunakan untuk membentuk pohon keputusan. Aturan yang terbentuk kemudian digunakan untuk memprediksi kelayakan lulusan beasiswa Magister menjadi Dosen Tetap, Dosen Kontrak, atau tidak memenuhi persyaratan. Hasil prediksi tersebut kemudian dievaluasi menggunakan Confusion Matrix dan memperoleh nilai akurasi sebesar 75%, Precision sebesar 77,78%, dan Recall sebesar 77,78%. Sehingga Algoritma C4.5 dengan menggunakan aplikasi RapidMiner cukup layak digunakan untuk mendukung pengambilan keputusan dalam pemilihan mahasiswa penerima beasiswa Magister yang akan diangkat menjadi Dosen Tetap, Dosen Kontrak maupun yang tidak memenuhi syarat sebagai Dosen di UTDI. Fakultas Teknologi Informasi
Dynamic Bitrate Adjustment in Web-based Video Streaming Applications Using HTTP Live Streaming (HLS) Roh Bintang Jaya, Mabrur; Andriyani, Widyastuti; Kristomo, Domy; Agung Nugroho, Muhammad
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1344

Abstract

This research aims to implement Adaptive Bit Rate (ABR) in the web-based video streaming application JBTV using HTTP Live Streaming (HLS). ABR is a technique that enables automatic adjustment of video bitrate according to user network conditions, while HLS is a streaming protocol that supports adaptive streaming based on HTTP. The research methodology encompasses requirements analysis, system design, implementation, and evaluation. During the requirements analysis phase, the identification of JBTV application requirements and the features needed to implement ABR with HLS were conducted. System design involves the selection of suitable ABR algorithms and the architecture design of the JBTV application that supports HLS. Implementation is carried out by developing the JBTV application capable of generating variant streams with various bitrates and performing adaptive playback according to network conditions
Deep Learning Architecture for Stock Price Prediction andi, tri; Andriyani, Widyastuti; Purnomosidi D.P, Bambang
Journal of Intelligent Software Systems Vol 3, No 1 (2024): July 2024
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v3i1.1343

Abstract

Dalam dunia investasi saham, kemampuan memprediksi pergerakan harga saham secara akurat sangatlah penting. Dua permasalahan utama yang menjadi fokus penelitian ini adalah, bagaimana pemodelan N-BEATS dibandingkan LSTM dan ARIMA pada harga saham Bank BCA, dan bagaimana hasil peramalan model N-BEATS, LSTM, dan ARIMA pada harga saham Bank BCA. Data saham Bank BCA. Untuk menjawab hal tersebut, penelitian ini membahas tentang pengembangan dan evaluasi model peramalan time series N-BEATS. Namun hasil analisis menunjukkan bahwa model ARIMA menunjukkan kinerja yang unggul, dengan pencapaian MAPE sebesar 0,001% pada data menit, 0,006% pada data jam, dan 0,018% pada data hari. Keunggulan ini signifikan dibandingkan model N-BEATS dan LSTM. Oleh karena itu, model ARIMA menunjukkan potensi besar untuk digunakan dalam peramalan deret waktu keuangan, penilaian risiko, dan pemodelan oleh analis keuangan.
Co-Authors Agung Nugroho, Muhammad Akhmad Dahlan Asyahri Hadi Nasyuha B.T. Sutrisno Bagas Triaji Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Cucut Hariz Pratomo Danny Kriestanto, Danny Domy Kristomo, Domy Duen Rakly, Brian Femi Dwi Astuti Femi Dwi Astuti Hamdani Hamdani Hendra Hengki Hengki Heri Muhrial Herwantono, Herwantono Kuindra Iriyanta Laksono, Triyan Agung Miftahul Huda Nugroho, Muhammad Agung P.D.P., Bambang Pangestika , Elza Qorina Pereira, Elisabet da Conceição Pujianto Pujianto Purnomosidi D.P, Bambang Purnomosidi Dwi Putranto, Bambang Putra, Fadhlih Girindra Rakly, Brian Duen Rifky Lana Rahardian Rikie Kartadie Roh Bintang Jaya, Mabrur Ruas da silva, Ivonia Fatima Said, Famidin Saputra, Andika Jodhi Saryanto Saryanto Sipayung, Hotma Sadariahta Suningrat, Nining Suryanto Suryanto Taufik Ismail Totok Suprawoto Tri Andi, Tri Wiwi Widayani, Wiwi Yohanni Syahra Yuli Astuti