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Dedy Sugiarto
Universitas Trisakti
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Journal : Intelmatics

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Perolehan Informasi Kembali (Information Retrieval/IR) Menggunakan Topic Modelling untuk Dataset Tempo Wilda Anggriani; Syandra Sari; Anung B. Ariwibowo; Dedy Sugiarto
Intelmatics Vol. 1 No. 2 (2021): Juli - Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v1i2.5030

Abstract

In the era of technology as it is today, many technologies and information are growing. The presence of information technology makes it easy for everyone to find information. Usually people use search engines like Google, Yahoo, etc. to find information., many technologies and information are growing. The presence of information technology makes it easy for everyone to find information. Usually people use search engines like Google, Yahoo, etc. to find information.Search engines really help humans to get information. Usually the search engine is one example of information retrieval (Information Retrieval / IR). Documents that produced by search engines are relevant documents based on user requests.In this study, the author implemented the IR process to find relevant documents based on existing queries. The results will be compared with relevant documents from previous research using the same dataset, namely the Tempo dataset from 2000 to 2002. This can find out how far the performance of the method used in this research is based on previous research. The method used in this research is the doc2vec method.From the results obtained using the doc2vec model, the smaller the epoch on the doc2vec model, the smaller the results of the average percentage similarity between the relevant documents produced by the doc2vec model and the relevant documents beforehand. While the results of the percentage similarity average of the doc2vec model are based on the vector size which is after the vector size 30 the result is above 35%. Epoch which produces the highest percentage average is epoch 25 from epoch 25, 50, 75, and 100. Vector size that produces the highest average percentage similarity is vector size 40 from vector size 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100. The highest results of the highest percentage similarity are generated by the doc2vec model that uses epoch 25 and vector size 40 is 41,930. In the era of technology as it is today, many technologies and information are growing. The presence of information technology makes it easy for everyone to find information. Usually people use search engines like Google, Yahoo, etc. to find information., many technologies and information are growing. The presence of information technology makes it easy for everyone to find information. Usually people use search engines like Google, Yahoo, etc. to find information.Search engines really help humans to get information. Usually the search engine is one example of information retrieval (Information Retrieval / IR). Documents that produced by search engines are relevant documents based on user requests.In this study, the author implemented the IR process to find relevant documents based on existing queries. The results will be compared with relevant documents from previous research using the same dataset, namely the Tempo dataset from 2000 to 2002. This can find out how far the performance of the method used in this research is based on previous research. The method used in this research is the doc2vec method.From the results obtained using the doc2vec model, the smaller the epoch on the doc2vec model, the smaller the results of the average percentage similarity between the relevant documents produced by the doc2vec model and the relevant documents beforehand. While the results of the percentage similarity average of the doc2vec model are based on the vector size which is after the vector size 30 the result is above 35%. Epoch which produces the highest percentage average is epoch 25 from epoch 25, 50, 75, and 100. Vector size that produces the highest average percentage similarity is vector size 40 from vector size 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100. The highest results of the highest percentage similarity are generated by the doc2vec model that uses epoch 25 and vector size 40 is 41,930.
Ekstraksi Informasi Menggunakan Named Entity Recognition dan Pembuatan Association Rule Pada Dokumen Direktori Putusan Mahkamah Agung Republik Indonesia Muhammad Rizky Fadila Afgan; Syandra Sari; Anung B. Ariwibowo; Dedy Sugiarto
Intelmatics Vol. 2 No. 1 (2022): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v2i1.5031

Abstract

Land is fundamental to the needs of human life. Humans carry out activities on the ground, so that they are obstructed from getting all human activities both directly and indirectly carried out on the ground. Land is a natural resource that is given by God Almighty to the Indonesian people as national wealth and is a means of meeting all life activities that are important for human life. In this case everyone must need land. Land is often used as a case by disputes, because of the limited area of landInvolved a lot of land The author will extract information in the Directory file Decision Mahkmah Agung is done to produce a named entity taken from the file. PDF extracted. In this study, the author uses the introduction of an entity named (NER Entity Recognition or NER). NER is used to retrieve named entities. After that the author uses the Association Rule to inform data in the form of graphs for analysis Land is fundamental to the needs of human life. Humans carry out activities on the ground, so that they are obstructed from getting all human activities both directly and indirectly carried out on the ground. Land is a natural resource that is given by God Almighty to the Indonesian people as national wealth and is a means of meeting all life activities that are important for human life. In this case everyone must need land. Land is often used as a case by disputes, because of the limited area of land                                Involved a lot of land The author will extract information in the Directory file Decision Mahkmah Agung is done to produce a named entity taken from the file. PDF extracted. In this study, the author uses the introduction of an entity named (NER Entity Recognition or NER). NER is used to retrieve named entities. After that the author uses the Association Rule to inform data in the form of graphs for analysis
Perancang Data Warehouse Dan Visualisasi Data Mutu Penerimaan Beras Nita Chairunnisa; Dedy Sugiarto; Teddy Siswanto
Intelmatics Vol. 2 No. 2 (2022): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v2i2.5037

Abstract

Rice is a staple food consumed by a large portion of the Indonesian population. Each region has its own rice production so that it has different qualities.. Indonesia itself has specific standards for good quality rice. In order for rice can be distributed evenly throughout the archipelago, Indonesia has a rice management organization, one of which is PT Food Station Tjipinang Jaya. Rice from various suppliers must be recorded and checked for quality. Making a Data Warehouse needs to be implemented so that it is easily collected and analyzes the data received and can be used as a reference for decision making. To build a data warehouse can use Extract, Transform and Load (ETL) available in Pentaho Data Integration. Data that has been entered into the data warehouse can be visualized using Python to make further decisions.
Designing Data Warehouse For Forecast and Data Visualization of Sales Nutrition Products Jeany Fadhilah Agatha Siahaan; Dedy Sugiarto; Teddy Siswanto
Intelmatics Vol. 1 No. 2 (2021): Juli - Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v1i2.5235

Abstract

Sales data can be processed in such a way that it can become information that is used as material for analysis and consideration in making decisions. This study aims to visualize PT XYZ sales data for nutritious intake products and predict sales figures for 2018 and 2019. Data is obtained directly from PT XYZ by submitting a request for data withdrawal. Data on sales of nutritious beverage products for the last 5 years are processed using Pentaho tools with ETL method (extract, transform, load) then predicted sales figures for 2019 using R programming language with ARIMA and Holt-Winters methods after which data will be visualized using Powe BI so that the display of data presentation is more interesting and informative. To find out the compatibility in using the forecasting method, the writer will compare RSME numbers from both methods and use the method with the smallest RSME number.
Business Intelligence Design for Data Visualization and Drug Stock Forecasting Novenia Eka Warestika; Dedy Sugiarto; Teddy Siswanto
Intelmatics Vol. 1 No. 1 (2021): January
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v1i1.7407

Abstract

Klinik Pratama is one form of service provided by the Ministry of Communication and Information of the Republic of Indonesia in protect employees from health disorders that could affect employee productivity. In its development, the clinic often finds problems, one of them is often a shortage or excess the drug stock on a running period. Therefore, it be required a design of an Business Intelligence that manages complex data into a data visualization forecasting of the future stock of drugs. Historical data processing of the drug is done with process of Extract, Transform and Load (ETL) using the Spoon Pentaho Data Integration tools. While the visualization of drug stock data and forecast results is done using Microsoft Power BI (Business Intelligence) tools and for forecasting is done with Artificial Neural Network method by RStudio tools. The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, the Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability because the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making The results of forecasting the amount of stock out of drug samples using the Artificial Neural Network method obtained an MSE value of 67.72 and RMSE 8.22 which means that this forecast has a good ability with the resulting error rate is relatively small. From this research, Klinik Pratama of the Ministry of Communication and Information can easily understand and analyze drug stock data and can support operational decision making.
Perancangan Business Intelligence Data Ketersediaan Obat di Puskesmas Curug Tangerang Mohamad Dimas Budisantoso; Dedy Sugiarto; Teddy Siswanto
Intelmatics Vol. 2 No. 1 (2022): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v2i1.12451

Abstract

Puskesmas Curug merupakan layanan pusat kesehatan masyarakat yang berlokasi di kecamatan Curug, Kabupaten Tangerang. Sebagai tempat pelayanan kesehatan, maka diperlukan ketersediaan stok obat untuk menjamin proses pelayanan Kesehatan berjalan dengan baik terutama dalam mendapatkan obat di Puskesmas. Namun faktanya di puskesmas sering terjadi jumlah obat mengalami surplus dan defisit stok pada saat periode berjalan. Hal ini dapat memengaruhi kegiatan operasional pelayanan dalam mengelola stok obat-obatan. Maka dari itu, dibutuhkan perancangan Business Intelligence yang mengelola sebuah data kompleks menjadi data yang tervisualisasikan untuk proses peramalan stok obat di periode yang akan datang. Pengolahan data stok obat dsetiap periode dilakukan dengan Proses ETL (Extract, Transform, and Load) menggunakan tools Spoon Pentaho Data Integration. Sedangkan visualisasi data stok obat dari hasil peramalan menggunakan tools Microsoft Power BI dan R Studio, untuk peramalan digunakan metode ARIMA yang memiliki signifikansi kemampuan peramalan yang baik, sebab memiliki MAPE diangka <10%, dan 10 – 20%.
Dashboard Design for New Employee Candidate Program at XYZ Bank Using Tableau Winona, Aisyah Vasthi; Siswanto, Teddy; Sugiarto, Dedy
Intelmatics Vol. 4 No. 1 (2024): Januari-Juni
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/itm.v4i1.17654

Abstract

Bank XYZ is a government-owned company engaged in the banking sector, requires data and information support to support decisions to be taken by Company leadership because the current system requires a fairly long process in presenting the supporting data. So this research aims to design a dashboard to make it easier for management to obtain important information for decision making. The research methodology uses a flow starting from problem identification, needs analysis, raw data acquisition, OLAP design, ETL process and visualization. This research uses Pentaho tools to carry out the ETL process and Tableau tools to create dashboards. Based on the OLAP design of this research, a fact table for the distribution of ODP and a dimension table consisting of location, trip, city, start date, end date, class, gender, university and major were formed. As a result of this research, the design that has been made means that dashboard users/managers can anticipate or detect early delays in the process of making a rundown for the next stage of the journey, users/managers of the dashboard can anticipate or detect early delays in the process of making a rundown for the next stage of the journey, can find out about universities and the most study programs for more precise decision making, and has a data filtering feature which is useful for searching for certain data more quickly so that the decision making process can be supported by the information produced.
Design of Study Program Performance Dashboard using Streamlit Ilham, Moch Ilham A; Siswanto, Teddy; Sugiarto, Dedy
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.20643

Abstract

To achieve certain performance goals, such as increasing the integration of study program performance data from various sources, such as student data, lecturers, curriculum, research, and so on, a performance dashboard is needed to monitor the performance of lecturers, students and curriculum to be more efficient. In this study, a dashboard was created with streamlit where the data was taken from the web scraping method. By utilizing the data visualization capabilities owned by Streamlit, study program performance information can be presented in the form of graphs, diagrams, or tables that are easier to understand and interpret.
Implementation Enterprise Resource Planning (ERP) ODOO Version 15.0 Manufacturing Module at CV. Razzaq Berkah Mulia Setiawan, Ibnu Fajar; Siswanto, Teddy; Sugiarto, Dedy
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.21073

Abstract

In the modern era, digitalization has had a significant impact on various business fields, with technology being the main key in fascilitating company performance. Digitalization of company systems is now an obligation, data integration is essential for analysis that supports company progress. ERP is software that simplifies company operations by integrating various business application modules such as Inventory, Accounting, Sales, Point of Sales, Manufacturing, Contact, Website, and Purchasing. CV Razzaq Berkah Mulia, which has the potential to compete with international furniture companies, has not fully adopted information systems in its management. This lack of digitalization can lead to losses and recording errors that hinder their business performance. An optimized and valid implementation of Odoo ERP is essential to improve the quality and operational efficiency of CV Razzaq Berkah Mulia. This Implementation will go through several phases, namely Discovery and Planning, Design, Development, Testing, Deployment, and Support. At the Discovery and Planning stage, the company needs will be identified and an implementation strategy will be planned. The Design stage will design the system as per requirements, while the Development Stage will involve the creation and configuration of the system. At the testing stage, the system willl be tested to ensure its functionality, and the Deployment stage will involve launching the system into the operational environtment. Finally, the Support stage will provide post-implementation support.  The final result of this development can be accepted and implemented for the implementation of Odoo at CV. Razzaq Berkah Mulia through UTAUT with a satisfaction value of Performance Expectancy 88%, Effort Expectancy 88%, Supporting Facilities 88,4%, Facilitating Conditions 91%, Attitude Towards Technology 89,2%, Behavioral Intention 90,8%.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Rochman Abdul Rochman Ahmad Zuhdi Ainul Yaqin Ainul Yaqin Aji Saputra, Aji Annisa Dewi Akbari, Annisa Dewi Anung B Ariwibowo Anung B. Aribowo Ariwibowo, Anung B Ariwibowo, Anung B. Arviandri Naufal Zaki Ashari, Krisna Aditama Azhar Rizki Zulma Betha Ariandini Binti Solihah Chani Anugerah Cicilia Puji Rahayu Dadan Umar Daihani Dadang Surjasa, Dadang Dara Mustika Dimmas Mulya Dita Mayasai Dorina Hetharia Dorina Hetharia Elfira Febriani Ema Utami Emelia Sari Faiz Kumara Fajar Anzari Farhan Hashfi Febriana Lestari Firdasari, Elita Wahyu Fitria Nabilah Putri Gatot Budi Santoso Grace Listiani Gunawan, Muhamad Ichsan Gunawan, Muhammad Ichsan Habyba, Anik Nur Harahap, S.TP, M.Si, Elfira Febriani Ida Jubaedah Ida Jubaidah Idriwal Mayusda Ilham, Moch Ilham A Illah Sailah Indah Permata Sari Is Mardianto Is Mardianto Is Mardianto Is Mardianto, Is Iveline Anne Marie Iwan Purwanto Jeany Fadhilah Agatha Siahaan Jeany Fadhilah Agatha Siahaan Johnson Saragih Khoirun nisa Kresna, I Nyoman Krisna Aditama Ashari Lukmanul Hakim Lukmanul Hakim M Arya Octavianus M Syamsul Ma’arif Marimin , Martino Luis Mohamad Dimas Budisantoso Muhamad Ichsan Gunawan Muhamad Ichsan Gunawan Muhamad Ichsan Gunawan Muhammad Hidayat Tullah Muhammad Rizky Fadila Afgan Nadia, Alya Shafa Nita Chairunnisa Noufal Zhafira Novenia Eka Warestika Nur Hadi Nurachman, Nurochman Nurlailah Badariah Octavianus, M Arya PUDJI ASTUTI Randy Andy Ratna Mira Yojana Ratna Shofiati Ratna Shofiati Reyhan Dwi Putra Reyhan Dwi Putra Rianti Dewi Sulamet-Ariobimo Ricky Saputera Ridho Rachmat Giffary Rina Fitriana Rina Fitriana, Rina S. Dewayana, Triwulandari Sari, Debby Kumala Sari, Syandra Setiawan, Ibnu Fajar Shabrina Teruri Shan ASP, Putri Shan, Putri Steven Sen Suharto Honggokusumo Sukardi Sukardi Syandra Sari Syandra Sari Syandra Sari Tamaulina Br Sembiring Tasya Aulia Teddy Siswanto Teddy Siswanto Teddy Siswanto Teddy Siswanto Teddy Siswanto, Teddy Tiena Gustina Amran Tiena Gustina Amran Titik Susilowati Titik Yusrini Triwulandari S Dewayana Triwulandari Satitidjati Dewayana Triwulandari Satitidjati Dewayana Viera Astry Wahyu Hidayat Wahyu Hidayat wahyuni wahyuni Wawan Kurniawan Wilda Anggriani Winnie Septiani Winnie Septiani Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Winona, Aisyah Vasthi Yojana, Ratna Mira Yuli Kurnia Ningsih