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Dedy Sugiarto
Fakultas Teknologi Industri, Universitas Trisakti

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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.
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%.