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<![CDATA[Rancang Bangun Virtual Reality Photography Berbasis Web untuk Menunjang Pariwisata ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Rinda Cahyana]]>;
<![CDATA[Dewi Tresnawati]]>
<![CDATA[ Jurnal Algoritma Vol 18 No 1 (2021): Jurnal Algoritma ]]>
Publisher : <![CDATA[Institut Teknologi Garut ]]>
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DOI: 10.33364/algoritma/v.18-1.980
<![CDATA[Pariwisata merupakan Industri sektor ekonomi non migas yang sangat berperan dalam peningkatan ekonomi dan proses pembangunan sebuah negara, dimana hal ini sangat berkaitan dengan pendapatan atau devisa negara serta pendapatan penduduk di sekitar objek wisata. Seiring perkembangan di bidang teknologi dan informasi, maka telah hadir suatu teknologi yang disebut dengan Virtual Reality. Teknologi Virtual Reality dalam dunia fotografi sekarang dikenal sebagai Virtual Reality Photography. Penelitian ini mengikuti tahapan survei literatur untuk mendeskripsikan cara penyajian informasi dalam dari tiga sampel situs web penyedia layanan informasi bagi masyarakat. Hasil menunjukkan bahawa format informasi yang disajikan pada rancang bangun untuk situs web Virtual Reality Photography adalah untuk Lokasi akan ditampilkan Peta, kemudian untuk Profil akan disajikan dalam bentuk Teks dan Gambar VR 360o dan untuk Konten akan disajikan dalam bentuk Teks, Peta, Gambar dan VR dalam Menu Opsional.]]>
<![CDATA[Rancang Bangun Virtual Reality Photography Berbasis Web untuk Menunjang Pariwisata ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Rinda Cahyana]]>;
<![CDATA[Dewi Tresnawati]]>
<![CDATA[ Jurnal Algoritma Vol 18 No 1 (2021): Jurnal Algoritma ]]>
Publisher : <![CDATA[Institut Teknologi Garut ]]>
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DOI: 10.33364/algoritma/v.18-1.980
<![CDATA[Pariwisata merupakan Industri sektor ekonomi non migas yang sangat berperan dalam peningkatan ekonomi dan proses pembangunan sebuah negara, dimana hal ini sangat berkaitan dengan pendapatan atau devisa negara serta pendapatan penduduk di sekitar objek wisata. Seiring perkembangan di bidang teknologi dan informasi, maka telah hadir suatu teknologi yang disebut dengan Virtual Reality. Teknologi Virtual Reality dalam dunia fotografi sekarang dikenal sebagai Virtual Reality Photography. Penelitian ini mengikuti tahapan survei literatur untuk mendeskripsikan cara penyajian informasi dalam dari tiga sampel situs web penyedia layanan informasi bagi masyarakat. Hasil menunjukkan bahawa format informasi yang disajikan pada rancang bangun untuk situs web Virtual Reality Photography adalah untuk Lokasi akan ditampilkan Peta, kemudian untuk Profil akan disajikan dalam bentuk Teks dan Gambar VR 360o dan untuk Konten akan disajikan dalam bentuk Teks, Peta, Gambar dan VR dalam Menu Opsional.]]>
<![CDATA[Design And Build Virtual Reality Photography Web-Based To Support Tourism ]]>
<![CDATA[Indri Tri Julianto]]>
<![CDATA[ Journal of Electrical, Electronic, Information, and Communication Technology Vol 3, No 2 (2021): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY ]]>
Publisher : <![CDATA[Universitas Sebelas Maret (UNS) ]]>
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DOI: 10.20961/jeeict.3.2.54833
<![CDATA[Tourism is the activity of visiting tourist objects for recreational and leisure purposes. Along with developments in the world of technology and information, there is a technology that can be used to promote a tourist attraction called Virtual Reality. Virtual Reality is a technology designed to allow users to interact with a computer-simulated environment. The problem is that there are still very few travel websites that provide this feature. Virtual Reality technology in the world of photography is known as Virtual Reality Photography. Virtual Reality Photography can produce images in 360o, so people can see the image as a whole and as if they were in place when the photographer was taking pictures. The purpose of this research is to design and build a website that provides information for tourists who want to visit tourist objects. The development method used is the Multimedia Development Life Cycle. The output of his research is a Virtual Reality Photography website that provides information in the form of 360o images equipped with location maps and static images. Augmented Reality technology can be applied on this website as a form of information presented as well as Virtual Reality technology.Keywords— Information, Multimedia, Tourism, Virtual Reality, Web.]]>
<![CDATA[COMPARISON OF DATA MINING ALGORITHM FOR FORECASTING BITCOIN CRYPTO CURRENCY TRENDS ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Dede Kurniadi]]>;
<![CDATA[Muhammad Rikza Nashrulloh]]>;
<![CDATA[Asri Mulyani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.2.194
<![CDATA[The popularity of cryptocurrencies has been increasing in the approximately 10 years since their emergence in 2008. Bitcoin is the most popular and the most instrumental in the existence of cryptocurrencies. The price of coins in cryptocurrencies is the same as the price of shares in the capital market which always fluctuates and even tends to be more volatile than the stock market. This condition is very influential for actors in cryptocurrencies. This study aims to compare the Algorithm Forecasting so that it can be known the right algorithm in Forecasting the trend of Bitcoin. The algorithm used is Algorithm Supervised Learning that is Neural Network, Linear Regression, Support Vector Machine, Gaussian Process, and polynomial Regression. Accuracy was measured using a 10 Fold Cross-validation model and evaluation is done by Root Mean Square Error (RMSE). The results showed that the Algorithm Neural Network is an Algorithm Forecasting best with RMSE value 277,237 +/- 74,736 (micro: 287,208 +/- 0.000) among other Algorithms so that Neural Network can be used for Forecasting cryptocurrency Bitcoin.]]>
<![CDATA[Performance Comparison of Data Mining Algorithms Which Occupy the Top: C4.5 and SVM ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Ricky Rohmanto]]>;
<![CDATA[Ujang Sarifudin]]>;
<![CDATA[Septian Rheno Widianto]]>
<![CDATA[ Jurnal Mantik Vol. 4 No. 4 (2021): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/mantik.Vol4.2021.1189.pp2499-2507
<![CDATA[Data is a collection of various kinds of facts that are stored but do not have meaning. Mining is a mining process. So data mining can be interpreted as the process of mining large and complex amounts of data for new knowledge or information that can be useful for data owners. There is a sequence of systematic ways to solve problems in Data Mining, known as Data Mining algorithms. The IEEE International Conference on data mining which was conducted in 2006 produced the 10 most frequently used data mining algorithms by the research community around the world. Two of the ten most commonly used algorithms are the C4.5 algorithm and the Support Vector Modeling (SVM) algorithm. The methodology used in this research is The Knowledge Discovery in Database (KDD) stage. This study aims to compare the C4.5 with the SVM in terms of performance where what will be seen is the value of Area Under Curve (AUC), Receiver Operating Characteristic (ROC), Accuracy, Error, Precison, and Recall.]]>
<![CDATA[COMPARISON OF CLASSIFICATION ALGORITHM AND FEATURE SELECTION IN BITCOIN SENTIMENT ANALYSIS ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Dede Kurniadi]]>;
<![CDATA[Muhammad Rikza Nashrulloh]]>;
<![CDATA[Asri Mulyani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.3.343
<![CDATA[Sentiment analysis is a process for extracting data in the form of textual, with the aim of obtaining information about the tendency to evaluate an object under study. Sentiments given by the general public can be used as a reference in making product decisions. Sentiment given can be in the form of positive, negative and neutral sentiments. One of the information technology products that has stolen enough attention in the last decade is Bitcoin. The purpose of this study is to compare several classification algorithms using Feature Selection. There are several classification algorithms that can be used for sentiment analysis, such as Deep Learning, Decission Tree, KNN, Naïve Bayes. Textual sentiment classification has constraints on datasets that have high dimensions. Feature Selection is a solution to reduce the dimensions of a dataset by reducing attributes that are less relevant. Feature Selection used is Information Gain and Chi Square. The method used to perform the comparison is by comparing the four classification algorithms to find the best algorithm, then comparing the Feature Selection to get the best between the two, then integrating the best classification algorithm and the best Feature Selection. The results showed that the best classification algorithm was Deep Learning with an accuracy value of 78.43% and a kappa of 0.625. The results of the comparison of Feature Selection, Information Gain get the best results with an average accuracy value of 63.79% and an average kappa of 0.382. The results of the integration of the best classification algorithm with the best Featrure Selection obtained an accuracy value of 78.63% and a kappa of 0.626 where the value was included in the Fair Classification category.]]>
<![CDATA[Analisis Sentimen Terhadap Sistem Informasi Akademik Institut Teknologi Garut ]]>
<![CDATA[Indri Tri Julianto]]>
<![CDATA[ Jurnal Algoritma Vol 19 No 1 (2022): Jurnal Algoritma ]]>
Publisher : <![CDATA[Institut Teknologi Garut ]]>
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DOI: 10.33364/algoritma/v.19-1.1112
<![CDATA[Analisis sentimen merupakan suatu proses untuk mengekstraksi data dalam bentuk teks untuk mendapatkan opini dari pengguna layanan. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap kepuasan pengguna Sistem Informasi Akademik Mahasiswa (SIAM) berbasis Android yang digunakan oleh Institut Teknologi Garut (ITG). Metode yang digunakan yaitu dengan mengumpulkan komentar-komentar di Google Play terhadap aplikasi ini, kemudian akan di klasifikasikan kedalam tiga kategori sentimen, yaitu Positif, Negatif dan Netral. Hasil penelitian menunjukkan bahwa 57,14% pengguna memberikan sentimen Positif, kemudian 37,14% pengguna memberikan sentimen Negatif dan sisanya yaitu 5,71% termasuk kedalam sentimen Netral.]]>
<![CDATA[Rancang Bangun Virtual Reality Photography Berbasis Web untuk Menunjang Pariwisata ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Rinda Cahyana]]>;
<![CDATA[Dewi Tresnawati]]>
<![CDATA[ Jurnal Algoritma Vol 18 No 1 (2021): Jurnal Algoritma ]]>
Publisher : <![CDATA[Institut Teknologi Garut ]]>
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DOI: 10.33364/algoritma/v.18-1.980
<![CDATA[Pariwisata merupakan Industri sektor ekonomi non migas yang sangat berperan dalam peningkatan ekonomi dan proses pembangunan sebuah negara, dimana hal ini sangat berkaitan dengan pendapatan atau devisa negara serta pendapatan penduduk di sekitar objek wisata. Seiring perkembangan di bidang teknologi dan informasi, maka telah hadir suatu teknologi yang disebut dengan Virtual Reality. Teknologi Virtual Reality dalam dunia fotografi sekarang dikenal sebagai Virtual Reality Photography. Penelitian ini mengikuti tahapan survei literatur untuk mendeskripsikan cara penyajian informasi dalam dari tiga sampel situs web penyedia layanan informasi bagi masyarakat. Hasil menunjukkan bahawa format informasi yang disajikan pada rancang bangun untuk situs web Virtual Reality Photography adalah untuk Lokasi akan ditampilkan Peta, kemudian untuk Profil akan disajikan dalam bentuk Teks dan Gambar VR 360o dan untuk Konten akan disajikan dalam bentuk Teks, Peta, Gambar dan VR dalam Menu Opsional.]]>
<![CDATA[Analisis Sentimen Terhadap Sistem Informasi Akademik Institut Teknologi Garut ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Lindawati Lindawati]]>
<![CDATA[ Jurnal Algoritma Vol 19 No 1 (2022): Jurnal Algoritma ]]>
Publisher : <![CDATA[Institut Teknologi Garut ]]>
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DOI: 10.33364/algoritma/v.19-1.1112
<![CDATA[Analisis sentimen merupakan suatu proses untuk mengekstraksi data dalam bentuk teks untuk mendapatkan opini dari pengguna layanan. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap kepuasan pengguna Sistem Informasi Akademik Mahasiswa (SIAM) berbasis Android yang digunakan oleh Institut Teknologi Garut (ITG). Metode yang digunakan yaitu dengan mengumpulkan komentar-komentar di Google Play terhadap aplikasi ini, kemudian akan di klasifikasikan kedalam tiga kategori sentimen, yaitu Positif, Negatif dan Netral. Hasil penelitian menunjukkan bahwa 57,14% pengguna memberikan sentimen Positif, kemudian 37,14% pengguna memberikan sentimen Negatif dan sisanya yaitu 5,71% termasuk kedalam sentimen Netral.]]>
<![CDATA[TWITTER SOCIAL MEDIA SENTIMENT ANALYSIS AGAINST BITCOIN CRYPTOCURRENCY TRENDS USING RAPIDMINER ]]>
<![CDATA[Indri Tri Julianto]]>;
<![CDATA[Dede Kurniadi]]>;
<![CDATA[Muhammad Rikza Nashrulloh]]>;
<![CDATA[Asri Mulyani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.20884/1.jutif.2022.3.5.289
<![CDATA[Cryptocurrency trends, especially Bitcoin, have gained a place in a group of people and there are even countries that already use Bitcoin as a legal transaction tool. The dynamics that occur in this Bitcoin trend make many new users. This lack of understanding of the technology can cast doubt on those who want to get started, so it is necessary to conduct sentiment analysis to increase knowledge of what Bitcoin is and how it works. This study aims to conduct a Sentiment Analysis regarding Bitcoin through Twitter social media, so that their opinion on this technology will be known. The method used is by using Tweet data that has been downloaded on the www.data.world.com website. The data is the result of using the Crawling technique, then sentiment analysis is carried out to classify a tweet into Neutral, Positive, or Negative. The results showed that from the 1998 dataset, 46.69% were classified as Neutral, then Positive, 43.54%, and 9.75% Negative.]]>
