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All Journal Jurnal Algoritma Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURTEKSI Jurnal Mantik Jurnal Teknik Informatika (JUTIF) Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Jurnal PkM MIFTEK Jurnal Algoritma INTERNAL (Information System Journal)
Indri Tri Julianto
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF DATA MINING ALGORITHM FOR FORECASTING BITCOIN CRYPTO CURRENCY TRENDS Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 2 (2022): JUTIF Volume 3, Number 2, April 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.2.194

Abstract

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.
COMPARISON OF CLASSIFICATION ALGORITHM AND FEATURE SELECTION IN BITCOIN SENTIMENT ANALYSIS Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 3 (2022): JUTIF Volume 3, Number 3, June 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.3.343

Abstract

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.
TWITTER SOCIAL MEDIA SENTIMENT ANALYSIS AGAINST BITCOIN CRYPTOCURRENCY TRENDS USING RAPIDMINER Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.5.289

Abstract

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.
THE PREDICTION OF PPA AND KIP-KULIAH SCHOLARSHIP RECIPIENTS USING NAIVE BAYES ALGORITHM Asri Mulyani; Dede Kurniadi; Muhammad Rikza Nashrulloh; Indri Tri Julianto; Meta Regita
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 4 (2022): JUTIF Volume 3, Number 4, August 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.4.297

Abstract

The aim of the research is was to predict the scholar recipient for Peningkatan Prestasi Akademik (PPA) and the Kartu Indonesia Pintar Kuliah (KIP-K). The prediction results of scholarship recipients will provide information in the form of the possibility of acceptance and non-acceptance of scholarship applicants. To achieve this goal, this study uses the Naive Bayes algorithm, where this algorithm predicts future opportunities based on past data by going through the stages of reading training data, then calculating the number of probabilities and classifying the values in the mean and probability table. The data analysis includes data collection, data processing, model implementation, and evaluation. The data needed for analysis needs to use data from the applicants for Academic Achievement Improvement (PPA) scholarship and the Indonesia Smart Education Card (KIP-K) scholarship. The data used for training data were 145 student data. The results of the study using the Naive Bayes algorithm have an accuracy of 80% for PPA scholarships and 91% for KIP-K scholarships.
DATA MINING CLUSTERING FOOD EXPENDITURE IN INDONESIA Indri Tri Julianto; Dede Kurniadi; Muhammad Rikza Nashrulloh; Asri Mulyani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.331

Abstract

The availability of food in a country is determined by a conducive climate. Prolonged droughts, floods, and natural disasters, especially for food crop production areas, will have an impact on the availability of natural disaster conditions faced by all countries including Indonesia is the Covid-19 pandemic, where this will affect food security in Indonesia. Data mining is the process of discovering the hidden meaning of a very large data set. The technique used in this study is Data Mining Clustering and the validity index used is Davies-Bouldin. This study aims to determine the Food Security Strategy in Indonesia through the Data Mining Clustering process based on food expenditure data and the Indonesian people's food expenditure per capita. The methodology used is Cross Industry Standard for Data Mining using the K-Means and K-Medoids Algorithm. The best cluster for the K-Means Algorithm is K=7 with a value of 0.341 and for the K-Medoids Algorithm, it is K=7 with a value of 0.362. This research produces the best algorithm, namely K-Means with a value of 0.341, which has a smaller value than K-Medoids with a value of 0.362. The results showed that the regional. cluster with the highest average expenditure on food was cluster 5 covering the DKI Jakarta area, while the cluster with the lowest expenditure was cluster 6 covering Central Java, East Nusa Tenggara, Southeast Sulawesi, Gorontalo, and West Sulawesi. In cluster 6, it is necessary to implement a strategy to increase food security by increasing production capacity and food reserves in each region.
Co-Authors Abdullah, Angga Abdulrohman, Muhammad Haviz Ade Sutedi Ade Sutedi, Ade Aditriyana, Muhammad Rizky Agisni Nurlela, Agni Akhdan Hidayat, Fairuz Alamsyah, Restu Ardana, Alwan Arif Rahman, Rifal Arif Syamsudin, Muhammad Asri Mulyani B. Balilo Jr , Benedicto B. Balilo Jr, Benedicto Balilo Jr, Benedicto B. Burhanudin, Asep Chaerunisa, Adinda Citra Indahsari, Ajeng Dede Kurniadi Dewi Tresnawati Dikdik, Dikdik Dinata, Messy Suryani Jaya Dwi Anggara, Krisna Dzulkhomzah, Moh Rival Fajar, Sigit Sihab Fauzi Pratama, Andhika Fauziah, Fathia Alisha Fikri Fahru Roji Fiqriansyah, Agung Firdaus, Ardy Reza Ginanjar, Ahmad Gotama, Dwi Hartono, Ali Hidayat, Ramdan Rahmat Hidayat, Rangga Huwaidah, Alya Ilham Maulana Ilyasin, Yasa Tiyas Kurnia, Ahmad Hopan Leni Fitriani, Leni Lindawati Lindawati Mahesa, Restu Gusti Malik Ibrahim, Maulana Meta Regita Muhammad Ajif, Arvin Muhammad Rikza Nashrulloh Muhammad Sambas, Phadil Mulyani, Neng Cici Munparik, Riyan Hakim Mutiara, Sani Muzaky, Rifky Khoerul N, Firza Much Asrizal Nawawi, Irfan Ahmad Nurandhini, Rosa Eliza Nurdiansyah, Farhan Nurdin, Kaila Fashla Nurfauziah, Hanifah Nurhalimah, Seli Nurhaqiqi, Lisda Nursalapiah, Sopa Nurul Muttaqin, Epwan Octaviansyah, Rizqi Moch Pardiansyah, Irgi Pratama, Rizky Muhammad Rahayu, Raden Erwin Gunadhi Rahman, Jaohari Rahmawati, Deby Ricky Rohmanto Ricky Rohmanto Ridwan Ridwan Setiawan Rinda Cahyana Rinda Cahyana Rohman, Fauza Rohmanto, Ricky Sadikin, M. Fitroh Saepul Jamil, Alwis Sanusi, Aini Fauziah Putu Septian Rheno Widianto Sermana, Elsa Maharani Setiawan Putra, Achmad Dhani Sirojudin, Naufal Suryadi, Khaila Thsabita Suryani, Isma Taupik Hidayat, Taupik Tria Afini Ujang Sarifudin Yoga Handoko Agustin Yosep Septiana