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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ULTIMATICS Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Journal of Community Development Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Asri Mulyani
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Nursing Public Health 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 AA Sudharmawan, AA Abania, Nia Abdurrohman, Muhammad Akmal Ade Sutedi Ade Sutedi, Ade Aditya Permana Aditya Permana Agung Gumelar Agus Hermawan Ahmad Badar Muttaqin, Dadan Ahmad Budiman Ahzam, Faiq Muhammad Ai Karlina Ainun, Taanafa Nurul Akbar, Fazri Haikal Alamsyah, Fathi Ridwan Aldiansah, Aldiansah Alfiansyah, Dandan Alhakim, Much Kahfi Amrulloh, Muhammad Fawaz Andarista, Hilda Dian Anggi Wandani Annisa Atmanati Ansori, Hasbi Hamdan Al Ardimansyah, Dendi Arifin, Pipin Zaenal Asep Deddy Supriatna Asri Indah Pertiwi Astri Yuliastri Asyah, Cha Cha Nisya Aulia, Husni Aulia, Wafa Gaida Ayu Latifah Balilo Jr, Benedicto B. Banowati, Rika Burdani, Aditya Mauludin Burhanuddin, Ridwan Choerunisa, Alma Deddy Supriatna, Asep Dede Kurniadi Deni Heryanto Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diazki, Moch. Haiqal Dini Destiani Siti Fatimah Dudy Mohammad Arifin Ependi, Nasep Eri Satria Erick Fernando B311087192 Erwin Gunadhi Erwin Gunadhi Rahayu, Raden Evita Prananda Dewi Fadiel Muhammad Fadillah, Hadi Bagus Fahmi Ilham Ardiansyah Fahru Nisa Aulia Faisal Nurur Ramadhan Faisal, Ridwan Nur Fathon, Ahmad Fatimah, Raden Dini Destiani Siti Faturrohman, Nadhif Fauzan, Muhammad Farhan Fauzi, Rizky Ahmad Fauziyah, Asyifa Febrianti, Tiara Fikri Fahru Roji Firdaus, Raden Syaban Firmansyah, Marshal Fitri Nuraeni FITRIANI, PIPIT Gina Muhtari Gugum Gumilar Setia Permana Gustiawan, Restu Fajar Halim, Muhammad Aufa Fauza Haolilah, Siti Hilmi Aulawi Homsatin, Asyifa Azsma Iis Oktaviani Ikbal Lukmanul Hakim Inda Muliana Indra Trisna Raharja Indrakusumah, Muhamamad Rafi Indri Tri Julianto Irfanov, Muhammad Irpan Ahmad Fauzi Ismaya, Karina Jajang Romansyah JAMALUDIN Jamiludin, Irfan Janatunnisa, Raisya Agni Juliansyah, Fauzan Romi Kamal, Chaerul Syah Al Karlina, Ai Khaifa, Raisha Sarah Kharisma Wiati Gusti Khoiriyyah, Fakhrun Mahda Kurniawan, Ihsan Hafiiz Latif, A. Abdul Latifah, Ayu Leni Fitriani, Leni Lufti Lukmanurkarim Lukmanurkarim, Lufti M Rafiq Syahputa M. Mesa Fauzi Mahendra Akbar Musadad Maulud, Restu Bagja Meta Regita Mubarok, Ilham Muhamad, Zaki Muhammad Abdul Yusup Hanifah Muhammad Rikza Nashrulloh Muharam, Muhamad Riyan Muhtari, Gina Muliana, Inda Mustaatinah, Tutin Nasrullah, Muhammad Rikza Nita Novianti Firmansyah Nugraha, Aldi Nugraha, Praja Salya Nugroho, Salmanudin Nuraisah, Tintin Nurazizah, Neng Putri Nurhaliza, Nabila Putri Nurhidayanti, Shopi Nurmahmudi, Raihan Nurpajar, Dini Siti Nurrifan Syabandhi Nursaidah, Syifa Nursofiana, Muhammad Fauzan Nursyaban, Dzikri Nurul Fauziah Nurusyam, Moh Algifari Oktavian, Gilang Anhari Oktavian, Muhamad Ar Rasyid Rizki Oktavian, Zordan Oktaviani, Iis Padilah, Eva Nurul Pasundan, Gia Aghista Prayoga, Hardi Putri, Elsinta Ismawati Putri, Mita Hidayani Qalam Ilmayasa, Muhammad Raharja, Indra Trisna Rahayu, Maulida Fasha Rahayu, Yari Ardiansyah Rahmat, Agil Rahmawan, Muhammad Kahfi Rais, Azfa Muhammad Ramdan, Galih Muhammad Ramdani Setiawan Ramdani, Dikri Ramdani, Idham Ramdhani, Nabila Aprilia Rangga, Wisnu Ranti Rahayu Pujianti Renaldy Alamsyah Rengganis, Nadia Fauziah Revi Rexi Muhamad Fadilah Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rima Ardianti Rinda Cahyana Riyad Sabilul Muminin Rizal Mulyana Rizki, Riyandi Muhamad Rizky Helmi Romadan, Mochamad Rizki Rosita Wulandari Rostilawati, Detila Ruli Ahmad Rusmana Ruspa, Rena Saadah, Roro Saepul Rochman Sahdan Hadianto Salsabila, Kailla Sambas, Keisha Aulia Saparudin, Hopid Saputri, Amellia Sarah Khoerunisa Saripudin Saripudin Setiawan, Ahmad Dandi Sidik, Muhammad Luthfi Farid Sinta Nurfatonah Siti Rima Fauziyah Slamet, Bagus Solahudin, Muhammad Husni Sopandi, Pendi Sopian, Alpi Sri Intan Multajam Sri Rahayu SRI RAHAYU Sugriantha, Irham Sukirno Sukirno Sukmawan, Tegar Sulaeman, Gilman Fajar Suwandy, Mochamad Riefky Rafliana Syabandhi, Nurrifan Tania Agusviani Wahidah Taupik Hidayat, Taupik Wahdaniah, Hamidah Nur Yana Nuryana Yoga Handoko Agustin Yosep Septiana Yuliastri, Astri Yundari, Yundari Yuni Yuliani Yusuf, Nadhif Murtadho Zaelani, Jaka Muhammad Zahra, Lubna Nur Zulkarnaen, Ade Iskandar