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All Journal Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi TEKNOLOGI: Jurnal Ilmiah Sistem Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Litbang Edusaintech Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Kohesi: Jurnal Sains dan Teknologi
Novanto Yudistira
Teknik Informatika, Fakultas Ilmu Komputer, Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Public Health Social Sciences Other

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Segmentasi Citra Makanan pada Tray Box menggunakan Metode Otsu Thresholding dengan Ruang Warna Griselda Anjeli Sirait; Novanto Yudistira; Achmad Ridok
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 2 (2022): Februari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Food is one of the basic needs needed by humans to fulfill the process of developing and growing needs. Serving food can be placed in containers such as plates, bowls, or lunch boxes. Tray box is a type of food lunch box consisting of 4 compartments. Rice, side dishes, vegetables are placed in each compartment so there aren't mix with each other and notice to the nutritional value of the food. An alternative way to know the nutritional content in food is digital image processing technology, by segmenting the image as the first step. In this research, the data used were 31 tray box images (full) consisting of 124 compartment images. The Otsu Thresholding method is used as a method for segmenting food images on a tray box with a color space. Each HSV channel is selected as color feature extraction for the compartment image segmentation process, the average of HSV and RGB are used for the full image segmentation process. The IoU accuracy results for compartmentalized image segmentation on each HSV channel are 0,6058237; 0,9006499, and 0,7726735. The results of IoU accuracy and MSE error for full image segmentation on the HSV average are 0,3069244 and 0,8644671, while the average RGB are 0,2761036 and 0,0267637. Based on the results, the Otsu Thresholding method with color space has good accuracy and provides a small error rate.
Klasifikasi Ekspresi Wajah dengan Convolutional Neural Network menggunakan Sobel Filter dan Inception Module Rian Nugroho; Fitra Abdurrachman Bachtiar; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Diterbitkan di JTIIK (Jurnal Teknologi Informasi dan Ilmu Komputer)
Analisis Sentimen terhadap Aplikasi PeduliLindungi menggunakan Metode Long Short-Term Memory (LSTM) Muhammad Rizaldi; Putra Pandu Adikara; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Dipublikasikan di Jurnal Teknologi dan Sistem Komputer
Prediksi Pergerakan Harga Cryptocurrency Bitcoin terhadap Mata Uang Rupiah menggunakan Algoritme LSTM Maulana Ahmad Maliki; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 7 (2022): Juli 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Investment is not new thing for most people, especially bitcoin which is very popular in recent years. Understanding the trend of price movements in investing is very important insight for investors to minimize investment risk, but predicting trend changes is very difficult challenge because it has fluctuating difference in value. The value of the increase and decrease in price of bitcoin is influenced by uncertainty factors such as political problems, economic problems at global level. So, need an algorithm that can predict prices in the future which is one strategy to maximize profits in investing. This study performs several processes to predict bitcoin price movements including, pre-processing, normalization, training the Long-Short Term Memory (LSTM) algorithm, evaluating regression matrix using Mean Square Error (MSE). Based on the results of tests that have been carried out in this study, LSTM algorithm can predict bitcoin price movements as evidenced by the MSE evaluation matrix value of 0.00374 with test parameters including 64 hidden_size, 18 sequence data, optimizer Adam, learning_rate of 0.005, and epoch 200. This research also involves several weight updated algorithms including Stochastic Gradient Descent (SGD), Stochastic Gradient Descent with Momentum (SGDM), and Adaptive Moment Estimation (ADAM) to find optimal prediction results.
Perbandingan Metode Fuzzy Time Series Average-Based Interval dan Long Short-Term Memory untuk Peramalan Harga Komoditi Kopi Arabika Sumatera Utara Cevita Detri Intan Suryaningrum; Novanto Yudistira; Khalid Rahman
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 8 (2022): Agustus 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Coffee is one of the leading commodities in Indonesia because it has good market opportunities both at Indonesia and overseas. Coffee commodities often experience price fluctuations as a result of an imbalance between coffee demand and supply. One of the efforts to anticipate price fluctuations is to do price forecasting. Many forecasting methods can be used, such as Fuzzy Time Series and Long Short-Term Memory which are used in this study to predict coffee prices on the next day. This study will use data on prices for North Sumatra Arabica coffee from January 2020 to August 2021 which were obtained from the official BAPPEBTI's website. In this study, the MAPE value generated in the Fuzzy Time Series Average-Based Interval is 0.016 and the smallest MAPE value in the Long Short-Term Memory method is obtained when the learning rate = 0.00001 with an initial weight value of 0.5 which means obtained MAPE value of 0.06. The MAPE value of both methods is below 10, so it can be said that both methods are categorized as very good. It can be said that in this study the Fuzzy Time Series Average-Based Interval method has better accuracy than Long Short-Term Memory in forecasting the price of Arabica coffee in North Sumatra.
Analisis Sentimen Tweet Covid-19 Varian Omicron pada Platform Media Sosial Twitter menggunakan Metode LSTM berbasis Multi Fungsi Aktivasi dan GLOVE Alfen Hasiholan; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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SARS-CoV-2 virus, also known as COVID-19, has become a very deadly epidemic for the past 2 years. At the end of 2021, the world was threatened by the emergence of a new Covid-19 variant, namely omicron. This variant is referred to as one that is very fast in transmission. The virus was first detected in South Africa and was designated by the world health agency (WHO) as a variant of concern under the name B.1.1.529. This has made omicron a big topic of discussion throughout the world community until now. Social media have played a crucial role in spreading information about the variant of omicron throughout the world. Twitter is a microblogging social media platform that is very effective in sharing lots of information. The number of tweets uploaded every minute is very large, up to 350,000 tweets. This number can be a very useful source of data for obtaining a public opinion on certain topics, especially the covid-19 omicron-related tweets. Sentiment analysis plays an important role in this issue. By using the sentiment analysis method, these opinions can be classified into positive or negative opinions. The long-Short Term Memory algorithm is one of the methods used in classifying the sentiment of public opinion. Optimization of this model is done by using the Glove word embedding method. This method works by counting the occurrence of a word with another word and then converting it into a vector. The result of sentiment analysis using the Long-Short Term Memory and GloVe Embedding method with 100 dimensions resulted in an accuracy rate of 82%.
Pengembangan Aplikasi Android Presensi Kehadiran Realtime menggunakan Pengenalan Wajah dengan Model Facenet Natanniel Eka Christyanto; Eriq Muhammad Adams Jonemaro; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 10 (2022): Oktober 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Attendance system is a system that regulates the presence or presence (a person, a group of people) at a place. There are various methods that can be done in recording attendance, namely the signature method, fingerprint method, card method, face recognition method, and so on. These various methods have their respective weaknesses and advantages, and the selection of methods can be adjusted to the prevailing conditions and situations. The Presence System application created has the aim of making it easier to take attendance without making direct contact with the tool quickly and integrated with the web to display attendance status in real time. This application is designed using Android because the use and availability of devices that use Android is very wide and available in various circles of society. Taken from one source of survey of the use of the Android Operating System (OS) states that 70% still use Android worldwide. In this research, the application is made using the Kotlin programming language on Android, web platform development using the Bubble.io tool, and PHP and SQL programming languages as supporting databases that contact Android devices with websites. Based on the results of the tests carried out, testing the Functional Suitability characteristics scored 100%, Compatibility characteristics with a score of 93.75%, Reliability characteristics with a score of 100%, and face recognition accuracy with a score of 100%.
Analisis Sentimen Tokocrypto pada Twitter menggunakan Metode Long Short-Term Memory Caesar Rio Anggina Toruan; Novanto Yudistira; Rizal Setya Perdana
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

PT. Digital Indonesia Berkat or what is called Tokocrypto is a cryptocurrency exchange company based in Jakarta that is officially registered with the Commodity Futures Trading Regulatory Agency (Bappebti). The level of customer satisfaction is of course very important to note so that companies can benefit and retain service users to continue using the company's services and attract potential service users. Public sentiment towards a cryptocurrency can also affect the price of the cryptocurrency such as the cryptocurrency owned by Tokocrypto called TKO. The amount of data provided by customers will take a long time to be analyzed manually. To overcome this, sentiment analysis can be carried out using a machine learning model that can understand the content of Tokocrypto customer feedback. This study applies the Bidirectional LSTM method to classify sentiment analysis using the tweet data of Tokocrypto service users. In addition, it is necessary to apply pre-processing of text data to overcome customer feedback which includes non-standard words and slang which causes the model to not understand the original meaning. The model is also adjusted for hyperparameters with the grid search method so that the model gets the optimal combination of parameters. Changing non-standard words does not guarantee increasing the accuracy of the model but can still help produce a better model with evaluation results including an f1-score value of 0.9485, a precision value of 0.9423, a recall value of 0.92, a training loss value of 0.0001 and a validation loss value of 0.0004.
Analisis Sentimen terhadap Perundungan Siber pada Twitter menggunakan Algoritma Bidirectional Encoder Representations from Transformer (BERT) Niluh Putu Vania Dyah Saraswati; Novanto Yudistira; Putra Pandu Adikara
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 2 (2023): Februari 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Bullying is the deliberate act of hurting physically, verbally, and psychologically someone who feels helpless. Bullying is an important issue that has been / is identified as a very serious social problem in the United States and many other parts of the world. Cyberbullying is the use of information and communication technologies for intentional/planned harassment and hostile activities, which are carried out repeatedly and structured in a potentially harmful manner. Types of cyberbullying include bullying someone through social media, harassment, sexting, fraud, impersonating, and sending malicious messages through chat rooms and instant messaging. Over the past few years, Bidirectional Encoder Representation from Transformers (BERT) has become a widely used and efficient representation model that achieves cutting-edge performance on sentence-level and token-level tasks, outperforming many specialized task architectures. The level of accuracy in the classification of cyberbullying using test data and validation data in Indonesian generated using the Bert algorithm is 0.81 which in percentage accuracy is 81%. BERT's algorithm can help identify inputs that lead to cyberbullying by providing outputs in the form of classes with predefined categories, namely neutral, abusive language or containing hate speech. The system also gives a percentage of the categories obtained through the program.
Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum Timothy Bastian Sianturi; Imam Cholissodin; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 7 No 3 (2023): Maret 2023
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One form of innovation of technological development is cryptocurrencies that have been widely recognized as an alternative to currency exchange. One of the cryptocurrencies that is quite popular today is Ethereum which started trading for the first time on August 7, 2015 at a price of US$2.83 and reached its highest price on November 8, 2021 at a price of US$4822.97. Ethereum has high price fluctuations and has many factors that affect the price of ethereum such as political or economic problems at the global level so as to cause sufficient investment risk This research performs several stages in predicting ethereum price movements, namely pre-processing, data normalization, training Long Short-Term Memory (LSTM) algorithms, and evaluating with Mean Square Error (MSE). Based on the results of this study, researchers succeeded in predicting the price of Ethereum using the multi-function activation based LSTM algorithm with testing parameters for the proportion of training data and testing data of 70%:30%, the number of sequences of 14 which describes data for 14 days, the hidden unit value of 64, the number of epochs of 150, and sigmoid as an activation function as evidenced by the MSE value of 0.0121..
Co-Authors Abdurrachman Bachtiar, Fitra Abel Filemon Haganta Kaban Achmad Basuki Achmad Ridok Adam Hendra Brata Adhi Setiawan Aditama, Gustian Agi Putra Kharisma Agus Wahyu Widodo Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Akbar, Alvin Tarisa Al Huda, Fais Aldi Fianda Putra Alfen Hasiholan Almasyhur, Muhammad Bin Djafar Alwan, Muhammad Fajrul Amin, Muhammad Basil Musyaffa Anarya Indika Putra Andina, Sherla Puspa Anggraheni, Hanna Shafira Annisa Sukmawati Apriyanti -, Apriyanti Ardhani, Luthfi Afrizal Ardhanto, Riyadh Ilham Arifandis Winata Arifien, Zainal Asmani, Wahayu Widyaning Austin, Yehezkiel Stephanus Bahrur Rizki Putra Surya Bana Falakhi Bayu Rahayudi Budi Darma Setiawan Caesar Rio Anggina Toruan Cahyo Prayogo, Cahyo Candra Dewi Cevita Detri Intan Suryaningrum Chindy Aulia Sari Christopher, Juan Young Darmawan, Abizard Hashfi Darmawan, Hanif Daud, Nathan Daut Daman Dewa Gede Trika Meranggi Dhaifullah, Afif Naufal Dhifan Diandra H Didik Suprayogo Dytha Suryani Edy Santoso Edy Santoso Elmira Faustina Achmal Eriq Muhammad Adams Jonemaro Fadhil Yusuf Rahadika Fadhil Yusuf Rahadika Fadhil Yusuf Rahadika Fahmi Achmad Fauzi Fajrina, Julia Nur Fathina Atsila F Fauzi, Muhammad Rifqi Firhan Fauzan Hamdani Fitra Abdurrachman Bachtiar Griselda Anjeli Sirait Griselda Anjeli Sirait Hafshah Durrotun Nasihah Hakim, Gibran Hakim, Sulthan Abiyyu Hanum, Assyfa Rasida Haris, Asmuni Harlan, Fajri Rayrahman Hawari, Rahmada Zulvia Azzahra Hermanto, Putri Tsania Maulidia Heru Nurwarsito Huda, Fais Al Hutamaputra, William Ikhwanul Kiram, Muh Zaqi Imam Cholissodin Indriati Indriati Iqra Ilhamsyah Irfan Ardiansyah Irfannanto, Adimas Irfano, Haikal Irwanto, M. Sofyan Izzatul Azizah Jauhar Bariq Rachmadi Javier Ardra Figo Karina Amadea Katrina Puspita Kevin Nadio Dwi Putra Khalid Rahman Khoirullah, Habib Bahari Krisnabayu, Rifky Yunus Kurnia Fakhrul Izza Kurnianingtyas, Diva Lailil Muflikhah Laksono, Khansa Salsabila Sangdiva Larasati, Saqina Salsabila Lutfi, Raniyah Mahardika, Mohammad Alfiano Rizky Manurung, Daniel Geoffrey Marasitua, Wahyu Valentino Marji Marpaung, Veronika Oktafia Maulana Ahmad Maliki Maulana, Muhammad Taufik Mawarni, Marrisaeka Meilinda Dwi Puspaningrum Michael David Muh. Arif Rahman Muhammad Rizaldi Muhammad Rizaldi Muhammad Tanzil Furqon Muhammad Zaini Rahman Natanniel Eka Christyanto Naufal, Muhammad Jilan Niluh Putu Vania Dyah Saraswati Nisa, Lisa N. Nisa, Septia Khoirin Novianti, Siska Nurannisa, Nadhira Oakley, Simon Pangondian, Yosia Permadhi, Raditya Atmaja Satria Pinasthika, Mohammad Ryan Prais Sarah Kayaningtias Prasetia, Anugrah Prayata, Rakan Fadhil Putra Pandu Adikara Putra, Octo Perdana Putri, Rania Aprilia Dwi Setya Putri, Salwa Cahyani Qurrata Ayuni Rahmadi, Anang Bagus Rahman, Muhammad Arif Raihan Hanif F RAMADHAN, ADITYA RIZKY Randy Cahya Wihandika Renata Rizki Rafi' Athallah Rian Nugroho Rilinka Rilinka Rishani Putri Aprilli Rizal Setya Perdana Rizky, Audhinata Bebytama RR. Ella Evrita Hestiandari Sabriansyah Rizqika Akbar, Sabriansyah Sahirah, Rafifa Addin Saputra, Kylix Eza Sastomo, Yogi Puji Selle, Nurfatima Setyawan Purnomo Sakti Sholeh, Mahrus Stephen Lui, Michael Sugihdharma, Joseph Ananda Sukma, Lintang Cahyaning Sulthon Akhdan G Suprapto Suprapto Sutrisna, Naufal Putra Syafira, Putri Amanda Tampubolon, Agustinus Parasian Thiodorus, Gustavo Timothy Bastian Sianturi Usfita Kiftiyani Vasya, Muhammad Azka Obila Wa Ode May Zhara Averina Wahyu Taufiqurrahman, Rayhan Waludi, Ikbal Wayan Firdaus Mahmudy Wulandari, Rafifah Ayud Yuita Arum Sari Yuita Arum Sari Zetha, Ivykaeyla Adriana