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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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Klasifikasi Jenis Tanaman Tembakau di Indonesia menggunakan Naive Bayes dengan Seleksi Fitur Information Gain Fahmi Achmad Fauzi; Muhammad Tanzil Furqon; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 2 (2021): Februari 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Tobacco plants are plantation products but not food crops, their leaves are usually used as the main ingredient in the making of cigarettes and cigars. Tobacco cultivation has been known for a long time in Indonesia, the cultivation of tobacco from generation to generation has resulted in the emergence of many new varieties in various regions in Indonesia. The number of tobacco varieties can be grouped by cultivation and type. The large number of tobacco varieties makes it difficult for farmers to distinguish the types of tobacco plants because the morphology and biology between tobacco plants are almost similar, so to make it easier to determine the type of tobacco plants, a system with a classification method is needed. One of the classification methods is the Naive Bayes algorithm. In this study, 11 classes were used and 19 features were used. In addition to classification, the feature selection method is also used to get a good combination of features and accuracy values, Information Gain used as the feature selection method. In the evaluation, the K-fold cross validation method is used to eliminate doubts on the data with k = 10. The result of all the tests carried out, the highest average accuracy for all balanced class tests was 52.72% using 17 features. Meanwhile, the highest accuracy of all unbalanced class tests is 64.06% when using 15 features.
Analisis Sentimen Penghapusan Ujian Nasional pada Twitter menggunakan Document Frequency Difference dan Multinomial Naive Bayes Rilinka Rilinka; Indriati Indriati; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 3 (2021): Maret 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

On Twitter, there was one topic that being discussed, it was about the new 2020 curriculum, elimination of The National Examination, a policy from Minister of Education and Culture of Indonesia, Mr. Nadiem Makariem. Public opinions on Twitter are matters as references for evaluating that policy on improving services from Ministry of Education and Culture of Indonesia (KEMENDIKBUD). That was why this research was conducted by analyzing the sentiment of Twitter users' opinions through tweets that they have sent about that policy and classifying it into two classes, there were positive and negative classes. The analysis sentiment consisted pre-processing, Document Frequency Difference (DFD) features selection, and Multinomial Naive Bayes classifier. The test consisted the amount of training data and testing data, it showed the best average accuracy using 600 training data and 200 testing data, was 72%. Then, the DFD testing showed the best result at threshold equal to 0.5, was 73.13%.
Analisis Sentimen Aplikasi E-Goverment berdasarkan Ulasan Pengguna menggunakan Metode Maximum Entropy dan Seleksi Fitur Mutual Information Abel Filemon Haganta Kaban; Indriati Indriati; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 4 (2021): April 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Mobile JKN is an e-government application that is used by the government as an innovative use of information and communication technology in government administration. Mobile JKN is an application as part of BPJS Kesehatan's commitment in providing services and easy access for BPJS Kesehatan users. Indonesians can try using the Mobile JKN application and review it on application providers such as the Google Play Store and the App Store. Reviews as a way of expressing opinions, offer data sources in the form of user sentiments regarding the features and services available on the Mobile JKN application. Sentiment analysis research is conducted to analyze the sentiments contained in user reviews by classifying reviews as positive or negative. In this research, Maximum Entropy is used as a classification method with Mutual Information as a feature selection to reduce the number of features used in the classification of user reviews of the Mobile JKN application. In testing, better evaluation results are shown by the use of the Mutual Information feature selection in the classification using Maximum Entropy with an accuracy value obtained of 82.5% compared to without the use of feature selection which results in an accuracy of 79.5%.
Klasifikasi Aritmia Dari Hasil Elektrokardiogram Menggunakan Metode Support Vector Machine Qurrata Ayuni; Randy Cahya Wihandika; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 6 (2021): Juni 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Arrhythmia (heart rhythm disorder) is a disorder of the cardiac elektrophysology caused by disruption of the conduction system as well as impaired formation and delivery of electrical impulses. Some factors that influence arrhythmia include age, blood pressure, height and weight. This arrhythmia can be recognized by using a cardiac record or electrocardiogram (ECG). Numerical data generated by ECG has many features that are not easily processed manually. Computer assistance with certain machine learning techniques can be used to automatically recognize diseases. One method of machine learning is support vector machine (SVM). In this study, a system was designed to classify arrhythmias using support vector machine (SVM) methods. The most optimal accuracy value or accuracy that has the highest value indicates that the system is in accordance with expectations, so that the support vector machine method is able to measure the accuracy of the classification of arrhythmias based on electrocardiogram results with RBF kernel function of 92%.
Penerapan Model Residual ConvNet dengan Augmentasi Citra untuk Klasifikasi Ekspresi Wajah Manusia Fadhil Yusuf Rahadika; Novanto Yudistira; Yuita Arum Sari
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID-19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model.
Implementasi Progressive Web Application dan Framework CodeIgniter pada Sistem Informasi Lomba Karya Tulis Ilmiah Mahasiswa Anarya Indika Putra; Agi Putra Kharisma; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 9 (2021): September 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The efficiency and effectiveness of information dissemination and communication are becoming increasingly important today. Centralization of dispersed information to cut effort and time is one way to streamline and make communication and information dissemination more effective. The centralization of information can be applied to the field of student scientific writing competitions by creating an Information System for Student Scientific Writing Contest which acts as an information center. The information center is developed in the form of a web because it is versatile and the development process will be more efficient. CodeIgniter framework and progressive web application (PWA) are implemented on the developed system. The lightweight CodeIgniter framework assisted by the use of caching in PWA is expected to provide good performance in terms of page load time. The versatility of web-based systems will be optimized by implementing PWA that allows native application features to be implemented. In this study, performance testing was carried out to provide an overview of system performance. This study produces a web-based system that implements the CodeIgniter and PWA frameworks with 100% validity in unit testing, integration, and validation. Performance tests show performance gains and losses against various page load time metrics. Performance metrics of the first byte, first contentful paint, speed index, and document complete time improve on first and repeat visits. The performance of the fully-loaded time metric increases on the first visit and decreases on repeat visits.
Klasifikasi Emosi berdasarkan Ekspresi Wajah menggunakan Support Vector Machine dan Ekstraksi Fitur Local Binary Pattern Kurnia Fakhrul Izza; Fitra Abdurrachman Bachtiar; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 13 (2021): Publikasi Khusus Tahun 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Untuk dipublikasikan di Jurnal Teknologi dan Sistem Komputer (JTSiskom)
Analisis Sentimen Tweet COVID-19 menggunakan Word Embedding dan Metode Long Short-Term Memory (LSTM) Muhammad Zaini Rahman; Yuita Arum Sari; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 11 (2021): November 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Government policies related to quarantine have generated various responses from the community, some people feel that The quarantine must be done so that the spread of the COVID-19 disease can be suppressed, but others also feel that this is detrimental to the community because their activities are being limited, this response can be found in their Twitter post. By analyzing the sentiments on people's Twitter posts, we can conclude whether a policy tends to get more positive or negative responses to the affected community. To carry out this analysis, deep learning method is used, namely Long-Short Term Memoryf (LSTM) with the addition of Word Embedding to 1364 independently crawled Indonesian people's Twitter data. Performance using the LSTM method produces 81% accuracy, 80% precision, 80% recall, and 81% f-measure. This LSTM method produces better performance than the other 2 methods, namely Naive Bayes and Recurrent Neural Network (RNN) with a difference of + 8%, with details of 74% accuracy, 72% precision, 74% recall, and 69% f-measure for the Naive Bayes method and 71% accuracy, 71% precision, 72% recall, and 72% f-measure for the RNN method.
Pengembangan Aplikasi Manajemen Tugas berbasis Android (Studi pada CV. Cheleron Production) Iqra Ilhamsyah; Agi Putra Kharisma; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 5 No 12 (2021): Desember 2021
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Management is an important sector in performing a task. One of the agencies that use management to perform a task is CV. Cheleron Productions. Currently CV. Cheleron Production has several problems in task management which lie in the absence of a place for integration of existing job data, no monitoring of work progress, no task scheduling, and no special format for work. Therefore, it takes a task management application that is expected to help CV. Cheleron Production solves these problems. This application can be developed using the Software Development Life Cycle (SDLC) Waterfall Model using an Object-oriented (OO) approach to the analysis and design process. Development is carried out with an Android-based implementation using the Java programming language. Testing is done by unit testing, integration testing, and validation testing. Unit testing is carried out using one of the White-box testing methods, namely Basis Path Testing, integration testing is carried out using Top-down Integration Testing, and validation testing is carried out using the Black-box method.
Klasifikasi Aktivitas Manusia menggunakan Algoritme Fuzzy Learning Vector Quantization (FLVQ) dengan Reduksi Dimensi Principal Component Analysis (PCA) Katrina Puspita; Fitra Abdurrachman Bachtiar; Novanto Yudistira
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 6 No 1 (2022): Januari 2022
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Human Activity Recognition is a popular research topic which aims to identify human activities using sets of data obtained with the help of sensor or image recorder. Research results regarding human activity recognition are widely used for military, health, and security purposes. Previously, research has been conducted regarding human activity recognition using Learning vector quantization (LVQ), but this method has several weaknesses which are LVQ accuracy rate rely heavily on the pre-processing of the data, sensitive to overlapping dataset, and requires a long computational time, in which these problems affect the performance of LVQ algorithm. One of the methods used to solve these problems is Fuzzy learning vector quantization (FLVQ) with dimensionality reduction Principal Component Analysis (PCA). FLVQ algorithm is a development from LVQ algorithm, where it uses batch of Fuzzy C-Means and LVQ in its calculations. The research was conducted using various tests on the parameters and the number of classes to be used in the classification stage. The highest accuracy was obtained from the classification using 2 classes, which gained 52.11% in accuracy with 30 features out of the total features (561 features) being used.
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