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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Nur Ghaniaviyanto Ramadhan
Institut Teknologi Telkom Purwokerto
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering

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Context-Aware Smart Door Lock with Activity Recognition Using Hierarchical Hidden Markov Model Aji Gautama Putrada; Nur Ghaniaviyanto Ramadhan; Maman Abdurohman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 1, February 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (806.27 KB) | DOI: 10.22219/kinetik.v5i1.904

Abstract

Context-Aware Security demands a security system such as a Smart Door Lock to be flexible in determining security levels. The context can be in various forms; a person’s activity in the house is one of them and is proposed in this research. Several learning methods, such as Naïve Bayes, have been used previously to provide context-aware security systems, using related attributes. However conventional learning methods cannot be implemented directly to a Context-Aware system if the attribute of the learning process is low level. In the proposed system, attributes are in forms of movement data obtained from a PIR Sensor Network. Movement data is considered low level because it is not related directly to the desired context, which is activity. To solve the problem, the research proposes a hierarchical learning method, namely Hierarchical Hidden Markov Model (HHMM). HHMM will first transform the movement data into activity data through the first hierarchy, hence obtaining high level attributes through Activity Recognition. The second hierarchy will determine the security level through the activity pattern. To prove the success rate of the proposed method a comparison is made between HHMM, Naïve Bayes, and HMM. Through experiments created in a limited area with real sensed activity, the results show that HHMM provides a higher F1-Measure than Naïve Bayes and HMM in determining the desired context in the proposed system. Besides that, the accuracies obtained respectively are 88% compared to 75% and 82%.
Comparison of Supervised Learning Methods for COVID-19 Classification on Chest X-Ray Image Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Arif Amrulloh; Arief Rais Bahtiar
CommIT (Communication and Information Technology) Journal Vol. 16 No. 2 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i2.7970

Abstract

The Coronavirus (COVID-19) pandemic is still ongoing in almost all countries in the world. The spread of the virus is very fast because the transmission process is through air contaminated with viruses from COVID-19 patients’ droplets. Several previous studies have suggested that the use of chest X-Ray images can detect the presence of this virus. Detection of COVID-19 using chest X-Ray images can use deep learning techniques, but it has the disadvantage that the training process takes too long. Therefore, the research uses machine learning techniques hoping that the accuracy results are not too different from deep learning and result in fast training time. The research evaluates three supervised learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Random Forest, to detect COVID-19. The experimental results show that the accuracy of the SVM method using a polynomial kernel can reach 90% accuracy, and the training time is only 462 ms. Through these results, machine learning techniques can compensate for the results of the deep learning technique in terms of accuracy, and the training process is faster than the deep learning technique. The research provides insight into the early detection of COVID-19 patients through chest X-Ray images so that further medical treatment can be carried out immediately.
Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata; Alon Jala Tirta Segara; Diovianto Putra Rakhmadani
JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i2.3979

Abstract

Fake news is information that is presented incorrectly or falsely. Of course, if the spread of fake news continues, it can result in wrong knowledge of the information obtained. One of the efforts to prevent the spread of fake news is by detecting whether the news is genuine or fake in order to provide an explanation to the readers of the related news. This study aims to detect fake news using a supervised learning random forest model. The news dataset used contains 6256 rows of titles that have a fake or real class. The dataset first goes through a cleaning, tokenization, and stemming process to break sentences into words. The results obtained using the random forest model of 84%, this result is higher than using the logistic regression model of 77%.
Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata
Indonesian Journal of Electrical Engineering and Computer Science Vol 26, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v26.i3.pp1765-1772

Abstract

Since the Coronavirus disease 2019 (COVID-19) pandemic hit the world, it had a significant negative impact on individuals, governments, and the global economy. One way to reduce the negative impact of COVID-19 is to vaccinate. Briefly, vaccination aims to enable the formed immune system to remember the characteristics of the targeted viral pathogen and be able to initiate an immune response that is rapid and strong enough to defeat future live viral pathogens. However, there are still many people in the world who are anti-vaccine. This certainly greatly hampers the process of accelerating the formation of the body's immune system widely in the community. Anti-vaccine people can be found on various social media platforms. Twitter was chosen as the data source because twitter is a common source of text for sentiment analysis. This study aims to analyze public sentiment on the COVID-19 vaccine through twitter in the form of tweets and retweets. This study uses the Gaussian Naïve Bayes method to see the results of the classification of sentiment analysis. The results obtained based on experiments prove that the Gaussian Naïve Bayes method can produce an average accuracy of 97.48% for each vaccine dataset used.
Pendekatan Deep Learning Untuk Prediksi Durasi Perjalanan Nur Ghaniaviyanto Ramadhan; Yohani Setiya Rafika Nur; Faisal Dharma Adhinata
Teknika Vol 11 No 2 (2022): Juli 2022
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v11i2.460

Abstract

Setiap orang dalam kehidupan memiliki kecenderungan untuk berpindah dari satu tempat ke tempat lainnya. Perpindahan tersebut dapat dilakukan dengan menggunakan berbagai macam cara seperti menggunakan transportasi pribadi atau umum (bus, taksi, pesawat, dan kereta api), Pada perkembangan teknologi saat ini mode transportasi sudah semakin canggih. Akan tetapi masih ada mode transportasi yang belum modern misalnya seperti taksi, dimana salah satunya tidak dapat memprediksi lama waktu perjalanan. Meskipun sudah ada taksi yang berbasis online seperti Uber, akan tetapi masih banyak taksi yang belum berbasis online sehingga tidak bisa dilakukan estimasi waktu dan jarak. Permasalahan di atas dapat diselesaikan dengan cara melakukan pendekatan berbasis pembelajaran mesin. Salah satu keuntungan yang didapatkan jika kita dapat mengetahui lama waktu estimasi perjalanan yaitu dapat mengatur waktu perjalanan sesuai dengan rutinitas yang sedang dikerjakan ataupun juga dapat menghemat biaya yang dikeluarkan dengan mengetahui jarak yang akan dijalankan. Pada penelitian ini bertujuan untuk memprediksi durasi perjalanan pada dataset New York taxi trip duration menggunakan pendekatan deep learning yaitu Long Short Term Memory Reccurent Neural Network (LSTM-RNN). Eksperimen dilakukan dengan melakukan tuning parameter terkait seperti epoch, nilai dropout, dan neurons. Pengukuran hasil menggunakan nilai Root Mean Square Error (RMSE) dan nilai loss. Hasil yang didapatkan menggunakan model LSTM-RNN sebesar 0,0012 untuk nilai loss dan RMSE 0,4.
Perancangan UI/UX Webinar Booking Terhadap Kepuasan Pengguna Menggunakan Metode Design Thinking Rachma Wukir Purwitasari; Purnama Dileon Yamora Nainggolan; Novi Rahmawati; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan
JURIKOM (Jurnal Riset Komputer) Vol 8, No 6 (2021): Desember 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v8i6.3700

Abstract

In order to meet user needs, designing an exemplary user interface requires in-depth knowledge of design principles and guidelines and an understanding of the multi-component design space. Multi-components are the image parts that can be used, their layout options, and visual effects options. This research was conducted to meet user needs. With the Design Thinking method, we can find out the user's needs and adjust to the user's interests. With the Webinar Booking application, it is hoped to be a solution for today's life
A Hybrid DenseNet201-SVM for Robust Weed and Potato Plant Classification Muhammad Dzulfikar Fauzi; Faisal Dharma Adhinata; Nur Ghaniaviyanto Ramadhan; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23886

Abstract

Potato plant growth needs to be protected from weeds that grow around it. Currently, the manual spraying of pesticides by farmers is not only precise on weeds but also on cultivated plants. Therefore, we need an intelligent system that can appropriately classify potato plants and weeds. The research contribution combines feature extraction and appropriate classification methods to obtain optimal accuracy. In addition, the small amount of data also contributes to this research. In this research, it is proposed to use a combination of feature extraction using deep learning techniques and classification using machine learning. We use the feature extraction method with the DenseNet201 model because this study's data is not too much. Complex vectors from DenseNet201 were reduced using Principal Component Analysis (PCA). Then we classified it with the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classification methods. The experimental results show that the PCA method can reduce the complexity of high-dimensional features into 2 and 3 dimensions. The average of the best classification results using SVM was obtained with a 3-dimensional PCA configuration, but on the contrary, using KNN obtained the best results in a 2-dimensional PCA configuration. The results showed 100% accuracy on the DenseNet201-SVM hybrid. The SVM kernel configuration used is a linear kernel. The results of this study can be an insight into an accurate classification method for separating weeds and potatoes so that agricultural technology can apply this method for classification.
Implementasi Website Rahayu River Tubing sebagai Media Promosi dan Reservasi bagi Wisatawan Faisal Dharma Adhinata Adhinata; Diovianto Putra Rakhmadani; Alon Jala Tirta Segara; Nur Ghaniaviyanto Ramadhan
Madani : Indonesian Journal of Civil Society Vol. 4 No. 2 (2022): Madani, Agustus 2022
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v4i2.1439

Abstract

Community service aims to implement website-based information technology as a media for promoting Rahayu River Tubing tourism and its reservation. The attractions offered are Tubing, Pangasinan Waterfall, Pengkol Hill, and Pedestal View. Before this website, promotions were only carried out on social media, and reservations were still manual by contacting the manager directly. This community service method is by identifying problems, developing the Rahayu River Tubing website at the address http://rahayurivertubing.com, and evaluating activities. This activity was attended by 15 participants from the manager and tour guide held at the Rahayu River Tubing basecamp. At the end of the activity, feedback is given to evaluate this community service activity. There are five statements on the questionnaire with an average result of 99%. These results indicate that community service partners are delighted with the socialization and training on the use of the Rahayu River Tubing tourism website.
Analisis Penerapan Metode Ensembled Learning Decision Tree Pada Klasifikasi Virus Hepatitis C Rifqi Alfinnur Charisma; Sofiyudin Pamungkas; Rifqi Akmal Saputra; Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2064

Abstract

Hepatitis C virus is a deadly virus that attacks the liver. This virus can cause chronic infections, even 80% of sufferers have experienced an illness. To minimize the risk of exposure to disease caused by the hepatitis C virus, consultation with a doctor or using an intelligent detection system can be conducted. Of course, if used a smart strategy, our need data that already contains parameters related to hepatitis C. This study uses a public dataset that the public can access. So, the purpose of this study is to classify patients with hepatitis C virus using a tree-based algorithm. The results obtained by applying the proposed algorithm are 93% accuracy, 92% precision, and 91% recall. This study also performs comparisons with other methods, namely naive bayes. The results show that the tree-based way is superior.
Perancangan Basis Data Menggunakan Normalisasi Tabel Pada Perusahaan Dagang Barokah Abadi Sayyid Yakan Khomsi Pane; Nur Ghaniaviyanto Ramadhan; Faisal Dharma Adhinata
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 2 No 2 (2022): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v2i2.563

Abstract

Many human activities are related to information systems. Not only in developed countries, in Indonesia, information systems have been widely applied everywhere, such as in offices, supermarkets, airports, and even at home when users interact with the internet. Increased company operations in business activities can not be separated from information technology. The use of information technology is one of the effective steps in data processing, as well as business transactions using increasingly sophisticated computer equipment. A good database design plays a very important role in the performance and smooth running of an agency. So, in this research, a database design will be carried out with table normalization using MySQL at the Barokah Abadi trading company. This research also designs using entity relationship diagram (ERD).
Co-Authors Abdurrahman Ibnul Rasidi Adil El-Faruqi Adiwijaya Agung Toto Wibowo Agus Supriatman Aji Gautama Putrada Alfian Akbar Gozali Alon Jala Tirta Segara Ananda Aulia Rizky Arief Rais Bahtiar Arif Amrulloh Azka Khoirunnisa Christoph Quix Diovianto Putra Rakhmadani Diva Angelica Enjeli Cistia Sukmawati Faisal Dharma Adhinata Fauzi, Muhammad Dzulfikar Hashimoto, Takako Hilda Fahlena Imelda Atastina Kridabayu, Adam Nur Kurnianingsih Kurnianingsih, Kurnianingsih Lintang Suryaningrum Maman Abdurohman Merlinda Wibowo Mohammad Fikri Nur Syahbani Nabilah Sharfina Nia Annisa Ferani Tanjung Nicolaus Euclides Wahyu Nugroho Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Fatin Liyana Mohd Rosely Purnama Dileon Yamora Nainggolan Purwitasari, Rachma Wukir Rachma Wukir Purwitasari Rahardian, Reva Ramadhan, Teguh Ikhlas Rifqi Akmal Saputra Rifqi Alfinnur Charisma Salsabila, Nisrinia Eka Sayyid Yakan Khomsi Pane Sigit Pramono Sofiyudin Pamungkas Warih Maharani Wibowo, Merlinda Yohani Setiya Rafika Nur