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All Journal Majalah Ilmiah Teknologi Elektro Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Transformatika JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Knowledge Engineering and Data Science JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING DoubleClick : Journal of Computer and Information Technology Journal of Information Technology and Computer Engineering JURIKOM (Jurnal Riset Komputer) Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Jurnal Riset Informatika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Building of Informatics, Technology and Science JTIM : Jurnal Teknologi Informasi dan Multimedia RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Informatics and Vocational Education Teknika ICTEE (Engineering Journals of Information, control, telecommunication and electrical) Insyst : Journal of Intelligent System and Computation Journal of Dinda : Data Science, Information Technology, and Data Analytics IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Embedded Systems, Security and Intelligent Systems El-Mujtama: Jurnal Pengabdian Masyarakat Majalah Ilmiah Teknologi Elektro JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Faisal Dharma Adhinata
Institut Teknologi Telkom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Mendeteksi Kematangan Pada Buah Mangga Garifta Merah Dengan Transformasi Ruang Warna HSI Ahmad Muslih Syafi’i; Muhammad Fajar Ahadi; Muhammad Iqbal Rasyid; Faisal Dharma Adhinata; Apri Junaidi
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i2.3217

Abstract

Garifta Mango is obtained from the combination of the best quality local mangoes. Garifta mango is said to have a sweeter taste variant than the quality of other types of mango. However, when choosing Red Garifta mangoes with a good level of ripeness, we are often confused. Sometimes Red Garifta mango entrepreneurs still use manual methods to distinguish the ripeness of Red Garifta mangoes. Therefore, this study carried out a systematic design using the HSI color space transformation method. We used 15 Red Garifta mangoes as test data and 30 Red Garifta mangoes as training data in the testing phase. After doing the test, we get the accuracy, precision, and recall of 15 test data, respectively 80%, 80%, and 87%. From this percentage value, it can be concluded that the method we use can be used to detect the ripeness of the Red Garifta mango fruit.
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.
DIAGNOSIS SYSTEM IN CHICKEN DISEASE USING FORWARD CHAINING METHOD Annisaa Utami; Faisal Dharma Adhinata; Yaqutina Marjani Santosa
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.2022.3.3.179

Abstract

Chickens are animals that are mostly kept by the community both on a large and small scale traditionally. Because the population is large, the disease is also more complex, from mild disease to diseases that can be transmitted to humans so that it can cause death such as bird flu. Diseases in chickens such as Newcastle Disease (ND), Infectious Bronchitis (IB), Gumboro Disease and Flu. Problems about chicken disease faced requires a system to help in diagnosing diseases in chickens. Expert system is a system in which a variety of knowledge comes from an expert so that users can consult. Forward Chaining method is a method of searching or forward-looking where tracking begins with information that da and combine rules to produce an expected conclusion or goal. The study used 9 disease data, 34 symptom data and 20 cases of chicken disease. The results of the accuracy test get a value of 90%. The purpose of this study is: diagnosing chicken disease as a first step to applying artificial intelligence in the medical world, designing and applying systems. Based on the data, it can be concluded that the protythepe expert system by implementing the Forward Chaining Method can help farmers and chicken owners in diagnosing chicken diseases.
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.
Aplikasi Klasifikasi SMS Berbasis Web Menggunakan Algoritma Logistic Regression Fitran Dwi Pramakrisna; Faisal Dharma Adhinata; Nia Annisa Ferani Tanjung
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.v11i2.466

Abstract

Jenis SMS spam adalah jenis pesan teks yang tidak diinginkan atau tidak diminta yang dikirim ke ponsel pengguna, seringkali untuk tujuan komersial. Untuk mengatasi masalah spam, diperlukan teknik untuk memilah kata atau kalimat termasuk spam atau bukan spam. Pada penelitian ini diusulkan menggunakan machine learning untuk mengklasifikasikan pesan mana yang spam dan mana yang tidak spam. Data yang digunakan pada penelitian ini terdiri dari 1140 pesan, dimana sudah diberi label 0 untuk pesan yang tidak spam dan 1 untuk pesan yang spam. Algoritma yang digunakan untuk kasus ini adalah Logistic Regression. Hasil penelitian menunjukkan model memiliki tingkat akurasi untuk mengklasifikasi pesan, sebesar 97%. Aplikasi yang dikembangkan untuk menerapkan hasil pemodelan machine learning menggunakan bentuk sebuah website sederhana dengan bantuan Flask framework dari Python. Hasil akhir dari aplikasi ini adalah model machine learning yang dapat dibuka melalui website.
Pengenalan Jenis Kelamin Manusia Berbasis Suara Menggunakan MFCC dan GMM Faisal Dharma Adhinata; Diovianto Putra Rakhmadani; Alon Jala Tirta Segara
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 1 No 1 (2021): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (686.041 KB) | DOI: 10.20895/dinda.v1i1.198

Abstract

Biometric information that exists in humans is unique from one human to another. One of the biometric data that is easily obtained is the human voice. The human voice is identic data that can differentiate between individuals. When we hear human voices directly, it is easy for our ears to tell the person who is speaking is male or female. But sometimes male voices can resemble girls and vice versa. Therefore, we propose a human voice detection system through Artificial Intelligence (AI) in machine learning. In this study, we used the Mel Frequency Cepstrum Coefficients (MFCC) method to extract human voice features and Gaussian Mixture Models (GMM) for the classification of female or male voice data. The experiment results showed that the system built was able to detect human gender through biometric voice data with an accuracy of 81.18%.
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.
Co-Authors Abdul Majid Abdurrahman Ibnul Rasidi Adam Nur Kridabayu Adil El-Faruqi Aditya Wijayanto Afzal Ziqri Ahmad Muslih Syafi’i Ajeng Fitria Rahmawati Akhmad Jayadi Aldhan Tri Maulana Alfan Adi Chandra Alissyah Putri Alon Jala Tirta Segara Alya Aulia Hanafi Ananda Aulia Rizky Ananda Aulia Rizky Andra Aulia Rizaldy Anshari Rusmeniar R.A Apri Junaidi, Apri Arief Rais Bahtiar Arif Amrulloh Ariq Cahya Wardhana Bagus Bayu Sasongko Christoph Quix Christyan, Timothy Condro Kartiko Dani Azka Faz Darmawan, Bagus Tri Yulianto Dayal Gustopo Setiadjit Dian Nugraha Diovianto Putra Rakhmadani Emmanuel Genesius Evan Devara Fadlan Raka Satura Fajar Malik Falah Arfani Fauzi, Muhammad Dzulfikar Fawwaz Muhammad Zulfikar Febry Ardiansyah Firdonsyah, Arizona Fitran Dwi Pramakrisna Fitran Dwi Pramakrisna Gilang Aditia GITA FADILA FITRIANA Gracia Rizka Pasfica Herman Yuliansyah Ibnul Rasidi, Abdurrahman Ikadhanny Yudyan Pratama Irsyad Zulfikar Jahfal Rizqi Putra Pradhana Kridabayu, Adam Nur M Alfian Maulana Al Azhar Merlinda Wibowo Metha Khafifah Isty Rikhanah Mohammad Rifqi Zein Muhammad Arif Saputra Muhammad Fajar Ahadi Muhammad Ikhsan Muhammad Iqbal Rasyid Muhammad Pajar Kharisma Putra Narantyo Maulana Adhi Nugraha Naseh Hibban Nasution, Annio Indah Lestari Nia Annisa Ferani Tanjung Nike Prasetyo Nisrina Eka Salsabila Novi Rahmawati Novi Rahmawati Nugraha, Narantyo Maulana Adhi Nur Ghaniaviyanto Ramadhan Nur Syahela Hussien Nursatio Nugroho Pasaribu, Yolanda Al Hidayah Purnama Dileon Yamora Nainggolan Putra, Muhammad Daffa Arviano Rachma Wukir Purwitasari Rahardian, Reva Rahmanda Trinova Putra Renna Nur Injiyani Retno Hendrowati Reva Rahardian Rifki Adhitama, Rifki Rifqi Akmal Saputra Rifqi Akmal Saputra Rifqi Alfinnur Charisma Rival Fahmi Hidayat Rizki Rafiif Amaanullah Rohman Beny Riyanto Saputro, Satria Nur Satria Adi Nugraha Sayyid Yakan Khomsi Pane Sofiyudin Pamungkas Teguh Rijanandi Teguh Rijanandi Teguh Rijanandi Tri Dimas Cipto Satrio Wibowo Try Susanto Ummi Athiyah Utama, Safitri Yuliana Utami, Annisaa Vincent Nathaniel Wahyono Wahyono Widi Widayat Wijayanto, Danur Winanto, Tawang Sahro Yaqutina Marjani Santosa Yohani Setiya Rafika Nur Yolanda Al Hidayah Pasaribu Yuni nur fari'ah Zanuar Rahmat Saputra Ziqri, Afzal