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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Madaniya Jurnal Teknik Informatika (JUTIF) Makara Journal of Science Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics

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Classification of Natural Disaster Reports from Social Media using K-Means SMOTE and Multinomial Naïve Bayes Nor Indrani; Mohammad Reza Faisal; Irwan Budiman; Dwi Kartini; Friska Abadi; Septyan Eka Prastya; Mera Kartika Delimayanti
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 7 No 1 (2023): June 2023
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v7i1.503

Abstract

Disasters can occur anytime and anywhere. Floods and forest fires are two types of disasters that occur in Indonesia. South Kalimantan Province is an area that frequently experiences floods and forest fires. The dataset used for previous research's flood and forest fire disaster data is unbalanced. Unbalanced data conditions can complicate the classification method in carrying out the classification process. The sampling method for the data level approach that can be used to solve imbalance problems is oversampling, one of the derivatives of oversampling, namely SMOTE. The K-Means SMOTE method is a modification of SMOTE. One Naïve Bayes model often used in text classification is Multinomial Naïve Bayes. Multinomial Naïve Bayes has a good performance in classifying text. The research results on flood disaster data using K-Means SMOTE with Multinomial Naïve Bayes yielded an f1 score of 66.04%, and forest fire disaster data using K-Means SMOTE with Multinomial Naïve Bayes produced an f1 score of 66.31%.
Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network Muhammad Fauzan Nafiz; Dwi Kartini; Mohammad Reza Faisal; Fatma Indriani; Triando Hamonangan Saragih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26374

Abstract

COVID-19 disease is known as a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55%). Given the prevalence of coughing as a symptom, artificial intelligence has been employed as a means of detecting COVID-19 based on cough sounds. This study aims to compare the performance of six different Convolutional Neural Network (CNN) models (VGG-16, VGG-19, LeNet-5, AlexNet, ResNet-50, and ResNet-152) in detecting COVID-19 using mel-spectrogram images derived from cough sounds. The training and validation of these CNN models were conducted using the Virufy dataset. Audio data was processed to generate mel-spectrogram images, which were subsequently employed as inputs for the CNN models. The AlexNet model, utilizing an input size of 227x227, exhibited the best performance with the highest Area Under the Curve (AUC) value of 0.930303. This study provides compelling evidence of the efficacy of CNN models in detecting COVID-19 based on cough sounds through the utilization of mel-spectrogram images. Furthermore, the study underscores the impact of input size on model performance. The primary contribution of this research lies in identifying the CNN model that demonstrates the best performance in COVID-19 detection based on cough sounds. Additionally, this study establishes the fundamental groundwork for selecting an appropriate CNN methodology for early detection of COVID-19.
Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory Kevin Yudhaprawira Halim; Dodon Turianto Nugrahadi; Mohammad Reza Faisal; Rudy Herteno; Irwan Budiman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26354

Abstract

Gender classification by computer is essential for applications in many domains, such as human-computer interaction or biometric system applications. Generally, gender classification by computer can be done by using a face photo, fingerprint, or voice. However, researchers have demonstrated the potential of the electrocardiogram (ECG) as a biometric recognition and gender classification. In facilitating the process of gender classification based on ECG signals, a method is needed, namely Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM). Researchers use these two methods because of the ability of these two methods to deal with sequential problems such as ECG signals. The inputs used in both methods generally use one-dimensional data with a generally large number of signal features. The dataset used in this study has a total of 10,000 features. This research was conducted on changing the input shape to determine its effect on classification performance in the LSTM and Bi-LSTM methods. Each method will be tested with input with 11 different shapes. The best accuracy results obtained are 79.03% with an input shape size of 100×100 in the LSTM method. Moreover, the best accuracy in the Bi-LSTM method with input shapes of 250×40 is 74.19%. The main contribution of this study is to share the impact of various input shape sizes to enhance the performance of gender classification based on ECG signals using LSTM and Bi-LSTM methods. Additionally, this study contributes for selecting an appropriate method between LSTM and Bi-LSTM on ECG signals for gender classification.
IMPLEMENTASI SMOTE DAN EXTREME LEARNING MACHINES PADA KLASIFIKASI DATASET MICROARRAY Ivan Sitohang; Triando Hamonangan Saragih; Dwi Kartini; Radityo Adi Nugroho; Mohammad Reza Faisal
Jurnal Informatika Polinema Vol. 8 No. 4 (2022): Vol 8 No 4 (2022)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v8i4.1029

Abstract

Tumor otak merupakan salah satu penyakit penyebab kematian terbesar secara global. Banyak cara untuk mendeteksi penyakit tumor otak dengan cara pengambilan struktur DNA microarray pada protein tumor otak lalu melakukan klasifikasi dengan menggunakan machine learning. Hasil penelitian ini adalah untuk mengetahui keakuratan dalam pengklasifikasian tumor otak dengan menggunakan metode Extreme Learning Machines dengan dan tanpa menggunakan oversampling SMOTE pada keseluruhan data. Performa kinerja klasifikasi tertinggi setiap model antara lain model Extreme Learning Machines mendapatkan akurasi sebesar 97.43% pada hidden neuron = 500. Lalu Extreme Learning Machines menggunakan oversampling SMOTE pada keseluruhan data menghasilkan akurasi sebesar 92.30% pada hidden neuron = 200. Pada penelitian ini didapatkan bahwa penggunaan hidden neuron serta penyeimbangan data pada klasifikasi data microarray sangat berpengaruh dalam akurasi yang akan didapatkan dalam penelitian ini.
Gender Classification of Twitter Users Using Convolutional Neural Network Fitra Ahya Mubarok; Mohammad Reza Faisal; Dwi Kartini; Dodon Turianto Nugrahadi; Triando Hamonangan Saragih
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 1 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i1.3318

Abstract

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
IMPLEMENTASI ALGORITMA GENETIKA UNTUK SELEKSI FITUR PADA KLASIFIKASI GENRE MUSIK MENGGUNAKAN METODE RANDOM FOREST Nurlatifah Amini; Triando Hamonangan Saragih; Mohammad Reza Faisal; Andi Farmadi; Friska Abadi
Jurnal Informatika Polinema Vol. 9 No. 1 (2022): Vol 9 No 1 (2022)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i1.1028

Abstract

Musik memiliki jenis yang beragam di Dunia. Adapun jenis musik yang paling popular diantaranya yaitu pop, disco, country, dangdut, jazz, blues, reggae, hiphop, rock, metal. Penelitian ini mengenai Klasifikasi genre musik menggunakan metode Random Forest menggunakan dataset dari GitHub atau GTZAN tentang genre musik dengan jumlah label ada 10, memiliki 26 fitur dan jumlah keseluruhan data ada 1000. Penelitian ini dibagi menjadi beberapa tahap, yaitu dengan klasifikasi seluruh data, mengklasifikasi dengan data yang dinormalisasi, melakukan klasifikasi dengan data asli menggunakan tahap seleksi fitur Algoritma Genetika, dan mengklasifikasi pada data yang dinormalisasi dengan seleksi fitur menggunakan Algoritma Genetika. Parameter yang digunakan pada Algoritma Genetika yaitu menggunakan Probabilitas Crossover, Probabilitas Mutasi. Pada penelitian ini Min-Max digunakan untuk metode normalisasi data, dan untuk perhitungan akurasi menggunakan metode Confusion Matrix. Peformasi terbaik dari parameter GA untuk Pc dan Pm menggunakan kombinasi 0.5 dan 0.2. Performasi populasi size terbaik adalah 26 dan iterasi atau max generasi terbaik ada pada 100 iterasi. Akurasi yang dihasilkan ketika menggunakan seluruh data menghasilkan akurasi sebesar 62%, 59% dengan data yang dinormalisasi, 64% dengan semua data menggunakan seleksi fitur Algoritma Genetika dan didapatkan akurasi sebesar 67% dengan menggunakan seleksi fitur Algoritma Genetika yang datanya dinormalisasi. Hasil ini memberi pengetahuan nilai rata-rata akurasi menunjukkan peningkatan dengan diterapkannya seleksi fitur Algoritma Genetika.
PERBANDINGAN METODE EXTREME GRADIENT BOOSTING DAN METODE DECISION TREE UNTUK KLASIFIKASI GENRE MUSIK SALLY LUTFIANI; Triando Hamonangan Saragih; Friska Abadi; Mohammad Reza Faisal; Dwi Kartini
Jurnal Informatika Polinema Vol. 9 No. 4 (2023): Vol. 9 No. 4 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i4.1319

Abstract

Musik merupakan sebuah “bahasa” yang mampu dimengerti dan dipahami oleh semua orang. Dalam musik sendiri, terdapat banyak genre musik yang berkembang yang dipengaruhi oleh budaya dari daerah-daerah yang berbeda-beda, seperti musik jazz, reggae, pop, rock, punk, dan masih banyak lagi genre musik yang ada seperti musik tradisional. Bertambahnya jumlah musik dalam bentuk digital secara pesat menyebabkan pemberian label genre secara manual menjadi tidak efektif. Pemberian label genre secara otomatis dapat dilakukan dengan menerapkan algoritma kecerdasan buatan yaitu salah satunya klasifikasi yang dapat mengelompokkan jenis musik berdasarkan genre dengan menggunakan fitur-fitur musik. Salah satu metode klasifikasi yang cukup sering digunakan adalah metode Extreme Gradient Boosting. Metode ini seringkali digunakan karena kecepatan, efisiensi dan skalabilitasnya untuk memecahkan beragam masalah klasifikasi ataupun regresi. Selain itu ada juga metode yang sering digunakan dalam melakukan klasifikasi yaitu metode Decision Tree yang merupakan metode pohon keputusan mengubah fakta yang sangat besar menjadi pohon keputusan yang merepresentasikan aturan dan juga berguna untuk mengekplorasi data, menemukan hubungan tersembunyi antara sejumlah calon variabel input dengan sebuah variabel target. Karena kedua metode ini termasuk dalam rumpun keluarga pohon atau ensemble learning, maka dilakukan perbandingan antara kedua metode tersebut. Pada penelitian ini melakukan perbandingan tingkat akurasi metode klasifikasi Extreme Gradient Boosting dan metode klasifikasi Decision Tree dengan melakukan pengujian parameter menggunakan nilai parameter terbaik yang didapatkan. Berdasarkan hasil penelitian metode klasifikasi Extreme Gradient Boosting dengan pengujian parameter menggunakan nilai parameter terbaik yang didapatkan menghasilkan kinerja akurasi yang lebih baik dibandingkan dengan metode klasifikasi Decision Tree yaitu sebesar 72% karena pada metode Extreme Gradient Bossting ini mampu meminimalisir eror dengan menggunakan data residu atau kesalahan prediksi pada model sebelumnya sehingga bisa mendapatkan dan mengoptimalkan hasil akurasi terbaik, yang membuktikan bahwa metode klasifikasi Extreme Gradient Boosting lebih baik dibandingkan dengan metode klasifikasi Decision Tree dengan pengujian parameter.
Pengembangan Aplikasi Edukasi Pengenalan Pohon Berbasis Qr Code Scanner Friska Abadi; Mohammad Reza Faisal; Radityo Adi Nugroho
Jurnal Pengabdian kepada Masyarakat TEKNO (JAM-TEKNO) Vol 4 No 2 (2023): Desember 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/jamtekno.v4i2.5577

Abstract

Natural tourist areas certainly have a variety of biodiversity that can be used as a place for education. Many types of trees can be found in the area, such as Beringin, Jeruju, Jingah, Nipah, and so on. However, the collection of data about trees has not been properly recorded and there is no comprehensive information about the trees there, so that when visitors travel there it will be difficult to identify them. Therefore, in an effort to help overcome existing problems, it is necessary to develop technology with QR Codes to be able to identify trees in natural tourism parks. The stages of implementing the service are creating an application where this application is made in the form of a website-based information system and socializing the use of the tree recognition educational application. The goal to be achieved is to create a website-based application for recognizing trees, so that visitors, whether they come directly or just want to see the tree collection in the natural tourism park but are constrained by distance, time, etc., visitors can access via a web browser on smartphones, laptops, etc. and personal computers wherever they are provided there is an internet connection.
Effect of Hyperparameter Tuning Using Random Search on Tree-Based Classification Algorithm for Software Defect Prediction Rizky, Muhammad Hevny; Faisal, Mohammad Reza; Budiman, Irwan; Kartini, Dwi; Abadi, Friska
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 1 (2024): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.90437

Abstract

The field of information technology requires software, which has significant issues. Quality and reliability improvement needs damage prediction. Tree-based algorithms like Random Forest, Deep Forest, and Decision Tree offer potential in this domain. However, proper hyperparameter configuration is crucial for optimal outcomes. This study demonstrates the use of Random Search Hyperparameter Setting Technique to predict software defects, improving damage estimation accuracy. Using ReLink datasets, we found effective algorithm parameters for predicting software damage. Decision Tree, Random Forest, and Deep Forest achieved an average AUC of 0.73 with Random Search. Random Search outperformed other tree-based algorithms. The main contribution is the innovative Random Search hyperparameter tuning, particularly for Random Forest. Random Search has distinct advantages over other tree-based algorithms
An Electrocardiogram Signal Preprocessing Strategy in LSTM Algorithm for Biometric Recognition Rahayu, Fenny Winda; Faisal, Mohammad Reza; Nugrahadi, Dodon Turianto; Nugroho, Radityo Adi; Muliadi, Muliadi; Redjeki, Sri
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 18, No 2 (2024): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.93895

Abstract

Electrocardiogram (ECG) signals are a very important tool for clinical diagnosis and can be used as a new biometric modality. The aim of this research is to determine the results of ECG signal processing using RNN methods such as the Long Short Term Memory (LSTM) algorithm by utilizing several preprocessing techniques. In this study, the ECG signal itself was previously tested by carrying out the LSTM classification process without preprocessing, and the results obtained were 0% accurate, so preprocessing was needed. The preprocessing methods tested with the LSTM classification method are Adjacent Segmentation and R Peak Segmentation to find out which preprocessing techniques greatly influence LSTM classification accuracy. The experimental results were that LSTM classification with R Peak Segmentation preprocessing obtained the highest accuracy on the two data used, namely filtered and raw data, with 80.7% and 78.95%, respectively. Meanwhile, the accuracy obtained from LSTM classification when using Adjacent Segmentation preprocessing is not good. This research compares LSTM accuracy from each preprocessing stage to determine which combination has the best results in the ECG data classification process. This research also offers new insights into the preprocessing stages that can be carried out on ECG data.
Co-Authors Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Admi Syarif Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Ghinaya, Helma Hanif Rahardian Herteno, Rudy Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Karlina Elreine Fitriani Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Mahmud Mahmud Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Ngo, Luu Duc Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Rahmina Ulfah Aflaha Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reza Rendian Septiawan Riadi, Putri Agustina Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizki, M. Alfi Rizky, Muhammad Hevny Rossyking, Favorisen Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Wildan Panji Tresna Winda Agustina Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi