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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL SISTEM INFORMASI BISNIS Epsilon: Jurnal Matematika Murni dan Terapan Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Mimbar Sekolah Dasar POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengembangan Riset dan Observasi Teknik Informatika Journal of Computer Science and Informatics Engineering (J-Cosine) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Formil (Forum Ilmiah) Kesmas Respati Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Altasia : Jurnal Pariwisata Indonesia Jurnal Mnemonic Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Data Science and Software Engineering Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Dwi Kartini, Dwi
Unknown Affiliation
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Public Health Social Sciences Transportation

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Backward Elimination for Feature Selection on Breast Cancer Classification Using Logistic Regression and Support Vector Machine Algorithms Salsha Farahdiba; Dwi Kartini; Radityo Adi Nugroho; Rudy Herteno; Triando Hamonangan Saragih
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 4 (2023): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.88926

Abstract

Breast cancer is a prevalent form of cancer that afflicts women across all nations globally. One of the ways that can be done as a prevention to reduce elevated fatality due to breast cancer is with a detection system that can determine whether a cancer is benign or malignant. Logistic Regression and Support Vector Machine (SVM) classification algorithms are often used to detect this disease, but the use of these two algorithms often doesn’t give optimal results when applied to datasets that have many features, so additional algorithm is needed to improve classification performance by using Backward Elimination feature selection. The comparison of Logistic Regression and SVM algorithms was carried out by applying feature selection to breast cancer data to see the best model. The breast cancer dataset has 30 features and two classes, Benign and Malignant. Backward Elimination has reduced features from 30 features to 13 features, thereby increasing the performance of both classification models. The best classification was obtained by using the Backward Elimination feature selection and linear kernel SVM with an increase in accuracy value from 96.14% to 97.02%, precision from 98.06% to 99.49%, recall from 90.48% to 92.38%, and the AUC from 0.95 to 0.96.
Implementation of Particle Swarm Optimization on Sentiment Analysis of Cyberbullying using Random Forest Helma Herlinda; Muhammad Itqan Mazdadi; Muliadi Muliadi; Dwi Kartini; Irwan Budiman
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17920

Abstract

Social media has exerted a significant influence on the lives of the majority of individuals in the contemporary era. It not only enables communication among people within specific environments but also facilitates user connectivity in the virtual realm. Instagram is a social media platform that plays a pivotal role in the sharing of information and fostering communication among its users through the medium of photos and videos, which can be commented on by other users. The utilization of Instagram is consistently growing each year, thereby potentially yielding both positive and negative consequences. One prevalent negative consequence that frequently arises is cyberbullying. Conducting sentiment analysis on cyberbullying data can provide insights into the effectiveness of the employed methodology. This research was conducted as an experimental research, aiming to compare the performance of Random Forest and Random Forest after applying the Particle Swarm Optimization feature selection technique on three distinct data split compositions, namely 70:30, 80:20, and 90:10. The evaluation results indicate that the highest accuracy scores were achieved in the 90:10 data split configuration. Specifically, the Random Forest model yielded an accuracy of 87.50%, while the Random Forest model, after undergoing feature selection using the Particle Swarm Optimization algorithm, achieved an accuracy of 92.19%. Therefore, the implementation of Particle Swarm Optimization as a feature selection technique demonstrates the potential to enhance the accuracy of the Random Forest method.
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
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
IMPLEMENTASI METODE PRINCIPAL COMPONENT ANALYSIS (PCA) DAN MODIFIED K-NEAREST NEIGHBOR PADA KLASIFIKASI CITRA DAUN TANAMAN HERBAL Nurdiansyah, Nurdiansyah; Muliadi, Muliadi; Herteno, Rudy; Kartini, Dwi; Budiman, Irwan
Jurnal Mnemonic Vol 7 No 1 (2024): Mnemonic Vol. 7 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v7i1.6664

Abstract

Klasifikasi citra tanaman herbal dapat dilakukan berdasarkan bentuk daun yang dikenal juga sebagai pengenalan citra tanaman herbal. Pada pengenalan citra dilakukan dengan mengidentifikasi ciri bentuk daun tanaman herbal dan dilakukan klasifikasi citra daun tersebut. Jumlah data citra yang digunakan sebagai 200 data yang terbagi kedalam 5 kelas. Sehingga masing-masing kelas terdiri dari 40 data citra. Masyarakat umumnya akan kesulitan untuk mengenal jenis tanaman herbal berdasarkan melihat secara sekilas pada daun. Pada metode PCA digunakan untuk mengurangi dimensi citra daun herbal dan metode Modified KNN digunakan untuk mengklasifikasikan citra daun herbal berdasarkan fitur. Pada pembagian data 6:4 nilai akurasi tertingginya sebesar 89 % pada K=2 dan akurasi terendahnya pada 78 % pada K=9. Pembagian data 7:3 nilai akurasi tertingginya sebesar 87 % pada K=4 dan akurasi terendahnya pada 82 % pada K=9. Dan Pada pembagian data 8:2 nilai akurasi tertingginya sebesar 93 % pada K=3 dan akurasi terendah sebesar 84 % pada K=4.
Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine Mafazy, Muhammad Meftah; Faisal, Mohammad Reza; Kartini, Dwi; Indriani, Fatma; Saragih, Triando Hamonangan
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2569

Abstract

A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.
Comparison of Industrial Business Grouping Using Fuzzy C-Means and Fuzzy Possibilistic C-Means Methods Lestari, Mega; Kartini, Dwi; Budiman, Irwan; Faisal, Mohammad Reza; Muliadi, Muliadi
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2548

Abstract

The industrial business sector plays a role in the development of the economic sector in developing countries such as Indonesia. In this case, many industrial businesses are growing, but the data has not been processed or analyzed to produce important information that can be processed into knowledge using data mining. One of the data mining techniques used in this research is data grouping, or clustering. This research was conducted to determine the comparison results of the Cluster Validity Index on Fuzzy C-Means and Fuzzy Possibilistic C-Means methods for clustering industrial businesses in Tanah Bumbu Regency. In each process, 5 trials were conducted with the number of clusters, namely 3, 4, 5, 6, and 7, and for the attributes used: Male Labor, Female Labor, Investment Value, Production Value, and BW/BP Value. Furthermore, this study will evaluate the Cluster Validity Index, namely the Partition Entropy Index, Partition Coefficient index, and Modified Partition Coefficient Index. This research provides the best performance results in the Fuzzy C-Means method with the results of the Cluster Validity Index on the Partition Entropy Index of 0.21566, Partition Coefficient Index of 0.88078, and Modified Partition Coefficient Index of 0.82117, and the best number of clusters is 3 with the labels of low competitive industry clusters, medium competitive industry clusters, and highly competitive industry clusters.
Uji Sensitivitas Metode Aras Dengan Pendekatan Metode Pembobotan Kriteria Sahnnon Entropy Dan Swara Pada Penyeleksian Calon Karyawan Halimah, Halimah; Kartini, Dwi; Abadi, Friska; Budiman, Irwan; Muliadi, Muliadi
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 4 No. 2 (2020)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v4i2.194

Abstract

Penelitian ini melakukan uji sensitivitas metode Additive Ratio Assessment (ARAS) pada penyeleksian calon karyawan dengan pendekatan pembobotan kriteria menggunakan metode Shannon Entropy dan metode Stepwise Weight Assessment Ratio Analysis (SWARA) yang bertujuan untuk mengukur seberapa sensitif metode ini jika diterapkan pada sebuah kasus pengambilan keputusan. Data yang digunakan ialah data penyeleksian calon karyawan. Uji Sentitivitas pada penelitian ini digunakan untuk mengetahui metode yang lebih sensitif saat diterapkan pada suatu kasus. Metode perangkingan menggunakan ARAS karena metode perangkingan ini memiliki fungsi utilitas dan nilai optimalisasi. Metode Shannon Entropy bobot kriteria diperoleh berdasarkan perhitungan data alternatif penyeleksian karyawan, sedangkan metode SWARA bobot kriteria diperoleh dari pakar atau si pengambil keputusan. Hasil penelitian ini menunjukkan bahwa metode yang paling sensitif dengan kasus penyeleksian calon karyawan adalah metode SWARA-ARAS yang pemberian bobotnya berdasarkan pakar atau si pengambil keputusan dengan hasil sebesar 91,24203% lebih tinggi dibandingkan metode Shannon Entropy-ARAS yang hasil sebesar 74,75263%.
Feature Selection Using Firefly Algorithm With Tree-Based Classification In Software Defect Prediction Maulida, Vina; Herteno, Rudy; Kartini, Dwi; Abadi, Friska; Faisal, Mohammad Reza
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.315

Abstract

Defects that occur in software products are a universal occurrence. Software defect prediction is usually carried out to determine the performance, accuracy, precision and performance of the prediction model or method used in research, using various kinds of datasets. Software defect prediction is one of the Software Engineering studies that is of great concern to researchers. This research was conducted to determine the performance of tree-based classification algorithms including Decision Trees, Random Forests and Deep Forests without using feature selection and using firefly feature selection. And also know the tree-based classification algorithm with firefly feature selection which can provide better software defect prediction performance. The dataset used in this study is the ReLink dataset which consists of Apache, Safe and Zxing. Then the data is divided into testing data and training data with 10-fold cross validation. Then feature selection is performed using the Firefly Algorithm. Each ReLink dataset will be processed by each tree-based classification algorithm, namely Decision Tree, Random Forest and Deep Forest according to the results of the firefly feature selection. Performance evaluation uses the AUC value (Area under the ROC Curve). Research was conducted using google collab and the average AUC value generated by Firefly-Decision Tree is 0.66, the average AUC value generated by Firefly-Random Forest is 0.77, and the average AUC value generated by Firefly-Deep Forest is 0, 76. The results of this study indicate that the approach using the Firefly algorithm with Random Forest classification can work better in predicting software damage compared to other tree-based algorithms. In previous studies, tree-based classification with hyperparameter tuning on software defect prediction datasets obtained quite good results. In another study, the classification performance of SVM, Naïve Bayes and K-nearest neighbor with firefly feature selection resulted in improved performance. Therefore, this research was conducted to determine the performance of a tree-based algorithm using the firefly selection feature.
Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection Ansyari, Muhammad Ridho; Mazdadi, Muhammad Itqan; Indriani, Fatma; Kartini, Dwi; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.322

Abstract

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.
Co-Authors A.A. Ketut Agung Cahyawan W Abadi, Friska Abdullayev, Vugar Adawiyah, Laila Adin Nofiyanto, Adin Ahdyani, Annisa Salsabila Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Ajwa Helisa Al Habesyah, Noor Zalekha Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Antoh, Soterio Arfan Eko Ari Widodo Aryastuti, Nurul Azizah, Siti Roziana Bachtiar, Adam Mukharil Badali, Rahmat Amin Budiman, Irwan Daduk Merdika Mansur Dalimunthe, Gallang Perdhana Deni Kurnia Diana Sari Dike Bayu Magfira, Dike Bayu Dina Arifah Dita Amara Dodon Turianto Nugrahadi Dzira Naufia Jawza Faisal, Mohammad Reza Faisal, Mohammad Reza Fathmah, Siti Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Friska Abadi Halimah Halimah Helma Herlinda Herteno, Rudy Ihsan, Muhammad Khairi Indriani, Fatma Irwan Budiman Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Jhondy Baharsyah Lestari, Mega Lilies Handayani Mafazy, Muhammad Meftah Mahmud Mahmud Maya Yusida Mera Kartika Delimayanti Miftakhul Huda Muhammad Fauzan Nafiz Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muhammad Syahriani Noor Basya Basya Muliadi Muliadi Muliadi Muliadi . Muliadi . Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Muliadi, Muliadi Musyaffa, Muhammad Hafizh Nafiz, Muhammad Fauzan Nor Indrani Nurcahyati, Ica Nurdiansyah Nurdiansyah Nurul Chamidah P., Chandrasekaran Padhilah, Muhammad Pirjatullah Pirjatullah Radityo Adi Nugroho Radityo Adi Nugroho Rahmat Hidayat Rahmat Ramadhani Reina Alya Rahma Riadi, Putri Agustina Rizky, Muhammad Hevny Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rusdiani, Husna Safitri, Yasmin Dwi Said, Muhammad Al Ichsan Nur Rizqi Salsha Farahdiba Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sari, Fitri Eka Septyan Eka Prastya Shalehah Siena, Laifansan Siti Aisyah Solechah Sulastri Norindah Sari Sule, Ernie Tisnawati Tri Mulyani Triando Hamonangan Saragih Vina Maulida, Vina Wahyu Caesarendra Wijaya Kusuma, Arizha Yevis Marty Oesman YILDIZ, Oktay Yuyus Suryana