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All Journal ComEngApp : Computer Engineering and Applications Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) Jurnal Teknologi dan Sistem Komputer Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic Computer Science and Information Technologies Jurnal Teknik Informatika (JUTIF) Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Herteno, Rudy
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Pengelompokan PMKS menggunakan Self Organizing Maps dengan perbaikan missing value Naïve Bayes Imputation Hidayah, Noor; -, Muliadi; Budiman, Irwan; Nugrahadi, Dodon Turianto; Herteno, Rudy
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14424

Abstract

Penyandang Masalah Kesejahteraan Sosial (PMKS) merupakan permasalahan pada kelompok masyarakat yang memiliki kesulitan dalam menjalankan fungsi sosial. Penelitian dilakukan untuk mengetahui karakteristik permasalahan di wilayah Kalimantan Selatan dengan menggunakan klasterisasi. Metode klasterisasi yang digunakan adalah SOM dan pengisian data kosong menggunakan NBI yang dibandingkan dengan Metode Statistik (Mean, Median, dan Modus). Proses dimulai dari mengisian data kosong dengan NBI dan Metode Statistik, dilanjutkan dengan klaster SOM dan hasil klaster dievaluasi menggunakan DBI. Hasil yang didapatkan adalah perbaikan NBI menempati hasil klasterisasi terbaik dengan nilai 0,032 pada pembagian 2 klaster. Klaster pertama berjumlah 8 wilayah yaitu Tanah Laut, Kota Baru, Tapin, Hulu Sungai Selatan, Hulu Sungai Tengah, Hulu Sungai Utara, Tabalong, dan Tanah Bumbu. Klaster kedua berjumlah 5 wilayah yaitu Banjar, Barito Kuala, Balangan, Banjarmasin, dan Banjarbaru. Tingkat prioritas yang diperoleh dari rata-rata klaster didapatkan bahwa klaster kedua sebagai prioritas pertama.
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.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN BIBIT PADI BERKUALITAS PADA LAHAN RAWA MENGGUNAKAN METODE DEMATEL DAN MFEP Ulya, Azizatul; Muliadi, Muliadi; Herteno, Rudy; Farmadi, Andi; Abadi, Friska
Sebatik Vol. 28 No. 1 (2024): June 2024
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v28i1.2291

Abstract

Bidang pertanian merupakan salah satu sektor penting di Indonesia. Sebagian besar masyarakat Indonesia bergantung pada sektor ini sebagai petani padi. Adapun tujuan penelitian ini adalah mengetahui hasil akurasi perankingan alternatif bibit padi pada lahan rawa menggunakan metode metode  Decision Trial Making And Evaluation Laboratory (DEMATEL) dan Multifactor Evaluation Process (MFEP). Data yang sudah dikumpulkan kemudian akan dianalisis untuk mendapatkan atribut apa saja yang akan dimasukkan dalam perancangan sistem untuk metode membobotan dan perankingan menggunakan metode DEMATEL dan MFEP. Hasil pembobotan yang didapatkan dengan metode Dematel yaitu umur tanaman adalah 0,2277, tinggi tanaman adalah 0,1961, anakan produktif tanaman adalah 0,1921, ketahanan terhadap hama adalah 0,1294, ketahanan terhadap penyakit adalah 0,0896 dan ketahanan terhadap genangan air adalah 0,1652. Jika nilai bobot dijumlahkan maka hasilnya sama dengan 1. Kesimpulan bahwa Nilai bobot kriteria menggunakan metode Dematel pada pemilihan bibit padi pada lahan rawa yang terdiri dari 6 kriteria, yaitu umur tanaman adalah 0,2277, tinggi tanaman adalah 0,1961, anakan produktif tanaman adalah 0,1921, ketahanan terhadap hama adalah 0,1294, ketahanan terhadap penyakit adalah 0,0896 dan ketahanan terhadap genangan air adalah 0,1652. Nilai perbandingan hasil pemilihan bibit padi pada lahan rawa menggunakan metode Dematel dan MFEP dengan keputusan pihak Balai Penyuluhan Pertanian (BPP) berdasarkan perhitungan akurasi yang didapatkan dari MAE (Mean Absolute Error) adalah 80,42%.
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, Mohammad Reza; Herteno, Rudy
Computer Engineering and Applications Journal Vol 13 No 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i2.469

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
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.
Comparative Study of Various Hyperparameter Tuning on Random Forest Classification With SMOTE and Feature Selection Using Genetic Algorithm in Software Defect Prediction Suryadi, Mulia Kevin; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.375

Abstract

Software defect prediction is necessary for desktop and mobile applications. Random Forest defect prediction performance can be significantly increased with the parameter optimization process compared to the default parameter. However, the parameter tuning step is commonly neglected. Random Forest has numerous parameters that can be tuned, as a result manually adjusting parameters would diminish the efficiency of Random Forest, yield suboptimal results and it will take a lot of time. This research aims to improve the performance of Random Forest classification by using SMOTE to balance the data, Genetic Algorithm as selection feature, and using hyperparameter tuning to optimize the performance. Apart from that, it is also to find out which hyperparameter tuning method produces the best improvement on the Random Forest classification method. The dataset used in this study is NASA MDP which included 13 datasets. The method used contains SMOTE to handle imbalance data, Genetic Algorithm feature selection, Random Forest classification, and hyperparameter tuning methods including Grid Search, Random Search, Optuna, Bayesian (with Hyperopt), Hyperband, TPE and Nevergrad. The results of this research were carried out by evaluating performance using accuracy and AUC values. In terms of accuracy improvement, the three best methods are Nevergrad, TPE, and Hyperband. In terms of AUC improvement, the three best methods are Hyperband, Optuna, and Random Search. Nevergrad on average improves accuracy by about 3.9% and Hyperband on average improves AUC by about 3.51%. This study indicates that the use of hyperparameter tuning improves Random Forest performance and among all the hyperparameter tuning methods used, Hyperband has the best hyperparameter tuning performance with the highest average increase in both accuracy and AUC. The implication of this research is to increase the use of hyperparameter tuning in software defect prediction and improve software defect prediction performance.
Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning Al Habesyah, Noor Zalekha; Herteno, Rudy; Indriani, Fatma; Budiman, Irwan; Kartini, Dwi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.381

Abstract

TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.
Comparison of CatBoost and Random Forest Methods for Lung Cancer Classification using Hyperparameter Tuning Bayesian Optimization-based Zamzam, Yra Fatria; Saragih, Triando Hamonangan; Herteno, Rudy; Muliadi; Nugrahadi, Dodon Turianto; Huynh, Phuoc-Hai
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.382

Abstract

Lung Cancer is a disease that has a high mortality rate and is often difficult to detect until it reaches a very severe stage. Data indicates that lung cancer cases are typically diagnosed late, posing significant challenges to effective treatment. Early detection efforts offer potential for better recovery chances. Therefore, this research aims to develop methods for the identification and classification of lung cancer in the hope of providing further knowledge on effective ways to detect this condition at an early stage. One approach under scrutiny involves employing machine learning classification techniques, anticipated to serve as a pivotal tool in early disease detection and enhancing patient survival rates. This study involves five stages: data collection, data preprocessing, data partitioning for training and testing using 10-fold cross validation, model training, and analysis of evaluation results. In this research, four experiments consist of applying two classification methods, CatBoost and Random Forest, each tested using default hyperparameter and hyperparameter tuning using Bayesian Optimization. It was found that the Random Forest model using hyperparameter tuning Bayesian Optimization outperformed the other models with accuracy (0.97106), precision (0.97339), recall (0.97185), f-measure (0.97011), and AUC (0.99974) for lung cancer data. These findings highlight Bayesian Optimization for hyperparameter tuning in classification models can improve clinical prediction of lung cancer from patient medical records. The integration of Bayesian Optimization in hyperparameter tuning represents a significant step forward in refining the accuracy and effectiveness of classification models, thus contributing to the ongoing enhancement of medical diagnostics and healthcare strategies.
Optimizing Software Defect Prediction Models: Integrating Hybrid Grey Wolf and Particle Swarm Optimization for Enhanced Feature Selection with Popular Gradient Boosting Algorithm Angga Maulana Akbar; Herteno, Rudy; Saputro, Setyo Wahyu; Faisal, Mohammad Reza; Nugroho, Radityo Adi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.388

Abstract

Software defects, also referred to as software bugs, are anomalies or flaws in computer program that cause software to behave unexpectedly or produce incorrect results. These defects can manifest in various forms, including coding errors, design flaws, and logic mistakes, this defect have the potential to emerge at any stage of the software development lifecycle. Traditional prediction models usually have lower prediction performance. To address this issue, this paper proposes a novel prediction model using Hybrid Grey Wolf Optimizer and Particle Swarm Optimization (HGWOPSO). This research aims to determine whether the Hybrid Grey Wolf and Particle Swarm Optimization model could potentially improve the effectiveness of software defect prediction compared to base PSO and GWO algorithms without hybridization. Furthermore, this study aims to determine the effectiveness of different Gradient Boosting Algorithm classification algorithms when combined with HGWOPSO feature selection in predicting software defects. The study utilizes 13 NASA MDP dataset. These dataset are divided into testing and training data using 10-fold cross-validation. After data is divided, SMOTE technique is employed in training data. This technique generates synthetic samples to balance the dataset, ensuring better performance of the predictive model. Subsequently feature selection is conducted using HGWOPSO Algorithm. Each subset of the NASA MDP dataset will be processed by three boosting classification algorithms namely XGBoost, LightGBM, and CatBoost. Performance evaluation is based on the Area under the ROC Curve (AUC) value. Average AUC values yielded by HGWOPSO XGBoost, HGWOPSO LightGBM, and HGWOPSO CatBoost are 0.891, 0.881, and 0.894, respectively. Results of this study indicated that utilizing the HGWOPSO algorithm improved AUC performance compared to the base GWO and PSO algorithms. Specifically, HGWOPSO CatBoost achieved the highest AUC of 0.894. This represents a 6.5% increase in AUC with a significance value of 0.00552 compared to PSO CatBoost, and a 6.3% AUC increase with a significance value of 0.00148 compared to GWO CatBoost. This study demonstrated that HGWOPSO significantly improves the performance of software defect prediction. The implication of this research is to enhance software defect prediction models by incorporating hybrid optimization techniques and combining them with gradient boosting algorithms, which can potentially identify and address defects more accurately
A Comparative Study: Application of Principal Component Analysis and Recursive Feature Elimination in Machine Learning for Stroke Prediction Hermiati, Arya Syifa; Herteno, Rudy; Indriani, Fatma; Saragih, Triando Hamonangan; Muliadi; Triwiyanto, Triwiyanto
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.446

Abstract

Stroke is a disease that occurs in the brain and can cause both vocal and global brain dysfunction. Stroke research mainly aims to predict risk and mortality. Machine learning can be used to diagnose and predict diseases in the healthcare field, especially in stroke prediction. However, collecting medical record data to predict a disease usually makes much noise because not all variables are important and relevant to the prediction process. In this case, dimensionality reduction is essential to remove noisy (i.e., irrelevant) and redundant features. This study aims to predict stroke using Recursive Feature Elimination as feature selection, Principal Component Analysis as feature extraction, and a combination of Recursive Feature Elimination and Principal Component Analysis. The dataset used in this research is stroke prediction from Kaggle. The research methodology consists of pre-processing, SMOTE, 10-fold Cross-Validation, feature selection, feature extraction, and machine learning, which includes SVM, Random Forest, Naive Bayes, and Linear Discriminant Analysis. From the results obtained, the SVM and Random Forest get the highest accuracy value of 0.8775 and 0.9511 without using PCA and RFE, Naive Bayes gets the highest value of 0.7685 when going through PCA with selection of 20 features followed by RFE feature selection with selection of 5 features, and LDA gets the highest accuracy with 20 features from feature selection and continued feature extraction with a value of 0. 7963. It can be concluded in this study that SVM and Random Forest get the highest accuracy value without PCA and RFE techniques, while Naive Bayes and LDA show better performance using a combination of PCA and RFE techniques. The implication of this research is to know the effect of RFE and PCA on machine learning to improve stroke prediction.
Co-Authors Adawiyah, Laila Aflaha, Rahmina Ulfah Akhtar, Zarif Bin Al Ghifari, Muhammad Akmal Al Habesyah, Noor Zalekha Alfando, Muhammad Alvin Andi Farmadi Angga Maulana Akbar Antoh, Soterio Aryanti, Agustia Kuspita Athavale, Vijay Anant Azizah, Azkiya Nur Azizah, Siti Roziana Bahriddin Abapihi Dodon Turianto Nugrahadi Dwi Kartini, Dwi Faisal, M. Reza Fatma Indriani Fauzan Luthfi, Achmad Favorisen R. Lumbanraja Febrian, Muhamad Michael Friska Abadi Ghinaya, Helma Halim, Kevin Yudhaprawira Hartati Hartati Hermiati, Arya Syifa Huynh, Phuoc-Hai Irwan Budiman Itqan Mazdadi, Muhammad Junaidi, Ridha Fahmi Lilies Handayani Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Reza Faisal, Muhammad Reza Muliadi Muliadi MULIADI -, MULIADI Ngo, Luu Duc Noor Hidayah Noryasminda Nur Hidayatullah, Wildan Nurdiansyah Nurdiansyah Pratama, Muhammad Yoga Adha Putri, Nitami Lestari Radityo Adi Nugroho Raidra Zeniananto Ramadhan, As`'ary Reza Faisal, Mohammad Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Saputro, Setyo Wahyu Saragih, Triando Hamonangan Setyo Wahyu Saputro Siti Aisyah Solechah Suryadi, Mulia Kevin Ulya, Azizatul Vina Maulida, Vina Wahyu Saputro, Setyo Zamzam, Yra Fatria