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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Radityo Adi Nugroho
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology

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SISTEM PEMANTAUAN LOKASI PEGAWAI ULM BERBASIS PRESENSI BERGERAK Ahmad Juhdi; Radityo Adi Nugroho; Friska Abadi; Andi Farmadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (558.312 KB)

Abstract

ULM attendance is usually done in each faculty using a fingerprint-based attendance machine. However, fingerprint-based presence during the pandemic is very dangerous due to the COVID-19 outbreak which allows the spread of the virus to be transmitted through finger intermediaries who use the presence machine simultaneously. As well as the existence of a letter prohibiting going home issued by the MENPENRB regarding "Restrictions on traveling activities outside the region or homecoming activities or leave for ASN in an effort to prevent the spread of Covid-18". In this study, we use a smartphone-based electronic system to overcome fingerprint-based attendance problems so that we can get an increase in terms of costs, and minimize the spread of the COVID-19 outbreak. By knowing the level of profit achieved through investment in the application development that the researcher has proposed, it is necessary to conduct a feasibility study (Feasibility Analysis) as a tool in drawing conclusions about what will be done electronically, a comparison will be made against the implementation of attendance in the previous year. The operational costs required are Rp. 27,665,070, while the costs incurred for application development are Rp. 1,613,666, it can be seen that there is an implementation cost savings of Rp. 26,051,404, when operational cost savings are included in the economic feasibility study, the Return on Investment (ROI) and Break-Event Point (BEP) values since the first year the application was implemented showed a positive value. Until the fourth year, ROI and BEP entered the feasible criteria so that from an Economic Feasibility perspective it can be seen that the application is economically feasible. And the application that is made is able to provide convenience in using the application as evidenced by validity and reliability tests.
Implementasi Metode Haralick dengan Random Forest Classifier untuk identifikasi Penyakit Kentang Pada Citra Daun Muhammad Syahriani Noor Basya Basya; Andi Farmadi; Dwi Kartini; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Potato plants are one of the most widely grown food crops in the highlands of Indonesia. Besides being used as food, potatoes are now known to be used to fight free radicals, control blood sugar, and nourish the digestive system. Therefore, potatoes have good prospects for development. In connection with efforts to develop potatoes in Indonesia, there are obstacles, namely the attack of potato plants by disease. As for the disease in potato plants, one of the characteristics of knowing it is on the leaves. To identify the leaf image, the texture feature is an important feature to recognize the leaf from an image. This is because there are differences in texture between normal and diseased leaves. To perform image processing through texture features, one method that can be used is haralick. In this study, a system was created to identify the types of diseases present in potato leaves using the Haralick method with the Random Forest Classifier. The image used is 300 data consisting of 3 classes, namely Late Blight, Early Blight, and Health. In this study, the testing was carried out by dividing the training and testing data with a percentage of 70:30, 80:20, and 90:10. The highest accuracy value in this study was obtained by using a combination of 80:20 split data, which was 0.88. The 70:30 data split gets an accuracy of 0.85 and the 90:10 data split gets an accuracy of 0.87.
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.
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.
Klasifikasi Harapan Hidup Pasien Karsinoma Hepatoseluler Menggunakan Extreme Learning Machine Dengan Perbaikan Data Hilang Suci Permata Sari; Triando Hamonangan Saragih; Andi Farmadi; Radityo Adi Nugroho; Rudy Herteno
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.v9i4.1287

Abstract

International Agency for Research on Cancer (IARC) mengestimasi bahwa pada tahun 2020 kanker hati primer berada di peringkat ke-6 sebagai kanker yang paling banyak didiagnosis dan peringkat ke-3 sebagai penyebab utama kematian akibat kanker di dunia. Mayoritas kanker hati primer muncul dari sel-sel hati dan disebut Karsinoma Hepatoseluler (KHS). Salah satu upaya yang dapat dilakukan untuk mengatasi permasalahan tersebut adalah dengan mengklasifikasikan harapan hidup pasien KHS. Terdapat banyak metode yang dapat digunakan dalam klasifikasi, salah satunya adalah menggunakan Extreme Learning Machine (ELM). Dataset yang digunakan pada penelitian ini adalah HCC Survival Data Set yang memiliki 49 fitur dengan rata-rata data hilang sebesar 10,22% secara keseluruhannya. ELM merupakan metode yang mengharuskan semua data pada datasetnya lengkap tanpa memiliki data hilang. Sehingga harus dilakukan penanganan data hilang terlebih dahulu sebelum dilakukan klasifikasi. Penanganan data hilang pada penelitian ini dilakukan dengan menggunakan teknik imputasi. Pada penelitian ini dilakukan perbandingan antara hasil klasifikasi dari data yang diimputasi menggunakan MissForest dengan hasil klasifkasi dari data yang diimputasi menggunakan K-Nearest Neighbors Imputation (KNNI). Perbandingan tersebut dilakukan untuk mengetahui metode imputasi mana yang menghasilkan data imputasi dengan kinerja terbaik pada klasifikasi kelangsungan hidup pasien KHS. Hasil menunjukkan bahwa data yang diimputasi menggunakan KNNI menghasilkan nilai akurasi rata-rata dan nilai rata-rata AUC yang lebih unggul dibandingkan dengan data yang diimputasi dengan MissForest, yaitu dengan nilai akurasi rata-rata sebesar 92,941% dan rata-rata AUC sebesar 0,9758.
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.
PENINGKATAN KINERJA PREDIKSI CACAT SOFTWARE DENGAN HYPERPARAMETER TUNING PADA ALGORITMA KLASIFIKASI DEEP FOREST Emma Andini; Faisal, Mohammad Reza; Rudy Herteno; Nugroho, Radityo Adi; Friska Abadi; Muliadi
Jurnal Mnemonic Vol 5 No 2 (2022): Mnemonic Vol. 5 No. 2
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v5i2.4793

Abstract

Prediksi cacat software adalah salah satu studi pada bidang Rekayasa Perangkat Lunak yang telah diteliti oleh banyak peneliti. Tujuan dari studi ini adalah untuk mencari tahu algoritma yang dapat memberikan kinerja prediksi cacat software yang lebih baik. Salah satu penelitian yang telah dilakukan adalah melakukan prediksi cacat software dengan menggunakan algoritma berbasis pohon seperti Decision Tree, Random Forest dan Deep Forest. Deep Forest adalah algoritma klasifikasi berbasis pohon yang baru yang merupakan perbaikan dari algoritma Random Forest. Namun implementasi Deep Forest dalam penelitian terdahulu masih belum memberikan kinerja yang maksimal. Hasil pada penelitian terdahulu menunjukan bahwa kinerja algoritma Deep Forest masih ada yang lebih rendah dibandingkan algoritma berbasis pohon yang lain. Pada penelitian ini berfokus pada peningkatan kinerja algoritma berbasis pohon dengan melakukan normalisasi pada dataset dan hyperparameter tuning pada algoritma klasifikasi dengan menggunakan pencarian grid. Dataset yang digunakan adalah 3 dataset dari ReLink yaitu Apache, Safe, dan Zxing. Setiap model prediksi divalidasi dengan Stratified 10-Fold Cross Validation dan kinerja dievaluasi menggunakan AUC. Dari hasil eksperimen yang didapatkan,hasil prediksi dari pendekatan yang diusulkan lebih baik daripada metode sebelumnya.
A Comparative Study of Machine Learning Methods for Baby Cry Detection Using MFCC Features Riadi, Putri Agustina; Faisal, Mohammad Reza; Kartini, Dwi; Nugroho, Radityo Adi; Nugrahadi, Dodon Turianto; Magfira, Dike Bayu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.350

Abstract

The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes. The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naïve Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naïve Bayes model achieved an accuracy 0.538 and F1 score of 0.539. Notably, no discernible differences were observed when evaluating the Support Vector Machine and Naïve Bayes methods across the 1-7 second time trial. The implication of this research is to establish a foundation for the advancement of premature illness identification techniques grounded in the vocalizations of infants, thereby facilitating swifter diagnostic processes for pediatric practitioners.
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.
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
Co-Authors Abdul Gafur Adi Mu'Ammar, Rifqi Adin Nofiyanto, Adin Ahmad Bahroini Ahmad Juhdi Ahmad Rusadi Aida, Nor Akhtar, Zarif Bin Alamudin, Muhammad Faiq Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Arie Sapta Nugraha Arie Sapta Nugraha Aryanti, Agustia Kuspita Athavale, Vijay Anant Aylwin Al Rasyid Bayu Hadi Sudrajat Dendy Fadhel Adhipratama Dendy Deni Kurnia Dike Bayu Magfira, Dike Bayu Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Efendi Mohtar Emma Andini Erdi, Muhammad Faisal, Mohammad Reza Fatma Indriani Fauzan Luthfi, Achmad Fenny Winda Rahayu Fhadilla Muhammad Friska Abadi Friska Abadi Hakim, Muhammad Ikhwanul Hanif Rahardian Herteno, Rudy Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Maya Yusida Muhammad Angga Wiratama Muhammad Azmi Adhani Muhammad Fikri Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Noor Muhammad Reza Faisal, Muhammad Reza Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Syahriani Noor Basya Basya Muhammad Zaien Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Nur Hidayatullah, Wildan Nur Ridha Apriyanti Oni Soesanto Pratama, Muhammad Yoga Adha Putri, Nitami Lestari Rahmat Ramadhani Raidra Zeniananto Reina Alya Rahma Reza Faisal, Mohammad Rezeki, Abdillah Riadi, Putri Agustina Rinaldi Rizal, Muhammad Nur Rizky Ananda, Muhammad Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Salsha Farahdiba Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Septiadi Marwan Annahar Setyo Wahyu Saputro Siena, Laifansan Suci Permata Sari Suryadi, Mulia Kevin Sutan Takdir Alam Wahyu Caesarendra Wahyu Ramadansyah Wahyu Saputro, Setyo Zaini Abdan