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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 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 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 Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology

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Journal : Journal of Data Science and Software Engineering

 1 
IMPLEMENTASI TEKNIK PENDEKATAN LEVEL DATA UNTUK MENYELESAIKAN KASUS DATA TIDAK SEIMBANG PADA KLASIFIKASI CACAT SOFTWARE Hanif Rahardian; Mohammad Reza Faisal; Friska Abadi; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 1 No 01 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (275.168 KB) | DOI: 10.20527/jdsse.v1i01.13

Abstract

Defects can cause significant software rework, delays, and high costs, to prevent disability it must be predictable the possibility of defects. To predict the disability the metrics software dataset is used. NASA MDP is one of the popular software metrics used to predict software defects by having 13 datasets and is generally unbalanced. The reward in the dataset can reduce the prediction of software defects because more unbalanced data produces a majority class. Data imbalance can be handled with 2 approaches, namely the data level approach technique and the algorithm level approach technique. The data level approach technique aims to improve class distribution by using resampling and data synthesis techniques. This research proposes a data level approach using resampling techniques, namely Random Oversampling (ROS), Random Undersampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), Tomek Link (TL) and One-Sided Selection (OSS) which are classified with Naïve Bayes was also validated using 10 Fold Cross-Validation, then evaluated with the Area Under ROC Curve (AUC). Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587. Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587. Prediction results based on the dataset obtained the best AUC value on MC2 with a value of 0.7277 using the Synthetic Minority Oversampling Technique (SMOTE). Prediction results based on the data level approach technique obtained the best average AUC value using Tomek Link (TL) with a value of 0.62587.
IMPLEMENTASI ALGORITMA C5.0 UNTUK MEMBENTUK POLA POHON KEPUTUSAN DIAGNOSA PENYAKIT DIABETES MELLITUS Muhammad Latief Saputra; Irwan Budiman; Radityo Adi Nugroho; Dwi Kartini; Muliadi
Journal of Data Science and Software Engineering Vol 1 No 02 (2020)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (464.45 KB)

Abstract

This study applies the C5.0 algorithm to form a decision tree pattern for diagnosing diabetes mellitus. C5.0 algorithm is a decision tree based classification algorithm. This algorithm focuses on the acquisition of information gain on all attributes. The data used is a diabetes mellitus dataset obtained from the Kaggle database website. Data preprocessing is done and data sharing is done 4 times with the distribution of training data 60% 70% 80% and 90%. Data sharing uses stratafied random sampling methods so that the distribution of training and testing data is in accordance with its portion. Calculation of accuracy performance using confusion matrix. Classification performance using C5.0 algorithm. With 90% training data get 72.73% accuracy of rules generated as many as 70 rules. With 80% training data the accuracy value is 74.03%. The rule is 64 rules. With 70% training data get an accuracy value of 76.52% of the rules generated 59 rules. With 60% training data get an accuracy value of 74.59% of the rules generated as many as 53 rules. From all the experiments that have been done, the best accuracy is found in experiments with 70% training data.
DEEP NEURAL NETWORK ON SOFTWARE DEFECT PREDICTION Arie Sapta Nugraha; Mohammad Reza Faisal; Friska Abadi; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (272.911 KB)

Abstract

Software defect prediction is often performed in research to determine the performance, accuracy, precision, and performance of the prediction model or method used in research, using various software metric datasets such as NASA MDP. In this research, we used Deep Neural Network to classify the software metrics dataset modules into Defective and Non-Defective. The data validation technique used to validate the model is Stratified 10-Fold Cross Validation. Performance of the Deep Neural Network model is reported using Area Under the Curve (AUC) for evaluation measurement. AUC of Deep Neural Network is obtained as 0.815 on MC1 dataset and 0.889 on PC1 dataset. Both AUC values obtained in the MC1 and PC1 datasets are included in Good Classification category.
IDENTIFIKASI PESAN SAKSI MATA PADA BENCANA KEBAKARAN HUTAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Rinaldi; Mohammad Reza Faisal; Muhammad Itqan Mazdadi; Radityo Adi Nugroho; Friska Abadi
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.011 KB)

Abstract

Social media, one of which is Twitter, is a medium for disseminating information that is growing rapidly at this time. The advantage of Twitter which has such a huge impact is its speed in spreading news and information that is happening. One of the information that is often reported through social media is information about natural disasters. Therefore, a lot of research on sensor social networks has been carried out by researchers using data from social media with the aim of obtaining valid data for the disaster emergency response process. In this study, the classification of eye witness messages for forest fires was carried out using Convolutional Neural Network and feature extraction Word2Vec with dimensions of 100. Twitter data used amounted to 3000 data and divided into 3 classes, namely eyewitnesses, non-eyewitnesses, and unknowns. The research was conducted to determine the accuracy performance obtained from testing using several types of configurations hyperparameter. Based on the results of the tests carried out, the best accuracy value was 81.97%.
PENGARUH OPTIMASI BOBOT MENGGUNAKAN ALGORITMA GENETIKA PADA KLASIFIKASI TINGKAT KERAWANAN DBD Bayu Hadi Sudrajat; Muliadi; Muhamad Reza Faisal; Radityo Adi Nugroho; Dwi Kartini
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.92 KB)

Abstract

Dengue Hemorrhagic Fever (DHF) is a disease transmitted by the Aedes Ageypti mosquito. In South Kalimantan, especially in the city of Banjarbaru, the number of cases tends to increase every year. Existing research has identified the level of dengue susceptibility by using computational methods, one of which is classification. The method used in this research is Neural Network Backpropagation with weight optimization using Genetic Algorithms for data classification of dengue disease in Banjarbaru City. The purpose of this study was to determine the performance of the classification of dengue susceptibility levels using Neural Network Backpropagation and weighting using Genetic Algorithms. The results showed that the performance obtained for the classification of the level of dengue susceptibility using the Neural Network Backpropagation Algorithm was 83.33% in the accuracy, 96.51% precision, and 84.69% recall, whereas when using the Neural Network Backpropagation Algorithm based on Genetic Algorithm for weight optimization, obtained an accuracy value of 96.29%, a precision of 98.97%, and a recall of 97%.
PENGARUH RESOLUSI CITRA DALAM MENDETEKSI RAMBU LALU LINTAS SIRKULER MENGGUNAKAN HOUGH CIRCLE TRANSFORM Zaini Abdan; Andi Farmadi; Rudy Herteno; Radityo Adi Nugroho; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 2 No 02 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (257.976 KB)

Abstract

The traffic signs have several shapes, one of which is circular. Hough Circle Transform is a function that detects a circular in an image based on the gradient. This function also needs some parameters, one of which is the image resolution. The traffic signs in the frame will have varying sizes. If after cropping, it will produce images with varying resolution sizes. Therefore, resizing image resolution is required so that all image data have the exact image resolution. Image resolutions to be tested are 25 × 25 pixels, 50 × 50 pixels, 75 × 75 pixels, 100 × 100 pixels, 125 × 125 pixels, 150 × 150 pixels, 175 × 175 pixels, and 200 × 200 pixels. This research proves that the image resolution in shape detection using Hough Circle Transform affects the shape detection accuracy. The data used are No Stopping signs and No Parking signs for True detection test, whereas Other Dangers signs and Pedestrian Crossing signs for False detection test. The highest accuracy was generated at a resolution of 75 × 75 pixels.
Optimasi Bobot Weighted Moving Average Dengan Particle Swarm Optimization Dalam Peramalan Tingkat Produksi Karet Dendy Fadhel Adhipratama Dendy; Irwan Budiman; Fatma Indriani; Radityo Adi Nugroho; Rudy Herteno
Journal of Data Science and Software Engineering Vol 2 No 03 (2021)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.447 KB)

Abstract

Rubber is a mainstay commodity in the country, in 2014 Indonesia ranked second as the largest natural rubber producing country in the world. However, rubber production in Indonesia experiences uncertain ups and downs so it is necessary to predict it in order to benefit small farmers and the state. Weighted Moving Average ( WMA) is a method for predicting time series data. However, the parameters on the WMA need to be optimized in order to get optimal weight results on the WMA and get accurate results. Algorithm Particle Swarm Optimization implemented to determine the weight value of the method Weighted Moving Average more optimal. PSO-WMA and WMA were carried out on three weights, namely from weighting 3 4 and 5 on rubber production data. So that the results of this study are WMA with 3 weights get 81% accuracy, 4 weight 80.5% and 5 weight 80.3%. And for PSO-WMA, the accuracy at weighting 3 is 81.4%, weighting 4 is 80.9% and for weighting 5 it is 81.6%. The test results of this study have the effect of the weight value on WMA in increasing the accuracy results.
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

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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.
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 Hanif Rahardian Irwan Budiman Irwan Budiman Itqan Mazdadi, Muhammad Ivan Sitohang Maya Yusida Muhammad Angga Wiratama Muhammad Azmi Adhani 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 Rahayu, Fenny Winda Rahmat Ramadhani Raidra Zeniananto Reina Alya Rahma Reza Faisal, Mohammad 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 Sri Redjeki Sri Redjeki Suci Permata Sari Suryadi, Mulia Kevin Sutan Takdir Alam Wahyu Caesarendra Wahyu Ramadansyah Wahyu Saputro, Setyo Zaini Abdan