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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) JURNAL SISTEM INFORMASI BISNIS Jupiter Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) 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 Jiko (Jurnal Informatika dan komputer) Journal of Computer Science and Informatics Engineering (J-Cosine) JURIKOM (Jurnal Riset Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Madaniya JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Data Science and Software Engineering
Irwan Budiman
Universitas Lambung Mangkurat
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science

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Penerapan Smart Monitoring Tarpaulin Fish bagi Pembudidaya Ikan Aliran Sungai Jembatan Kembar di Kelurahan Loktabat Utara Banjarbaru berbasis MQTT Dodon Turianto Nugrahadi; Irwan Budiman; Muliadi Muliadi; M. Reza Faisal
Madaniya Vol. 3 No. 4 (2022)
Publisher : Pusat Studi Bahasa dan Publikasi Ilmiah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53696/27214834.310

Abstract

Smart Monitoring Tarpaulin Fish merupakan pengelolaan kualitas air tentang upaya memantau kualitas air sehingga dapat tercapai kualitas air kondisi yang diinginkan sesuai dengan kondisi alamiahnya. Pada kegiatan budidaya perikanan, untuk keseimbangan ekosistem perairan dalam suatu wadah yang terbatas bahwa pH akan rendah dan kandungan oksigen terlarut akan berkurang, sebagai akibatnya konsumsi oksigen akan menurun, aktivitas pernafasan ikan naik dan selera makan ikan akan berkurang. Menurut Rochyani (2018) bahwa faktor penentu kualitas air untuk kolam budidaya ikan antara lain keasaman atau kebasaan air, kekeruhan air, suhu air, kandungan oksigen, dan kandungan garam. Warga di pesisir sungai jembatan kembar Loktabat Utara Kota Banjarbaru saat ini telah berbudidaya perikanan. Pengelolaan budidaya perikanan memerlukan pemantauan secara berkala dikarenakan perlunya pengamatan kualitas air budidaya perairan. Pembudidaya ikan sungai jembatan kembar Loktabat Utara rata-rata bekerja juga sebagai buruh harian, sehingga ada kalanya tidak dapat memantau kondisi kolam. Maka dibutuhkan teknologi yang dapat memudahkan dalam memantau pengelolaan kondisi kolam budidaya perikanan. Penggunaan smart monitoring tarpaulin fish ini menjadi salah satu solusi untuk mengatasi masalah tersebut, yaitu kolam terpal berbasis IoT (Internet of Things). Kondisi ini memantau kondisi suhu, dan kondisi tds air dengan menggunakan koneksi internet broadband berbasis MQTT (Message Queuing Telemetry Transport) serta bertenaga surya. Hasil implementasi ini terpenuhinya pemantauan secara real time kondisi kolam budidaya ikan hingga 80%. Penurunan kematian ikan hingga 30% karena percepatan penanganan kualitas air.
Using Social Media Data to Monitor Natural Disaster: A Multi Dimension Convolutional Neural Network Approach with Word Embedding Mohammad Reza Faisal; Irwan Budiman; Friska Abadi; Muhammad Haekal; Mera Kartika Delimayanti; Dodon Turianto Nugrahadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 6 (2022): Desember 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v6i6.4525

Abstract

Social media has a significant role in natural disaster management, namely as an early warning and monitoring when natural disasters occur. Artificial intelligence can maximize the use of natural disaster social media messages for natural disaster management. The artificial intelligence system will classify social media message texts into three categories: eyewitness, non-eyewitness and don't-know. Messages with the eyewitness category are essential because they can provide the time and location of natural disasters. A common problem in text classification research is that feature extraction techniques ignore word meanings, omit word order information and produce high-dimensional data. In this study, a feature extraction technique can maintain word order information and meaning by using three-word embedding techniques, namely word2vec, fastText, and Glove. The result is data with 1D, 2D, and 3D dimensions. This study also proposes a data formation technique with new features by combining data from all word embedding techniques. The classification model is made using three Convolutional Neural Network (CNN) techniques, namely 1D CNN, 2D CNN and 3D CNN. The best accuracy results in this study were in the case of earthquakes 78.33%, forest fires 81.97%, and floods 78.33%. The calculation of the average accuracy shows that the 2D and 3D v1 data formation techniques work better than other techniques. Other results show that the proposed technique produces better average accuracy.
Efek Transformasi Wavelet Diskrit Pada Klasifikasi Aritmia Dari Data Elektrokardiogram Menggunakan Machine Learning Dodon Turianto Nugrahadi; Tri Mulyani; Dwi Kartini; Rudy Herteno; Mohammad Reza Faisal; Irwan Budiman; Friska Abadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.4859

Abstract

Arrhythmia is one of the abnormalities of the heart rhythm, and some patients who suffer from arrhythmia do not feel any symptoms. Automating the early detection of arrhythmia is necessary by using an electrocardiogram. Previous research that had been done conducted classifications using several methods of data mining. In this research, the transformation for processing signals used is Discrete Wavelet Transformation, where a filtering process occurs that separates signals into high and low-frequency signals without losing the information from signals and is carried out with a two-level decomposition. After that, data normalization was performed using min-max normalization and was put into the model classification using the Support Vector Machine method with a Gaussian Radial Basis Function kernel of Naïve Bayes and K-Nearest Neighbor. Each data that was being used consisted of 140 data with a total of 35 data for each label. This research shows that at level 1 decomposition, the highest accuracy was obtained at db7 for the classification using Support Vector Machine with an accuracy of 73,57%, 68,57% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 59,64%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 63,57% while at level 2 decomposition the highest accuracy was obtained at db6 dan db8 for the classification using Support Vector Machine with an accuracy of 70,71%, 67,50% for Naïve Bayes, K-Nearest Neighbor with k=3 resulting in an accuracy of 66,07%, and K-Nearest Neighbor with k=5 resulting in an accuracy of 65%. From this research, it can be concluded that the highest accuracy is produced by decomposition level 1 using Support Vector Machine classification and that the Daubechies wavelet type has better results than the Haar wavelet.
Implementasi Seleksi Fitur Binary Particle Swarm Optimization pada Algoritma K-NN untuk Klasifikasi Kanker Payudara Rahmat Hidayat; Dwi Kartini; Muhammad Itqan Mazdadi; Irwan Budiman; Rahmat Ramadhani
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 11, No 1 (2023)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v11i1.53608

Abstract

Kanker Payudara adalah jenis kanker paling umum yang sering menyerang kalangan wanita di seluruh dunia. Diagnosa awal yang akurat dalam mendeteksi kanker payudara memainkan peran penting dalam pengobatan pasien karena semakin cepat kanker di diagnosa semakin cepat juga pengobatan dapat diberikan. Untuk menghasilkan diagnosa yang akurat terhadap pasien kanker payudara maka dilakukan penelitian dengan tujuan mendapatkan model klasifikasi yang dapat memberikan klasifikasi yang akurat terhadap penyakit kanker payudara. Algoritma klasifikasi yang sering digunakan dan cukup terkenal adalah K-Nearest Neighbor (K-NN). Algoritma K-NN melakukan klasifikasi menggunakan konsep tetangga terdekat berdasarkan pada data terdahulu, akan tetapi algoritma K-NN lemah terhadap data dengan jumlah fitur yang besar. Maka dari itu, untuk kasus dataset dengan banyak fitur dapat dilakukan seleksi fitur terlebih dahulu untuk meningkatkan performa klasifikasi K-NN. Salah satu yang sering digunakan untuk seleksi fitur adalah algoritma Binary Particle Swarm Optimization (BPSO). Pada penelitian ini akan dibuat 2 model klasifikasi K-NN yaitu model klasifikasi K-NN saja tanpa seleksi fitur dan model klasifikasi K-NN dengan seleksi fitur Binary Particle Swarm Optimization, Kemudian dilakukan perbandingan hasil akurasi yang didapat. Dataset yang akan digunakan adalah Breast Cancer Wincosin (Diagnostic) dari UCI Machine Learning Repository yang memiliki 569 data dan 30 fitur. Hasil penelitian menunjukan model K-NN+BPSO menghasilkan akurasi sebesar 95,32% dan model K-NN menghasilkan akurasi sebesar 94,15%. Berdasarkan akurasi yang didapatkan algoritma K-NN dengan seleksi fitur menghasilkan akurasi yang lebih baik daripada algoritma K-NN tanpa seleksi fitur sebesar 1,17%. Algoritma Binary PSO juga berhasil mereduksi fitur dari 30 fitur menjadi 5 fitur dengan tidak mengurangi akurasi dari model klasifikasi. Sehingga dapat disimpulkan bahwa pada kasus klasifikasi kanker payudara dengan dataset Breast Cancer Wincosin (Diagnostic) algoritma K-NN dapat dikombinasikan dengan seleksi fitur Binary PSO untuk membuat model klasifikasi yang memberikan performa cukup baik.
PENGARUH SOFTWARE METRIK PADA KINERJA KLASIFIKASI CACAT SOFTWARE DENGAN ANN Achmad Zainudin Nur; Mohammad Reza Faisal; Friska Abadi; Irwan Budiman; 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 (180.324 KB) | DOI: 10.20527/jdsse.v1i01.5

Abstract

Software Defect Prediction has an important role in quality software. This study uses 12 D datasets from NASA MDP which then features a selection of metrics categories software. Feature selection is performed to find out metrics software which are influential in predicting defects software. After the feature selection of the metric software category, classification will be performed using the algorithm Artificial Neural Network and validated with 5-Fold Cross Validation. Then conducted an evaluation with Area Under Curve (AUC), From datasets D” 12 NASA MDP that were evaluated with AUC, PC4, PC1 and PC3 datasets obtained the best AUC performance values. Each value is 0.915, 0.828, and 0.826 using the algorithm Artificial Neural Network.
Penyeleksian Calon Karyawan Menggunakan Metode Pembobotan Shannon Entropy dan Metode ARAS Halimah; Dwi Kartini; Friska Abadi; Irwan Budiman; Muliadi
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 (220.092 KB) | DOI: 10.20527/jdsse.v1i01.7

Abstract

This study discusses the selection of prospective employees using the Shannon Entropy weighting method and the Additive Ratio Assessment (ARAS) method which aims to determine the accuracy of the results obtained from the method. The Shannon Entropy method is a weighting method that assigns criteria weights based on the calculation of alternative employee selection data and the Additive Ratio Assessment (ARAS) method is a ranking method that has a utility function. Testing the data in this study using the Mean Absolute Error (MAE) method to get system accuracy results. Based on testing conducted using 6 criteria and 56 alternative data for prospective employees, the accuracy of the method used was 85.34%.
Penerapan Long Short Term Memory RNN untuk Prediksi Transaksi Penjualan Minimarket Patrick Ringkuangan; Fatma Indriani; Muhammad Itqan Mazdadi; Irwan Budiman; Andi Farmadi
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 (475.488 KB)

Abstract

This study aims to determine whether it can build a prediction of sales of goods at the Lapan-Lapan Mart by using the Long Short Term Memory Recurrent Neural Network method that can be used to predict the sale of goods. In this study, the data was taken from the Lapan-Lapan Mart, together with data on 10 different items sold every day. The data is then compiled for the level of sales to be weekly and a total of 52 data is obtained for each item so that the total data is amounted to 520. To get the weight in the LSTM calculation, there are two processes, namely forward and backward . the weight will be used to make predictions using the basic formula of the LSTM.Based on the research that has been done, it is known that the highest accuracy of using MAD (Mean Absolute Deviation) is 91 gr (11.61803507) indomie goods and 1.8kg of lemon daia (2.077000464) for the lowest MAD
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.
IMPLEMENTASI METODE CONVOLUTIONAL NEURAL NETWORK UNTUK PREDIKSI HARGA SAHAM LQ45 Aris Pratama; Dwi Kartini; Akhmad Yusuf; Andi Farmadi; Irwan Budiman
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 (195.714 KB)

Abstract

Stock are securities of ownership of a company. Investments in the stock market on average can produce a return rate of 10-30% per year, this amount is about two to three times higher than the rate of return on deposits or savings in banks which are only 5-10 % every year. One problem is the stock price is fluctuating or changing due to certain factors. This study compares several window size data with different amounts of data, aiming to find window size data with a more accurate amount of data for stock price predictions. Convolutional neural network algorithm with window size data of 7 days, 14 days, 21 days and 28 days in the amount of data 1 year and 2 years for stock price predictions. The results of this study are the convolutional neural network algorithm with a data window size of 7 days at the amount of data 2 years is more accurate than the window size data and the amount of other data. Because the smallest error result is 0.000201587.
IMPLEMENTATION OF LOAD BALANCE EQUAL COST MULTI PATH (ECMP) BETWEEN ROUTING PROTOCOL BORDER GATEWAY PROTOCOL (BGP) AND OPEN SHORTEST PATH FIRST (OSPF) USING DUAL CONNECTION Aji Triwerdaya; Dodon Trianto Nugrahadi; Muhammad Itqan Masdadi; Irwan Budiman; Ahmad Rusadi Arrahimi
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 (601.437 KB)

Abstract

Currently, Internet is needed by everyone to lighten their work, then a method has been developed to be able to access the internet using 2 ISPs (Internet Service Providers), namely using load balance. This method can perform bandwidth management so that it can balance the bandwidth of 2 ISPs. To support this method, Load Balance Equal Cost Multi Path (ECMP) is used. Another innovation that continues to be developed routing, the process of exchange data packets between different IP networks and to identify the best route to each connected network, that can make routing better by using dynamic routing types, to unify the network if a change occurs of topology by exchanging new topology information with each other on a network using the Open Shortest Path First (OSPF) routing or using the Border Gateway Protocol (BGP). OSPF is an open source routing protocol that is often used[4] and OSPF is a link-state in the routing algorithm. This routing use the Dijkstra or SPF (Short Path First) algorithm to calculate the shortest path from each route. Coinciding with the increase in routers in an area, the information that routers in the same area must have at the same time will increase, then the Border Gateway Protocol (BGP) is the new routing protocol[7]. BGP is a vector-path protocol where each router decides locally the "best AS" line per destination. The local preference attribute is used to set the policy for outgoing traffic. Testing is done by comparing the performance of an ECMP network using OSPF routing and an ECMP network using BGP routing[3]. Testing is done by measuring based on the throughput and data delay parameters using 16, 32, 48 routers. the topology is divided into 3 areas, namely area 1 for user load balance, area 2 for ISP 1 and area 3 for ISP 2. Throughput is used to measure routing performance on the TCP transport protocol and UDP transport protocol. Then, data delay is for measuring the performance of routing on the TCP and UDP transport protocol with the addition of variations. The testing that have been carried out show that the network throughput with OSPF routing (764.13 bps) has a lower performance than the network with BGP routing (818.81 bps) when sending TCP and UDP data, and network delay with OSPF routing (85.61 ms) has a significant increase than the network with BGP routing (89.23 ms) when sending TCP and UDP data.
Co-Authors A.A. Ketut Agung Cahyawan W Abdul Gafur Achmad Zainudin Nur Ahmad Faris Asy'arie Ahmad Faris Asy’arie Ahmad Rusadi Arrahimi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Shofi Khairian Aji Triwerdaya Ajwa Helisa Akhmad Yusuf Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmandi Antar Sofyan Aris Pratama Artesya Nanda Akhlakulkarimah Dendy Fadhel Adhipratama Dendy Dita Amara Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Faisal Murtadho Fatma Indriani Fatma Indriani Fitrinadi Friska Abadi Halimah Halimah Halimah Ichwan Dwi Nugraha Kevin Yudhaprawira Halim Lutfi Salisa Setiawati M Kevin Warendra Mera Kartika Delimayanti Muflih Ihza Rifatama Muhammad Adhitya Pratama Muhammad Darmadi Muhammad Haekal Muhammad Halim Muhammad Haris Qamaruzzaman Muhammad I Mazdadi Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Latief Saputra Muhammad Mada Muhammad Nazar Gunawan Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rizky Adriansyah Muhammad Rusli Muliadi Muliadi Muliadi - Muliadi Aziz Muliadi Muliadi Muliadi Muliadi muliadi muliadi Muliadi Muliadi Mutiara Ayu Banjarsari Nahdhatuzzahra Nahdhatuzzahra Nor Indrani Nursyifa Azizah Oni Soesanto Patrick Ringkuangan Radityo Adi Nugroho Rahman Hadi Rahman Rahmat Hidayat Rahmat Ramadhani Retma Ramadina Riana Riana Riza Susanto Banner Rizki Amelia Rudy Herteno Rudy Herteno Salsabila Anjani Sam'ani Sam'ani Saragih, Triando Hamonangan Septiadi Marwan Annahar Septyan Eka Prastya Setyo Wahyu Saputro Sofyan, Antar Sulastri Norindah Sari Sutami Sutan Takdir Alam Toni Prahasto Tri Mulyani Wahyu Caesarendra Wahyudi Wahyudi Yuli Christyono