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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika Jurnal Fisika FLUX KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal IMPACT: Implementation and Action Journal of Electronics, Electromedical Engineering, and Medical Informatics Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat (Mediteg) Jurnal Abdimas Madani dan Lestari (JAMALI) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Computer Science and Information Technologies Madaniya Jurnal Teknik Informatika (JUTIF) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Ilung (Inovasi Lahan Basah Unggul) Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Dodon Turianto Nugrahadi
Lambung Mangkurat University
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Journal : Journal of Data Science and Software Engineering

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Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Gambar X-Ray Penyakit Covid-19 dan Pneumonia Fitria Agustina fitria; Andi Farmadi; Dwi Kartini; Dodon Turianto Nugrahadi; Ando Hamonangan Saragih
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Abstrak Pneumonia caused by the corona virus is different from ordinary pneumonia. One way to find out which pneumonia is caused by the corona virus is to do an X-ray. The disadvantage of this examination is that it requires a radiologist and the analysis time is relatively long. Therefore, to overcome this problem, deep learning methods can be used by implementing the Convolutional Neural Network (CNN) Algorithm method for X-ray image classification. The implementation of the Convolutional Neural Network (CNN) Algorithm is done by using training data of 4800 images which are trained using batch size values ​​of 16, 32, and 64. The train process with batch size values ​​of 16, 32 and 64 produces an average accuracy of 90%, 91% and 92%, while the loss values ​​are 0.22, 0.16 and 0.25. From this process it was found that batch 64 was the best loss and accuracy result for training data. The test data with batch values ​​of 16, 32, and 64 resulted in an accuracy of 76%, 82% and 76%, while the loss values ​​were 0.79, 0.53 and 0.63. The results of this manual testing of 30 photos contained 7 images that are not recognized by the model because of the images look similar to each other with an accuracy of 76%. From this process it was found that batch 32 was the best loss and accuracy result for testing data.
COMPARATIVE ANALYSIS OF FUZZY TIME SERIES METHOD WITH FUZZY TIME SERIES MARKOV CHAIN ON RAINFALL FORECAST IN SOUTH KALIMANTAN M Kevin Warendra; Irwan Budiman; Rudy Herteno; Dodon Turianto Nugrahadi; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract Time series data (TS) is a type of data that is collected according to the order of time within a certain time span. Time Series data analysis is one of the statistical procedures applied to predict the probability structure of future conditions for decision making. FTS (FTS) is a data forecasting method that uses fuzzy principles as its basis. Forecasting systems with FTS capture patterns from past data and then use them to project future data. FTS Markov Chain is a new concept that was first proposed by Tsaur, in his research to analyze the accuracy of the prediction of the Taiwan currency exchange rate with the US dollar. In his research, Tsaur combines the FTS method with Markov Chain, The merger aims to obtain the greatest probability using a transition probability matrix. The results obtained from this research are tests with the best number of presentation values ​​from FTS Markov Chain with FTS, resulting in different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%. produce different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%. produce different accuracy values ​​depending on the two methods. The best accuracy performance is obtained by the Markov Chain FTS method with an error value of 1.6% and an accuracy value of 98.4% and for FTS with an error value of 7.4% and an accuracy value of 92.6%.
PERBANDINGAN ADAPTIVE MOMENT ESTIMATION OPTIMIZATION DAN NESTEROV-ACCELERATED ADAPTIVE MOMENT ESTIMATION OPTIMIZATION PADA METODE CONVOLUTIONAL NEURAL NETWORK UNTUK MELAKUKAN DETEKSI BUAH Ismail Didit Samudro; Andi Farmadi; Dwi Kartini; Dodon Turianto Nugrahadi; Muliadi
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Convolutional Neural Networks are often used in research to conduct training, validation, classification, prediction and detection of images using Deep Neural Network. Optimization algorithm is used to change the hyperparameter values ​​in the Neural Network such as learning rate, optimization is needed to reduce losses and increase the accuracy of the model. Optimization algorithm that is widely used because of its good performance is Adam and Nadam optimization, but the learning rate setting still needs to be updated manually. In this research architecture that was based on VGG16 will be used, Learning Rate Scheduler is used in optimization to control the learning rate value by updating the learning rate value in each step during model training. In this study, a comparison of the optimization of Adam and Nadam was carried out when the Learning Rate Scheduler was used to update the learning rate value in model training and obtained prediction accuracy using Adam 98.85% and Nadam 95.02% and then obtained MAP model performance value using Adam 93.58%. and Nadam 75.28%.
PREDIKSI DATA PENARIKAN UANG TUNAI DI MESIN ATM MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) Fitrinadi; Irwan Budiman; Andi Farmadi; Dodon Turianto Nugrahadi; Muhammad Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 3 No 02 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract Data mining is a series of processes to explore the added value of knowledge that has been unknown from a data set. Many algorithms can be used in solving a problem related to prediction or forecasting a new data value for the future based on pre-existing data. Sarima model is a model in time series analysis. The performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) method produces a suitable or good model used to predict cash withdrawal data at ATM machines. The data used in the study is a dataset of ATM transactions originating from Finhacks. The result of error using MAPE (Mean Absolute Percenttage Error) on the predicted result of cash withdrawal data at atm machines is K1 16.75%, K2 18.09%, K3 7.85%, K4 5.67%, and K5 11.80%. So it can be concluded that the data matches using the SARIMA model that has been selected because the MAPE value is smaller than 20%.
THE EFFECT THE EFFECT OF SPREADING FACTOR ON LORA TRANSMISSION Muhammad Khairin Nahwan; Dodon Turianto Nugrahadi; M. Itqan Mazdadi; Andi Farmadi; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

The conditions of a different area can affect the transmission of data so that transmission is needed that is resistant to interference and in certain conditions a device that can monitor several places is needed at once. The concept of Wireless Sensor Network (WSN) is applied to meet these demands. This research is shown to determine the effect of Spreading Factor (SF) on Long Range (LORA) transmission on distance by analyzing Quality of Service (QOS). The test is divided into 2 conditions, namely: The Line of Sight (LOS) condition & Non-Line of Sight (NLOS) condition. The test results show that the maximum distance that the LoRa transmitter can reach is 1100m in LOS conditions while for NLOS conditions it can only reach a distance of 300m. The QOS parameters used to consist of Delay, Throughput, RSSI, & SNR. Spreading Factor (SF) affects Delay and Throughput, not RSSI and SNR. The best value of Delay (9.64 ms), Throughput (667.60 Bps), and RSSI ( -94.25 dBm) is at Spreading Factor (SF) 6 and SNR (5.23 dB) is Spreading Factor (SF) 8 and for the distance, the value of RSSI (-76.45 dBm) and SNR (5.23 dB) is at a distance of 10m. This applies in LOS and NLOS conditions.
Implementasi Implementasi Kinerja Transmisi Data Dengan Modul Komunikasi LoRa dan Protokol MQTT-SN Pada Gateway Untuk Mendukung Transmisi Data Sensor Kelembapan Tanah Djordi Hadibaya; Dodon Turianto Nugrahadi; M. Reza Faisal; Andi Farmadi; M. Itqan Mazdadi
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Wireless sensor network can help remote data transfer. Implementation of wireless sensor network in IoT system must be done with a good planning because IoT system typically have limited system resources. This limitation can affect performance of a wireless network sensor. The purpose of this study is to find out the effect of node range to the data transfer performance in terms of delay, throughput, RSSI, and SNR by using QOS (quality of service) analysis for LoRa and MQTT protocol. The results of LoRa’s protocol delay are between 2,82 ms to 37,27 ms. Throughput between 0,61 Kb/s to 24,29 Kb/s. SNR between 2,7 dBm to 8,34 dBm, and RSSI between -74,92 dBm to -122,36 dBm. On the other hand, the results of MQTT’s protocol delay are between 677,49 ms to 1182,69 ms. Throughput between 0,60 Kb/s to 1,12 Kb/s. SNR between 2,7 dBm to 8,34 dBm and RSSI between -74,92 dBm to -122,36 dBm.
Optimasi SVR dengan PSO untuk peramalan harga Cryptocurrency Arifin Hidayat; Andi Farmadi; Mohammad Reza Faisal; Dodon Turianto Nugrahadi; Rudy Herteno
Journal of Data Science and Software Engineering Vol 3 No 01 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Cryptocurrency is the nickname given to a system that uses Cryptography technology to securely transmit data and process digital currency exchanges in a dispersed manner. A Cryptocurrency is a form of risky investment, Cryptocurrency prices are very volatile (changing) making Cryptocurrency prices need to be predicted to make a profit. Support Vector Regression (SVR) is one method for predicting time series data such as Cryptocurrency prices. However, the SVR parameters need to be optimized to get accurate results. The Particle Swarm Optimization (PSO) algorithm is implemented to determine the effect on the optimization of SVR parameters. The implementation of SVR and SVR-PSO is carried out on Bitcoin and Shiba Inu Coin Cryptocurrency data. The result of this research is that the SVR algorithm has an accuracy of 13.19082% (Bitcoin) and 68.3221% (Shiba Inu Coin). The SVR-PSO algorithm obtained an accuracy of 96.92359% (BTC) and 94.74245% (SHIB).
IMPLEMENTASI PROTOKOL MQTT-SN PADA INTERNET GATEWAY DEVICE DENGAN PENGIRIMAN PAKET DATA UDP Wahyu Dwi Styadi; Dodon Turianto Nugrahadi; M. Itqan Mazdadi; Mohammad Reza Faisal; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

Internet of Things (IoT) is one of the new trends in the world of technology that is likely to become a trend in the future, to be able to make this happen, communication protocols such as MQTT-SN are needed which is a variant of the MQTT protocol and the connection protocol that supports IoT is NB- IoT to support this. Unlike MQTT which uses TCP as its communication protocol, MQTT-SN uses UDP as its data communication protocol. The purpose of this study is to determine the results of Quality of Service on the value of delay and throughput at QoS levels 0, 1, and 2. There are 2 test scenarios, namely real-time test scenarios and phased test scenarios. The design of the instrument consists of sensor instruments, Raspberry Pi microcontrollers for internet gateway device, and NB-IoT modules to then be tested with scenarios to get test results. Based on the test results, the best QoS results for the delay parameter in the real-time scenario are QoS level 2 with a delay value of 1.602 seconds, while for the gradual scenario there is QoS 0 with a delay value of 1.622 seconds. Furthermore, the best QoS results for throughput parameters in real-time scenarios are found at QoS level 2 with a throughput value of 245.79 bits per second and in a phased scenario found at QoS level 1 with a throughput value of 286.42 bits per second.
IMPLEMENTATION OF LORA WITH TEMPERATURE SENSORS IN IRRIGATION AREA (CASE STUDY: MARTAPURA CITY) Muhammad Mirza Hafiz Yudianto; Dodon Turianto Nugrahadi; Dwi Kartini; M. Itqan Mazdadi; Friska Abadi
Journal of Data Science and Software Engineering Vol 3 No 03 (2022)
Publisher : Fakultas MIPA Universitas Lambung Mangkurat

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Abstract

This study applies to the concept of a Wireless Sensor Network (WSN) consisting of a transmitting instrument and a receiving instrument using Long Range (LoRa) data transmission with a frequency of 915 MHz and LoRa 920 MHz. The test is divided into 2 tropical weather conditions, namely when the weather is sunny and rainy. The test results show that the maximum distance that the LoRa transmitter can reach is 1 kilometer. The QoS (Quality of Service) parameters used to consist of Delay, Throughput, RSSI, & SNR. Based on the test results of the QoS parameters, both frequencies affect tropical weather conditions and increase as the distance of data collection increases. LoRa Frequency 915 MHz and Frequency 920 MHz have their respective differences and advantages, which are uncertain on weather conditions and data transmission distances.
Co-Authors Abadi, Friska Abdul Gafur Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Adi, Puput Dani Prasetyo Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Aji Triwerdaya Alfando, Muhammad Alvin Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Apriana, Susi Ardiansyah Sukma Wijaya Arfan Eko Fahrudin Arifin Hidayat Azwari, Ayu Riana Sari Azwari, Ayu RianaSari Bachtiar, Adam Mukharil Badali, Rahmat Amin Bahriddin Abapihi Bedy Purnama Cahyadi, Rinova Firman Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emy Iryanie, Emy Faisal Murtadho Faisal, Mohammad Reza Fajrin Azwary Fatma Indriani Fhadilla Muhammad Fitra Ahya Mubarok Fitria Agustina fitria Fitriani, Karlina Elreine Fitrinadi Friska Abadi Gunawan Gunawan Gunawan Gunawan Halim, Kevin Yudhaprawira Hariyady, Hariyady Herteno, Rudy Herteno, Rudy Heru Kartika Candra, Heru Kartika Huynh, Phuoc-Hai Ichsan Ridwan Indah Ayu Septriyaningrum Irwan Budiman Irwan Budiman Irwan Budiman Ismail Didit Samudro Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kartika, Najla Putri Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Liling Triyasmono M Kevin Warendra M. Apriannur Martalisa, Asri Maulidha, Khusnul Rahmi Mera Kartika Delimayanti Miftahul Muhaemen Mohammad Reza Faisal Muhamad Ihsanul Qamil Muhammad Alkaff Muhammad Anshari Muhammad Haekal Muhammad Hasan Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muhammad Sholih Afif Muhammad Solih Afif Muliadi Muliadi Muliadi MULIADI -, MULIADI Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Musyaffa, Muhammad Hafizh Nafis Satul Khasanah Nahdhatuzzahra Nahdhatuzzahra Ngo, Luu Duc Noor Hidayah Nursyifa Azizah Ori Minarto Padhilah, Muhammad Pirjatullah Pirjatullah Pirjatullah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani, Rahmat Ramadhan, Muhammad Rizky Aulia Riadi, Putri Agustina Rifki Izdihar Oktvian Abas Pullah Rifki Riza Susanto Banner Rizal, Muhammad Nur Rizki Amelia Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Saman Abdurrahman Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satou, Kenji Selvia Indah Liany Abdie Setyo Wahyu Saputro sholih Afif Siti Napi'ah Soesanto, Oni Sri Cahyo Wahyono Sri Rahayu Sri Redjeki Sri Redjeki Totok Wianto Totok Wiyanto Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utomo, Edy Setyo Wahyu Dwi Styadi Wahyu Saputro, Setyo Wardana, Muhammad Difha Winda Agustina Yabani, Midfai Yanche Kurniawan Mangalik YILDIZ, Oktay Yudha Sulistiyo Wibowo Zamzam, Yra Fatria