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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Komputasi Sebatik Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Andi Farmadi
Universitas Lambung Mangkurat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Library & Information Science Materials Science & Nanotechnology

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (325.963 KB)

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (675.556 KB)

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).
Implementation of Particle Swarm Optimization Feature Selection on Naïve Bayes for Thoracic Surgery Classification Shalehah; Muhammad Itqan Mazdadi; Andi Farmadi; Dwi Kartini; Muliadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 3 (2023): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeemi.v5i3.305

Abstract

Thoracic surgery is among the operations that are most often performed on patients with lung cancer. Naive Bayes is one of the data mining classification techniques that may be used to handle thoracic surgery data. Therefore, the goal of this study is to assess the precision of all research models using Naive Bayes with and without Particle Swarm Optimization. This study's methodology includes the dataset used, the Naive Bayes algorithm theory, the particle swarm optimization algorithm, test validation using split validation, and performance assessment using the confusion matrix and AUC evaluation approaches. In this inquiry, secondary data are retrieved via the UCI Repository website. Thoracic surgery weight optimization accuracy is increased using particle swarm optimization. The test results of the Naive Bayes technique utilizing the thoracic surgery dataset showed the highest accuracy of 81.91% at a ratio of 80:20 and an AUC value of 0.620. The highest accuracy score is 93.62% with an AUC value of 0.773 at a ratio of 90:10, with three characteristics, namely PRE6, PRE14, and PRE17, having zero weight. This accuracy score was achieved when Particle Swarm Optimization was used to refine feature selection for attribute weighting. As a consequence, Naïve Bayes accuracy in thoracic surgery has increased as a result of attribute weighting on feature selection utilizing Particle Swarm Optimization. In turn, this research contributes to increasing the precision and efficiency with which thoracic surgical data are processed, which benefits lung cancer diagnosis in both speed and accuracy.
IMPLEMENTASI ALGORITMA GENETIKA UNTUK SELEKSI FITUR PADA KLASIFIKASI GENRE MUSIK MENGGUNAKAN METODE RANDOM FOREST Nurlatifah Amini; Triando Hamonangan Saragih; Mohammad Reza Faisal; Andi Farmadi; Friska Abadi
Jurnal Informatika Polinema Vol. 9 No. 1 (2022): Vol 9 No 1 (2022)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v9i1.1028

Abstract

Musik memiliki jenis yang beragam di Dunia. Adapun jenis musik yang paling popular diantaranya yaitu pop, disco, country, dangdut, jazz, blues, reggae, hiphop, rock, metal. Penelitian ini mengenai Klasifikasi genre musik menggunakan metode Random Forest menggunakan dataset dari GitHub atau GTZAN tentang genre musik dengan jumlah label ada 10, memiliki 26 fitur dan jumlah keseluruhan data ada 1000. Penelitian ini dibagi menjadi beberapa tahap, yaitu dengan klasifikasi seluruh data, mengklasifikasi dengan data yang dinormalisasi, melakukan klasifikasi dengan data asli menggunakan tahap seleksi fitur Algoritma Genetika, dan mengklasifikasi pada data yang dinormalisasi dengan seleksi fitur menggunakan Algoritma Genetika. Parameter yang digunakan pada Algoritma Genetika yaitu menggunakan Probabilitas Crossover, Probabilitas Mutasi. Pada penelitian ini Min-Max digunakan untuk metode normalisasi data, dan untuk perhitungan akurasi menggunakan metode Confusion Matrix. Peformasi terbaik dari parameter GA untuk Pc dan Pm menggunakan kombinasi 0.5 dan 0.2. Performasi populasi size terbaik adalah 26 dan iterasi atau max generasi terbaik ada pada 100 iterasi. Akurasi yang dihasilkan ketika menggunakan seluruh data menghasilkan akurasi sebesar 62%, 59% dengan data yang dinormalisasi, 64% dengan semua data menggunakan seleksi fitur Algoritma Genetika dan didapatkan akurasi sebesar 67% dengan menggunakan seleksi fitur Algoritma Genetika yang datanya dinormalisasi. Hasil ini memberi pengetahuan nilai rata-rata akurasi menunjukkan peningkatan dengan diterapkannya seleksi fitur Algoritma Genetika.
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.
Implementasi Algoritma Enkripsi AES-128 bit pada Data RFID pada Jarngan 802.11n dan 802.11ac dengan Monitoring Jarak Jauh Berbasis MQTT M. Apriannur; Dodon Turianto Nugrahadi; Andi Farmadi; Muhammad Itqan Mazdadi; Fatma Indriani
Jurnal Informatika Polinema Vol. 10 No. 1 (2023): Vol 10 No 1 (2023)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i1.1494

Abstract

Message Queue Telemetry Transport atau yang biasa disebut MQTT yaitu protokol untuk komunikasi yang bersifat machine to machine atau M2M dan bekerja pada layer ketujuh atau aplikasi dan bersifat lightweight message. Protokol MQTT secara default hanya memiliki mekanisme autentikasi saja secara default masih belum terenkripsi. Maka dilakukan penelitian implementasi algoritma enkripsi AES-128 bit dengan koneksi 802.11n berbasis mqtt pada sistem kunci RFID pintu otomatis dengan monitoring jarak jauh. Dalam penelitian ini pembacaan sensor rfid dilakukan menggunakan skenario dengan enskripsi dan tanpa enkripsi dengan pengiriman data sebanyak 50 kali. Hardware yang digunakan pada penelitian yaitu mikrokontroler NodeMCU ESP8266, adaptor 5V, dan sensor MFRC522 yang diimplementasikan pada Tp-link Archer C54 dan Tp-link TL-MR100. Tujuan pada penelitian ini adalah untuk mengetahui pengaruh proses enkripsi AES 128-bit data RFID pada mikrokontroler NodeMCU melalui protokol MQTT menggunakan jaringan 802.11n Archer dan jaringan 802.11n MR100 terhadap penggunaan RAM, delay dan throughput. Pada parameter penggunaan RAM, tipe router 802.11n Archer, skenario enkripsi menghasilkan sisa RAM rata – rata sebesar 18,829 KB. Sedangkan skenario tanpa enkripsi sebesar 23,225 KB. Tipe router 802.11n MR100, skenario enkripsi menghasilkan sisa RAM rata – rata sebesar 18,828 KB. Sedangkan skenario tanpa enkripsi sebesar 23,287 KB. Terjadi peningkatan penggunaan RAM 32 KB dari penggunaan tanpa enkripsi dan enkripsi yaitu pada router 802.11n Archer 23%, sedangkan pada router 802.11n MR100 24%. Pada parameter delay, tipe router 802.11n Archer, Transfer data enkripsi memiliki delay rata – rata sebesar 137,79 ms. Sedangkan Transfer data tanpa enkripsi sebesar 128,08 ms. Tipe router 802.11n MR100, Transfer data enkripsi memiliki delay rata – rata sebesar 145,71 ms. Sedangkan transfer data tanpa enkripsi sebesar 126,45 ms. Terjadi peningkatan delay dari penggunaan tanpa enkripsi dan enkripsi yaitu pada router 802.11n Archer 8%, sedangkan pada router 802.11 MR100 15%. Pada parameter ukuran throughput, tipe router 802.11n Archer, skenario enkripsi memiliki throughput sebesar 1,659 KB/s sedangkan tanpa enkripsi sebesar 0,491 KB/s. Tipe 802.11n MR100, skenario enkripsi memiliki throughput sebesar 1,586 KB/s sedangkan tanpa enkripsi sebesar 0,513 KB/s. Terjadi peningkatan throughput dari penggunaan tanpa enkripsi dan enkripsi.
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%.
An Approach to ECG-based Gender Recognition Using Random Forest Algorithm Arif, Nuuruddin Hamid; Faisal, Mohammad Reza; Farmadi, Andi; Nugrahadi, Dodon; Abadi, Friska; Ahmad, Umar Ali
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.v6i2.363

Abstract

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.
Gender Classification on Social Media Messages Using fastText Feature Extraction and Long Short-Term Memory Sa’diah, Halimatus; Faisal, Mohammad Reza; Farmadi, Andi; Abadi, Friska; Indriani, Fatma; Alkaff, Muhammad; Abdullayev, Vugar
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.v6i3.407

Abstract

Currently, social media is used as a platform for interacting with many people and has also become a source of information for social media researchers or analysts. Twitter is one of the platforms commonly used for research purposes, especially for data from tweets written by individuals. However, on Twitter, user information such as gender is not explicitly displayed in the account profile, yet there is a plethora of unstructured information containing such data, often unnoticed. This research aims to classify gender based on tweet data and account description data and determine the accuracy of gender classification using machine learning methods. The method used involves FastText as a feature extraction method and LSTM as a classification method based on the extracted data, while to achieve the most accurate results, classification is performed on tweet data, account description data, and a combination of both. This research shows that LSTM classification on account description data and combined data obtained an accuracy of 70%, while tweet data classification achieved 69%. This research concludes that FastText feature extraction with LSTM classification can be implemented for gender classification. However, there is no significant difference in accuracy results for each dataset. However, this research demonstrates that both methods can work well together and yield optimal results.
Comparison of the Adaboost Method and the Extreme Learning Machine Method in Predicting Heart Failure Muhammad Nadim Mubaarok; Triando Hamonangan Saragih; Muliadi; Fatma Indriani; Andi Farmadi; Rizal, Achmad
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.v6i3.440

Abstract

Heart disease, which is classified as a non-communicable disease, is the main cause of death every year. The involvement of experts is considered very necessary in the process of diagnosing heart disease, considering its complex nature and potential severity. Machine Learning Algorithms have emerged as powerful tools capable of effectively predicting and detecting heart diseases, thereby reducing the challenges associated with their diagnosis. Notable examples of such algorithms include Extreme Learning Machine Algorithms and Adaptive Boosting, both of which represent Machine Learning techniques adapted for classification purposes. This research tries to introduce a new approach that relies on the use of one parameter. Through careful optimization of algorithm parameters, there is a marked improvement in the accuracy of machine learning predictions, a phenomenon that underscores the importance of parameter tuning in this domain. In this research, the Heart Failure dataset serves as the focal point, with the aim of demonstrating the optimal level of accuracy that can be achieved through the use of Machine Learning algorithms. The results of this study show an average accuracy of 0.83 for the Extreme Learning Machine Algorithm and 0.87 for Adaptive Boosting, the standard deviation for both methods is “0.83±0.02” for Extreme Machine Learning Algorithm and “0.87±0.03” for Adaptive Boosting thus highlighting the efficacy of these algorithms in the context of heart disease prediction. In particular, entering the Learning Rate parameter into Adaboost provides better results when compared with the previous algorithm. Our research findings underline the supremacy of Extreme Learning Machine Algorithms and Adaptive Improvement, especially when combined with the introduction of a single parameter, it can be seen that the addition of parameters results in increased accuracy performance when compared to previous research using standard methods alone.
Co-Authors Abdilah, Muhammad Fariz Fata Abdullayev, Vugar Achmad Rizal Ahdyani, Annisa Salsabila Ahmad Bahroini Ahmad Faris Asy’arie Ahmad Juhdi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Tajali Akhmad Yusuf Ando Hamonangan Saragih Ardiansyah Sukma Wijaya Arif, Nuuruddin Hamid Arifin Hidayat Aris Pratama Azizah, Siti Roziana Bachtiar, Adam Mukharil Bahriddin Abapihi Deni Sutaji Dita Amara Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Dzira Naufia Jawza Efendi Mohtar Erdi, Muhammad Evi Nadya Prisilla Faisal Murtadho Fathul Hadi Fatma Indriani Fayyadh, Muhammad Naufaldi Fitria Agustina fitria Friska Abadi Ghinaya, Helma Gita Malinda Heru Candra Kartika Heru Kartika Chandra I Gusti Ngurah Antaryama Irwan Budiman Irwan Budiman Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Keswani, Ryan Rhiveldi Khairunnisa Khairunnisa Lisnawati M. Apriannur Miftahul Muhaemen Muhammad Alkaff Muhammad Halim Muhammad Itqan Mazdadi Muhammad Khairin Nahwan Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muhammad Rusli Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi muliadi muliadi Musyaffa, Muhammad Hafizh Mutiara Ayu Banjarsari Nafis Satul Khasanah Ngo, Luu Duc Noryasminda Nugraha, Muhammad Amir Nugrahadi, Dodon Nurcahyati, Ica Nurlatifah Amini P., Chandrasekaran Patrick Ringkuangan Pirjatullah Pirjatullah Pirjatullah Radityo Adi Nugroho Raidra Zeniananto Ramadhan, As`'ary Rifki Izdihar Oktvian Abas Pullah Rifki Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rusdiani, Husna Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sa’diah, Halimatus Setyo Wahyu Saputro Shalehah Suci Permata Sari Syahputra, Muhammad Reza Tajali, Ahmad Ulya, Azizatul Umar Ali Ahmad Wijaya Kusuma, Arizha Winda Agustina YILDIZ, Oktay