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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Pertanian Indonesia ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) POSITIF KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Edu Komputika Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Komputasi Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Computer Science and Informatics Engineering (J-Cosine) Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic Madaniya Jurnal Teknik Informatika (JUTIF) Makara Journal of Science Journal of Data Science and Software Engineering Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Masyarakat Tekno Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Integra: Journal of Integrated Mathematics and Computer Science
Muhammad Reza Faisal, Muhammad Reza
Universitas Lambung Mangkurat
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics

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PENENTUAN FITUR WEBSITE BIDANG PARIWISATA DAN KEBUDAYAAN DENGAN METODE FEATURE-ORIENTED DOMAIN ANALYSIS (FODA) Muhammad Iqbal; Muhammad Reza Faisal; Irwan Budiman
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 3, No 2 (2016)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v3i2.53

Abstract

Abstract Determination of  the features in creating a tourism and cultures websites is required to find out which features are can be implemented. To help determination of the feature, we can use a domain analysis method Feature-Oriented Domain Analysis (FODA). The method has some step, starting with application review to three sample websites to take on the features. The next step are the context analysis to gain a structure diagram and a context diagram. The next step are the modeling domain which divided into two steps, first, features analysis to get the features of the web application through the features diagram with an explanation through domain terminology dictionary. The next step is the entity-relationship modeling by making entity-relationship diagrams for database creation. The final step are architecture modelings to create a domain architecture for application development that only focus on the features. The results from the feature analysis get 38 mandatory features which be implemented on a web application for tourism and culture.Keywords: Tourism, Culture, Website, Features, Feature-Oriented Domain Analysis Abstrak Penentuan fitur dalam membuat website bidang pariwisata dan kebudayaan dibutuhkan untuk mengetahui fitur yang bisa diimplementasikan. Untuk membantu menentukan fitur tersebut, digunakan analisis domain dengan metode Feature-Oriented Domain Analysis (FODA). Metode tersebut mempunyai tahapan dimulai dari tinjauan aplikasi terhadap ketiga website sebagai sampel untuk mengambil fitur. Selanjutnya tahapan analisis konteks yang mendapatkan diagram struktur dan diagram konteks. Berikutnya tahapan pemodelan domain yang dibagi dua langkah yaitu analisis fitur untuk mendapatkan fitur-fitur pada aplikasi web melalui diagram fitur dengan penjelasan melalui kamus terminologi domain. Langkah berikutnya adalah pemodelan entity-relationship dengan membuat diagram entity-relationship untuk pembuatan database. Terakhir, pemodelan arsitektur dengan membuat arsitektur domain untuk pengembangan aplikasi yang hanya fokus pada fitur.  Hasil dari analisis fitur adalah didapatkan sebanyak 38 fitur mandatory yang berarti fitur tersebut wajib diimplementasikan dalam aplikasi web untuk pariwisata dan kebudayaan. Kata kunci: Pariwisata, Kebudayaan, Website, Fitur, Feature-Oriented Domain Analysis
PERANCANGAN SISTEM APLIKASI TERHADAP PENENTUAN TULANG OSTEOPOROSIS PADA CITRA X-RAY TULANG PAHA DENGAN THRESHOLDING METODE OTSU Muhammad Angga Wiratama; Muhammad Reza Faisal; Radityo Adi Nugroho
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 2, No 2 (2015)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v2i2.28

Abstract

Femur has a function to support the upper body that causes the femur can be affected by osteoporosis due to its function , it is supported by singh index rating system that has a grade each - each seeing femur bone affected by osteoporosis or normal bone. This study aimed to test the Otsu thresholding method for determining the femur bone osteoporosis and Singh index , the results of the study showed the percentage of correctness of 80 % to determine osteoporotic bone but getting the 31 % to determine the index singh, so the Otsu thresholding method can be concluded successfully to determine the femur bone osteoporosis but did not succeed in determining the index singh. Keywords: Thresholding, Otsu, indeks singh. Tulang paha memiliki fungsi untuk menyokong tubuh bagian atas yang menyebabkan tulang paha dapat terkena osteoporosis karena fungsinya, hal ini didukung oleh sistem pemeringkatan indeks singh yang memiliki grade masing - masing melihat tulang paha yang terkena tulang osteoporosis atau tulang normal. Penelitian ini bertujuan untuk menguji thresholding metode otsu untuk menentukan tulang paha yang terkena osteoporosis dan indeks singh , dari hasil penelitian didapatkan hasil presentase kebenaran sebesar 80% untuk menentukan tulang osteoporosis tetapi mendapatkan hasil 31% untuk menentukan indeks singhnya, sehingga thresholding metode otsu dapat disimpulkan berhasil untuk menentukan tulang paha terkena osteoporosis akan tetapi tidak berhasil dalam menentukan indeks singh. Kata kunci : Thresholding , Otsu , indeks singh.
Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa Ahmad Rusadi Arrahimi; Muhammad Khairi Ihsan; Dwi Kartini; Mohammad Reza Faisal; Fatma Indriani
Jurnal Sains dan Informatika Vol. 5 No. 1 (2019): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v5i1.171

Abstract

Undergraduate Students data in academic information systems always increases every year. Data collected can be processed using data mining to gain new knowledge. The author tries to mine undergraduate students data to classify the study period on time or not on time. The data is analyzed using CART with bagging techniqu, and CART with boosting technique. The classification results using 49 testing data, in the CART algorithm with bagging techniques 13 data (26.531%) entered into the classification on time and 36 data (73.469%) entered into the classification not on time. In the CART algorithm with boosting technique 16 data (32,653%) entered into the classification on time and 33 data (67,347%) entered into the classification not on time. The accuracy value of the classification of study period of undergraduate students using the CART algorithm is 79.592%, the CART algorithm with bagging technique is 81.633%, and the CART algorithm with boosting technique is 87.755%. In this study, the CART algorithm with boosting technique has the best accuracy value.
PENGENALAN SUARA PADA KAMUS BANJAR-INDONESIA DAN INDONESIA-BANJAR MENGGUNAKAN STATISTIK INFERENSI Akhmad Rezki Purnajaya; Fatma Indriani; Mohammad Reza Faisal
JURNAL ILMIAH INFORMATIKA Vol 8 No 01 (2020): Jurnal Ilmiah Informatika (JIF)
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (401.634 KB) | DOI: 10.33884/jif.v8i01.1727

Abstract

Banjar language used in conversation and daily life around the area. So foreigners who come to the regions of South Kalimantan will have difficulty in communicating. Besides, most local residents in the backwoods of South Kalimantan can not use Indonesian language properly, they would be more convenient to use regional language to interact. For that reason we need an Android application can help users to find the translation of a word or phrase whenever and wherever. With the help of Google Voice Search, this application can also listen to the voice of the user to be converted into text and insert into the input translation. Speech recognition of Banjar language required a literacy training data by using the method of statistical inference to make results appropriated. Testing using method of Black Box Testing to measure the percentage of suitability of the results of translation, speech recognition for Indonesian language and speech recognition Banjar language using method of Statistical inference. So the results of translation accuracy 100% and accuracy of speech recognition Indonesian language and Banjar language by 97.85% and 82.74%.
Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search Rahmad Ubaidillah; Muliadi Muliadi; Dodon Turianto Nugrahadi; M Reza Faisal; Rudy Herteno
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4146

Abstract

Liver disease is a disorder of liver function caused by infection with viruses, bacteria or other toxic substances so that the liver cannot function properly. This liver disease needs to be diagnosed early using a classification algorithm. By using the Indian liver patient dataset, predictions can be made using a classification algorithm to determine whether or not patients have liver disease. However, this dataset has a problem where there is an imbalance of data between patients with liver disease and those without, so it can reduce the performance of the prediction model because it tends to produce non-specific predictions. In this study, classification uses the XGBoost method which is then added with SMOTE to overcome class imbalances in the dataset and/or combined with Bayesian search hyperparameter tuning so that the resulting model performance is better. From the research, the results obtained from the XGBoost model get an AUC value of 0.618, for the XGBoost model with Bayesian search the AUC value is 0.658, then for the XGBoost SMOTE model the AUC value is 0.716, then for the XGBoost SMOTE model with Bayesian search the AUC value is 0.767. From the comparison of the four models, XGBoost SMOTE with Bayesian search obtained the highest AUC results and has an AUC difference of 0.149 compared to the XGBoost model without SMOTE and Bayesian search.
An in Silico Study of the Cathepsin L Inhibitory Activity of Bioactive Compounds in Stachytarpheta jamaicensis as a Covid-19 Drug Therapy Utami, Juliyatin Putri; Kurnianingsih, Nia; Faisal, Mohammad Reza
Makara Journal of Science Vol. 26, No. 1
Publisher : UI Scholars Hub

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

Abstract

Inhibition of cathepsin L (Cat L) can be considered a target for COVID-19 treatment. Starchytapheta jamaicensis is a plant from the Verbenaceae family that is commonly used for medicinal purposes. This study aims to analyze the inhibitory activities of compounds of Stachytarpheta jamaicensis toward Cat L by computational docking analysis. Ten compounds contained in the extracts (i.e., α-spinasterol, apigenin, luteolol-7-glucuronide, friedelin, hispidulin, chlorogenic acid, ipolamiide, geraniol, hentriacontane, and γ-aminobutyric acid) were selected as ligands; decanoyl-arg-val-lys-arg-chloromethylketone and oxocarbazate were used as the reference. Computational docking analysis was performed using Autodock Vina integrated into PyRx 8.0 and visualized using the Discovery Studio Visualizer v19.1.0.18287 (2019 version) based on the scoring functions. Seven bioactive compounds were bound more strongly than decanoyl-arg-val-lys-arg-chloromethylketone: α-spinasterol, apigenin, luteolol-7-glucuronide, friedelin, hispidulin, chlorogenic acid, and ipolamiide. However, all bioactive compounds were bound with less strength than oxocarbazate. Apigenin showed the best affinity, with much hydrogen bonding, and had the same ASN18 residue as Cat L inhibitor 1. PreADMET showed that all compounds of S. jamaicensis did not have hepatotoxicity, mutagenic, and carcinogenic criteria. The current research indicates that S. jamaicensis compounds can be used as an inhibitor for Cat L and as a COVID-19 drug candidate.
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.
Combination of texture feature extraction and forward selection for one-class support vector machine improvement in self-portrait classification Reina Alya Rahma; Radityo Adi Nugroho; Dwi Kartini; Mohammad Reza Faisal; Friska Abadi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp425-434

Abstract

This study aims to validate self-portraits using one-class support vector machine (OCSVM). To validate accurately, we build a model by combining texture feature extraction methods, Haralick and local binary pattern (LBP). We also reduce irrelevant features using forward selection (FS). OCSVM was selected because it can solve the problem caused by the inadequate variation of the negative class population. In OCSVM, we only need to feed the algorithm using the true class data, and the data with pattern that does not match will be classified as false. However, combining the two feature extractions produces many features, leading to the curse of dimensionality. The FS method is used to overcome this problem by selecting the best features. From the experiments carried out, the Haralick+LBP+FS+OCSVM model outperformed other models with an accuracy of 95.25% on validation data and 91.75% on test data.
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.
Co-Authors Abdul Gafur Abdullayev, Vugar Achmad Zainudin Nur Adawiyah, Laila Admi Syarif Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Andi Farmadi Andi Farmadi Andi Farmadi Angga Maulana Akbar Annisa Rizqiana Arie Sapta Nugraha Arif, Nuuruddin Hamid Arifin Hidayat Azizah, Azkiya Nur Bachtiar, Adam Mukharil Bahriddin Abapihi Bayu Hadi Sudrajat Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emma Andini Fatma Indriani Fatma Indriani Fatma Indriani Favorisen R. Lumbanraja Fitra Ahya Mubarok Fitriyana, Silfia Friska Abadi Friska Abadi Friska Abadi Ghinaya, Helma Hanif Rahardian Herteno, Rudy Irwan Budiman Irwan Budiman Irwan Budiman Ivan Sitohang Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Karlina Elreine Fitriani Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Kurnianingsih, Nia Lilies Handayani Liling Triyasmono Lisnawati Mahmud Mahmud Mauldy Laya Mera Kartika Delimayanti Miftahul Muhaemen Muflih Ihza Rifatama Muhamad Ihsanul Qamil Muhammad Al Ichsan Nur Rizqi Said Muhammad Alkaff Muhammad Angga Wiratama Muhammad Fauzan Nafiz Muhammad Haekal Muhammad Haekal Muhammad Iqbal Muhammad Irfan Saputra Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairi Ihsan Muhammad Mada Muhammad Mursyidan Amini Muhammad Rizky Adriansyah Muhammad Rusli Muhammad Sholih Afif Muhammad Zaien MUJIZAT KAWAROE Muliadi Muliadi Muliadi Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Mustofa, Fahmi Charish Ngo, Luu Duc Nor Indrani Noryasminda Nugrahadi, Dodon Nurlatifah Amini Nursyifa Azizah Oni Soesanto Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putri Nabella Radityo Adi Nugroho Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani Rahmat Ramadhani Rahmina Ulfah Aflaha Ratna Septia Devi RAUDLATUL MUNAWARAH Reina Alya Rahma Reza Rendian Septiawan Riadi, Putri Agustina Rinaldi Riza Susanto Banner Rizal, Muhammad Nur Rizki, M. Alfi Rizky, Muhammad Hevny Rossyking, Favorisen Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Rudy Herteno Rudy Herteno Said, Muhammad Al Ichsan Nur Rizqi SALLY LUTFIANI Salsabila Anjani Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sarah Monika Nooralifa Sari, Risna Sa’diah, Halimatus Septyan Eka Prastya Septyan Eka Prastya Setyo Wahyu Saputro Setyo Wahyu Saputro Siti Aisyah Solechah Solly Aryza Sri Redjeki Sri Redjeki Sugiarto, Iyon Titok Sulastri Norindah Sari Suryadi, Mulia Kevin Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utami, Juliyatin Putri Vina Maulida, Vina Wahyu Caesarendra Wahyu Dwi Styadi Wahyudi Wahyudi Wildan Panji Tresna Winda Agustina Yenni Rahman YILDIZ, Oktay Yudha Sulistiyo Wibowo Yunida, Rahmi