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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta JURNAL MEDIA INFORMATIKA BUDIDARMA Faktor Exacta Conference on Innovation and Application of Science and Technology (CIASTECH) Petir Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Telematika Progresif: Jurnal Ilmiah Komputer Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Scientific Student Journal for Information, Technology and Science Jurnal Buana Pengabdian Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Scientific Journal of Informatics
Siregar, Amril Mutoi
Teknik Informatika, Universitas Buana Perjuangan Karawang
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The effect of segmentation on the performance of machine learning methods on the morphological classification of Friesien Holstein dairy cows Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p59-68

Abstract

Many classification algorithms are in the form of image pattern recognition; the approach to the complexity of the problem should be a feature of feasibility for representing images. The morphology of dairy cows greatly affects their health and milk production. The paper will apply several classification methods based on the morphology of Holstein Friesian dairy cows. To improve the accuracy of the model used, the segmentation process is the right step. In this paper, we compare several machine learning algorithms to get optimal accuracy. The algorithm used a support vector machine (SVM), artificial neural networks, random forests and logistic regression. Segmentation methods used are mask region-based convolutional neural network (R-CNN) and Canny; optimal accuracy is expected to create intelligent applications. The success of the method is measured with accuracy, precision, recall, and F1 Score, as well as testing by conducting a training-testing ratio of 90:10 and 80:20. This study discovered an artificial neural network optimal model with Canny with an accuracy of 82.50%, precision of 87.00%, recall of 79.00%, F1-score of 81.62%, and testing ratio of 90:10. While the models with the highest 80:20 ratio achieved 84.39% accuracy, 88.46% precision, 80.47%, and 83.00% F1-score with mask R-CNN with logistic regression.
Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp2093-2100

Abstract

Computer vision deals with image-based problems, such as deep learning, classification, and object detection. This study classifies the quality of dairy parents into three, namely high, medium, and low based on morphology by focusing on Bogor Indonesia farms. The morphological images used are the side and back of dairy cows and the challenge is to determine the optimal accuracy of the model for it to be implemented into an automated system. The 2-step mask region-based convolutional neural network (mask R-CNN) and Canny segmentation algorithm were continuously used to classify the convolutional neural network (CNN) in order to obtain optimal accuracy. When testing the model using training and testing ratios of 90:10 and 80:20, it was evaluated in terms of accuracy, precision, recall, and F1-score. The results showed that the highest model produced an accuracy of 85.44%, 87.12% precision, 83.79% recall, and 84.94% F1-score. Therefore, it was concluded that the test result with 2-stages of segmentation was the best.
PENINGKATAN PRESTASI SISWA SMK TEKNIKOM CIKARANG MELALUI PEMANFAATAN INTERNET DI ERA INDUSTRI 4.0 Ahmad Fauzi; Sutan Faisal; Amril Mutoi
JURNAL BUANA PENGABDIAN Vol 4 No 1 (2022): JURNAL BUANA PENGABDIAN
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat, Universitas Buana Perjuangan Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36805/jurnalbuanapengabdian.v4i1.3167

Abstract

SMK Teknikom Cikarang merupakan sekolah kejuruan di kabupaten Bekasi pada bidang teknik pemesinan, teknik pengendalian produksi, teknik kendaraan ringan, teknik komputer dan jaringan, dan multimedia. Letaknya yang berada pada Kawasan industri potensial menuntut lulusan yang memiliki keahlian sesuai kebutuhan industri pada era industri 4.0. Para siswa perlu diberikan motivasi, pengetahuan dan keahlian dalam pemanfaatan internet untuk meningkatkan potensi yang dimiliki. Internet merupakan teknologi yang menyediakan berbagai data dan informasi dari penulis atau pengisi konten mengenai topik tertentu. Melalui pelatihan pemanfaatan internet untuk siswa SMK Teknikom Cikarang, siswa dapat menggunakan internet secara sehat, mendapatkan data dengan pencarian melalui key yang tepat, memanfaatkan Google Applications dengan lebih baik. Hal tersebut menunjang peningkatan prestasi siswa dalam pembelajaran di sekolah maupun peran di masyarakat. Kata kunci—era industri, internet, prestasi siswa SMK Teknikom Cikarang is a vocational school in Bekasi district in the fields of engineering, production control techniques, light vehicle engineering, computer and network engineering, and multimedia. Its location in a potential industrial area demands graduates who have skills according to industrial needs in the industrial era 4.0. Students need to be given motivation, knowledge and skill in using the internet to increase their potential. Internet is a technology that provides various data and information from writers or content fillers on certain topics. Through internet use training for SMK Teknikom Cikarang students, students can use the internet healthily, get data by searching through the right key, make better use of Google Applications . This supports the improvement of student achievement in learning at school and the role in society. Keywords—industrial era, internet, student achievement
Analisis Sentimen Gojek Indonesia Pada Twitter Menggunakan Algoritme Naïve Bayes Dan Support Vector Machine Yusuf Khoiruddin; Ahmad Fauzi; Amril Mutoi Siregar
Progresif: Jurnal Ilmiah Komputer Vol 19, No 1: Februari 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v19i1.1173

Abstract

Transportation is an important element in everyday life and serves as a means of connecting between places. Online motorcycle taxi services such as Gojek have penetrated all regions, enabling users to order transportation services such as motorcycle taxis, taxis and cars online through applications. With the large number of content tweeted by Twitter users related to the use of the Gojek application, therefore sentiment analysis is needed to determine user perceptions of a topic or event. This study aims to analyze people's responses to online transportation through data collected from tweets. The data is then classified into two sentiment classes, namely positive and negative. The classification results using the Naive Bayes algorithm show an accuracy of 91%, while the use of the SVM (Support Vector Machine) algorithm produces a higher accuracy, which is equal to 99%. This indicates that the SVM algorithm is more effective in producing higher classification accuracy than the Naive Bayes algorithm.Keywords: Sentiment analysis; Online transportation; Naive bayes; Support Vector Machine AbstrakTransportasi merupakan elemen penting dalam kehidupan sehari-hari dan berfungsi sebagai sarana untuk menghubungkan antar tempat. Layanan ojek online seperti Gojek telah merambah di seluruh wilayah, memungkinkan pengguna untuk memesan layanan transportasi seperti ojek, taksi, dan mobil secara online melalui aplikasi. Dengan banyaknya isi tweet pengguna twitter terkait dengan penggunaan aplikasi Gojek, oleh karena itu diperlukan analisis sentimen untuk mengetahui persepsi pengguna terhadap suatu topik atau peristiwa. Penelitian ini bertujuan untuk melakukan analisis respons masyarakat terhadap transportasi online melalui data yang terkumpul dari tweet. Data tersebut kemudian diklasifikasikan ke dalam dua kelas sentimen yaitu positif dan negatif. Hasil klasifikasi menggunakan algoritme Naive Bayes menunjukkan akurasi sebesar 91%, sedangkan penggunaan algoritme SVM (Support Vector Machine) menghasilkan akurasi yang lebih tinggi, yaitu sebesar 99%. Hal ini mengindikasikan bahwa algoritme SVM lebih efektif dalam menghasilkan akurasi klasifikasi yang lebih tinggi dibandingkan dengan algoritme Naive Bayes.Kata kunci: Analisis sentimen; Transportasi online; Naive bayes; Support Vector Machine
PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE DENGAN DECISION TREE PADA APLIKASI RUANG GURU Indi Nurul Hassanah; Sutan Faisal; Amril Mutoi Siregar
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 1 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i1.602

Abstract

Mobile Learning is electronic-based learning using a computer or computer-based. One of the most widely known Mobile Learning applications today is Ruang Guru. One way to determine the success of an application is to do a sentiment analysis of the application. The purpose of this study was to find the best accuracy model for classifying data in the SVM and Decision Tree algorithms. The data is taken from the comments column in the playstore on the Ruang Guru application as much as 1500 data. Then the data is labeled into 2 classes, namely positive and negative. After that, the data is divided into 70% training data and 30% testing data. The results of the comparison show that the best test model for sentiment classification cases is found in the SVM algorithm with an accuracy value of 84.2%, while the Decision Tree algorithm gets an accuracy value of 70%. So it can be concluded that the SVM algorithm has a better value for classification of review data in the Ruang Guru application compared to the Decision Tree algorithm. Keywords: Ruang Guru, sentiment analysis, SVM, Decision Tree Mobile Learning merupakan pembelajaran berbasis elektronik dengan menggunakan komputer atau berbasis komputer. Salah satu aplikasi Mobile Learning yang banyak dikenal saat ini adalah Ruang Guru . Salah satu cara untuk mengetahui keberhasilan suatu aplikasi adalah dengan melakukan analisis sentimen terhadap aplikasi tersebut. Tujuan penelitian ini untuk menemukan pemodelan akurasi terbaik terhadap pengklasifikasian data pada algoritma SVM dan Decision Tree. Data diambil dari kolom komentar di playstore pada aplikasi Ruang Guru sebanyak 1500 data. Kemudian data tersebut dilabelkan menjadi 2 kelas yaitu positif dan negatif. Setelah itu, data dibagi 2 menjadi data training sebanyak 70% dan data testing 30%. Hasil perbandingan menunjukkan model uji terbaik untuk kasus klasifikasi sentimen terdapat pada algoritma SVM dengan nilai akurasi sebesar 84.2% sedangkan pada algoritma Decision Tree mendapatkan nilai akurasi sebesar 70%. Sehingga dapat disimpulkan bahwa algoritma SVM memiliki nilai yang lebih baik untuk klasifikasi data ulasan pada aplikasi Ruang Guru dibandingkan algoritma Decision Tree.Kata kunci: Ruang Guru, analisis sentimen, SVM, Decision Tree
Implementasi Clustering Data Kasus Covid 19 Di Indonesia Menggunakan Algoritma K-Means Nofita Sari; Hanny Hikmayanti Handayani; Amril Mutoi Siregar
Bianglala Informatika Vol 11, No 1 (2023): Bianglala Informatika 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bi.v11i1.14762

Abstract

Covid19 adalah virus pertama kali terdeteksi di Wuhan, Cina pada akhir Desember 2019. Kasus Covid-19 masuk di Indonesia pada Maret 2020, tercatat mencapai 1.511.712 dengan jumlah kematian 40,858 dan sembuh 1.348.330 kasus. Di Indonesia terdapat 34 provinsi yang menjadi persebaran kasus Covid19. Penelitian ini bertujuan untuk mengelompokkan setiap provinsi di Indonesia ke dalam beberapa cluster tertentu agar mengetahui daerah dengan jumlah kasus yang tergolong tinggi, sedang, rendah. Mengelompokan data kasus Covid19 di provinsi Indonesia menggunakan teknik  clustering dengan menggunakan algoritma K-means. Data yang digunakan sebanyak 7098 data dari tanggal 1 Maret hingga 11 Oktober 2020. Dataset yang digunakan dari website AtapData (atapdata.ai). Mengolah data tersebut menggunakan Google Collaboratory dengan bahasa pemrograman python. Pada penelitian dilakukan optimasi menggunakan metode elbow yang menghasilkan jumlah cluster sebanyak 3 cluster. Pengujian dilakukan untuk mendapatkan nilai K yang optimal. Melakukan evaluasi menggunakan Sum of Square Error (SSE). Dari hasil evaluasi memiliki jumlah optimal K: 3 yaitu 228913736548657.56.Kata Kunci : Covid19, algoritma K means, Clustering, Metode ElbowCovid19 is a virus that was first detected in Wuhan, China at the end of December 2019. Covid-19 cases entered Indonesia in March 2020, it was recorded that it had reached 1,511,712 with 40,858 deaths and 1,348,330 cases of recovery. In Indonesia there are 34 provinces where the spread of Covid19 cases. This study aims to classify each province in Indonesia into certain clusters in order to identify areas with high, medium, low number of cases. The grouping of Covid19 case data in the Indonesian province uses a clustering technique using the K-means algorithm. The data used is 7098 data from March 1 to October 11 2020. The dataset used is from the AtapData website (atapdata.ai). Processing the data using Google Collaboratory with the python programming language. In this research, optimization was carried out using the elbow method which resulted in a total of 3 clusters. Tests are carried out to obtain optimal K values. Evaluation using Sum of Square Error (SSE). From the evaluation results, it has an optimal number of K: 3, namely 228913736548657.56.Keywords: Covid19, K mean algorithm, Clustering, Elbow Method
Optimization of Machine Learning Models with Segmentation to Determine the Pose of Cattle Amril Mutoi Siregar; Sony Hartono Wijaya; Ahmad Fauzi; Tjong Wan Sen; Sutan Faisal; Tukino Tukino; Yana Cahyana
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26750

Abstract

Image pattern recognition poses numerous challenges, particularly in feature recognition, making it a complex problem for machine learning algorithms. This study focuses on the problem of cow pose detection, involving the classification of cow images into categories like front, right, left, and others. With the increasing popularity of image-based applications, such as object recognition in smartphone technologies, there is a growing need for accurate and efficient classification algorithms based on shape and color. In this paper, we propose a machine learning approach utilizing Support Vector Machine (SVM) and Random Forest (RF) algorithms for cow pose detection. To achieve an optimal model, we employ data augmentation techniques, including Gaussian blur, brightness adjustments, and segmentation. The proposed segmentation methods used are Canny and Kmeans. We compare several machine learning algorithms to identify the optimal approach in terms of accuracy. The success of our method is measured by accuracy and Receiver Operating Characteristic (ROC) analysis. The results indicate that using the Canny segmentation, SVM achieved 74.31% accuracy with a testing ratio of 90:10, while RF achieved 99.60% accuracy with the same testing ratio. Furthermore, testing with SVM and K-means segmentation reached an accuracy of 98.61% with a test ratio of 80:20. The study demonstrates the effectiveness of SVM and Random Forest algorithms in cow pose detection, with Kmeans segmentation yielding highly accurate results. These findings hold promising implications for real-world applications in image-based recognition systems. Based on the results of the model obtained, it is very important in pattern recognition to use segmentation based on color even though shape recognition.
Perbandingan Algoritme Klasifikasi Untuk Prediksi Cuaca Amril Mutoi Siregar; Sutan Faisal; Yana Cahyana; Bayu Priyatna
Jurnal Accounting Information System (AIMS) Vol. 3 No. 1 (2020)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v3i1.280

Abstract

Weather conditions is an air condition in a place with a relatively short time, which is expressed by the value of parameters such as temperature, wind speed, pressure, rainfall, which is another atmospheric phenomenon as the main component. Human activities can be influenced by weather conditions, such as transportation, agriculture, plantation, construction or even sports. Therefore, for determining the weather, getting weather information needs to be made so that it can be utilized by the community. Problems that arise how to make weather predictions automatically so that it can be done by everyone. In this study proposed several algorithms Navie Bayes, Decision Tree, Random Forest to calculate the opportunities of one class from each of the existing group attributes and determine which class is the most optimal, meaning that grouping can be done based on the categories that users enter in the application. The prediction system has been made to obtain an accuracy rate of Navie Bayes of 77.22% with a standard deviation of 29%, a Decision Tree accuracy rate of 79.46% with a standard deviation of 15%, a random forest accuracy rate of 82.38% with a standard deviation of 43%.
PENGELOMPOKAN BIDANG LAJU PERTUMBUHAN EKONOMI INDONESIA MENGGUNAKAN ALGORITMA K-MEANS Amril Mutoi Siregar
Jurnal Accounting Information System (AIMS) Vol. 2 No. 2 (2019)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v2i2.342

Abstract

Indonesian is one of countries with economic development in the very good category. Economic growth is seen from several supporting fields, Indonesia has a lot of excess natural resources, which can support the economy compared to other countries. But the problem faced is the lack of maximum management of the economy, Indonesia has economic support categorized into 17 fields. Among the fields not in the same development because they are still stuck in one area, it turns out that Indonesia has all the potential to improve all fields. To increase the growth of all fields, the government must have correct, accurate and relevant data to group these fields. In this study using the Decision Tree algorithm to classify fields supporting economic growth automatically. The grouping results into three classes, namely high, medium, low. After the research was conducted the results were that the high category group was Mining and Excavation, Construction, transportation and warehousing, Provosion of accommodation and food Drinking, Information and Communication, Financial Services and Insurance, Real Estate, Educational Services, Health Services and Social Activities, medium groups were Procurement of Electricity and Gas, Company Services and low-income groups are in the fields of Agriculture, Forestry, and Fisheries, Processing Industry, water supply , waste management, Waste and Recycling, large Trade and retail, car and motorcycle repair, Government Administration, Defense and Compulsory Social Security, Other Services.
KLASIFIKASI ALGORITMA TF DAN NEUTRAL NETWORK DALAM SENTIMEN ANALISIS Amril Mutoi Siregar
Jurnal Accounting Information System (AIMS) Vol. 1 No. 2 (2018)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v1i2.359

Abstract

Nowadays social media has become one of the tools to express idea or opinion. They are more active expressing it on social media instead of speaking directly. Twitter is the most popular among them to express idea, also share news, picture, music and etc. Twitter users are increasing significantly each year as the result the information grows in same way. Due too much information flow, people get difficulties to make sure or clarify the news. For example, Looking for the information about a figure who will participate in a Pilkada. There are many researchers analyze subjectively and haven’t given the maximum result yet. This research is trying to clarify information and divided them into positive, negative and neutral information. It is using TF algorithm and Neutral Network as the tools. The dataset is taken from a figure’ twitter which is participate in Pilkada. And the result shows that accuracy 66.92%, positive precision 67.80%, negative precision  64.29%, neutral precision 73.33%, and positive recall 80%, negative recall 70%, neutral recall 36.67%.
Co-Authors Abdul Mufti Ahmad Fauzi Ahmad Fauzi Alma Hidayanti Andri Juliyanto Anton Romadoni Junior ARIF, SITI NOVIANTI NURAINI Baihaqi, Kiki Ahmad Basuni, Nursela Bunga Tiara, Vira Deden Wahiddin Dwi Sulistya Kusumaningrum Dwi Sulistya Kusumaningrum Dwi Vina Wijaya Faisal, Sutan Fariz Duta Nugraha Farkhina Dwi Utari Fauzi Ahmad Muda Fitri Nur Masruriyah, Anis Hanny Hikmayanti Handayani Hartono Wijaya, Sony Hilda Yulia Novita Indi Nurul Hassanah Indra Maulana, Indra Indra, Jamaludin Jaman, Jajam Haerul Jayidan, Zirji Juwita, Ayu Ratna Koirunnisa, Koirunnisa Kusumaningrum, Dwi Sulistya Kusumaningrum, Dwi Sulistya Kusumaningrum Lestari, Santi Arum Puspita Lilis Kartika Lutfiah Adeliana Maulana Abdur Rofik Maulana, Ikhsan Mulya Cahya Ramadanty Murniasih nabila, putri Nahrowi Nahrowi Nahrowi Nofita Sari Nurani Khoerunnisa Permana, Tedi Pratama, Adi Rizky Pratama, Rio Priyatna, Bayu Rahmad Nahar Siregar Rahmat Rahmat Ramadhan, Naufal Cahya Rizqi Fahrozi Rohana, Tatang Romadoni, Nurul Salsa Desmalia Santi Arum Puspita Lestari Sekar Wuni Sinta Candra Dewi Sinung Suakanto SITI NURJANAH Siti Silvia Arifin Sony Hartono Wijaya Sony Hartono Wijaya Sutan Faisal Sutan Faisal Sutan Faisal Tatang Rohana Tia Astiyah Hasan Tjong Wan Sen Tjong Wan Sen Tohirin Al Mudzakir Tohirin Al Mudzakir Tria Pratiwi Sutriyani Tukino Tukino Tukino, Tukino Wilda Amalia Y Aris Purwanto Yana Cahyana Yana Cahyana Yana Cahyana Cahyana Yholanda Maldini Yogi Firman Alfiansyah Yusuf Khoiruddin