Article Per Year (5 Year)
p-Index From 2021 - 2026
2.794
P-Index
This Author published in this journals
All Journal Dinamik Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Data Sciences Indonesia (DSI)
Amir Mahmud Husein, Amir Mahmud
Unknown Affiliation
Computer Science & IT Education Electrical & Electronics Engineering Engineering Physics

Published : 29 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 29 Documents
Search

 1   2   3 
Literature Review Application of YOLO Algorithm for Detection and Tracking Feri Imanuel; Waruwu, Seven Kriston; Linardy, Alvin; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4374

Abstract

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.
YOLO-Based Vehicle Detection: Literature Review: English Kosasi, Tommy; Sihombing, Zein Adian Laban; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4377

Abstract

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.
Model Prediksi Prestasi Mahasiswa Berdasarkan Evaluasi Pembelajaran Menggunakan Pendekatan Data Science Tommy, Tommy; Husein, Amir Mahmud
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1168

Abstract

Perguruan tinggi merupakan satuan penyelenggara pendidikan tinggi sebagai tingkat lanjut jenjang pendidikan menengah di jalur pendidikan formal. Aspek prestasi belajar merupakan salah satu aspek penilaian keberhasilan perguruan tinggi dalam proses belajar. Dalam makalah ini menyajikan hasil analisis hubungan antara pembelajaran dengan prestasi mahasiswa dimana tahapan yang dilakukan menggunakan pendetakan data science. Berdasarkan Analisis data terdapat tiga indikator penting dalam penilaian prestasi belajar yaitu pedagogi, profesional dan kepribadian. Ketiga fitur digunakan sebagai variabel dependen untuk memprediksi prestasi belajar dimana algoritma DecisionTree menghasilkan akurasi lebih baik dari pada model k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine, Naive Bayes dan dengan tingkat akurasi 68%, kemudian KNN dengan akurasi 66% dan lainnya sebesar 55% pada masing-masing algoritma yang diusulkan.
Pendekatan Data Science untuk Menemukan Churn Pelanggan pada Sector Perbankan dengan Machine Learning Husein, Amir Mahmud; Harahap, Mawaddah; Fernandito, Peter
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1169

Abstract

Peralihan pelanggan merupakan fenomena dimana pelanggan perusahaan berhenti membeli atau berinteraksi sehingga sangat penting bagi perusahaan khususnya perbankan untuk memprediksi kemungkinan churn pelanggan dan hasilnya dapat digunakan untuk membantu retensi pelanggan dan bagian dari strategi perusahaan. Makalah ini menyajikan analisis dan prediksi churn pelanggan dengan menggunakan lima model berbeda yaitu Kneighbors Classifier, Logistic Regression, Linear SVC, Random Tree Classifier dan Random Forest Classifier. Berdasarkan hasil pengujian pendekatan model Random Forest Classifier dan Kneighbors Classifier lebih baik dari pada model lain dengan akurasi sebesar 86% dan 84%. Rekayasa fitur dengan pendekatan Anova dan Chi Square memiliki pengaruh yang signifikan terhadap peningkatan kinerja model prediksi.
Analisis Prediktif untuk Keputusan Bisnis : Peramalan Penjualan Husein, Amir Mahmud; Lubis, Fachrul Rozi; Harahap, Muhammad Khoiruddin
Data Sciences Indonesia (DSI) Vol. 1 No. 1 (2021): Article Research Volume 1 Issue 1, June 2021
Publisher : Yayasan Cita Cendikiawan Al Kharizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dsi.v1i1.1196

Abstract

Peramalan penjualan produk adalah aspek utama dari manajemen pembelian, persediaan yang melebihi permintaan atau kekurangan akan berdampak pada manajemen pelayanan maupun secara ekominis. Makalah ini fokus mencoba menyajikan penerapan analisis prediktif dengan mengadopsi kerangka kerja Data Science (ilmu data) untuk menemukan wawasan yang berguna dalam pengambilan keputusan bisnis khususnya tentang peramalan penjualan produk di masa depan. Kerangka CRISP-DM diusulkan dengan tahapan pemahasan bisnis, pemahaman dan persiapan data, exploratory data analysis (EDA) dan pemodelan. Berdasarkan hasil pengujian data penjualan yang dievaluasi berdasarkan RMSE dan MAE, algoritma XGBoost menghasilkan prediksi berada dalam 1,3% kemudian ARIMA sebesar 1.6%, masih lebih baik dibandingkan LinearRegression, RandomForestdan LSTM dengan tingkat kesalahan sebesar 1.81%, 1.97%, 2.21% pada masing-masing algoritma dari data aktual.
Analisis Sinyal EKG untuk Deteksi Gangguan Jantung pada Mahasiswa dengan Gangguan Tidur Menggunakan Algoritma K-Nearest Neighbor (KNN) Anugrah Putri, Gustie Vaniest; Damanik, Melky Eka Putra; Hendiko, Kennyzio; Simarmata, Harry Binur Pratama; Husein, Amir Mahmud
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10256

Abstract

Gangguan tidur pada mahasiswa merupakan permasalahan yang dapat berdampak pada kesehatan jantung, khususnya melalui perubahan aktivitas listrik jantung yang terekam dalam sinyal EKG. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis untuk mendeteksi kondisi jantung berdasarkan sinyal EKG menggunakan algoritma K-Nearest Neighbor (KNN) dan reduksi fitur dengan Principal Component Analysis (PCA). Dataset yang digunakan terdiri dari 159 citra sinyal EKG yang dibagi menjadi dua kelas, yaitu Good Heart dan Bad Heart. Citra diproses melalui tahap preprocessing, reduksi dimensi menggunakan PCA, dan diklasifikasikan menggunakan KNN dengan berbagai nilai parameter. Model terbaik diperoleh pada kombinasi 20 komponen PCA dan nilai K = 6, dengan akurasi mencapai 96,23%, precision 98,46%, recall 92,75%, dan f1-score 95,50%. Hasil penelitian menunjukkan bahwa metode ini mampu mengenali kondisi jantung dengan baik dan efisien. Sistem ini berpotensi dikembangkan sebagai alat bantu deteksi dini gangguan jantung, khususnya pada kelompok mahasiswa yang mengalami gangguan tidur.
Analisis Tren dan Perkiraan Pandemi COVID-19 di Indonesia Menggunakan Peramalan Metode Prophet :Sebelum dan Sesudah Aturan New Normal Harahap, Mawaddah; Andika, Ahmad Zaki; Husein, Amir Mahmud; Dharma, Abdi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 1: Februari 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022914060

Abstract

Dalam menanggulangi penyebaran pandemi Covid-19 di Indonesia, pemerintah telah menetapkan PSBB dan aturan Normal Baru namun laju penyebaran pandemi terus meningkat dari waktu ke waktu. Selain itu, ketidakpastian akan berakhirnya pandemi ini berdampak pada perubahan kondisi sosial. Makalah ini bertujuan untuk memfasilitasi perbandingan antara PSBB dan regulasi New Normal tentang perkembangan jumlah kasus Covid-19 di Indonesia dengan memetakan jumlah kumulatif kasus (kasus aktif, sembuh, dikonfirmasi dan meninggal). Metode Prophet digunakan untuk memprediksi kasus kematian dan terkonfirmasi dalam 30 hari ke depan. Analisis data visual dengan pendekatan Exploratory Data Analysis (EDA) disajikan untuk memberikan pemahaman tentang perkembangan penyebaran pandemi di Indonesia. Pengujian kerangka analisis dilakukan dengan eksperimen untuk mengukur tingkat ketepatan prediksi metode Prophet dengan membagi kumpulan data historis periode 23 Maret 2020 - 31 Juli 2020, sedangkan data bulan terakhir dari kumpulan data periode 01 Agustus 2020 hingga 5 September 2020 digunakan sebagai target prediksi. Berdasarkan hasil pengujian metode Prophet memprediksi Indonesia akan mengalami peningkatan jumlah kasus terkonfirmasi sekitar 238.322 kasus dan kematian sekitar 9.609 hingga akhir September dengan tingkat kesalahan relatif dari estimasi yang dievaluasi dengan MAPE sekitar 23,9%. dan MAE sekitar 73,12 MAE. Hasil analisis visual penyebaran suatu pandemi juga disajikan dengan harapan dapat bermanfaat sebagai wawasan perkembangan jumlah kasus pandemi di Indonesia. Abstract In countering the spread of the Covid-19 pandemic in Indonesia, the government has set PSBB and New Normal rules but the rate of spread of the pandemic continues to increase from time to time. In addition, the uncertainty about the end of this pandemic has resulted in changing social conditions. This paper aims to facilitate a comparison between the PSBB and New Normal regulations on the development of the number of Covid-19 cases in Indonesia by mapping the cumulative number of cases (active, cured, confirmed and death cases). The Prophet method is used to predict confirmed cases and deaths within the next 30 days. Visual data analysis using the Exploratory Data Analysis (EDA) approach is presented to provide an understanding of the development of the pandemic spread in Indonesia. The testing analysis framework was carried out by experiments to measure the level of prediction accuracy of the prophet method by dividing the historical data set for the period 23 March 2020 - 31 July 2020, while the last month data from the data set for the period 01 August 2020 to 5 September 2020 were used as prediction targets. Based on the results of the Prophet method testing it is estimated that Indonesia will experience an increase in the number of confirmed cases around 238,322 and cases of death around 9,609 until the end of September with the relative error rate of estimates evaluated with MAPE around 23.9% and MAE around 73.12 MAE. The results of a visual analysis of the spread of a pandemic are also presented in the hope that they will be useful as an insight into the development of the number of pandemic cases in Indonesia.
Literature Review Application of YOLO Algorithm for Detection and Tracking Feri Imanuel; Waruwu, Seven Kriston; Linardy, Alvin; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4374

Abstract

A vehicle tracking system is a computer program that utilizes devices to monitor the position, movement and condition of a vehicle or fleet of vehicles. Multi-vehicle tracking on highways has significant research interest and practical value in building intelligent transportation systems. Nevertheless, traffic road video frames consist of various complex backgrounds and objects. Detection and tracking are very challenging because foreground to background switching occurs frequently. One-stage algorithm approaches such as YOLO and its various variants have been proven to be accurate for detecting vehicles. Meanwhile, the SORT, DeepSORT, ByteTrack and other algorithms can be combined in YOLO. The aim of this study is to highlight existing research on the application of YOLO and its variants in detecting and tracking vehicles, especially in traffic management. The journals used are limited to 2019 – 2024 and the journal sources consist of Hindawi, IEEE, MDPI, Research Gate, Science Direct, and Springer. Based on the research that has been reviewed, the YOLO variant algorithm approach has been successfully applied in the field of vehicle monitoring to support smart cities. In addition, many new model combinations and improvements have been proposed, proving that this algorithm has a big influence in the field of computer vision.
YOLO-Based Vehicle Detection: Literature Review: English Kosasi, Tommy; Sihombing, Zein Adian Laban; Husein, Amir Mahmud
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4377

Abstract

This research aims to evaluate the implementation of the You Only Look Once (YOLO) algorithm and its variants in the context of vehicle detection in traffic management systems. The importance of implementing intelligent transportation systems (ITS) in increasing transportation efficiency and reducing traffic problems such as congestion and accidents. The methodology used involves a critical review of current literature utilizing the YOLO algorithm for vehicle detection, with a focus on improving the accuracy of detection models. The research results show that the YOLO algorithm and its variants, such as YOLOv4 and YOLOv8, show a significant increase in vehicle detection accuracy reaching 90% in various environmental conditions. However, weaknesses in detecting small objects and in extreme lighting conditions still need further attention. This study also reviews several improvement approaches proposed in the literature, including the use of image augmentation techniques and the integration of deep learning models to improve the performance of the YOLO algorithm. The implementation of the YOLO algorithm in vehicle detection in intelligent transportation systems has great potential in increasing the efficiency and accuracy of traffic monitoring. This research provides recommendations for further development so that the YOLO algorithm can be better adapted to various environmental conditions and different types of data.
Co-Authors Abdi Dharma Ambarwati, Lita Andika Andika Andika Rahmad Kolose Sumangunsong Andika, Ahmad Zaki Andreas Simatupang Anugrah Putri, Gustie Vaniest Astasachindra, Rishi Banjarnahor, Prayoga Br Sihotang, Nurseve Lina Brandlee, Rio Christopher Christopher Damanik, Melky Eka Putra Dashuah, Ramonda Daulay, Tri Agustina Dodi Setiawan Fauza, Ra'uf Harris Feri Imanuel Fernandito, Peter Ginting, Deskianta Gracia, Andy Gulo, Befi Juniman Gulo, Steven Eduard Gultom, Atap Gunawan, Nico Hasibuan, Muhammad Haris Hendiko, Kennyzio HS, Christnatalis Hutauruk, Eben Kevin kevin Kevry Kosasi, Tommy Kwok, Shane Christian Larosa, Tri Putra Laurentius, Laurentius Leonardi, Jocelyn Linardy, Alvin Livando, Nicholas Lovely, Veryl Lubis, Fachrul Rozi Manik, David Hamonangan D. Mawaddah Harahap, Mawaddah Muhammad Arsyal, Muhammad Muhammad Khoiruddin Harahap Nainggolan, Yandi Tumbur Noflianhar Lubis, Kevi Ong, Derrick Kenji Phan, Gary Pratama, Panji Dika PUJI LESTARI Purba, Windania Purwanto, Eko Paskah Jeremia Salim Sidabutar, Daniel Shela Aura Yasmin Sihombing, Zein Adian Laban Silitonga, Benny Art Simanggungsong, Antonius Moses Simanjuntak, Andre Juan Simarmata, Allwin M Simarmata, Harry Binur Pratama Sinaga, Candra Julius Sinaga, Sutrisno Sinurat, Watas Sipahutar, Berninto Sirait, Agrifa Darwanto Siringo-Ringo, Dewi Sahputri Siti Aisyah Situmorang, Priskila Natalia C. Sormin, Pedro Samuel Syahputa, Hendra Tambun, Bella Siska Tambunan, Razana Baringin Daud Tampubolon, Hotman Parsaoran Tampubolon, Mei Monica Telaumbanua, Agustritus Pasrah Hati Tommy, Tommy Waren, Ashwini Waruwu, Seven Kriston William Chandra Willim, Alfredy Wizley, Vincent Yuanda, Yansan Yulizar, Dian Zagoto, Mariana Erfan Kristiani