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All Journal Jurnal Penelitian Komunikasi dan Opini Publik Sisforma: Journal of Information Systems Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah KOMPUTASI Jurnal Dimensi Pendidikan dan Pembelajaran JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Tekno Kompak Jurnal Pengabdian Masyarakat Teknik Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia Brilliance: Research of Artificial Intelligence Jurnal Algoritma
Eka Angga Laksana
Universitas Widyatama
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Other

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Analisis Kinerja Pasar Saham Berbasis Business Intelligence secara Realtime Maulana, Rafli Iqbal; Laksana, Eka Angga
Jurnal Tekno Kompak Vol 18, No 1 (2024): FEBRUARI
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i1.3266

Abstract

Penelitian ini mengeksplorasi penggunaan widget Streamlit dan TradingView Python untuk membuat aplikasi dan dasbor pasar saham khusus. Streamlit adalah kerangka kerja sumber terbuka untuk tim Pembelajaran Mesin dan Ilmu Data, sedangkan TradingView adalah platform pembuatan grafik yang populer. Aplikasi ini memungkinkan pengguna untuk memilih simbol saham dan tanggal mulai untuk melihat harga saham dan indikator teknis. Aplikasi ini menggunakan modul yfinance dan ta untuk mengunduh harga saham dan menghitung indikator teknis. Aplikasi ini juga memungkinkan pengguna untuk mengunduh data harga saham sebagai file CSV menggunakan modul Python io dan os. Keuntungan menggunakan Streamlit dan TradingView adalah berbagai macam fiturnya, yang memungkinkan pengguna untuk membuat aplikasi dan dasbor yang disesuaikan. Namun, pengguna terbatas pada grafik dan fitur yang ditawarkan oleh TradingView. Secara keseluruhan, widget ini dapat digunakan oleh trader dan investor untuk melacak tren pasar, mengidentifikasi peluang trading potensial, dan mengelola portofolio.
TRAFFIC FLOW AND CONGESTION DETECTION WITH YOLOV8 AND BYTETRACK-BASED MULTI OBJECT TRACKING Fahrezi, Marchel Maulana; Laksana, Eka Angga
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2063

Abstract

The rapid urbanization witnessed in cities like Bandung, Indonesia, has emerged as a pressing issue, precipitating severe traffic congestion that poses challenges to economic growth and diminishes overall quality of life. This study endeavors to confront these multifaceted challenges through the development of a sophisticated real-time traffic surveillance and control system. The proposed system utilizes the current CCTV infrastructure in the city and incorporates advanced technologies like YOLOv8 for accurate vehicle detection and ByteTrack for dynamic real-time vehicle tracking. This system utilizes a comprehensive strategy, including multi-object tracking techniques to improve the precision of congestion detection. The system was thoroughly assessed in several places in Bandung, and it showed remarkable performance metrics. Specifically, YOLOv8 achieved an impressive 80% accuracy rate in vehicle detection, showcasing its efficacy in discerning vehicles within complex urban environments. Simultaneously, ByteTrack exhibited an average error rate of 17% in vehicle counting, further Strengthening the system's capabilities in accurately quantifying vehicular traffic. Furthermore, the combination of YOLOv8 and ByteTrack in a multi-object tracking paradigm yielded an 80% accuracy rate in congestion detection, emphasizing the system's robustness in real-time traffic management scenarios. These findings underscore the immense potential of the integrated YOLOv8 and ByteTrack system in traffic management strategies and alleviating congestion in smart cities like Bandung. This research has produced precise outcomes in identifying and quantifying the traffic congestion in various scenarios.
Rancang Bangun Teknologi Edukasi Berbasis Moodle dan Private Cloud di SMAN 26 Bandung Nafysah, Mutiara; Laksana, Eka Angga
JURNAL DIMENSI PENDIDIKAN DAN PEMBELAJARAN Vol 11 No 2 (2023): July 2023
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/dpp.v11i2.7151

Abstract

Teknologi informasi dan komunikasi berkembang dengan pesat dan telah menyentuh banyak bidang dalam membantu pekerjaan manusia. Salah satu bidang yang telah disentuh oleh teknologi informasi dan komunikasi adalah bidang pendidikan dengan penggunaan tekonologi e-learning dalam membantu kegiatan belajar mengajar. E-learning digunakan oleh banyak sekolah maupun universitas dalam melaksanakan kegiatan belajar mengajar terutama di masa pandemi. Meskipun sekarang kebanyakan kegiatan belajar mengajar telah dilakukan secara offline, website e-learning masih bisa digunakan untuk melaksanakan ujian maupun ulangan harian. Moodle adalah salah satu framework LMS (Learning Management System) open-source yang dapat diperoleh secara gratis dengan portal e-learning yang bisa dimodifikasi agar dapat disesuaikan sebagaimana kebutuhan penggunanya. Moodle yang telah dimodifikasi tentu baru dapat digunakan jika sudah diisi dengan data-data tertentu mulai dari pengguna aplikasi sampai pembagian kelas dan mata pelajaran. Setelah data primer didapat, data tersebut perlu diolah terlebih dahulu sesuai format sistem sebelum bisa di input ke dalam Moodle.
Optimasi LDA untuk Analisis Keluhan Nasabah Perbankan dengan Grid Search: Grid Search Parameter Tuning Rika Afriyani; Eka Angga Laksana
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 2 (2025): Agustus 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i2.2025.98-106

Abstract

This study aims to analyze topics in banking customer complaint data using the Latent Dirichlet Allocation (LDA) method, enhanced with parameter tuning via Grid Search. The dataset is sourced from ConsumerFinance.gov, containing a total of 6.3 million complaint entries from 2011 to 2024, with 50% of the data used to maintain representation and simplify analysis. In this analysis, the LDA method is employed to identify hidden topics, while Grid Search enhances model coherence. The results indicate that customer complaints can be categorized into 10 main topics, including complaint report issues (25.67%), payment errors (18.10%), data authorization (12.20%), and credit policy (10.77%). Parameter optimization successfully improved the model's coherence score from 0.49 to 0.56, reflecting an enhancement in topic clustering quality. A comparison between standard LDA and LDA with Grid Search reveals that the optimization method yields a higher average coherence score (0.52 vs. 0.42). This study provides insights into common complaints received by banks and key terms such as "report," "authorization," and "investigation," which can assist banks in better understanding and addressing customer complaints more effectively.
Assessment of E-learning Activity During COVID-19 Pandemic using Data Science Technique Eka Angga Laksana; Viddi Mardiasyah; Sunjana Sunjana; Yosi Malatta Madsu; Iwa Ovyawan Herlistiono; Andry Septian Syahputra Tumaruk
Brilliance: Research of Artificial Intelligence Vol. 6 No. 1 (2026): Brilliance: Research of Artificial Intelligence, Article Research May 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i1.7979

Abstract

The emerging of COVID-19 pandemic has become a threat to humanity, many activities of higher education forced to use Learning Management Systempad. This sudden transition significantly changed traditional face-to-face learning into fully online or blended learning environments, requiring both lecturers and students to quickly adapt to digital platforms and new methods of interaction.It provides various tools such as online quizzes, discussion forums, assignment submissions, and learning resources that can be accessed anytime and anywhere. Through these features, lecturers are able to distribute materials, monitor student participation, and evaluate learning outcomes more efficiently.The log records include information such as login frequency, access to learning materials, participation in discussion forums, quiz attempts, and assignment submissions.By applying data mining, statistical analysis, and data visualization methods, complex and unstructured log data can be transformed into meaningful insights. These visual representations help management identify trends, monitor student engagement, evaluate learning effectiveness, and support strategic decision-making in improving the quality of education.Processing log large data was optimized by the use of Graphics Processing Unit (GPU) and python programming language to extract, transform and load data (ETL) then convert the information to specific chart. By analyzing the result, we found some information regarded to student total activities by date, day, hour and also heatmap chart which represent total student activities by hour and day. Finally, the whole series of the processes are proposed as the assessment of e-learning activity on higher education.
Co-Authors Adrian, Mochammad Ilham Andry Septian Syahputra Tumaruk Ardhian Ekawijana Ase Suryana Azizah Zakiah Benny Yustim Fahrezi, Marchel Maulana Heri Heryono Iwa Ovyawan Herlistiono Mahendra, Randa Maulana, Rafli Iqbal Muhammad Fadzryan, Reza Nafysah, Mutiara Rika Afriyani Sukenda . Sukenda Sukenda Sunjana, Sunjana Viddi Mardiansyah Yosi Malatta Madsu