Article Per Year (5 Year)
p-Index From 2020 - 2025
3.021
P-Index
This Author published in this journals
All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika Jurnal Masharif al-Syariah: Jurnal Ekonomi dan Perbankan Syariah Indonesian Journal of Artificial Intelligence and Data Mining JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Kumparan Fisika Jurnal Teknologi Dan Sistem Informasi Bisnis Jambura Journal of Electrical and Electronics Engineering Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri Jurnal Sistem Komputer & Kecerdasan Buatan Jurnal Pengabdian kepada Masyarakat Nusantara Action Research Literate (ARL) Jurnal Sisfotek Global International Journal Science and Technology (IJST) Journal of Scientech Research and Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Community Service
M. Asep Rizkiawan
Politeknik Takumi
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Social Sciences Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
Business Intelligence Visualisasi Data Penerimaan Mahasiswa Baru Menggunakan Tableau di Universitas ABC Anhari, Tirta; Alim, Endy Sjaiful; Rizkiawan, M. Asep; Hasan, Firman Noor; Aulia, Muhammad Fathan
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.570

Abstract

This study aims to analyze the application of Business Intelligence (BI) using Tableau in the new student admission process at ABC University. Tableau is used to visualize admission data for the period 2021 to 2023, including the number of applicants, geographic distribution, and course preferences. The research methodology involves data collection, cleaning, and integration which is then visualized in an interactive dashboard. The results showed a decrease in the number of applicants during the study period, with the lowest applicants in 2024. Geographic distribution analysis shows that DKI Jakarta and West Java provinces still dominate, indicating the need for expansion in conducting promotions and also data-based marketing strategies. In addition, the shift in the interest of applicants from Communication Science study programs to Pharmacist and Management Professions is an important finding, indicating a changing trend in prospective students' preferences for the fields of Communication Science and Business. This study concludes that the implementation of BI using Tableau provides significant benefits in improving the efficiency of decision-making, expanding the range of admissions, and strengthening the competitiveness of ABC University amid changing educational trends. The findings contribute to the literature related to BI implementation in the education sector and recommend further development to optimize university management in the future
Rancang Bangun Sistem Kendali Temperatur Pendingin Portable Menggunakan Thermoelectric FADLY, SINGGI; Avista, Zeluyvenca; Kurniawan, Eko; Witanto, Yudha; Ajitomo, Dimas Suryo; Rizkiawan , M. Asep; Ardiansyah , Bintang
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 8 No. 2 (2025): Volume VIII - Nomor 2 - Februari 2025
Publisher : Teknik Informatika, Sistem Informasi dan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v8i1.784

Abstract

Sistem kendali temperatur pendingin portable merupakan aspek penting untuk penyimpanan bahan makanan dan minuman dalam temperatur dingin. Mengendalikan termoelektrik dapat memudahkan pengguna dalam mengatur temperatur menggunakan dimmer yang mempengaruhi efek elemen peltier temperatur tinggi hingga rendah. Penelitian ini merancang sebuah kulkas mini yang ramah lingkungan serta hemat energi tidak menggunakan refrigerant kompresi yang mudah ditempatkan dimana saja. Sistem ini dirancang menggunakan dua sisi termoelektrik dilengkapi dengan kipas DC sebagai penyerap kalor didalam cooler box dan melepas kalor untuk tercapainya temperatur rendah, power supply 12VDC sebagai konversi tegangan AC ke DC dimana komponen termoelektrik menggunkakan tegangan DC dan dimmer DC sebagai pengatur tegangan, arus serta daya yang terhubung dengan peltier. Berdasarkan hasil pengujian, sistem dapat berfungsi dengan baik karena adanya waktu pengujian 20 menit awal menghasilkan 18.1°C dengan putaran dimmer 20% hingga menambahkan waktu pengujian selama 160 menit menghasilkan 12.8°C maka tercapainya temperatur tinggi ke rendah didalam pendingin box.
Pengenalan Internet of Things menggunakan Simulasi Cisco Packet Tracer untuk Siswa dan Mahasiswa di BPTI UHAMKA Rizkiawan, M. Asep Rizkiawan; Kurniawan, Eko Kurniawan
Sinesia : Jurnal Pengabdian Masyarakat Vol 1 No 2 (2024): Pemberdayaan Masyarakat dan Peningkatan Kualitas Layanan melalui Inovasi Pendidik
Publisher : Yayasan Penelitian Dan Pengabdian Masyarakat Sisi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69836/sinesia-jcs.v1i2.145

Abstract

The Internet of Things (IoT) Basic Introduction Training using Cisco Packet Tracer was held at the UHAMKA Information Technology Development Agency (BPTI) with the aim of increasing the understanding of students and the general public about the basic concepts of IoT and its use through software simulations. IoT is a technology that is increasingly developing and plays an important role in various industrial sectors and daily life. However, limited access to IoT hardware is a challenge in the learning process. Therefore, simulation using Cisco Packet Tracer was chosen as a practical and effective solution. The method used in this training is action research with quantitative and qualitative approaches. The training involved a theory session on the basic concepts of IoT and a simulation practice session using Cisco Packet Tracer, in which participants created mini IoT projects such as a network simulation of a Smart Home System based on the Internet of Things. Evaluation was conducted through pre-test and post-test to measure the improvement of participants' understanding, as well as observation and interviews to obtain qualitative feedback. The results showed that the average pre-test score of the participants was 45.3, while the average post-test score increased significantly to 82.7. A total of 85% of participants successfully completed the IoT mini project, demonstrating their mastery of basic IoT concepts.
Pengenalan dan Implementasi Smart Bell Berbasis Internet of Things DI SMK S Jakarta 1 Fadly, Singgi; Rizkiawan, M. Asep; Avista, Zeluyvenca; Kurniawan, Eko; Witanto, Yudha; Ajitomo, Dimas Suryo
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 2 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi April - Juni
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i2.5728

Abstract

Pengoperasian bel sekolah secara manual masih menjadi kendala dalam menjaga ketepatan jadwal di SMK S Jakarta 1. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut melalui implementasi Smart Bell berbasis Internet of Things (IoT) dalam program Pengabdian kepada Masyarakat (PKM). Metode yang digunakan meliputi identifikasi masalah, perancangan sistem, implementasi, serta pelatihan bagi pengguna. Smart Bell dirancang menggunakan mikrokontroler ESP32, pemrograman Arduino, dan platform Blynk untuk kendali jarak jauh melalui smartphone. Hasil implementasi menunjukkan bahwa sistem Smart Bell berfungsi dengan baik, menghasilkan suara yang jelas, serta meningkatkan efisiensi operasional sekolah. Berdasarkan survei Google Form yang diisi oleh siswa, mayoritas responden merasa bahwa sistem ini bermanfaat, materi yang diberikan mudah dipahami, serta program ini menambah wawasan mereka mengenai teknologi IoT. Kesimpulannya, penerapan Smart Bell berbasis IoT dapat menjadi solusi inovatif untuk meningkatkan efisiensi pengelolaan sekolah dan memperkenalkan teknologi modern kepada siswa dan tenaga pendidik.
Implementasi Load Balancing pada Google Cloud Platform Untuk Membangun Online Learning Sjaiful Alim, Endy; Rizkiawan, M. Asep; Subagyo, Ahmad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 4: Agustus 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.124

Abstract

Dalam era digital, institusi pendidikan menghadapi tantangan dalam menyediakan sistem pembelajaran daring yang andal, skalabel, dan responsif terhadap lonjakan pengguna. Salah satu permasalahan utama yang sering terjadi adalah bottleneck pada server web dan database, yang dapat menyebabkan penurunan performa saat jumlah pengguna meningkat secara signifikan. Penelitian ini bertujuan untuk mengatasi permasalahan tersebut dengan mengimplementasikan load balancing pada Google Cloud Platform (GCP) guna membangun platform pembelajaran daring berbasis Moodle yang optimal. Metode yang digunakan dalam penelitian ini mencakup perancangan dan implementasi infrastruktur berbasis layanan GCP, termasuk Compute Engine untuk hosting server web, Cloud SQL sebagai database terkelola, Cloud Memorystore Redis untuk caching guna mengurangi beban query pada database, serta Cloud Filestore untuk penyimpanan data. HTTPS Load Balancer digunakan untuk mendistribusikan lalu lintas pengguna secara merata ke beberapa instance server, sementara autoscaler diaktifkan untuk menyesuaikan kapasitas sumber daya secara dinamis sesuai kebutuhan pengguna. Hasil pengujian menunjukkan bahwa bottleneck utama pada sistem e-learning terjadi pada beban tinggi di database dan server web, yang dapat diatasi dengan caching dan load balancing. Implementasi ini memungkinkan sistem menangani lonjakan lalu lintas hingga 5.000 pengguna simultan. dengan penggunaan moodle data base mencapai 80,28 %, penggunaan autoscaling mencapai level 1,916. Utilisasi mulai menurun dan menunjukkan stabilisasi mendekati nilai 1. Stabilitas ini mengindikasikan bahwa autoscaler berhasil menyesuaikan jumlah instance dengan kebutuhan beban kerja, menjaga performa optimal aplikasi. Dengan demikian, penggunaan load balancing pada GCP terbukti meningkatkan keandalan, skalabilitas, dan efisiensi platform pembelajaran daring, serta memberikan panduan praktis bagi institusi pendidikan dalam mengadopsi teknologi cloud untuk mendukung kegiatan belajar-mengajar secara daring.   Abstract In the digital era, educational institutions face the challenge of providing a reliable, scalable and responsive online learning system to the surge of users. One of the main problems that often occurs is bottleneck on the web server and database, which can cause performance degradation when the number of users increases significantly. This research aims to overcome this problem by implementing load balancing on Google Cloud Platform (GCP) to build an optimal Moodle-based online learning platform. The method used in this research includes the design and implementation of GCP service-based infrastructure, including Compute Engine for web server hosting, Cloud SQL as a managed database, Cloud Memorystore Redis for caching to reduce query load on the database, and Cloud Filestore for data storage. HTTPS Load Balancer is used to distribute user traffic evenly across multiple server instances, while autoscaler is enabled to dynamically adjust resource capacity according to user needs. The test results show that the main bottleneck in the e-learning system occurs at high loads on the database and web server, which can be addressed by caching and load balancing. This implementation allows the system to handle traffic spikes of up to 5,000 simultaneous users. with moodle data base utilization reaching 80.28%, autoscaling utilization reaching a level of 1.916. This stability indicates that the autoscaler successfully adjusts the number of instances to the needs of the workload, maintaining optimal application performance. Thus, the use of load balancing on GCP is proven to improve the reliability, scalability, and efficiency of the online learning platform, and provides practical guidance for educational institutions in adopting cloud technology to support online teaching and learning activities.
Pose Analysis and Classification in Shooting Sport Using Convolutional Neural Network and Long Short-Term Memory Sobari, Bahar; Moedjiono, Moedjiono; Rizkiawan, M. Asep
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25566

Abstract

Shooting sport requires high accuracy and speed, making training evaluation essential for athlete performance improvement. Conventional evaluation methods are often limited, thus the application of Artificial Intelligence (AI) and Computer Vision provides an effective alternative. This research aims to analyze and classify shooting sport poses using Deep Learning methods. A dataset consisting of several thousand pose images was collected from both field recordings and publicly available sources, followed by preprocessing for coordinate extraction. Convolutional Neural Network (CNN) was employed to extract coordinate data from shooting pose images, while Long Short-Term Memory (LSTM) was applied for pose classification. Experimental results demonstrated 94% accuracy, 95% Percentage of Correct Keypoints (PCK), and 4 mm Mean Per Joint Position Error (MPJPE), with training conducted at a learning rate of 0.0001 over 150 epochs on 5% test data, involving a total of 596,642 parameters. These results indicate that the proposed CNN–LSTM model provides a reliable approach for pose analysis and classification in shooting sport. The contribution of this study lies in presenting a novel dataset and framework for AI-based shooting sport evaluation, which can enhance training feedback and broaden AI applications in sports. 
Evaluasi Proses Bisnis Layanan Mahasiswa Baru Di Perguruan Tinggi Menggunakan Capability Maturity Model Integration Anhari, Tirta; Gaol , Ford Lumban; Matsuo , Takuro; Rizkiawan , M. Asep
Action Research Literate Vol. 8 No. 9 (2024): Action Research Literate
Publisher : Ridwan Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/arl.v8i9.532

Abstract

Proses bisnis merupakan hal penting yang dapat menentukan kesuksesan dan keselarasan dari tujuan suatu perusahaan atau organisasi. Penelitian ini bertujuan untuk mengetahui seberapa matang proses bisnis layanan mahasiswa baru di Universitas XYZ, yang mencakup semua langkah dari pendaftaran hingga orientasi mahasiswa baru. Proses bisnis di ukur tingkat kematangannya menggunakan capability maturity model integration (CMMI). Tingkat kematangan CMMI terdiri dari Initial, managed, defined, quantitatively managed, dan optimizing. Data dikumpulkan melalui kuesioner yang di isi oleh 16 informan yang terlibat pada layanan mahasiswa baru. Setelah itu, Dilakukan analisis data untuk menemukan kekuatan dan kelemahan dalam proses bisnis saat ini. Menurut hasil penelitian, proses bisnis layanan mahasiswa baru di Universitas XYZ berada pada tingkat maturitas pada level 4 “quantitatively managed” dengan skor 5,34. Dalam makalah ini juga peneliti memberikan saran untuk melakukan perbaikan yang berkelanjutan agar dapat meningkatkan maturitas ke-yang lebih tinggi pada Universitas XYZ dan tentunya dapat meningkatkan layanan dan kepuasan mahasiswa baru melalui penelitian ini
Smart Building Design Web based Room Temperature and Humidity M. Asep Rizkiawan; Zeluyvenca Avista; Singgi Fadly; Eko Kurniawan; Hoirul Anam
Jurnal Elektronika dan Otomasi Industri Vol. 12 No. 1 (2025): Vol 12 No 1 (Mei 2025): Jurnal Elkolind Vol 12 No 1 (Mei 2025)
Publisher : Program Studi Teknik Elektronika Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/elkolind.v12i1.7474

Abstract

Pemantauan kondisi suhu dan kelembaban ruangan secara real-time menjadi aspek penting dalam mendukung kenyamanan dan efisiensi energi dalam gedung pintar (smart building). Penelitian ini bertujuan untuk merancang dan membangun sistem monitoring suhu dan kelembaban ruangan berbasis web yang terintegrasi dengan perangkat Internet of Things (IoT). Sistem ini menggunakan sensor DHT22 untuk mengukur suhu dan kelembaban, mikrokontroler ESP32 untuk mengirimkan data secara nirkabel melalui koneksi Wi-Fi, serta antarmuka web untuk menampilkan hasil pengukuran secara real-time. Data yang diperoleh tidak disimpan dalam database, namun ditampilkan secara dinamis pada web setiap kali halaman diakses atau di-refresh. Rancang bangun sistem ini juga dilengkapi dengan perancangan fisik kotak alat menggunakan perangkat lunak Autodesk Inventor, sehingga sistem dapat ditempatkan dengan rapi dalam lingkungan ruangan. hasil menunjukkan bahwa sistem mampu menampilkan suhu dan kelembaban secara akurat dan stabil. Implementasi sistem pada sebuah ruangan memperlihatkan keberhasilan sistem dalam membaca kondisi lingkungan dengan baik. Penelitian ini diharapkan menjadi dasar dalam pengembangan sistem monitoring lingkungan berbasis web yang lebih lanjut, termasuk penambahan fitur alarm pada suhu dan kelembaban yang melebihi ambang batas.
Peningkatan dan Efisiensi Operasional Supply Chain Management (SCM) dengan Memanfaatkan Teknologi M. Asep Rizkiawan; Harry Ramza
Jurnal Masharif al-Syariah: Jurnal Ekonomi dan Perbankan Syariah Vol 9 No 1 (2024)
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/jms.v9i1.21488

Abstract

Abstract- Abstract Supply chain management (SCM) is an integral part of any business that involves the movement of goods and services from one place to another. It covers a wide range of activities, from sourcing raw materials to delivering finished products to customers. However, traditional supply chain management systems are plagued with challenges such as lack of transparency and others. In recent years, emerging technologies such as blockchain, machine learning (ML) and artificial intelligence (AI) have shown promise in improving supply chain management efficiency, security, and transparency. This research investigates the potential of blockchain, machine learning (ML), and artificial intelligence (AI) technologies in improving supply chain management. This paper will summarize the challenges of traditional supply chain management systems and how blockchain, Machine Learning (ML), and Artificial Intelligence (AI) technologies can overcome these challenges. The paper will also evaluate real-life examples of blockchain and Artificial Intelligence (AI) use in supply chain management and their impact on operational efficiency and security. The purpose of this research is to identify the impact of Artificial Intelligence (AI) in supply chain management, the impact of Machine Learning in supply chain management, and the impact of Blockchain in supply chain management. With the results of the study Integrating new technologies such as AI and blockchain in managing supply chains can provide great benefits to organizations. These technologies can increase work efficiency, reduce costs, strengthen the footprint of goods, and increase security. AI can help with predictive analysis, demand forecasting, and automation. Meanwhile, blockchain can provide a guaranteed, open, and secure record for end-to-end tracking, and reduce the risk of fraud and errors. Keywords: Supply chain management 1, Efficiency 2, Technology 3, Blockchain 4, Machine Learning 5, Artificial Intelligence 6
Internet of Things (IoT) Based Temperature and Humidity Detector Prototype in the UHAMKA Data Center Room Rizkiawan, M. Asep; Ramza, Harry; Sofwan, Agus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28035

Abstract

Internet of Things (IoT) is a concept where an object or entity is imbued with technology such as sensors and software, aiming to communicate, control, connect, and exchange data with other devices as long as they remain connected to the internet. In this research, the developed IoT is employed to monitor and control the conditions of a data center space. The research methodology follows the system development life cycle, utilizing the Blynk application and a modified Arduino Uno with the esp8266 microcontroller, relay, and DHT-22 sensor for real-time temperature and humidity detection. The IoT development's outcomes were tested through black box and white box approaches. The research results demonstrate that the IoT network prototype functions effectively, enhancing the performance of the data center space. Temperature measurements were acquired from the DHT22 sensor, and alternative temperature measurements were taken without utilizing the DHT22 sensor, instead using a tool known as a thermometer, revealing measurement errors. Based on the calculation of the average percentage of temperature error on the DHT22 sensor, it can be concluded that the temperature error rate reaches 0.051%, while for humidity it reaches 0.064%, with an average delay time of 6.542 ms. Additionally, users have convenient access through both a website and mobile platform for seamless monitoring.
Co-Authors Abdul Multi Agus Sofwan Sofwan, Agus Sofwan Aji, Arielio Bayu Ajitomo, Dimas Suryo Alim, Endi Sjaiful Alim, Endy Sjaiful Anhari, Tirta Ardiansyah , Bintang Aulia, Muhammad Fathan Avista, Zeluyvenca Eko Kurniawan Eko Kurniawan Emilia Roza Endy Sjaiful Alim FADLY, SINGGI Firman Noor Hasan Gaol , Ford Lumban Hadi, Nofiar Harry Ramza Harry Ramza Hoirul Anam Kurniawan, Eko Kurniawan Matsuo , Takuro Moedjiono Moedjiono Nuroji Nuroji Nuroji Nuroji Prananda, Dimas Adi Rahmi Imanda Ramza, Harry Rosalina Singgi Fadly Sobari, Bahar Sofwan, Agus subagyo, Ahmad Sulaeman, Mia Kamayani Wijaya, Farid Rizky Yudha Witanto, Yudha Zeluyvenca Avista