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All Journal Jurnal Teknologi Terpadu Jurnal Informatika Terpadu The Indonesian Journal of Computer Science DBESTI: Journal of Digital Business and Technology Innovation
adriansyah, ahmad rio
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science

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Analisis dan Pengembangan Sistem Informasi Pengelolaan Masjid berbasis Mobile dengan Teknologi API Web Service Mujahid, Ahmad; Abdullah, Muhammad Yahya; Suharya, Suharya; Adriansyah, Ahmad Rio
Jurnal Informatika Terpadu Vol 7 No 2 (2021): September, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v7i2.368

Abstract

YukAmal (yukamal.com) is a web-based mosque information and management system. Feature on YukAmal website is a donation, finance, construction progress, and information mosques. At present, the application cannot integrate with mobile apps. The research aims to design the YukAmal application based on Android Kotlin, integrated using REST API web service technology. The method applied in this research uses Scrum to get optimal results. This application divide into three modules: Information and Mosque Search, Donation and Mosque Finance, and REST API Web Service. The method used is UAT (User Acceptance Testing). For web service, REST API feature gets 85% test results, mosque information features and online donations get 80% test results, financial report feature gets 25% test results or can only view financial information. Results of the research, this application was proper for use by worshipers and mosque administrators in Depok City.
Analisis dan Perancangan Aplikasi Penganggaran Barang berbasis Web pada Unit Sarana Prasarana Perguruan Tinggi Maharani, Siti Zahra; Adriansyah, Ahmad Rio
Jurnal Informatika Terpadu Vol 8 No 1 (2022): Maret, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v8i1.402

Abstract

This study discusses the budget approval process at the Nurul Fikri Integrated Technology High School Facilities and Infrastructure Unit, which is inflexible because it requires a physical supervisor's signature, management of the procurement of goods that have not been carried out centrally and is expected to reduce delays in the fulfillment of goods and repetitive work. Therefore, an STT-NF Goods Budgeting application is needed to facilitate submissions and streamline time in meeting the needs of goods and data backup. The method used in designing the STT-NF Goods Budgeting application uses a literature review, interviews with the head of the Integrated High School Facilities and Infrastructure Section, Nurul Fikri, and the Incremental Development System method in stages. The study results showed the suitability of the features as expected; 96% of users stated that the features in the information system were by the process of submitting existing goods.
Pengenalan Pola Fonem Vokal menggunakan Short Time Fourier Transform (STFT) dan Fitur Mel Frequency Cepstral Coefficient (MFCC) Adriansyah, Ahmad rio; Prasetyo, Kurniawan Dwi; Atmam Al Faruqi, Hamdan Ainul
Jurnal Teknologi Terpadu Vol 7 No 1: Juli, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v7i1.298

Abstract

Phonemes are the building blocks of every oral language. Every utterance is composed of one or more phonemes. To improve the accuracy of acoustic models, the researchers attempted to identify the pattern of vowel phonemes in bahasa Indonesia using STFT and MFCC features. This paper analyzes 398 audio files gathered from 51 participants and explores the difference of phonemes a, i, u, e,o. Using SVM and Neural Network, the features are classified and tested. The result gave 93.8% accuracy using SVM with radial based kernel  
Pengembangan Sistem Deteksi Tuberkulosis pada Citra X-Ray Menggunakan Metode Convolutional Neural Network (CNN) dengan Framework Laravel Alimi, Aldi Akbar; Adriansyah, Ahmad Rio; Prima, Pudy
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1437

Abstract

Tuberculosis or TB is a disease caused by Mycobacterium Tuberculosis, which has a high transmission level. TB disease can be diagnosed through several methods, namely, using sputum samples and using x-ray scans. However, both methods take a long time to detect. Therefore, a detection system is needed to detect TB disease quickly and can be done by anyone. This research creates a detection system that can detect TB disease through chest x-ray images. The detection system is a web-based application built using the Laravel framework and a machine learning model with the Convolutional Neural Network (CNN) method for X-ray image analysis. This research will apply the CNN model that has been made into a web-based application through an API created using the FastAPI framework. The results of research on the detection system show that the detection system can detect TB disease. Proven by the results of testing conducted using the black box testing method, the test results show that the test success rate is 87%. In addition, the machine learning model with the CNN method can also provide classification on x-ray images well, where an accuracy of 93% is obtained on training data and 85% on test data.
Reengineering REST API Monolit ke Microservice Menggunakan Framework Laravel Sahrudin, Asep; Adriansyah, Ahmad Rio
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1158

Abstract

The increasing population growth in Jakarta has also led to a surge in demand for housing, including boarding houses. This surge in demand has become a challenge in managing applications and websites that provide information and services related to boarding houses, particularly in terms of managing user activity within the system. Therefore, researchers are trying to restructure the existing boarding house application using a microservice model. This research discusses the restructuring of the backend application from a monolithic to a microservice model, with a case study on Kos Jenggot. The development method employed in this research is Extreme Programming (XP), which incorporates testing methods, including black-box and performance testing. The results of this research show that using the microservice model is superior to the monolithic model; with black box testing results of 100%, the feature can run well through performance for the most significant scenario, namely an access load of 200 users in 100 seconds which has a total error of 100% for the monolithic model and 40 % for microservice models. These results indicate that the microservice model is more effective than the monolithic model for restructuring boarding applications.
Model Deep Learning untuk Penerjemah Bahasa Isyarat SIBI dengan Arsitektur Transfer Learning Al Fajri, Fikri Pratama; Adriansyah, Ahmad Rio; Munir, Sirojul
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1476

Abstract

People with speech and hearing disabilities have difficulties when communicating with non-disabled people because they use sign language which is rarely learned in general. To solve this problem, a deep learning model is needed that can detect sign language hand gestures which can then be made a sign language translator application that can facilitate communication between non-disabled people and people with disabilities. The purpose of this research is to create a deep learning model that is able to detect SIBI alphabet type sign language hand gestures with good accuracy. The CNN algorithm model uses Transfer Learning MobilenetV2 architecture and transfer learning method. The results of this study show that the model evaluation reaches 95.45% and the next model can be applied to the sign translator application, for further development it is expected to use more datasets so that the model gets a lot of variation during the training process.
Perbandingan XGB Regressor dengan Algoritma Lain untuk Prediksi Tarif Tol Al Khairi, Said; Adriansyah, Ahmad Rio; Rosyidi, Lukman
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1477

Abstract

In recent years, toll roads in Indonesia have grown rapidly, many of which were built to facilitate traffic in developed areas and improve the distribution of goods and services to support economic growth. In addition, toll roads play an important role as part of efforts to improve connectivity between cities and regions and accelerate community mobility. Many benefits of toll roads have been felt by the people of Indonesia such as, the Jagorawi toll road which smooths traffic so as to shorten the travel time from one region to another, and many more. The purpose of this research is to create a machine learning prediction of toll road tariffs to provide a reference to the public, optimise toll tariffs in Indonesia, and provide input on toll tariffs as a consideration for the relevant government. This research approach is quantitative using linear regression with XGB Regressor algorithm. The results of making machine learning toll tariff predictions are quite accurate where the accuracy test results using the root mean squared error (RMSE) metric are at 3390.691, with the testing results showing that there are several predicted tariffs that match the original tariff.
Penerapan Micro Frontend dengan Next.js dan Module Federation pada Aplikasi Cash Management Fikri, Muhammad; Drehem, Ishom Muhammad; Adriansyah, Ahmad Rio
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1631

Abstract

The digitalization of banking has made cash management applications essential for business efficiency; however, the monolithic architecture of the front-end in PT. Bank XYZ's cash management application presents significant challenges in terms of scalability and flexibility, particularly with the cash pooling feature. This study implements a Micro Frontend architecture using Next.js and Module Federation to enhance development and maintenance efficiency. The results show that build times have been reduced from 1 hour to 37 minutes, with an average build time of 12 minutes achieved through parallel processes. Deployments have also become faster, with only a few hours between releases. A survey of 11 respondents, including developers, quality assurance personnel, and other key stakeholders, recorded an average satisfaction score of 4.44 out of 5, indicating strong support. This implementation accelerates feedback loops, facilitates parallel development, and streamlines the feature release process, making it highly applicable to all features within the cash management application, thereby improving scalability and flexibility comprehensively.
Klasifikasi Gerakan Olahraga Panahan Menggunakan YOLO dan Metode LSTM Adriansyah, Ahmad Rio; Wibowo, Edi; Panji, Krisna
The Indonesian Journal of Computer Science Vol. 14 No. 3 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i3.4664

Abstract

The classification of archery movements presents a significant challenge in the development of technology to support athlete training. This study aims to develop an archery movement classification system using a combination of YOLO for pose detection and Long Short-Term Memory (LSTM) for temporal classification. The system processes archery training videos into a sequence of images. The aim of this research is to train LSTM model to recognize patterns from four predefined archery movement classes: stand, extend, hold, and release. Evaluation results show that the system achieved an accuracy of 64.47%. Furthermore, analysis using precision, recall, and F1-score metrics indicates varying performance across the movement classes, with the highest F1-score of 83.75% achieved in the release class. This study contributes to the development of machine learning-based technology to support sports training, particularly in archery, by offering a data-driven approach capable of automatically recognizing and evaluating athletes' movements.
Implementasi Sistem Pengolahan Data Terintegrasi dengan Algoritma K-Means pada KNIME Nabila, Zakiah; Adriansyah, Ahmad Rio
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 2 (2025): November, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i2.2059

Abstract

This research aims to develop and implement an integrated data processing system based on KNIME to analyze employee satisfaction at X School. The methodology involved collecting data via a survey distributed to 125 employees, integrating data from Google Sheets, preparing the data, applying the K-Means algorithm to cluster employees by satisfaction levels, and visualizing the results in an interactive dashboard. The research results indicate that the system was successfully built and can group employees into three clusters: Very Satisfied, Satisfied, and Less Satisfied. User acceptance testing (UAT) showed that the system met 80% of the testing criteria, indicating that most features functioned as expected by users. Evaluation using the Silhouette Coefficient produced an average value of 0.19, indicating less-than-optimal clustering quality, but the system still provided an overview of employee satisfaction levels. This system supports KNIME use for employee satisfaction analysis and provides strategic recommendations for X School to improve employee satisfaction and retention.
Co-Authors Abdullah, Muhammad Yahya Ahmad Mujahid, Ahmad Al Fajri, Fikri Pratama Al Khairi, Said Alimi, Aldi Akbar Atmam Al Faruqi, Hamdan Ainul Edi Wibowo Ishom Muhammad Drehem, Ishom Muhammad Lukman Rosyidi Maharani, Siti Zahra Muhammad Fikri Nabila, Zakiah Panji, Krisna Prasetyo, Kurniawan Dwi Prima, Pudy Sahrudin, Asep Sirojul Munir Suharya, Suharya