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All Journal Journal of Telematics and Informatics JUITA : Jurnal Informatika Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal Teknik Komputer AMIK BSI Cakrawala : Jurnal Humaniora Bina Sarana Informatika Paradigma Jurnal Sains dan Informatika Jurnal Teknik Informatika STMIK Antar Bangsa JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi IAIC Transactions on Sustainable Digital Innovation (ITSDI) Computer Science (CO-SCIENCE) Conten : Computer and Network Technology Indonesian Journal of Networking and Security - IJNS Jurnal Insan Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Media Teknologi dan Informatika Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Teknik Informatika dan Teknologi Informasi
Rachmat Adi Purnama
Universitas Bina Sarana Informatika
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Social Sciences Other

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Pemilihan Provider Internet Ponsel Terbaik Dengan Metode Weighted Sum Model Dyas, Pramudya Widyastama; Purnama, Rachmat Adi; Triyanto, Dedi; Kurniawan, Deny
Media Teknologi dan Informatika Vol. 1 No. 4 (2024): Oktober
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/mti.v1i4.7623

Abstract

Penelitian ini bertujuan untuk menganalisis pemilihan provider jaringan ponsel terbaik berdasarkan pengalaman pelanggan menggunakan metode Weighted sum model (WSM). Enam provider utama yang diteliti adalah XL Axiata, Telkomsel, Three, Smartfren, Indosat, dan Axis. Data dikumpulkan melalui kuesioner dengan skala Likert 1-5, di mana nilai 1 menunjukkan "Sangat Tidak Baik" dan nilai 5 menunjukkan "Sangat Baik." Analisis dilakukan secara manual dan dengan menggunakan MATLAB untuk memastikan validitas hasil. Hasil penelitian menunjukkan bahwa Telkomsel mendapatkan skor tertinggi sebesar 205,45, mengindikasikan bahwa provider ini memberikan pengalaman pelanggan terbaik dibandingkan dengan provider lainnya. Faktor-faktor yang paling mempengaruhi kepuasan pelanggan meliputi kualitas sinyal, kecepatan internet, cakupan geografis, harga layanan, dan kualitas layanan pelanggan. Konsistensi antara perhitungan manual dan MATLAB menunjukkan keandalan metode WSM yang digunakan. Penelitian ini memberikan wawasan berharga bagi provider untuk meningkatkan kualitas layanan mereka dan membantu konsumen dalam membuat keputusan yang lebih terinformasi. Saran diberikan kepada Telkomsel untuk mempertahankan kualitas layanannya, sementara provider lain disarankan untuk meningkatkan aspek-aspek layanan yang kurang memuaskan. Penelitian ini juga membuka peluang bagi penelitian lanjutan untuk mencakup lebih banyak variabel dan provider.   This study aims to analyze the selection of the best mobile internet provider based on customer experience using the Weighted sum model (WSM). The six main providers studied are XL Axiata, Telkomsel, Three, Smartfren, Indosat, and Axis. Data were collected through a questionnaire using a Likert scale of 1-5, where 1 indicates "Very Poor" and 5 indicates "Very Good." The analysis was conducted manually and using MATLAB to ensure result validity. The results showed that Telkomsel obtained the highest score of 205.45, indicating that this provider offers the best customer experience compared to other providers. Factors that most influence customer satisfaction include signal quality, internet speed, geographic coverage, service price, and customer service quality. The consistency between manual calculations and MATLAB shows the reliability of the WSM method used. This research provides valuable insights for providers to improve their service quality and helps consumers make more informed decisions. Recommendations are given to Telkomsel to maintain its service quality, while other providers are advised to improve aspects of their services that are less satisfactory. This research also opens opportunities for further studies to include more variables and providers.
Penerapan Model Design Thinking Pada Perancangan Aplikasi Informasi Desa Wisata Kabupaten Bantul Hidayat, Wahyutama Fitri; Malau, Yesni; Purnama, Rachmat Adi; Setiadi, Ahmad
Computer Science (CO-SCIENCE) Vol. 4 No. 2 (2024): Juli 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i2.3459

Abstract

Tourism actors in the current technological era have implemented information systems. With the rapid growth of tourist villages in Bantul Regency, there is a need for promotion and digital information delivery media. However, in developing digital media it is also necessary to pay attention to aspects of the users who are the target market. The design of the application called sidewi mobile (mobile tourist village information system) is based on user experience and needs, using the Design Thinking methodology which has five stages as follows: Empathize, Define, Ideate, Prototype, and Test. The design of the Sidewi mobile application was created using FIGMA software. This research has direct benefits, namely that it can be used as a benchmark for design needs before the development process. The results of the design are then tested using the usability testing method. Using a user friendly design approach and conducting testing using usability testing with the results of five users being able to complete the testing proves that when it was created using user experience there were no significant difficulties when used and it covered all needs.
Usability Testing Aplikasi Informasi Desa Wisata Menggunakan Metode Cognitive Walkthrough dan System Usability Scale (SUS) Hidayat, Wahyutama Fitri; Setiadi, Ahmad; Malau, Yesni; Purnama, Rachmat Adi
Jurnal INSAN Journal of Information System Management Innovation Vol. 5 No. 1 (2025): Juni 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/j-insan.v5i1.8887

Abstract

Penelitian ini membahas pengujian usability pada aplikasi Sidewi Mobile guna mengevaluasi tingkat keterpakaian dan kepuasan pengguna. Metode yang digunakan adalah System Usability Scale (SUS) dan Cognitive Walkthrough untuk mengidentifikasi masalah serta memberikan rekomendasi perbaikan berdasarkan pengalaman pengguna. Hasil pengujian menunjukkan bahwa aplikasi memiliki tingkat keberhasilan penyelesaian tugas sebesar 89,2% dan efisiensi penggunaan dengan rata-rata waktu penyelesaian sebesar 0,01 goals/seconds. Nilai SUS yang diperoleh sebesar 52,1, menunjukkan bahwa aplikasi masih berada dalam kategori marginal low dan belum sepenuhnya dapat diterima oleh pengguna. Oleh karena itu, rekomendasi perbaikan mencakup peningkatan antarmuka pengguna serta penambahan fitur pencarian untuk meningkatkan pengalaman pengguna dalam mengakses informasi desa wisata.
Virtual Link Aggregation Network Performance Using MikroTik Bonding Firmansyah, Firmansyah; Wahyudi, Mochamad; Purnama, Rachmat Adi
IAIC Transactions on Sustainable Digital Innovation (ITSDI) Vol 2 No 2 (2021): April
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/itsdi.v2i2.394

Abstract

Quality of Service in a network is a big thing that must be resolved and dealt with as best as possible. The limitation of the maximum transfer rate in network devices creates an obstacle in the process of transferring data packets. To maximize the transfer rate in network devices, you can use Virtual Link Aggregation which can offer bandwidth optimization and failover in the network. Link aggregation is a solution in combining several physical links into one logical link. The method used in this research is to consider the allocation of bandwidth, load balancing and failover in the link aggregation. From the results of the link aggregation test using two (2) interface bonding, the results of the bandwidth averages when there is a UPD data packet transfer to 0 bps / 184.9 Mbps, which was previously around 0 bps / 91.6 Mbps. While the result of the bandwidth averages when the TCP data packet transfer occurs is 0 bps / 105.5 Mbps, which was previously around 0 bps / 93.8 Mbps. Link Aggregation using a Mikrotik Router is a solution to produce a larger Throughput Bandwidth by combining two (2) Ethernet Physical Links into one logical link.
Implementation of YOLOv8 and DETR for Multi-Level Tomato Ripeness Detection with Real-Time Bounding Boxes Muhammad Rizky Heriadi Putra; Deni Setiawan; Ahnaf putra hafezi; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5083

Abstract

Tomato ripeness detection is an essential component in the development of automated agricultural systems, enabling improvements in harvesting accuracy, sorting consistency, and supply chain standardization. Conventional grading processes rely heavily on manual observation, which is subjective, labor-intensive, and unsuitable for large-scale operations. Recent advancements in deep learning enable automated recognition of visual maturity indicators through object detection frameworks, offering a more reliable and scalable solution. This study examines the implementation of two modern detection models, YOLO and DETR, for multi-level tomato ripeness classification involving four distinct maturity stages. The research workflow includes dataset collection, annotation, preprocessing, model training, threshold calibration, and systematic evaluation to assess detection stability and classification behavior under real-world variability.Analysis of model outputs demonstrates that both architectures are capable of identifying multiple ripeness stages with useful levels of consistency, although each model exhibits strengths under different operational conditions. YOLO provides advantages in scenarios requiring real-time responsiveness and deployment on resource-limited hardware, making it suitable for mobile automation and field-based harvesting systems. DETR shows improved interpretive behavior in visually complex environments, particularly when samples exhibit subtle maturity differences or appear in overlapping cluster formations. The findings indicate that no single model is universally optimal and that deployment choice should be based on application requirements, environmental constraints, and operational objectives. This research contributes practical insight to the integration of artificial intelligence in agriculture and provides a foundation for future work exploring model fusion, advanced feature learning, or multispectral input integration to further enhance maturity classification performance.
Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning Napiah, Musriatun; Heristian, Sujiliani; Raharjo, Mugi; Purnama, Rachmat Adi
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10445

Abstract

Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi,” “sehat,” “anak sekolah,” and “gratis.” The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.
Komparasi SVM dan Random Forest Berbasis Histogram Warna untuk Deteksi Penyakit Anggur Faqihuddin, Muhammad; Purnama, Rachmat Adi
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2340

Abstract

The decline in grape (Vitis vinifera) productivity is often caused by leaf diseases such as Black Rot, which are challenging to detect accurately through manual visual inspection The key point of this research is to compare the performance of two Machine Learning classification algorithms, namely Support Vector Machine (SVM) and Random Forest, to identify the most optimal model for disease detection. The methodology employs digital image processing with Histogram Color (HSV) feature extraction, which is chosen for its efficiency in representing color changes caused by infection. The grape leaf disease image dataset was classified and evaluated. The comparative results demonstrate that Random Forest achieved the highest accuracy of 95.32%, slightly surpassing SVM which reached 94.48%. These findings prove that both algorithms perform excellently, but Random Forest is more recommended for this dataset due to its superior robustness in accurately predicting disease classes.
Real-Time Detection of Huanglongbing (HLB) Disease in Citrus Leaves Using Enhanced YOLO V8 Algorithm Sumanto Sumanto; Rachmat Adi Purnama; Hendra Supendar; Ade Christian; Teuku Vaickal Rizki irdian; Kaisar Ages Querio
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.82

Abstract

This study addresses the complex challenge of detecting Huanglongbing (HLB) disease in citrus leaves, which is known as one of the most lethal plant diseases with no known cure. The primary issue in HLB detection is the difficulty in identifying symptoms early and accurately, particularly in dynamic and uncontrolled field environments. Therefore, the main focus of this research is the development of a real-time detection approach using the YOLO V8 algorithm to more accurately detect and classify HLB symptoms in citrus leaf images. The objective of this study is to design a technique that can enhance the detection of HLB disease and compare its performance with the conventional YOLO V8 method. This research also aims to address the limitations of previous studies that used the Support Vector Machine (SVM) method, which only achieved an accuracy of 80%. To achieve this objective, the study utilizes a dataset consisting of 1200 citrus leaf images, representing various levels of severity, including mild, moderate, severe, and healthy leaves. The method employed in this research involves the use of the YOLO V8 algorithm to detect and classify HLB symptoms in citrus leaf images. This approach was tested through a series of experiments to measure accuracy, precision, recall, and computational efficiency. The experimental results consistently demonstrate that the developed approach outperforms the basic YOLO V8 and previous methods using SVM, with an improvement in HLB disease detection accuracy reaching 98%. This study provides critical insights into early detection of HLB disease, potentially serving as a powerful tool to support efforts in preventing the spread of this disease across citrus orchards. Additionally, this research opens opportunities for further development in real-time plant disease detection by integrating more advanced AI technologies and applying similar methods to other plant diseases. Future research can focus on developing more efficient and scalable algorithms for use in various field conditions, as well as exploring the integration of sensors and IoT technology for more comprehensive plant health monitoring.
Sistem Deteksi Penggunaan Helm Pada Pengendara Sepeda Motor di Indonesia Menggunakan Perbandingan Model YOLOv8 dan RT-DETR Samuel Orief Rosario; Agustinus Aditya Bintara; Muhammad Rifki Zhaki; Rachmat Adi Purnama; Rame Santoso; Veti Apriana
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6314

Abstract

Road safety is an important aspect in reducing accident risks, especially for motorcycle riders. To improve compliance with helmet use, this study compares the performance of two deep learning–based object detection models, namely YOLOv8 and RT-DETR, using a Roboflow dataset consisting of 3,735 images with two classes: with helmet and without helmet. The research process includes data acquisition, preprocessing (512×512 pixels), model training conducted in Visual Studio Code using an Nvidia GTX 1070 Ti GPU with the Ultralytics framework (100 epochs, AdamW optimizer, 0.0005 learning rate, 25 patience), testing on images, videos, and real-time inputs using last.pt, as well as evaluation through precision, recall, mAP, and confusion matrix, followed by implementation of the best algorithm in a local Streamlit web application.The results show that RT-DETR achieved slightly better training performance in terms of mAP50–95, while YOLOv8 performed better during real-world testing with more stable accuracy, particularly for the with helmet class. YOLOv8 reached up to 100% accuracy in video and real-time testing, whereas RT-DETR performed better in the without helmet class, achieving 95% accuracy on image data and up to 100% in video testing. Overall, YOLOv8 was selected as the best model for implementation in the Streamlit-based helmet detection application because it is faster, more stable, and more accurate. This system has the potential to support intelligent ETLE enforcement to enhance traffic safety in Indonesia.
Klasifikasi Wajah untuk Rekomendasi Gaya Rambut Menggunakan SVM dan Random Forest Mochamad Rizky Ainur Ridho; Mahatma Mahesa; Bagus Adi Wibowo; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6360

Abstract

The goal this project is to create a face-shape classification and hairstyle recommendation system by combining Support Vector Machine (SVM) and Random Forest (RF) algorithms with Histogram of Oriented Gradients (HOG) feature extraction. This study is motivated by the growing demand for individualized appearance support, as many users find it difficult to find haircuts that complement their face features. The method first preprocesses facial photos, uses HOG to extract key geometric and texture-based features, and then uses SVM and RF models to categorize the images. For training, validation, and testing, a dataset of five different face shapes is utilized. According to experimental results, the Random Forest model has an accuracy of about 89%, while the SVM model achieves an accuracy of about 95%. These findings suggest that SVM is better suited for managing high-dimensional feature spaces generated by HOG extraction. A recommendation system that offers hairstyle recommendations based on the anticipated face shape is then integrated with the trained model. The system is useful for real-time use since it can process pictures taken with the camera or uploaded from the gallery. Overall, this study shows that integrating HOG with SVM offers a dependable basis for creating customized hairdo recommendations as well as an efficient method for face-shape classification.  
Co-Authors Ade Christian Ade Setiawan Adi Tri Laksono Putra Aditya, Tri Putra Agus Tri Purnomo Agustinus Aditya Bintara Ahmad Setiadi Ahnaf putra hafezi Andy Rafsandi Anton Atrinawati, Lovinta Happy Ayi Rohmayati - STMIK Nusa Mandiri Jakarta Baginda Oloan Lubis Bagus Adi Wibowo Dedi Triyanto DENI SETIAWAN Dennis Gunawan, Dennis DENY KURNIAWAN Dyas, Pramudya Widyastama Ehon Abdulmanan Eka Kusuma Pratama Eka Ria Nugraha El Faraboy Endri Maulana Fadhil Naufal Shodiq Faqihuddin, Muhammad Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Gregorius Bayu Listyoputro Hanggoro Aji Al Kautsar Kaisar Ages Querio Kartika Handayani Krisnavaro Raihananta Mahatma Mahesa Mochamad Rizky Ainur Ridho Mochamad Wahyudi Mugi Raharjo Muhammad Rifki Zhaki Muhammad Rizky Heriadi Putra Musriatun Napiah Mustofa Mustofa Pedro Lucio Parera Prima Cop Rachmawati Darma Astuti Raharjo, Mugi Rame Santoso Rame Santoso Rian Septian Anwar Samuel Orief Rosario Sari Dewi Sari Dewi Sujiliani Heristian Sumanto, Sumanto Teuku Vaickal Rizki irdian Tri Putra Aditya Veti Apriana, Veti Waeisul Bismi Wahyutama Fitri Hidayat wati erawati Yesni Malau