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All Journal Indonesian Journal on Software Engineering (IJSE) Jurnal Teknik Komputer AMIK BSI Indonesian Journal of Business Intelligence (IJUBI) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi dan Sistem Komputer Indonesian Journal of Networking and Security - IJNS SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Teknik Informatika dan Teknologi Informasi
Rame Santoso
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Other

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Analisis Sentimen Terhadap Game Clash of Clans Berdasarkan Ulasan Pemain Menggunakan Metode Support Vector Machine Agustian, Satria Bayu; Tengku Pasyah, Ahmad Dani; Vinaro, Lahenda; Santoso, Rame; Purwandani, Indah
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 1 (2026): Vol 11 No 1 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i1.1163

Abstract

Popularitas game Clash of Clans menghasilkan volume ulasan yang besar pada platform Google Play Store hingga saat ini. Studi ini mengevaluasi opini pemain menggunakan pendekatan Support Vector Machine (SVM) terhadap 3.287 data ulasan yang dihimpun pada periode April-Mei 2025. Serangkaian tahapan preprocessing diterapkan, mulai dari pembersihan data hingga stemming. Selanjutnya, ulasan dikategorikan ke dalam label sentimen positif dan negatif. Data tersebut kemudian diproses melalui pembobotan teks TF-IDF untuk selanjutnya diklasifikasikan menggunakan algoritma SVM. Hasil pengujian menunjukkan dominasi sentimen positif dengan tingkat akurasi mencapai 89%. Temuan ini memberikan wawasan bagi pengembang dalam memetakan preferensi serta aspirasi pemain, sekaligus mengonfirmasi keandalan teknik machine learning untuk analisis sentimen yang presisi.
KLASIFIKASI KEMATANGAN PISANG BERDASARKAN CITRA WARNA KULIT MENGGUNAKAN DECISION TREE DAN SUPPORT VECTOR MACHINE DENGAN INTEGRASI YOLOV8 Gitisari, Deva; Nisrina, Restu Putri; Putri, Nayla Natania; Heristian, Sujiliani; Apriana, Veti; Santoso, Rame
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 2 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i2.6488

Abstract

  Di Indonesia, panen pisang sering dilakukan sebelum buah mencapai kematangan fisiologis. Akibatnya, seringkali pisang yang belum matang beredar di pasaran. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dua algoritma Machine Learning, yaitu Decision Tree dan Support Vector Machine (SVM) untuk menentukan tingkat kematangan pisang dengan  menggunakan dataset 6000 gambar pisang yang dikategorikan unripe, ripe, overripe, dan rotten. Dataset dipecah dalam rasio 80:20 untuk data latih dan data uji. Kemudian, metrik akurasi, presisi, recall, dan skor F1 digunakan untuk menguji. Hasil pengujian menunjukkan algoritma SVM memiliki akurasi tertinggi 92%, melampaui Decision Tree yang memiliki akurasi 82%. Model SVM Terbaik kemudian dikombinasikan dengan YOLOv8 untuk identifikasi kematangan pisang secara real-time menggunakan kamera. Penelitian ini memberikan kontribusi dengan menunjukkan efektivitas kombinasi HSV-SVM serta implementasi real-time menggunakan YOLOv8 menawarkan solusi praktis untuk pemantauan kualitas pisang secara otomatis.
Sistem Kecerdasan Buatan Untuk Deteksi Kondisi Daun Berbasis Metode Klasifikasi Fahrozi, Habil; Adiansyah, Rifky Ramadhan; Samit, Zaidan; Sujiliani, Sujiliani; Santoso, Rame; Apriana, Veti
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.v8i1.2315

Abstract

Plant diseases pose a significant threat to agricultural productivity. This study aims to develop and evaluate an artificial intelligence system capable of automatically detecting leaf health conditions and comparing the performance of two different deep learning architectures. Leaf image data obtained from the Kaggle dataset were processed and classified using Convolutional Neural Network (CNN) and MobileNetV2, while the YOLOv8 algorithm was applied to detect leaf objects within the images. The main evaluation metric used was classification accuracy to assess the model’s ability to identify whether a leaf is healthy or diseased. The results demonstrate the efficiency and comparative performance of both classification methods. The best-performing model was then implemented into a Python-based web application, enabling users to upload leaf images and obtain real-time health detection results. This implementation provides a practical contribution to the development of precision agriculture systems.
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.  
Sistem Deteksi Penyakit pada Tanaman Cabai Menggunakan RT-DETR dan YOLLOv8 Pedro Lucio Parera; Gregorius Bayu Listyoputro; Krisnavaro Raihananta; 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.v5i3.6373

Abstract

This study investigates the performance of two state-of-the-art object detection models, YOLOv8 and RT-DETR, in identifying diseases in chili plants, which represent a major challenge affecting horticultural productivity. Diseases such as anthracnose and Cercospora leaf spot often cause significant yield losses, and traditional manual identification tends to be inefficient, subjective, and error-prone due to the visual similarities found among disease symptoms. The objective of this research is to evaluate and compare the capabilities of both models using the Chili dataset from Roboflow Universe consisting of four classes: Anthracnose, Cercospora Leaf Spot, Healthy Fruit, and Healthy Leaf. The methodology includes data preprocessing, training using identical hyperparameters, and performance evaluation through accuracy and model behavior analysis during real-world testing. The findings indicate that RT-DETR achieves higher accuracy in controlled testing, reaching 90% for Anthracnose, 95% for Healthy Leaf, 100% for Healthy Fruit, and 85% for Cercospora Leaf Spot, supported by its transformer-based architecture that enhances spatial understanding. However, YOLOv8 demonstrates superior stability and consistency in real-world scenarios involving varying lighting, leaf orientations, and natural texture variations. The model also produces fewer misclassification errors, making it more reliable for practical field deployment. The implications of these results show that YOLOv8 is the most suitable model for integration into a Streamlit-based application due to its fast, responsive, and accurate inference, supporting early disease detection for chili farmers.
Library Information System Implementation MTs Ittihaadul Umam Depok Case Study Ishaq, Ahmad; Sumbaryadi, Achmad; Saputro, M. Ikhsan; Santoso, Rame; Sukendar, Toni
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3334

Abstract

Many organizations encounter challenges in managing information effectively and efficiently. A significant number of data management processes are still performed manually, which increases the risk of errors and information loss. Similar to libraries, which require quick and accurate access to information, this study aims to implement a web-based library information system for the MTs Ittihaadul Umam Depok library. The research employs the Rapid Application Development (RAD) methodology, which includes stages such as business modeling, data modeling, process modeling, application creation, and testing and turnover. At this stage, an analysis of the book management needs at MTs Ittihaadul Umam Depok was conducted to guide the system development. Based on the research findings and the challenges faced at the MTs Ittihaadul Umam Depok library, designing and implementing an integrated web-based library information system is expected to simplify the process of borrowing and returning books for all library members. Additionally, it will assist library staff in managing library data more efficiently and help the school advance its technological capabilities.
Co-Authors - AMIK BSI Bogor, Abdul Hamid - STMIK Nusa Mandiri, Firwan Santoso -STMIK Nusamandiri, Navy Apriyafiki Widanarko -UBSI, Sidik Abdul Hamid Achmad Sumbaryadi, Achmad Ade Sudaryana Adiansyah, Rifky Ramadhan Agustian, Satria Bayu Agustinus Aditya Bintara Ahmad Ishaq Ahmad Ishaq - Universitas Nusa Mandiri Anak Agung Gede Sugianthara Bagus Adi Wibowo Basuki Manto, Agung Doris Doris Doris, Doris Fahrozi, Habil Gitisari, Deva Gregorius Bayu Listyoputro Imanuddin, Ikhlas Irmayani Ningsih - Universitas Nusa Mandiri Ishak Kholil Ishaq, Ahmad Krisnavaro Raihananta Mahatma Mahesa Mochamad Rizky Ainur Ridho Mohammad Ikhsan Saputro Mohammad Ikhsan Saputro Mohammad Ikhsan Saputro, Mohammad Ikhsan Muhammad Rifki Zhaki Nisrina, Restu Putri Pedro Lucio Parera Purwandani, Indah Putri, Nayla Natania Putri, Wanda Rachmat Adi Purnama Rastono . Samit, Zaidan Samuel Orief Rosario Saputro , M. Ikhsan Saputro, M. Ikhsan Sidik . Sidik Sidik Sidik Sidik Sismadi, Sismadi Soni Kurniawan Sujiliani Heristian Sujiliani, Sujiliani Tengku Pasyah, Ahmad Dani Toni Sukendar Veti Apriana, Veti Vinaro, Lahenda Wahyuni, Ani