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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengabdian UntukMu NegeRI Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat INTEK: Informatika dan Teknologi Informasi Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Software Engineering and Information System (SEIS) Teknomatika: Jurnal Informatika dan Komputer
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Universitas Muhammadiyah Riau
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Computer Science and Information Technology (CoSciTech)

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Sentimen Analisis Masyarakt terhadap Kasus Penembakan Brigadir J Menggunakan Algoritma Naïve Bayes Classifier Febby Apri Wenando; Regiolina Hayami; Soni Soni; Ananda Fitria; Deyola Shifana
Computer Science and Information Technology Vol 4 No 2 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i2.5686

Abstract

Analisis sentimen merupakan riset komputasional dari opini, sentimen dan emosi yang di ekspresikan secara tekstual dengan menggunakan metode pengelompokkan sehingga menghasilkan penilaian bernilai positif atau negatif. Proses analisis ini umumnya dimulai dengan pengumpulan data yang kemudian di olah melalui pendekatan machine learning. Salah satu teknik pengumpulan data tersebut yaitu menggunakan internet dan beragam platform media sosial lainnya. Salah satu jenis platform media sosial yang sangat berkembang saat ini adalah Twitter. Media sosial Twitter mempermudah masyarakat untuk bebas berpendapat melalui cuitan atau biasa disebut dengan tweets. Netizen dapat dengan bebas menyampaikan opini pribadinya untuk topik apapun, termasuk persepsi terhadap kasus kriminal yang terjadi di Indonesia. Salah satu kasus terbaru yang tengah menjadi topik perbincangan hangat saat ini adalah kasus pembunuhan Brigadir Joshua dengan tersangka yaitu seorang Irjen Polri yaitu Ferdy Sambo. Sehingga di dalam penelitian ini opini masyarakat yang terdapat pada platform Twitter tersebut dapat dimanfaatkan sebagai bahan analisis sentimen untuk mengetahui pendapat publik terhadap kasus Ferdy Sambo. Data yang digunakan terdiri dari 234 data tweet dengan persentase opini positive sebesar 51,50% dan negative sebesar 48,50% yang kemudian diklasifikasikan dengan Algoritma Naive Bayes Classifier dengan hasil yang didapat nilai f1-score sebesar 75%.
Klasifikasi Kebakaran Hutan Dan Lahan Dengan Algoritma You Only Learn One Representation Rizki, Yoze; Yogi Alfinaldo; Soni; Sy, Yandiko Saputra; Rahmad Firdaus
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6434

Abstract

Forest areas have a function of storing carbon dioxide and producing oxygen from trees and plants. The function of forests is very important for life, so forests are highly protected. One solution that can be taken is to take preventive measures, namely monitoring fire hotspots in forest and land areas by air. This research was tested using the same dataset as the YOLO (You Only Look Once) algorithm against the You Only Learn One Representation (YOLOR) algorithm with a train data division model of 1188 image data and test data of 75 image data with mAP results of 66.36%. . So it can be confirmed that the YOLOR algorithm is better than the YOLO algorithm which gets an mAP value of 50.65%.
Perbandingan Algoritma SIMON dan SPECK Dalam Pengamanan Citra Digital Fatma, Yulia; Soni; Mikdad Amseno
Computer Science and Information Technology Vol 5 No 3 (2024): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v5i2.7619

Abstract

Cryptography is a data security technique by encoding data that is to be kept secret so that the original meaning of the data can no longer be understood. SIMON and SPECK are modern cryptographic algorithms issued by the National Security Agency (NSA). SIMON and SPECK are said to be algorithms that are known for their efficiency and strong security. This research will compare the performance of the SIMON and SPECK algorithms in securing digital images. Comparisons were made by testing time performance, changes in file size, and the level of randomness of image files using the Unified Average Changing Intensity (UACI) and Number of Pixels Change Rate (NPCR) metrics. The research results show that the average encryption and decryption time required by the SIMON algorithm is greater when compared to the SPECK algorithm. The image file size resulting from encryption using the SIMON and SPECK algorithms both increased by 24% from the original image. The level of randomness of the resulting image based on the UACI value obtained using the SIMON algorithm was found to be an average of 19.65%, while the UACI value obtained using the SPECK algorithm was an average of 20.94%. This shows that there is a significant change in intensity between the original image and the encrypted image. However, not all pixels in the encrypted image change when compared to the original image, this is shown by the NPCR value obtained from the SIMON and SPECK algorithm encrypted image, with average results of 49.98% and 50.17%.
Deteksi Serangan Dalam Ekosistem Iot Melalui Analisis Multi-Class Dengan Model Xgboost Dan Penerapan Teknik Imbalance Ratio Pada Dataset IoTID20 Amien, Januar Al; Sunanto, Sunanto; Rangkuti, Muhammad Al-Ikhsan; Soni, Soni
Computer Science and Information Technology Vol 6 No 3 (2025): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v6i3.9861

Abstract

This research focuses on attack detection in the Internet of Things (IoT) ecosystem using the XGBoost algorithm and the Imbalance Ratio technique on the IoTID20 dataset. The main goal is to overcome the problem of data imbalance that is common in IDS datasets and improve accuracy in classifying attack types. The methodology used includes data preprocessing, feature selection, and applying the Imbalance Ratio technique to handle class imbalance in the IoTID20 dataset. Next, the XGBoost model is implemented with the scale_pos_weight parameter to handle the class imbalance problem. This model is trained on training data and evaluated using metrics such as accuracy, precision, recall, and F1-score. The research results show that the combination of the XGBoost algorithm and the Imbalance Ratio technique is able to overcome data imbalance problems effectively. The resulting model achieved an accuracy rate of 99.32%, precision 99.32%, recall 99.32%, and F1-score 99.32% in classifying attack types on the IoTID20 dataset. These results demonstrate excellent capabilities in detecting attacks and distinguishing between normal and anomalous traffic in the IoT ecosystem. This research contributes to improving IoT network security by applying an effective Machine Learning approach to accurately detect attacks, while also addressing data imbalance problems that often occur in IDS datasets.
Co-Authors Ab Ghani, Hadhrami Agusriadi, - Al Amien, Januar Alris Gusnanda Aminullah, Rabiah Aminuyati Amran, Hasanatul Fu'adah Anam, M Khairul Ananda Fitria Andesa, Khusaeri ANDRIANSYAH Arkan, M Alif Baidarus Bambang Sugiantoro Bayu Anugerah Putra Br Bangun, Elsi Titasari Daud, Kauthar Mohd Deprizon, Deprizon Desti Mualfah Deyola Shifana Diah Angraina Fitri Diah Angraini Putri Dian Utami Didik Sudyana Edi Ismanto Eka Putra Eka Ramadhan Evans Fuad Fakhira Frisya Ramadhani Falda Dimantara Fatma, Yulia Febby Apri Wenando Fitri Handayani Fitri, Nurkhairi Fitria Aini, Fitria Fransiskus Zoromi, Fransiskus Gunawan, Rahmad Hadi Nasbey Hafid, Afdhil Hanum Salsabila Hari Sepdian Harun Mukhtar Hasanuddin Hasanuddin Hayami, Regiolina Hendri, Yusriadi Herianto Herianto Hul Hasanah, Sifa Ilham Firdaus Irzi Gunawan Januar Al Amien Januar Al Amien Jihan Aulia Kultum, Fi Ardhi Laksono Trisnantoro Lisman, Muhammad Mas’yuri, Dhina Nurriska Md Saleh, Nurul Izrin Miftakhul Jannah Mikdad Amseno Mohamad, Mohd Saberi Mohd Daud, Kauthar Muhammad Fajri Jamil Muhammad Hamadi Muzahaffar, Fatih Al Nengsih, Rafni Yulia Prastiwi, Adila Pramudiah Putra, Reza Tanujiwa Rahmad Firdaus Rahmad Firdaus Rahmaddeni Rahmaddeni Ramadhanti, Nurul Randra Aguslan Pratama Rangkuti, Muhammad Al-Ikhsan Remli, Muhammad Akmal Reny Medikawati Taufik Ricinur Ricinur Rico Apriandika Ridhollah, Farhan Rinaldi Rinaldi Rizki Anwar Rizki, Yoze Rizky Rahman Salam Septiana Srinandini Sofhia Mohnica Sunanto Sunanto Sy, Yandiko Saputra Torkis Nasution Unik, Mitra Vanama, Melsa Wan Salihin Wong, Khairul Nizar Syazwan Yogi Alfinaldo Yoze Rizki Yudi Prayudi Yulia Fatma Yulia Fatma Yusril Ibrahim