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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Applied Technology and Computing Science Journal Informatika Jurnal Informasi dan Teknologi Jatilima : Jurnal Multimedia Dan Teknologi Informasi Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Ipteks Terapan : research of applied science and education Journal of Student Development Information System (JoSDIS) Internet of Things and Artificial Intelligence Journal Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat
Hendra, Yomei
Informatika, Universitas Adzkia
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Sosialisasi Keamanan Cyber Kepada Siswa Sekolah Paket C : Menjaga data dan Privasi Maulana, Fajar; Hendra, Yomei; Thoriq, Muhammad; Eirlangga, Yofhanda Septi; Syaputra, Aldo Eko; Manurung, Kiki Hariani; Hayati , Nova; Mufid, Muhammad Fauzan
Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 01 (2025): Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat
Publisher : CV. Devi Tara Innovations

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Community service is one of the obligations of lecturers in implementing the Tri Dharma of Higher Education, aimed at assisting and supporting activities within the community. One such activity is carried out at PKBM Suka Maju Sejahtera (SMS) Padang, a non-formal educational institution focusing on educating disadvantaged communities through Programs A, B, and C. In the increasingly digital era, students face various challenges related to cyber security, such as threats to personal data and a lack of understanding about privacy protection in the online world. The low awareness among students about these threats increases the risk of misuse of personal information. Therefore, the community service conducted by lecturers from the Information Systems Study Program of Universitas Adzkia aims to provide students of the PKBM SMS Padang Program C with knowledge about cyber security, data protection, and privacy. This activity is expected to help students become more prudent in using the internet and understand how to effectively protect their personal information.
Sentiment Analysis of Gojek App Reviews on Google Play Store with Natural Language Processing Using Naive Bayes' Algorithm Rahman, Zumardi; Sakinah, Putri; Hendra, Yomei; Satria, Budy; Maulana, Fajar; Ayun, Aisyah Qurrata
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 6 No. 03 (2024): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v6i03.1189

Abstract

In the digital era, sentiment analysis is an important tool to understand user perceptions of applications, including the Gojek application. This study aims to analyze the sentiment of Gojek application user reviews on the Google Play Store using the Naive Bayes algorithm. The research process involved collecting 5,000 reviews, preprocessing the text, weighting with TF-IDF, and applying the Naive Bayes algorithm to classify sentiment into negative, neutral, and positive. The evaluation results show that the model has the best accuracy of 76% after applying the data balancing technique. The model's performance for negative sentiment is very good with a precision of 91% and an F1 score of 87%. Positive sentiment shows quite good performance with a precision of 76% and an F1 score of 65%. However, neutral sentiment has low precision (23%) although recalls increased to 51%. Sampling techniques such as SMOTE have succeeded in improving the model's ability to recognize underrepresented classes. With an overall evaluation of weighted average precision of 82% and an F1 score of 78%, this model is considered quite reliable in analyzing the sentiment of Gojek app reviews. This research provides insights for application developers in improving service quality based on user perception..
Development of a Rice Leaf Disease Detection Application Using Python-Based Computer Vision and YOLO Fajar Maulana; Yomei Hendra; Guswita Helmi; Radhiatul Husna; Amma Liesvarastranta Haz; Evianita Dewi Fajrianti
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 2 (2025): December
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/atcsj.v8i2.8486

Abstract

Rice is one of Indonesia’s primary agricultural commodities and is highly vulnerable to various leaf diseases, including blast, blight, brown spot, and tungro, which can significantly reduce crop productivity. To address this issue, an automated and accurate detection system is needed to assist farmers in identifying rice leaf diseases at an early stage. This study aims to develop a rice leaf disease detection application using computer vision technology based on Python and the YOLO (You Only Look Once) algorithm. The research methodology consisted of several stages: problem identification, data acquisition, data exploration, model development, evaluation, and deployment. The dataset was obtained from Roboflow and comprised five classes: blast, blight, brown spot, healthy, and tungro. The YOLO model was trained using Google Colab with optimized parameters to enhance detection performance. Experimental results demonstrate that the proposed model achieved an accuracy of 95% and a mean Average Precision (mAP) of 95%, indicating strong performance in detecting and classifying rice leaf diseases. The system was implemented as a web-based application using Flask and Bootstrap, allowing users to upload images of rice leaves and obtain real-time detection results. This application enables farmers to identify plant diseases quickly and accurately, facilitating timely and effective intervention to minimize crop losses.
Co-Authors Ainil Mardiah Aldo Eko Syaputra Aly, Moustafa H Amma Liesvarastranta Haz Asyahri Hadi Nasyuha Ayun, Aisyah Qurrata Budy Satria Evianita Dewi Fajrianti Fajar Maulana . Guswita Helmi Hamsar , Ali Hamsar, Ali Hariani Manurung, Kiki Hayati , Nova Hayati, Nova Husna, Radhiatul Manurung, Kiki Hariani Maulana , Fajar Maulana, Fajar Mufid, Muhammad Fauzan Muhammad Imam Akbar Muhammad Thoriq Radhiatul Husna Rahman, Zumardi SAKINAH, PUTRI Thoriq, Muhammad Yofhanda Septi Eirlangga