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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL Jurnal Eduscience (JES) Jurnal Informasi dan Teknologi Jurnal AbdiMas Nusa Mandiri Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Minfo Polgan (JMP) Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Algoritma Jurnal Teknologi Terapan and Sains 4.0 Dikdimas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Inovasi Masyarakat Indonesia Jurnal Rekayasa Sistem Informasi dan Teknologi Journal Of Artificial Intelligence And Software Engineering Pengabdian Pendidikan Indonesia (PPI) Jurnal Ilmu Komputer dan Sistem Informasi Portal Riset dan Inovasi Sistem Perangkat Lunak Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Proceedings of Malikussaleh International Conference on Multidisciplinary Studies (MICoMS) ITEJ (Information Technology Engineering Journals) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Rizki Suwanda
Universitas Malikussaleh, Aceh, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Human Face Deepfake Detection Using the YOLO Algorithm Sutri Wandani; Zara Yunizar; Rizki Suwanda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

Deepfake technology has emerged as a significant challenge to digital security because it can generate highly realistic manipulated facial images and videos. The rapid spread of deepfake content has increased the risks of identity fraud, misinformation, privacy violations, and various forms of cybercrime. This study proposes an Android-based human face deepfake detection system using the YOLOv8 algorithm. The dataset consisted of authentic facial images collected from Universitas Malikussaleh students and deepfake facial images generated using artificial intelligence techniques. The research methodology included data collection, image preprocessing, annotation using Roboflow, YOLOv8 model training, model evaluation, TensorFlow Lite (TFLite) conversion, and Android application development. Experimental results demonstrated that the proposed model achieved 95% precision and 95% recall in detecting real and manipulated facial images from both images and videos, indicating reliable detection performance. Nevertheless, several limitations remain. The dataset does not fully represent real-world facial variations, including differences in ethnicity, illumination, facial expressions, head poses, and occlusion caused by masks, glasses, or other objects covering facial regions. These limitations may reduce the model's generalization capability when deployed in real-world environments outside the testing dataset. Furthermore, the deepfake dataset only includes several manipulation techniques and has not been evaluated using more recent deepfake generation methods, such as diffusion model-based face swapping or other advanced generative approaches. Consequently, the model's performance may decrease when encountering manipulation techniques that were not included during training. In addition, the evaluation has not comprehensively considered challenging imaging conditions, such as motion blur, image noise, low-bitrate video compression, and quality variations introduced by different mobile device cameras, which may affect the robustness of the proposed deepfake detection system in practical applications.
IMPLEMENTASI METODE WEIGHTED AGGREGATED SUM PRODUCT ASSESSMENT (WASPAS) DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PESTISIDA TANAMAN PADI Muhammad Naufal; Muchlis Abdul Muthalib; Rizki Suwanda
Jurnal Teknologi Terapan and Sains 4.0 Vol 7 No 1 (2026): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v7i1.26696

Abstract

Penelitian ini mengembangkan sistem pendukung keputusan berbasis web untuk mengatasi subjektivitas dalam pemilihan pestisida pada tanaman padi di Dinas Pertanian Kabupaten Bireuen. Sistem ini menerapkan metode Weighted Aggregated Sum Product Assessment (WASPAS) untuk mengevaluasi 29 alternatif pestisida berdasarkan lima kriteria, yaitu harga (30%), volume racun per hektar (25%), ukuran kemasan (10%), masa kedaluwarsa (20%), dan luas cakupan (15%). Sistem dikembangkan menggunakan bahasa pemrograman PHP dan basis data MySQL dengan model pengembangan waterfall, serta divalidasi melalui pengujian BlackBox Testing. Hasil perhitungan metode WASPAS menunjukkan bahwa Sidabas 500 EC memperoleh nilai tertinggi sebesar 0,82936 sehingga direkomendasikan sebagai pestisida terbaik untuk pencegahan hama pada tanaman padi. Sistem yang dibangun mampu melakukan proses perangkingan secara otomatis dan menyajikan hasil secara terstruktur, sehingga mendukung pengambilan keputusan yang lebih objektif dan efisien dalam upaya meningkatkan produktivitas padi secara berkelanjutan di Kabupaten Bireuen. Kata kunci: Sistem Pendukung Keputusan, Pestisida, WASPAS, Tanaman Padi
PERBANDINGAN PREDIKSI HARGA SAHAM DENGAN MENGGUNAKAN METODE RECURRENT NEURAL NETWORK DAN LONG SHORT TERM MEMORY Fatia Naura; Safwandi; Rizki Suwanda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8345

Abstract

Unpredictable stock price fluctuations encourage the use of artificial intelligence methods based on deep learning. This study compares the performance of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) in predicting the share price of PT Pacific Strategic Financial Tbk. using 346 daily historical data (January 2024–June 2025) sourced from Investing.com. The data collected includes daily stock prices, such as opens, closes, highs, lows, and trading volumes, which will be used to train and test the prediction model. The research stages include pre-processing of data (Min-Max normalization and windowing), model design and training, evaluation using Mean Squared Error (MSE) and Mean Absolute Error (MAE), 5-fold cross-validation, and window size sensitivity analysis. The results showed that RNN was slightly superior to LSTM in prediction accuracy (MSE 0.001417 vs 0.001514; MAE 0.030937 vs 0.031938), inter-fold consistency, and computational efficiency of RNN parameters is only a quarter and memory usage is 1.5 times more efficient than LSTM. In contrast, LSTMs produce predictive patterns that are more visually refined and potentially more suitable for long-term trend analysis. The limited number of data (346 observations) on one issuer is allegedly a factor that limits the theoretical advantages of LSTM gating architecture. These findings imply that RNNs are a more efficient option for short-term stock price predictions with limited data, while LSTMs are more relevant for long-term trend analysis needs on larger volumes of data.  
IMPLEMENTASI FRAMEWORK CODEIGNITER DALAM PENGEMBANGAN SISTEM MANAJEMEN DATA DAN INFORMASI ALUMNI BERBASIS WEB Rizki Suwanda; Said Fadlan Anshari; Rizal Rizal
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 2 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i2.10470

Abstract

Data alumni salah unsur yang paling penting dalam peningkatan mutu perguruan tinggi. Alumni menjadi perpanjangan tangan sebagai pembentuk jaringan kerja yang diharapkan dapat membuka relasi instansi untuk memudahkan para mahasiswa dan alumni  saling berbagi informasi dalam dunia kerja dan ilmu pengetahuan. Sebagai upaya pengembangan dan peningkatan sistem manajemen informasi alumni diperlukan sebuah sistem pengelolaan data yang bisa diakses dengan waktu yang tidak terbatas sesuai dengan kebutuhan. Pengembangan sistem berbasis web dengan menerapkan Codeigniter berbasis php dengan menerapkan konsep  MVC. Codeigniter menjadi sebuah toolkit yang diminati ditujukan kepada pengembang aplikasi web dalam bahasa PHP dengan berbagai macam library yang disediakan yang dapat mempermudah dalam pengembangan aplikasi web. Hasil penelitian ini dapat diterapkan pada penelusuran data alumni dengan sistem manajemen data alumni berbasis web yang terkomputerisasi dan terstruktur dapat memudahkan program studi maupun universitas dalam mengelola data dan informasi para lulusan.
Implementation of Convolutional Neural Network for Leaf Disease Detection in Cayenne Pepper Plantsaper Ayu Suningsih; Zara Yunizar; Rizki Suwanda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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Abstract

Cayenne pepper (Capsicum frutescens L.) is an economically important horticultural crop in Indonesia; however, its productivity is frequently affected by leaf diseases, including leaf curl, leaf spot, and yellow leaf disease. Conventional disease identification mainly relies on visual inspection, making the diagnosis highly dependent on farmers’ experience and increasing the possibility of inaccurate identification and delayed treatment. This study proposes a Convolutional Neural Network (CNN)-based approach for automatic chili leaf disease classification and implements the trained model in an Android application for offline real-time detection. A total of 2,000 images were collected from chili plantations located in Lhokseumawe City and Aceh Tamiang Regency. The dataset was organized into five balanced categories, namely healthy leaf, leaf curl, leaf spot, yellow leaf, and non-leaf, with 400 images assigned to each category. The addition of a non-leaf category enables the application to distinguish chili leaves from irrelevant objects during mobile-based detection. Before training, all images were resized to 150 × 150 pixels, normalized, and partitioned into training, validation, and testing sets using an 80:10:10 ratio. The proposed CNN architecture comprised three convolutional layers followed by max-pooling layers, a flatten layer, a fully connected layer, a dropout layer, and a Softmax output layer. Experimental evaluation on the testing dataset produced an overall accuracy of 89.50%, while the macro-average precision, recall, and F1-score reached 90%, 89%, and 89%, respectively. The trained model was successfully converted into TensorFlow Lite (TFLite) format and integrated into an Android application capable of providing real-time disease prediction, confidence scores, disease descriptions, treatment recommendations, and detection history without requiring an Internet connection. These results indicate that the proposed system is suitable for practical field deployment to support early identification of chili leaf diseases.
Student Creativity Education in Plastic Waste Processing Innovation Tulus Setiawan; Rizki Suwanda; Said Fadlan Anshari; Nur Fazri Husna; Tiara Sartika
DIKDIMAS : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 3 (2024): DIKDIMAS : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Asosiasi Profesi Multimedia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/dikdimas.v3i3.350

Abstract

This community service activity aims to address environmental problems, emphasizing project-based learning, where participants are invited to solve real-world problems in their environment. The implementation of the Kurikulum Merdeka (Independent Curriculum) offers a great opportunity to utilize the Project for Strengthening Pancasila Student Profiles (P5) as a platform to encourage student involvement in environmental issues. One tangible form of P5 implementation is through a project converting plastic waste into oil. The activity was conducted at SMA Negeri 5 Kota Lhokseumawe, employing the creative education method for plastic waste processing, including education and training for teachers and students, as well as project assignments for processing plastic waste through the pyrolysis process. The results of the community service activity showed that more than 80% of respondents rated the educational activities, such as material delivery, awareness-raising, and providing new insights on plastic waste processing, as excellent. Moreover, 95% expressed satisfaction with the implementation of the plastic waste processing project via the pyrolysis process, which successfully provided educational, technical, and motivational experiences to participants. Education and the plastic waste processing project have proven to offer practical solutions that not only enhance students' knowledge and skills but also drive behavioral and attitudinal changes toward the environment.
Co-Authors ., Bustami Absa, Munzir Adek, Rizal Tjut Amanda, Sherly Dwi Ananda, Ria Anggara, Aji Armelia Dafrina Ayu Rosmala, Ayu Rosmala Ayu Suningsih Bayu Winata, Bayu Bustami Bustami . Bustami Bustami Dahlan Abdullah Dendy K. Pramudito Desi Efendi, Syahril Erna Budhiarti Eva Darnila Fadillah, Rizky Fadlisyah Fadlisyah Fanita Fanita, Fanita Fatia Naura Fatih Dwi Laksana Haj, Shafly Ulya Halimatus Sakdiah, Halimatus Hamdhana, Defry Judijanto, Loso Kamilah, Muna Laksana, Fatih Dwi Mardhatillah, Mardhatillah Maryana Maryana Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Daud Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Iqbal Muhammad Khardawi Muhammad Khoiruddin Harahap Muhammad Naufal Muhammad Zakaria Muhammad Zikri Mukti Qamal Muna, Ninal Munirul Ula Munzir Nazaruddin Ahmad Nur Fazri Husna Nur Mauliza Nurdin Nurdin Nurul Nafisa Pangestu, Aridho Rangkuti, Irsan Efendi Raudhatul Fazira Rini Meiyanti Rivalzi, Tri Ananda Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Roslaini Roslaini Rozi Kesuma Dinata Rozzi Kesuma Dinata Safwandi Safwandi Safwandi Safwandi, Safwandi Sagala, Wulandari Shakinahwati Said Fadlan Anshari Said Fadlan Anshari Siti Nadilla Sutri Wandani Syahriani Putri Ayu Syibral Malasyi, Syibral Taufiq Taufiq Teguh Brahmana Tiara Sartika Tri Ananda Rivalzi Tri Ramdhany Tulus Setiawan Wardina Ningsih Yudhi Franata Zahra, Annisa Afrilia Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar