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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 JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL INSTEK (Informatika Sains dan Teknologi) 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 Bulletin of Network Engineer and Informatics (BUFNETS) 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 Jurnal Polimesin 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 Automotive Engineering 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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Design of a Web-Based Self-Reporting Information System for Teacher Professional Education Fatih Dwi Laksana; Rizki Suwanda
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13932

Abstract

The manual self-reporting process for students in the Teacher Professional Education (PPG) program leads to delays, data inaccuracies, and administrative complexities. This research focuses on personal information of PPG students, educational history, and assignment documents. Data collection methods included interviews, observations, and document studies. The system development method followed the waterfall approach, involving requirements analysis, system design, implementation using PHP as the primary programming language, testing, and evaluation. The resulting information system leverages web technology and PHP to facilitate self-reporting for PPG students. Key features include online reporting forms, student data management, and handling PPG-related documents. Evaluation results indicate improved efficiency, accuracy, and speed in administrative processes, along with increased student engagement in self-reporting. The conclusion of this research is that the implementation of the web-based PPG Self-Reporting Information System using PHP successfully addresses administrative challenges in the Faculty of Teacher Training and Education. This system contributes positively to data efficiency and accuracy, enhances the quality of academic services, and strengthens administrative transparency. Therefore, the application of web technology and PHP in the PPG Self-Reporting Information System can serve as a model for developing similar systems in other educational institutions.
Penerapan Metode Naïve Bayes Dalam Sistem Rekomendasi Pemilihan Program Studi Pendidikan Tinggi Berbasis Website Rizki Suwanda; Said Fadlan Anshari; Rizky Putra Fhonna; Tulus Setiawan
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15858

Abstract

Pemilihan program studi pendidikan tinggi merupakan keputusan penting yang berdampak pada arah karir dan pengembangan potensi siswa. Namun, banyak siswa mengalami kesulitan dalam menentukan pilihan yang sesuai dengan minat dan kemampuan akademik mereka. Dalam praktiknya, pemilihan program studi masih sering dilakukan secara subjektif tanpa dukungan data atau sistem yang dapat membantu proses pengambilan keputusan secara rasional dan terukur. Penelitian ini bertujuan untuk mengembangkan sebuah sistem rekomendasi berbasis metode Naïve Bayes yang mampu memberikan saran program studi kepada siswa berdasarkan data minat dan prestasi akademik. Metode Naïve Bayes dipilih karena mampu mengklasifikasikan data secara efisien dengan pendekatan probabilistik, meskipun asumsi antar atribut bersifat independen. Sistem ini diharapkan dapat menjadi alat bantu bagi siswa maupun pihak sekolah (seperti guru BK) dalam memberikan arahan akademik berbasis data. Tahapan penelitian dimulai dari studi literatur dan perancangan sistem, dilanjutkan dengan pengumpulan data berupa minat siswa dan nilai akademik, baik melalui dataset simulasi maupun data uji terbatas dari responden nyata. Data tersebut kemudian diproses dan digunakan untuk membangun model klasifikasi menggunakan algoritma Naïve Bayes. Selanjutnya, sistem diuji untuk mengukur akurasi dan efektivitasnya dalam memberikan rekomendasi program studi yang sesuai. Penelitian ini juga mencakup evaluasi sistem berdasarkan hasil klasifikasi serta analisis keterkaitan antara input (minat dan prestasi) dan output rekomendasi program studi.
Web-Based Stroke Disease Classification System Using the Modified K-Nearest Neighbors Method Rizki Suwanda; Bustami Bustami; Muhammad Khardawi
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.16046

Abstract

Classification is a systematic method of grouping data based on predefined analytical rules and principles. One of the classification methods employed in this study is the Modified K-Nearest Neighbors (MKNN) algorithm, which is recognized for its potential to achieve higher accuracy. MKNN is an extension of the traditional K-Nearest Neighbors (KNN) method, incorporating an additional ranking stage and a weighted voting mechanism using an alpha value of 0.5. The object of this study is stroke disease. In the medical context, stroke occurs due to a disruption of blood flow to the brain. Ischemic stroke is caused by the obstruction of blood vessels and is generally considered less severe, whereas hemorrhagic stroke results from the rupture of blood vessels and is categorized as a severe condition. Hospitals in Indonesia are required to provide prompt and accurate healthcare services, in accordance with Law Number 36 of 2009 concerning Health. Approximately 70% of stroke patients have a history of hypertension and heart disease, while around 87% experience psychological disorders such as anxiety and depression. Based on data obtained from Cut Meutia Regional General Hospital (RSUD Cut Meutia) in Lhokseumawe, the classification of stroke types is still performed manually through clinical observation. Therefore, this study proposes a stroke classification system based on the MKNN algorithm. The system utilizes 11 features and two diagnostic classes, namely ischemic stroke and hemorrhagic stroke, with a total of 100 medical record datasets divided into 80 training data and 20 testing data. Using a value of K = 5, the system achieved an average confidence accuracy of 81.19%, with a precision of 85.71%, recall of 80%, F1-score of 82.75%, and overall accuracy of 75%. The system was developed using the PHP programming language and a MySQL database.
Prediction Of Industrial Waste Using The Autoregressive Integrated Moving Average Method Roslaini Roslaini; Dahlan Abdullah; Rizki Suwanda
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.624

Abstract

This study presents the development of a web-based industrial waste prediction system using the Autoregressive Integrated Moving Average (ARIMA) method to forecast the volume of liquid and solid waste generated by PT Pupuk Iskandar Muda (PIM). The predictive model is built upon historical waste data collected between 2020 and 2023, serving as the foundation for the statistical analysis. The system is developed using the Flask web framework, offering an interactive and user-friendly interface, while SQLite3 is employed as a lightweight local database solution for efficient data handling. The ARIMA (1,1,1) model was selected based on stationarity testing and examining ACF and PACF patterns. The results suggest that the model can moderately capture prediction trends, although limitations in accuracy are evident. For 2024, liquid waste is projected to decrease from 30,600 tons in January to 29,400 tons in December. In contrast, solid waste displays a more stable trend, with an average monthly generation of approximately 23.2 tons. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) method, yielding high error rates—166.11% for liquid waste and 100% for solid waste, highlighting the significant impact of data quality and completeness on prediction accuracy. The system generates visual outputs through interactive graphs and tables accessible via a web browser, supporting data-driven decision-making. This research is a predictive tool for PT PIM and a reference for future development of technology-driven waste management systems to promote environmental sustainability.
RANCANG BANGUN SMART GREENHOUSE OTOMATIS BERBASIS INTERNET OF THINGS DENGAN KONTROL RULE-BASED UNTUK OPTIMALISASI PERTUMBUHAN TANAMAN TOMAT: DESIGN AND CONSTRUCTION OF AN AUTOMATIC SMART GREENHOUSE BASED ON THE INTERNET OF THINGS WITH RULE-BASED CONTROL FOR OPTIMIZING TOMATO PLANT GROWTH Muhammad Zikri; Muhammad Fikry; Rizki Suwanda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6435

Abstract

This study aims to develop and implement an automated greenhouse system based on the Internet of Things (IoT) to monitor and analyze environmental conditions, including temperature, humidity, and light intensity. The system utilizes an ESP32 microcontroller integrated with a DHT22 sensor, soil moisture sensor, and LDR light sensor. Sensor data is transmitted in real time to the Blynk application, enabling users to monitor and control the system remotely via smartphone. The system uses a rule-based approach, such as activating a fan when the temperature exceeds 32°C, activating a water pump when soil moisture is below 30%, and turning on lights when light intensity is low. The experiment was conducted using a laboratory-scale prototype with kangkung plants. The system demonstrated its ability to provide timely and accurate environmental updates, enhancing greenhouse management efficiency. This system shows potential to support optimal plant growth while conserving energy.
PENGEMBANGAN APLIKASI AUGMENTED REALITY UNTUK PEMBELAJARAN BIOLOGI MENGGUNAKAN MARKER BASED TRACKING PADA PLATFORM ANDROID Teguh Brahmana; Taufiq; Rizki Suwanda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6460

Abstract

The development of Augmented Reality (AR) technology offers significant opportunities for creating interactive learning media, especially for abstract Biology topics. This study aims to develop an AR-based learning application using the marker-based tracking method on the Android platform. The developed application, named BioAR, presents a three-dimensional model of animal cell structures that can be displayed through the device’s camera with the help of markers. The development method used in this application is the Waterfall model, which consists of several stages: literature review, data collection, system design, application development, and testing. The 3D models of the organelles are created using Blender, then integrated into Unity with the use of Vuforia SDK. The application is tested using the black-box testing method, focusing on the functionality of navigation buttons, the distance between the camera and marker, light intensity, marker type, and testing on various Android devices with different specifications. The testing results show that the application performs well according to the designed functions. The application successfully displays 3D objects in real-time at an ideal distance between the camera and marker, ranging from 20–30 cm, with adequate lighting. Devices with at least 3 GB of RAM and high-quality cameras provide optimal results. This application is expected to serve as an alternative interactive learning media, easily accessible, and help visualize Biology material in a more engaging way.
IMPLEMENTASI ALGORITMA BOYER-MOORE DAN BRUTE FORCE UNTUK PENCARIAN TAFSIR DI ENSIKLOPEDIA AL-QUR’AN BERBASIS WEBSITE: MPLEMENTATION OF BOYER-MOORE AND BRUTE FORCE ALGORITHMS FOR TAFSIR SEARCH IN A WEB-BASED QUR’ANIC ENCYCLOPEDIA Siti Nadilla; Rizal Tjut Adek; Rizki Suwanda
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6552

Abstract

The growing demand for structured and efficient access to Qur’anic content based on specific themes underscores the need for a thematic search system for verses and tafsir. This study aims to develop a web-based Qur’anic encyclopedia featuring a keyword-based search function that employs string matching algorithms. We implemented and compared two algorithms Boyer-Moore and Brute Force to assess their performance in processing large textual tafsir datasets. The system was built using PHP and MySQL, utilizing a dataset comprised of categorized tafsir and verses according to themes. Our methodology included a literature review, interface design, system development, algorithm implementation, and performance testing. The results indicate that the Boyer-Moore algorithm is significantly faster and more efficient than the Brute Force algorithm for large-scale text searches. Testing with ten different keywords revealed that the average search time for the Boyer-Moore algorithm was 0.030 seconds, while the Brute Force algorithm took 0.069 seconds, making Boyer-Moore approximately 56% faster. The search feature reliably retrieved relevant verses and tafsir based on user input. Additionally, the system demonstrated user-friendliness through a responsive web interface. These findings highlight the effectiveness of string matching algorithms in digital Islamic tools and emphasize the advantage of the Boyer-Moore algorithm in optimizing the complexity of thematic Qur’anic searches.  
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
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i2.27553

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.  
Co-Authors ., Bustami Absa, Munzir Adek, Rizal Tjut Amanda, Sherly Dwi Ananda, Ria Anggara, Aji Armelia Dafrina Arnawan Hasibuan Ayu Rosmala, Ayu Rosmala Ayu Suningsih Bayu Winata Bustami Bustami . Bustami Bustami Cut Rifa Salsabil Dahlan Abdullah Defry Hamdhana Dendy K. Pramudito Desi Dini Fariha Efendi, Syahril Erna Budhiarti Eva Darnila Fadillah, Rizky Fadlisyah Fadlisyah Fadlisyah Fanita Fanita, Fanita Fatia Naura Fatih Dwi Laksana Haj, Shafly Ulya Halimatus Sakdiah, Halimatus Hamdhana, Defry Irsan Efendi Rangkuti Judijanto, Loso Laksana, Fatih Dwi Maisarah Mardhatillah, Mardhatillah Maryana Maryana Muchlis Abdul Muthalib Muh Fahrudin Alawi Muhammad Daud Muhammad Fathurrahman Fathurrahman Muhammad Fikry Muhammad Furqan Muhammad Habibi Muhammad Iqbal Muhammad Iqbal Muhammad Khardawi Muhammad Khoiruddin Harahap Muhammad Naufal Muhammad Zakaria Muhammad Zikri Mukti Qamal Muna Kamilah Muna, Ninal Munirul Ula Munzir Nazaruddin Ahmad Nur Fazri Husna Nur Mauliza Nurdin Nurdin Nurul Nafisa Pangestu, Aridho Raudhatul Fazira Rini Meiyanti Rivalzi, Tri Ananda Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek 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 Salshabila Putri Siti Nadilla Sutri Wandani Syahriani Putri Ayu Syibral Malasyi, Syibral Taufiq Taufiq Teguh Brahmana Tiara Sartika Tri Ananda Rivalzi Tri Ramdhany Tulus Setiawan Usman Usman Wardhiah Wardhiah Wardina Ningsih Yudhi Franata Zahra, Annisa Afrilia Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar