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All Journal International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian UntukMu NegeRI JURNAL MEDIA INFORMATIKA BUDIDARMA JPT : JURNAL PROTEKSI TANAMAN (JOURNAL OF PLANT PROTECTION) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Journal of Software Engineering and Information System (SEIS)
Gunawan, Rahmad
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Physics Social Sciences

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POTENSI IOT DALAM INDUSTRI 4.0 Zilham, Adib; Gunawan, Rahmad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 2 (2024): JATI Vol. 8 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i2.9209

Abstract

Penerapan Internet of Things (IoT) dalam konteks Industri 4.0 telah menjadi sorotan utama dalam pengembangan teknologi industri modern. Dengan menggunakan jaringan sensor, perangkat terkoneksi, dan sistem cerdas, IoT membuka peluang baru untuk meningkatkan efisiensi, produktivitas, dan inovasi dalam berbagai sektor industri. Studi literatur ini mengkaji potensi penerapan IoT dalam industri kesehatan, pertanian, telekomunikasi, ketenagalistrikan, kecerdasan buatan, smart home, dan data center. Tujuan utama dari integrasi IoT dalam Industri 4.0 adalah untuk memberikan efisiensi operasional dan kemudahan dalam melaksanakan berbagai tugas. Keberlanjutan perkembangan teknologi ini menjanjikan terus munculnya inovasi baru, yang diharapkan dapat menggerakkan kemajuan signifikan di berbagai sektor industri, termasuk peningkatan hasil dan efektivitas dalam pertanian. Dengan evolusi teknologi ini, Industri 4.0 dapat mencapai tingkat kematangan yang lebih tinggi, membentuk lanskap industri yang lebih pintar, adaptif, dan terkoneksi.
METODE DEEP LEARNING UNTUK ANALISIS TEKS: LITERATUR REVIEW Wesley, Royman; Gunawan, Rahmad
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 5 (2024): JATI Vol. 8 No. 5
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i5.11780

Abstract

Penelitian ini mengeksplorasi metode analisis sentimen teks, seperti Artificial Neural Network (ANN), Convolutional Neural Network (CNN) dengan Long Short-Term Memory (LSTM), Deep CNN, dan Bidirectional LSTM (BiLSTM). Hasil menunjukkan bahwa kombinasi ANN dan Synthetic Minority Over-sampling Technique (SMOTE) mencapai akurasi 87,06%, sementara CNN dan LSTM masing-masing mencapai 0,88 dan 0,84. BiLSTM mencatat akurasi terbaik sebesar 91%, dan BERT mencapai 73%, dengan potensi peningkatan melalui dataset yang lebih besar. Analisis komentar YouTube mengenai keputusan Mahkamah Konstitusi menemukan dominasi sentimen negatif, dengan model Multi-Layer Perceptron (MLP) menggunakan SMOTE mencapai akurasi 99%. Dalam penelitian ini, peneliti menggunakan metode kajian literatur, mengumpulkan data dari 10 artikel terakreditasi SINTA 1 - SINTA 5 yang diterbitkan antara 2019 hingga 2023. Analisis dilakukan untuk mengevaluasi kondisi terkait topik penelitian dan merumuskan kesimpulan mendalam dari berbagai sumber literatur. Temuan ini menekankan pentingnya teknik pengolahan data yang tepat dan keseimbangan dataset untuk meningkatkan performa model analisis sentimen, terutama dalam konteks isu sosial.
Pendekatan Transfer Learning untuk Klasifikasi Penyakit Mata Menggunakan Citra dengan CNN InceptionV3 Gunawan, Rahmad; Fathurrahman, Raihan; Widyaningrum, Amelia Ismania Sita; Issandra, Febri; Abdurachman, Muhammad Andhika; Putra, Yogi Ernanda; Naufal
Computer Science and Information Technology Vol 6 No 1 (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.v6i1.8509

Abstract

Eye diseases are a leading cause of vision impairment and blindness worldwide. Therefore, detection of eye diseases is crucial in the prevention of blindness. This study develops an eye disease classification model based on Convolutional Neural Network (CNN) using Transfer Learning with InceptionV3. The dataset consists of 1559 images, divided into 1249 training images and 310 validation images, covering 8 eye disease classes. The model was trained using 40 epochs with the Adam optimizer. Evaluation results show a validation accuracy of 81.29%. While the model performed well, some classes, such as hordeolum, showed lower accuracy, indicating areas that need further improvement. This study confirms that Transfer Learning with InceptionV3 is an effective approach for eye disease classification.
Klasifikasi Penyakit Daun Kentang dengan Transfer Learning Menggunakan CNN optimalisasi Arsitektur MobileNetV2 Gunawan, Rahmad; Fauzan Salim; Wahyudhy, Adhe Indra; Wibowo, Angga Yudha; Yordan, Gibril; Filamori, Refly Fauzan
Computer Science and Information Technology Vol 6 No 2 (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.v6i2.8599

Abstract

Potatoes are a major food crop with high economic value, but they are susceptible to various Diseases impacting potato leaves can significantly influence their quality and productivity. This research focuses on identifying diseases in potato leaves through the Convolutional Neural Network (CNN) approach, leveraging transfer learning with the MobileNetV2 architecture. The dataset utilized comprises 4,072 images of potato leaves. categorized into three groups: non-infected leaves (healthy ), Early Blight-infected leaves, and Late Blight-infected leaves. The dataset is processed through data augmentation and normalization to enhance data quality. The resulting model demonstrates excellent performance, achieving an accuracy of 95.31%, a precision of 95.81%, a recall of 95.31%, and an F1-Score of 95.38%. These findings indicate the approach demonstrates its ability to identify the condition of potato leaves with a low classification error rate, especially in the healthy category. However, there are challenges in classifying between Early Blight and Late Blight that require further analysis and method improvement. This study contributes to the development of efficient and accurate plant disease detection systems.
Inovasi Pemanfaatan Limbah Air Kelapa Menjadi Pupuk Organik Cair (POC) dalam Mendukung Pertanian Ramah Lingkungan di Desa Teluk Merbau Gunawan, Rahmad; Rais, Muhammad_Akmal; Ramadhan, Syahrudin; Damayanti, Risma; Maysa Putri, Yulia; Zaskiv S, Marshal Khairana; Pratiwi, Husnatul Fadillah; Pratama, Ade; Sugiyadi, Riski; Riani, Della Ayunda; Arfa, Laura Zevira; Rahmania, Marsha Nailah; Yanto, Apri; Yanti, Elis
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10283

Abstract

Sektor pertanian di Indonesia masih menghadapi tantangan dalam mengurangi ketergantungan terhadap pupuk anorganik. Penggunaan pupuk kimia yang berlebihan selain meningkatkan biaya produksi juga menimbulkan dampak negatif terhadap kualitas tanah dan lingkungan. Salah satu alternatif yang dapat dikembangkan adalah pupuk organik cair (POC) berbahan dasar air kelapa, yang memiliki kandungan unsur hara makro, mikro, dan hormon pertumbuhan tanaman. Kegiatan pengabdian masyarakat ini dilaksanakan bersama Ibu-Ibu PKK Desa Teluk Merbau melalui sosialisasi dan pelatihan pembuatan POC dari limbah air kelapa. Metode penelitian menggunakan pendekatan kualitatif dengan desain studi kasus yang melibatkan sosialisasi, demonstrasi, serta evaluasi terhadap pemahaman dan keterampilan petani dalam pembuatan POC. Hasil kegiatan menunjukkan peningkatan pengetahuan dan keterampilan peserta dalam memanfaatkan limbah air kelapa menjadi produk pupuk organik cair yang bernilai guna. Dampak lain yang terlihat adalah tumbuhnya kesadaran akan pertanian berkelanjutan serta peluang ekonomi melalui pemanfaatan limbah rumah tangga. Kegiatan ini diharapkan menjadi awal bagi masyarakat desa dalam mengembangkan inovasi pengolahan limbah organik yang ramah lingkungan dan mendukung kemandirian pertanian.
Penerapan Teknologi Rocket Stove untuk Mengurangi Polusi Pembakaran Sampah di Kampung Merangkai Pradipa, Raditya; Gunawan, Rahmad; Alfiah Insani Amin, Andi Nur; Yuliskania, Aisyara; Tania, Manzilah Ditiara; Harmawan, Muhamad Rizki; Jasmin, Muhammad Iqbal; Nugroho, Altaric; Fadilla, Niken Rahma; Vania, Azra Gusti; Avicenna, Achyar Zein; Nofrial, Nofrial; Razkia, Binta; Nadira, Besti Zahratul
Jurnal Pengabdian UntukMu NegeRI Vol. 9 No. 3 (2025): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v9i3.10343

Abstract

Ineffective plastic waste management remains a significant problem in rural communities, particularly due to traditional burning practices that generate air pollution, odors, and health risks. The Muhammadiyah ‘Aisyiyah Community Service Program (KKNMAs) in Merangkai Village, Dayun District, Siak Regency aimed to provide an alternative solution through the application of appropriate technology in the form of a rocket stove. The implementation method consisted of preparation (literature study, field observation, and design), execution (socialization, construction of a rocket stove unit, and technical training), and evaluation (monitoring, interviews, and design improvements). The results indicated that the rocket stove reduced smoke and odor emissions by 60–80% compared to traditional burning, improved efficiency in processing dry waste, and encouraged active community participation in environmental management. Success factors included technology design, support from local government, and community awareness, although the limited number of units remained a challenge. This program demonstrated that rocket stove technology offers a sustainable small-scale waste management solution with potential replication in other rural areas.
Klasifikasi Algoritma Kriptografi pada Pesan Terenkripsi menggunakan Support Vector Machine (SVM) Fatma, Yulia; Gunawan, Rahmad; Fitri, Nurkhairi; Firdaus, Rahmad; Hayami, Regiolina; Soni, Soni
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10843

Abstract

Data protection has become a highly critical aspect, particularly in addressing ransomware threats that illegally encrypt data. This study is important to evaluate the capability of machine learning techniques in identifying encryption algorithms used in encrypted data, especially in ransomware attacks. This work represents an initial step that can assist cybersecurity practitioners in more rapidly understanding attack patterns, determining appropriate response strategies, and enhancing proactive mitigation and response efforts to protect data against increasingly complex cyber threats. The machine learning algorithm employed in this study is the Support Vector Machine (SVM). The dataset consists of ciphertext generated using the AES, DES, and Vigenère Cipher cryptographic algorithms. The feature extraction process utilizes ten statistical features to capture the distinctive patterns of each type of ciphertext. The SVM model is developed using a data split of 90% for training and 10% for testing. Performance evaluation is conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The result demonstrate an average accuracy 0f 92,33%, with the vigenere cipher being perfectly classified (100% accuracy). Howefer, slight misclassifications occured beetween AES and DES duet o their similiar entropy chraracteristic. Experimental results demonstrate that the SVM model is capable of identifying encryption algorithms with high accuracy and balanced classification performance across the three algorithm classes. These findings highlight the potential of machine learning approaches for detecting encryption algorithms in cyber-attacks, thereby making a meaningful contribution to the improvement of proactive data security mitigation and response strategies.
Perbandingan model SARIMA dan Prophet dalam memprediksi jumlah kunjungan wisatawan mancanegara ke Indonesia berdasarkan data deret waktu bulanan Alfaridzi, M Ilmi; Gunawan, Rahmad; Alfian, Haris; Mirano, Muhammad Fitter; Nazifah, Hayatun; Wahyuni, Sri; Illahi, Kevanda Sondani
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.v6i3.9963

Abstract

Forecasting international tourist arrivals is a critical aspect of tourism planning and policy-making. This study compares two time series forecasting methods, Seasonal Autoregressive Integrated Moving Average (SARIMA) and Prophet in modeling and predicting the monthly number of international tourists visiting Indonesia, based on data from January 2018 to May 2025. The methodology includes data preprocessing, stationarity testing using the Augmented Dickey-Fuller test, and selecting optimal SARIMA parameters based on the lowest AIC. Model performance was evaluated using MAE and RMSE on the testing data from January to May 2025. The results indicate that SARIMA outperforms Prophet, with a lower average MAE of 1336.41 and RMSE of 1616.67, compared to Prophet’s MAE of 5591.33 and RMSE of 5739.71. Based on this evaluation, SARIMA was selected as the best model and used to project international tourist visits for the period June to December 2025. These findings highlight SARIMA’s superior ability to capture seasonal patterns in tourism data, making it a reliable tool for short-term tourism forecasting in Indonesia.
Klasifikasi serangan DDoS dengan metode random forest dan teknik class weight pada dataset CICDDoS2019 Mualfah, Desti; Ardiansyah, Rudi; Gunawan, Rahmad
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.v6i3.10731

Abstract

The rapid advancement of information technology has significantly influenced various aspects of life, including an increasing reliance on network-based services. However, this dependence has also led to the emergence of more complex cybersecurity threats, one of the most prominent being Distributed Denial of Service (DDoS) attacks. These attacks can disrupt service availability by overwhelming target systems with excessive traffic. A major challenge in detecting DDoS attacks lies in the wide variety of attack patterns and the class imbalance that commonly occurs in network traffic datasets. To address these issues, a machine learning–based approach capable of handling complex attack behaviors while compensating for imbalanced data distribution is required. One potential solution is the use of the Random Forest algorithm with class-weight techniques, applied to the CICDDoS2019 dataset. The research procedure includes data collection and exploration, preprocessing steps such as handling missing and infinite values, encoding categorical attributes, and feature normalization. The dataset is then divided into training and testing subsets before being processed by the Random Forest model. Model evaluation is conducted using a confusion matrix along with accuracy, precision, recall, and F1-score metrics. Experimental results show that incorporating class weight significantly improves model performance, achieving an accuracy of 99.98%, precision of 99.98%, recall of 99.97%, and an F1-score of 99.97%. These findings demonstrate that the proposed approach is highly effective for accurately detecting and classifying DDoS attacks.
Intelligent hyperparameter optimization of multilayer perceptron for water quality classification Edi Ismanto; Rahmad Gunawan; Harun Mukhtar; Rahmad Al Rian; Vitriani Vitriani; Hadhrami Ab Ghani
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp490-500

Abstract

Assessment of water quality is essential for monitoring the environment and sustainable resource management. However, complex physicochemical parameters and class imbalance pose challenges for reliable predictive modeling. Although the multilayer perceptron (MLP) is widely applied for classification tasks, its performance depends strongly on effective hyperparameter optimization. This study evaluates three metaheuristic approaches—Bayesian optimization (BO), genetic algorithm (GA), and particle swarm optimization (PSO)—to enhance the performance of MLP on an imbalanced water potability dataset, which is addressed using synthetic minority over-sampling technique (SMOTE). Experimental results obtained using stratified 10-fold cross-validation demonstrate consistent improvements over the baseline MLP (accuracy of 0.7899±0.013). BO and GA improve predictive performance, while PSO achieves the best overall results with an accuracy of 0.9324±0.006, F1-score of 0.9338±0.006, and receiver operating characteristic–area under the curve (ROC–AUC) of 0.9652±0.005. The findings indicate that PSO provides superior convergence stability and generalization by effectively balancing exploration and exploitation during hyperparameter search. The integration of class balancing and swarm-based optimization substantially enhances classification robustness and discriminative capability, supporting its applicability for intelligent water quality monitoring systems.
Co-Authors . Reflinaldon Abadi, Abdi Nauli Abdurachman, Muhammad Andhika Ade Pratama Adityo, Tri Novian Alfaridzi, M Ilmi Alfian, Haris Amin Hariyanto Aminuyati Andi Nur Insani ANDRIANSYAH Apri Yanto Aprilya, Regiesta Lintang Arfa, Laura Zevira Avicenna, Achyar Zein Baidarus Bayu Anugerah Putra Damayanti, Risma Danang Mulyadipa Suratno Desti Mualfah Edi Ismanto Edi Rian Kartiko Erik Suanda Handika Evans Fuad Fadilla, Niken Rahma Fathurrahman, Raihan Fatma, Yulia Fauzan Azim Fauzan Salim Febby Apri Wenando Filamori, Refly Fauzan Fitri Handayani Fitri, Nurkhairi Hadhrami Ab Ghani Hadhrami Ab Ghani Harmawan, Muhamad Rizki Harun Mukhtar Hasanatul Fu’adah Amran Hayami, Regiolina Ilham Pratama, Muhammad Illahi, Kevanda Sondani Issandra, Febri Januar Al Amien Jasmin, Muhammad Iqbal Maysa Putri, Yulia Mirano, Muhammad Fitter Mualfah, Desti Nadira, Besti Zahratul Naufal Nazifah, Hayatun Nofrial . Nugroho, Altaric Nurul Izrin Md Saleh Nurul Izrin Md Saleh Nurulaqilla Khamis Pradipa, Raditya Pratiwi, Husnatul Fadillah Putra, Yogi Ernanda Rahmad Al Rian Rahmad Firdaus Rahman Septiadi Rahmania, Marsha Nailah Rais, Muhammad_Akmal Ramadhan, Rafi Fakhri Ramadhan, Syahrudin Ramadhoni Razkia, Binta Riani, Della Ayunda Rudi Ardiansyah Soni Soni Soni, Soni Sri Wahyuni Sugiyadi, Riski Syahril Tania, Manzilah Ditiara Taufiq, Reny Medikawati Ulva Elviani Vania, Azra Gusti Vitriani Wahyudhy, Adhe Indra Wesley, Royman Wibowo, Angga Yudha Wide Mulyana Widyaningrum, Amelia Ismania Sita Yaherwandi Yanti, Elis Yordan, Gibril Yulia Fatma Yuliskania, Aisyara Zaskiv S, Marshal Khairana Zilham, Adib