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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Indonesian Journal of Electrical Engineering and Computer Science
Al Amien, Januar
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Computer Science & IT Social Sciences

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Pemanfaatan Limbah Pelepah Pohon Kelapa Sawit Menjadi Anyaman Piring Lidi di RT 001 RW 003 Kelurahan Muara Fajar Barat Wisesa, Raden Muhammad Bima; Arnas, Jonri; Manullang, Irene Terauchi; Yanti, Della Afri; Irawati, Irawati; Amien, Januar Al
Jurnal Pendidikan Tambusai Vol. 5 No. 2 (2021): 2021
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1105.337 KB)

Abstract

Kelurahan Muara Fajar Barat ,Kecamatan Rumbai Barat ,kota Pekanbaru ,khususnya RT001 RW003 merupakan daerah yang banyak tanaman Kelapa sawit. Sekeliling jalan menuju RT001 terdapat pohon kelapa sawit milik warga setempat. Kurangnya pengetahuan warga setempat mengenai pemanfaatn limbah pelepah kelapa sawit menyebabkan Limbah pelepah sawit tersebut terabaikan begitu saja.Luasnya perkebunan kelapa sawit masyarakat berbanding lurus dengan banyaknya limbah yang dihasilkan salah satunya pelepah kelapa sawit. Limbah ini sama sekali belum termanfaatkan oleh masyarakat sehingga membakar limbah menjadi satu-satunya cara dalam menanganinya. Tujuan dari kegiatan pengabdian ini adalah meningkatkan sumberdaya manusia (SDM) melalui pelatihan pemanfaatan limbah kelapa sawit (pelepah dan lidi) menjadi produk yang bernilai ekonomis. Mekanisme pelaksanaan kegiatan tersebut dengan cara melakukan pendampingan kepada masyarakat khususnya ibu PKK secara berkala dan dilanjutkan dengan memberikan pelatihan-pelatihan untuk memaksimalkan potensi yang ada di Desa Muara Fajar Barat. Pelatihan tersebut juga diharapkan berdampak terhadapperubahan pendapatan peserta dan peningkatan perekonomian masyarakat di Desa Muara Fajar Barat. Capaian hasil kegiatan pengabdian kepada masyarakat di Desatahun pertama ini adalah keterampilan masyarakat Desa yang pada awalnya tidak mengetahui cara membuat kerajinan tangan dari anyaman lidi kelapa sawit. Namun setelah program ini dilaksanakan, masyarakat Desa mampu membuat anyaman lidi sawit dengan berbagai macam bentuk seperti piring, mangkok, tempat buah, dan lain sebagainya.
Pemberdayaan UMKM Berbasis Teknologi & Informasi di Kelurahan Maharani Latif, Fuad; Putri, Dilla Annisa; Amanda, Yona; Putri, Nadila Ramadhania; Wulandari, Rahayu; Amien, Januar Al
Jurnal Pendidikan Tambusai Vol. 5 No. 2 (2021): 2021
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (930.829 KB)

Abstract

Usaha Mikro, Kecil dan Menengah (UMKM) merupakan salah satu bagian penting dari perekonomian suatu negara ataupun daerah. Peran penting tersebut telah mendorong banyak negara termasuk Indonesia untuk terus berupaya mengembangakan UMKM. Maharani adalah salah satu kelurahan di Kecamatan Rumbai Barat, Kota Pekanbaru, Provinsi Riau, Indonesia. Pemberdayaan dilakukan di kelurahan Maharani adalah dengan menggunakan teknologi yang terkini untuk membantu memasarkan produk seperti E-commerce, Marketplace, dan Social Media. Metode Penelitiaan menggunakan metode PDCA (Plan, Do, Check, Act) yaitu suatu proses sederhana yang dilakukan untuk terus menerus mendukung peningkatan ke arah perbaikan. Potensi wilayah di Maharani memiliki sumber daya alam yang melimpah seperti tanaman ubi, wilayah tersebut memiliki lahan yang luas dan subur, sehingga dapat dimanfaatkan menjadi sumber bahan baku dalam keripik ubi.
Pemanfaatan Limbah Pelepah Pohon Kelapa Sawit Menjadi Anyaman Piring Lidi di RT 001 RW 003 Kelurahan Muara Fajar Barat Wisesa, Raden Muhammad Bima; Arnas, Jonri; Manullang, Irene Terauchi; Yanti, Della Afri; Irawati, Irawati; Amien, Januar Al
Jurnal Pendidikan Tambusai Vol. 5 No. 2 (2021): 2021
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v5i2.1983

Abstract

Kelurahan Muara Fajar Barat ,Kecamatan Rumbai Barat ,kota Pekanbaru ,khususnya RT001 RW003 merupakan daerah yang banyak tanaman Kelapa sawit. Sekeliling jalan menuju RT001 terdapat pohon kelapa sawit milik warga setempat. Kurangnya pengetahuan warga setempat mengenai pemanfaatn limbah pelepah kelapa sawit menyebabkan Limbah pelepah sawit tersebut terabaikan begitu saja.Luasnya perkebunan kelapa sawit masyarakat berbanding lurus dengan banyaknya limbah yang dihasilkan salah satunya pelepah kelapa sawit. Limbah ini sama sekali belum termanfaatkan oleh masyarakat sehingga membakar limbah menjadi satu-satunya cara dalam menanganinya. Tujuan dari kegiatan pengabdian ini adalah meningkatkan sumberdaya manusia (SDM) melalui pelatihan pemanfaatan limbah kelapa sawit (pelepah dan lidi) menjadi produk yang bernilai ekonomis. Mekanisme pelaksanaan kegiatan tersebut dengan cara melakukan pendampingan kepada masyarakat khususnya ibu PKK secara berkala dan dilanjutkan dengan memberikan pelatihan-pelatihan untuk memaksimalkan potensi yang ada di Desa Muara Fajar Barat. Pelatihan tersebut juga diharapkan berdampak terhadapperubahan pendapatan peserta dan peningkatan perekonomian masyarakat di Desa Muara Fajar Barat. Capaian hasil kegiatan pengabdian kepada masyarakat di Desatahun pertama ini adalah keterampilan masyarakat Desa yang pada awalnya tidak mengetahui cara membuat kerajinan tangan dari anyaman lidi kelapa sawit. Namun setelah program ini dilaksanakan, masyarakat Desa mampu membuat anyaman lidi sawit dengan berbagai macam bentuk seperti piring, mangkok, tempat buah, dan lain sebagainya.
Pemberdayaan UMKM Berbasis Teknologi & Informasi di Kelurahan Maharani Latif, Fuad; Putri, Dilla Annisa; Amanda, Yona; Putri, Nadila Ramadhania; Wulandari, Rahayu; Amien, Januar Al
Jurnal Pendidikan Tambusai Vol. 5 No. 2 (2021): 2021
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v5i2.1986

Abstract

Usaha Mikro, Kecil dan Menengah (UMKM) merupakan salah satu bagian penting dari perekonomian suatu negara ataupun daerah. Peran penting tersebut telah mendorong banyak negara termasuk Indonesia untuk terus berupaya mengembangakan UMKM. Maharani adalah salah satu kelurahan di Kecamatan Rumbai Barat, Kota Pekanbaru, Provinsi Riau, Indonesia. Pemberdayaan dilakukan di kelurahan Maharani adalah dengan menggunakan teknologi yang terkini untuk membantu memasarkan produk seperti E-commerce, Marketplace, dan Social Media. Metode Penelitiaan menggunakan metode PDCA (Plan, Do, Check, Act) yaitu suatu proses sederhana yang dilakukan untuk terus menerus mendukung peningkatan ke arah perbaikan. Potensi wilayah di Maharani memiliki sumber daya alam yang melimpah seperti tanaman ubi, wilayah tersebut memiliki lahan yang luas dan subur, sehingga dapat dimanfaatkan menjadi sumber bahan baku dalam keripik ubi.
Feature selection technique on convolutional neural network – multilabel classification task Hayami, Regiolina; Yusoff, Nooraini; Daud, Kauthar Mohd; Mukhtar, Harun; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp2001-2009

Abstract

Automated text-based recommendation, an artificial intelligence development, finds application in document analysis like job resumes. The classification of job resumes poses challenges due to the ambiguity in categorizing multiple potential jobs in a single application file, termed multi-label classification, deep learning, particularly convolutional neural networks (CNN), offers flexibility in enhancing feature representations. Despite its robust learning capabilities, the black-box design of deep learning lacks interpretability and demands a substantial number of parameters, requiring significant computational resources. The primary challenge in multilabel learning is the ambiguity of labels not fully explained by traditional equivalence relations. To address this, the research employs feature selection techniques, specifically the Chi-square method. The goal is to reduce features in deep learning models while considering label relevance in multi-label text classification, easing computational workload while preserving model performance. Experimental tests, both with and without the Chi-square feature selection technique on the dataset, underscore its substantial impact on the classification model's ability. The conclusion emphasizes the influence of the Chi-square feature selection technique on performance and computational time. In summary, the research underscores the importance of balancing computational efficiency and model interpretability, especially in complex multi-label classification tasks like job applications.
Improving imbalanced class intrusion detection in IoT with ensemble learning and ADASYN-MLP approach Soni, Soni; Remli, Muhammad Akmal; Mohd Daud, Kauthar; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1209-1217

Abstract

The exponential growth of the internet of things (IoT) has revolutionized daily activities, but it also brings forth significant vulnerabilities. intrusion detection systems (IDS) are pivotal in efficiently detecting and identifying suspicious activities within IoT networks, safeguarding them from potential threats. It proposes a ensemble approach aimed at enhancing model performance in such scenarios. Recognizing the unique challenges posed by imbalanced class distribution, the research employs three sampling techniques LightGBM adaptive synthetic sampling (ADASYN) with multilayer perceptron (MLP), XGBoost ADASYN with MLP, and LightGBM ADASyn with XGBoost to address class imbalance effectively. Evaluation confusion matrix performance metrics underscores the efficacy of ensemble models, particularly LightGBM ADASYN with MLP, XGBoost ADASYN with MLP, and LightGBM ADASYN with XGBoost, in mitigating imbalanced class issues. The LightGBM ADASYN with MLP model stands out with 99.997% accuracy, showcasing exceptional precision and recall, demonstrating its proficiency in intrusion detection within minimal false positives negatives. Despite computational demands, integrating XGBoost within ensemble frameworks yields robust intrusion detection results, highlighting a balanced trade-off between accuracy, precision, and recall. This research offers valuable insights into the strengths with different ensemble models, significantly contributing to the advancement of accurate and reliable IDS in realm of IoT.
Performance evaluation of multiclass classification models for ToN-IoT network device datasets Soni, Soni; Remli, Muhammad Akmal; Daud, Kauthar Mohd; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 1: July 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i1.pp485-493

Abstract

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.
Enhancing attack detection in IoT through integration of weighted emphasis formula with XGBoost Al Amien, Januar; Ab Ghani, Hadhrami; Md Saleh, Nurul Izrin; Soni, Soni
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp641-648

Abstract

This research addresses the challenge of detecting attacks in the internet of things (IoT) environment, where minority classes often go unnoticed due to the dominance of majority classes. The primary objective is to introduce and integrate the imbalance ratio formula (IRF) into the XGBoost algorithm, aiming to provide greater emphasis on minority classes and ensure the model's focus on attack detection, particularly in binary and multiclass scenarios. Experimental validation using the IoTID20 dataset demonstrates the significant enhancement in attack detection accuracy achieved by integrating IRF into XGBoost. This enhancement contributes to the consistent improvement in distinguishing attacks from normal traffic, thereby resulting in a more reliable attack detection system in complex IoT environments. Moreover, the implementation of IRF enhances the robustness of the XGBoost model, enabling effective handling of imbalanced datasets commonly encountered in IoT security applications. This approach advances intrusion detection systems by addressing the challenge of class imbalance, leading to more accurate and efficient detection of malicious activities in IoT networks. The practical implications of these findings include the enhancement of cybersecurity measures in IoT deployments, potentially mitigating the risks associated with cyber threats in interconnected smart environments.
Deep learning-based cryptanalysis in recovering the secret key and plaintext on lightweight cryptography Fatma, Yulia; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp1115-1123

Abstract

The development of machine learning (ML) technologies provide a new development direction for cryptanalysis. Several ML research in the field of cryptanalysis was carried out to identify the cryptographic algorithm used, find out the secret key, and even recover the secret message The first objective of this study is to see how much influence optimization and activation function have on the multi-layer perceptron (MLP) model in performing cryptanalysis. The second research objective, which is to compare the performance of cryptanalysis in recovering keys and the plaintext. Several experiments have been carried out, the observed parameters found that the use of the rectified linear unit (ReLU) activation function and the ADAM optimizer improves the performance of deep learning (DL)-based cryptanalysis as evidenced by a significantly smaller error rate. DL-based cryptanalysis works more effectively in recovering keys than recovering plaintext. DL-based cryptanalysis managed to recover the keys with an average loss of 0.007, an average of 49 epochs, and an average time of 0.178 minutes.
A Comparison of Enhanced Ensemble Learning Techniques for Internet of Things Network Attack Detection Ismanto, Edi; Al Amien, Januar; Vitriani, Vitriani
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3885

Abstract

Over the past few decades, the Internet of Things (IoT) has become increasingly significant due to its capacity to enable low-cost device and sensor communication. Implementation has opened up many new opportunities in terms of efficiency, productivity, convenience, and security. However, it has also brought about new privacy and data security challenges, interoperability, and network reliability. The research issue is that IoT devices are frequently open to attacks. Certain machine learning (ML) algorithms still struggle to handle imbalanced data and have weak generalization skills when compared to ensemble learning. The research aims to develop security for IoT networks based on enhanced ensemble learning by using Grid Search and Random Search techniques. The method used is the ensemble learning approach, which consists of Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). This study uses the UNSW-NB15 IoT dataset. The study's findings demonstrate that XGBoost performs better than other methods at identifying IoT network attacks. By employing Grid Search and Random Search optimization, XGBoost achieves an accuracy rate of 98.56% in binary model measurements and 97.47% on multi-class data. The findings underscore the efficacy of XGBoost in bolstering security within IoT networks.
Co-Authors Ab Ghani, Hadhrami Amanda, Yona Arnas, Jonri Daud, Kauthar Mohd Edi Ismanto, Edi Fatma, Yulia Harun Mukhtar Hayami, Regiolina Irawati Irawati Latif, Fuad Manullang, Irene Terauchi Md Saleh, Nurul Izrin Mohamad, Mohd Saberi Mohd Daud, Kauthar Putri, Dilla Annisa Putri, Nadila Ramadhania Remli, Muhammad Akmal Soni Vitriani, Vitriani Wisesa, Raden Muhammad Bima Wulandari, Rahayu Yanti, Della Afri Yusoff, Nooraini