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Sistem Pemantau dan Pengontrol Suhu dan pH Air Otomatis pada Budidaya Ikan Gabus Meutia, Ernita Dewi; Utama, Muhammad Yoga; Munadi, Rizal; Irhamsyah, Muhammad
Journal of Engineering and Science Vol. 2 No. 2 (2023): July-December 2023
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jes.v2i2.143

Maintaining the temperature and pH of water in fish farming is important to maintain the survival of the commodities being cultivated. Snakehead fish as one of the freshwater fish cultivation commodities, live in a temperature range of 25° to 32°C, and a pH of 4,5 to 6. Changes in temperature and pH due to differences in day and night and weather conditions can be mortal for the fish. To help the fish farmers maintain water quality, in this research a prototype of IoT based temperature and pH monitor and control was built using Arduino microcontroller, that can be accessed through a mobile application on Android smartphone. The test results show that the prototype has successfully control the temperature by turning the heater on when it dropped below 25°C, turned the acid solution pump on when the pH felt below 4.5, and alkaline solution pump when pH was above 6. Continuous sensor readings are able to maintain the stability of water quality.

Performance Analysis of H2O and H2O with HCl Material Image Classification Using Inception V3, VGG19, DenseNet201, and Otsu Segmentation Yunidar, Yunidar; Melinda, Melinda; Putri, Mauliza; Irhamsyah, Muhammad; Basir, Nurlida; Khairah, Alfita
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1253

Challenges in classifying signals with fluctuations remain a focus in the field of image and signal processing. Deep learning technology, especially CNN (Convolutional Neural Network), has proven effective for complex visual classification; however, its performance can still be improved, particularly for signal nonlinearity distributions that are not evenly distributed. This study develops a system for classifying signals that exhibit high fluctuations using a merged Otsu segmentation and deep learning ensemble approach with InceptionV3, VGG19, and DenseNet201 models. The methodology employed is a quantitative study based on a deep learning ensemble. H?O and H?O with HCL signal datasets were processed using Otsu segmentation and then extracted using three CNN architectures, which were then combined with the methods of soft voting and stacking. Evaluation is conducted through the analysis of accuracy, precision, recall, loss, and a confusion matrix. DenseNet201 records the highest accuracy of 95%, precision of 0.90, recall of 0.86, and f1-score of 0.95. InceptionV3 achieves equivalent accuracy (95%) but with a recall of 0.83. VGG19 noted an accuracy of 91%, a precision of 0.82, and a recall of 0.78. The ensemble results show improvement in stability classification, especially in class H?O segmentation. However, the classification class HCL segmentation still shows more mistakes. The integration of Otsu segmentation and deep learning ensemble models has been proven effective in increasing the accuracy of classifying signal fluctuations. Segmentation helps highlight the importance of spatial features, while ensemble enhances model generalization. Research furthermore recommended exploring method segmentation and adaptive data augmentation to handle more complex and unbalanced distributions.

Penerapan Sistem Identifikasi Ekspresi Wajah Anak Penyandang Autisme Berbasiskan Citra Termal pada Sekolah Berkebutuhan Khusus di Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Mina Rizky, Muharratul; Leo, Hendrik; Fahmi, Fahmi
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 1 (2025)
Publisher : Universitas Syiah Kuala

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

This community service activity aims to apply technology to detect facial expressions of children with autism through thermal images. The activity was carried out at My Hope Special Need Center, Banda Aceh, an educational center for orphans and children with special needs. By utilizing a combination of psychological and technological approaches, data collection is carried out in the form of thermal images of the faces of children with and without autism. The data obtained was analyzed using the Convolutional Neural Network (CNN) approach to develop an automatic facial expression detection method. The results of this activity show the potential use of facial recognition technology in supporting education and therapy for children with special needs.

Implementasi Sistem Informasi Pengelolaan Aset Desa Berbasis Web pada Desa Tingkeum, Kabupaten Aceh Besar Yunidar, Yunidar; Irhamsyah, Muhammad; Amalia, Amalia; Akbar, Muhazir; Rafiqi, Ashaf
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 1, No 2 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v1i2.42914

Desa Tingkeum, Kabupaten Aceh Besar, menghadapi tantangan dalam pengelolaan aset desa yang masih dilakukan secara manual, menyebabkan kesulitan dalam pelacakan dan pengelolaan aset secara efisien. Untuk mengatasi masalah ini, dikembangkan Sistem Informasi Pengelolaan Aset Desa Berbasis Web menggunakan platform SIMAssets yang dirancang dengan framework CodeIgniter dan basis data MySQL. Sistem ini memungkinkan pencatatan aset secara terpusat, otomatisasi kodefikasi aset, dan pelabelan aset dengan kode unik untuk memudahkan pelacakan. Implementasi sistem dilakukan dengan pelatihan kepada perangkat desa, termasuk Keuchik dan pengelola aset. Hasil pengujian menunjukkan bahwa sistem ini berhasil meningkatkan efisiensi pengelolaan aset, memudahkan pembuatan laporan, dan mempercepat proses inventarisasi. Pengguna melaporkan bahwa antarmuka sistem user-friendly dan fitur kodefikasi sangat bermanfaat dalam pengelolaan aset secara berkelanjutan.

Pembangkit Listrik Dengan Sistem Multihybrid dari Tenaga Fotovoltaik dan Mikrohidro Berbasis Fingerprint dan Internet of Thing (IoT) Roslidar, Roslidar; Irhamsyah, Muhammad; Sara, Ira Devi; Syukriyadin, Syukriyadin
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 1, No 1 (2024)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jprw.v1i1.36541

Abstrak Pembangkit listrik yang berkelanjutan dan dapat diakses oleh masyarakat luas menjadi kunci untuk memenuhi kebutuhan energi global dan meningkatkan kesejahteraan masyarakat. Pelaksanaan pengabdian kepada masyarakat ini mengusulkan dan mengimplementasikan pembangkit listrik tenaga multihybrid yang mengintegrasikan sistem tenaga surya fotovolatik dan sistem tenaga air mikrohidro. Penambahan sistem keamanan berbasis fingerprint dan teknologi Internet of Things (IoT) menjadikan pembangkit multihybrid ini lebih aman dan dapat diatur penggunaan energi listriknya. Langkah-langkah dalam pengembangan sistem kelistrikan ini melibatkan pemilihan lokasi di Desa Bung Pageu, Kecamatan Blang Bintang Kab. Aceh Besar, perancangan sistem kelistrikan dan instalasi fotovoltaik dan pembangkit mikrohidro sesuai potensi air setempat, serta integrasi sistem keamanan dengan pengelolaan energi menggunakan teknologi fingerprint. Selain itu, sensor IoT diterapkan untuk pemantauan real-time dan pengendalian jarak jauh. Dengan menggabungkan beberapa teknologi dan partisipasi aktif masyarakat setempat, kegiatan ini memberikan solusi terhadap permasalahan energi dan pengetahuan baru terkait dengan implementasi sistem energi terbarukan dengan pendekatan multihybrid. Keberhasilan kegiatan ini memberikan kontribusi positif terhadap pembangunan berkelanjutan dan memberdayakan masyarakat untuk turut serta dalam pemanfaatan energi terbarukan secara berkesinambungan.Kata kunci: Energi Terbarukan, Pembangkit Tenaga Multihybrid, Fotvoltaic, Microhydro

Sistem Pemantau dan Pengontrol Suhu dan pH Air Otomatis pada Budidaya Ikan Gabus Meutia, Ernita Dewi; Utama, Muhammad Yoga; Munadi, Rizal; Irhamsyah, Muhammad
Journal of Engineering and Science Vol. 2 No. 2 (2023): July-December 2023
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jes.v2i2.143

Maintaining the temperature and pH of water in fish farming is important to maintain the survival of the commodities being cultivated. Snakehead fish as one of the freshwater fish cultivation commodities, live in a temperature range of 25° to 32°C, and a pH of 4,5 to 6. Changes in temperature and pH due to differences in day and night and weather conditions can be mortal for the fish. To help the fish farmers maintain water quality, in this research a prototype of IoT based temperature and pH monitor and control was built using Arduino microcontroller, that can be accessed through a mobile application on Android smartphone. The test results show that the prototype has successfully control the temperature by turning the heater on when it dropped below 25°C, turned the acid solution pump on when the pH felt below 4.5, and alkaline solution pump when pH was above 6. Continuous sensor readings are able to maintain the stability of water quality.

FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION Syakir, Fakhrus; Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Zulhelmi, Zulhelmi; Miftahujjannah, Rizka
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6665

Lake Maninjau experiences periodic upwelling events that disrupt water quality, harm fish stocks, and pose socioeconomic challenges to surrounding communities. This study aimed to enhance upwelling prediction accuracy by integrating Vector Autoregressive (VAR) time series modelling with Support Vector Machine (SVM) classification. A five-year dataset (2020–2024) of daily climate variables surface temperature, precipitation, and wind speed was collected from NASA. Data stationarity was confirmed using Box-Cox transformations and Augmented Dickey-Fuller tests, while Granger Causality analysis revealed bidirectional relationships among the variables. The optimal forecasting model, VAR(17), was selected based on the Akaike Information Criterion (AIC), ensuring residuals met white-noise criteria. K-means clustering then labelled potential upwelling days, and these labels were employed to train SVM classifiers. An interactive dashboard was developed using Python and Streamlit to facilitate real-time forecasts and classification outputs. The VAR(17) model produced highly accurate forecasts, reflected by minimal error metrics (e.g., RMSE < 0.60). SVM classification of potential upwelling events achieved strong performance, consistently attaining F1-scores above 0.95. By merging time series forecasts with event classification, the hybrid VAR–SVM framework outperformed single-method approaches in identifying and predicting upwelling episodes. This integrated modelling strategy effectively addresses the complexity of upwelling in Lake Maninjau, enabling timely decision-making for fisheries management and local tourism stakeholders. Future work may incorporate additional environmental indicators (e.g., dissolved oxygen, pH) and extend dashboard functionalities to bolster sustainable resource management and community resilience

Pengenalan Robotika sebagai Media Pembelajaran STEM di SMA Labschool Unsyiah Banda Aceh Melinda, Melinda; Yunidar, Yunidar; Irhamsyah, Muhammad; Islamy, Fajrul; Priandana, Karlisa; Basir, Nurlida; Safitri, Rini
Jurnal Pengabdian Rekayasa dan Wirausaha Vol 2, No 2 (2025)
Publisher : Universitas Syiah Kuala

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

Perkembangan teknologi robotika telah membawa perubahan signifikan dalam berbagai aspek kehidupan, termasuk dunia pendidikan. Robotika, sebagai bidang multidisiplin yang menggabungkan aspek mekanika, elektronika, dan ilmu komputer, memiliki potensi besar untuk diterapkan dalam pembelajaran berbasis Science, Technology, Engineering, and Mathematics (STEM). Artikel ini bertujuan mendeskripsikan pelaksanaan kegiatan pengabdian masyarakat berupa pengenalan sistem robotika di SMA Labschool Unsyiah Banda Aceh serta mengkaji dampaknya terhadap pengetahuan dan motivasi siswa. Metode pelaksanaan kegiatan meliputi identifikasi kebutuhan mitra, perencanaan materi, pelaksanaan kegiatan, praktik sederhana, hingga evaluasi. Dokumentasi kegiatan menunjukkan antusiasme tinggi dari siswa selama mengikuti seluruh rangkaian kegiatan. Hasil evaluasi melalui kuesioner dan observasi lapangan mengindikasikan bahwa siswa memperoleh peningkatan pemahaman tentang konsep dasar robotika serta terdorong untuk lebih berminat pada bidang STEM. Guru pendamping juga menilai kegiatan ini relevan dengan kebutuhan pembelajaran dan membuka peluang pengembangan robotika sebagai kegiatan ekstrakurikuler di sekolah. Dengan demikian, kegiatan pengenalan robotika tidak hanya berkontribusi pada peningkatan literasi teknologi siswa, tetapi juga menjadi langkah awal dalam membangun ekosistem pendidikan berbasis teknologi yang berkelanjutan di SMA Labschool Unsyiah Banda Aceh.

Metodologi Takhrīj Berbasis Rijāl: Menggabungkan Jarḥ wa al-Taʻdīl dan Evidence-Based Approach Juwita, Frasiska Sari; Munthe , Khoirotun Nisah Br; Irhamsyah, Muhammad; Rifa’i, M. Mashduqi; Siregar, Idris
Nizamiyah: Jurnal Sains, Sosial dan Multidisiplin Vol. 1 No. 4 (2025): Nizamiyah: Jurnal Sains, Sosial dan Multidisiplin
Publisher : Yayasan Albahriah Jamiah Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64691/nizamiyah.v1i4.122

The study of takhrīj hadith has so far been dominated by a normative approach that positions the assessment of rijāl primarily as a reproduction of classical authority, so that the process of verifying narrator and sanad data often lacks a systematic, transparent, and methodologically traceable evaluation mechanism. This condition has implications for the limited consistency and replicability of hadith assessment in the context of contemporary hadith studies that demand higher scientific accountability. This study aims to reconstruct the methodology of takhrīj based on rijāl through the integration of the principles of jarḥ wa al-Taʻdīl with an evidence-based approach, so that the assessment of narrators and sanad is not only based on normative authority, but also on structured data management and verification. The study uses a qualitative-analytical approach with a critical analysis of rijāl literature, works of jarḥ wa al-Taʻdīl, and the scientific evidence framework that has developed in the modern research tradition. The results of this study indicate the formation of a takhrīj methodological framework that places rijāl data as the primary basis of analysis through the stages of source inventory, cross-verification of narrator information, assessment of the evidential weight of jarḥ and taʻdīl, and consistent synthesis of findings in the evaluation of sanad. This framework allows for a more structured, transparent, and consistent hadith assessment process, while minimizing subjectivity and fragmentation of assessment. The implications of this research lie in strengthening the validity and reliability of hadith assessment, as well as its contribution to developing a takhrīj methodology that is adaptive to the demands of contemporary hadith studies without abandoning the foundations of classical scholarship.

FORECASTING UPWELLING IN LAKE MANINJAU USING VECTOR AUTOREGRESSIVE, SUPPORT VECTOR MACHINE AND DASHBOARD VISUALIZATION Syakir, Fakhrus; Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Zulhelmi, Zulhelmi; Miftahujjannah, Rizka
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6665

Lake Maninjau experiences periodic upwelling events that disrupt water quality, harm fish stocks, and pose socioeconomic challenges to surrounding communities. This study aimed to enhance upwelling prediction accuracy by integrating Vector Autoregressive (VAR) time series modelling with Support Vector Machine (SVM) classification. A five-year dataset (2020–2024) of daily climate variables surface temperature, precipitation, and wind speed was collected from NASA. Data stationarity was confirmed using Box-Cox transformations and Augmented Dickey-Fuller tests, while Granger Causality analysis revealed bidirectional relationships among the variables. The optimal forecasting model, VAR(17), was selected based on the Akaike Information Criterion (AIC), ensuring residuals met white-noise criteria. K-means clustering then labelled potential upwelling days, and these labels were employed to train SVM classifiers. An interactive dashboard was developed using Python and Streamlit to facilitate real-time forecasts and classification outputs. The VAR(17) model produced highly accurate forecasts, reflected by minimal error metrics (e.g., RMSE < 0.60). SVM classification of potential upwelling events achieved strong performance, consistently attaining F1-scores above 0.95. By merging time series forecasts with event classification, the hybrid VAR–SVM framework outperformed single-method approaches in identifying and predicting upwelling episodes. This integrated modelling strategy effectively addresses the complexity of upwelling in Lake Maninjau, enabling timely decision-making for fisheries management and local tourism stakeholders. Future work may incorporate additional environmental indicators (e.g., dissolved oxygen, pH) and extend dashboard functionalities to bolster sustainable resource management and community resilience

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