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All Journal HAYATI Journal of Biosciences Teknosains: Media Informasi Sains dan Teknologi Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JURNAL NASIONAL TEKNIK ELEKTRO JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) Jurnal Ilmiah Ecosystem Building of Informatics, Technology and Science Jurnal Aplikasi dan Inovasi Iptek (JASINTEK) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat KLIK: Kajian Ilmiah Informatika dan Komputer eProceedings of Engineering Eduvest - Journal of Universal Studies AMMA : Jurnal Pengabdian Masyarakat Jurnal Pemanfaatan Teknologi untuk Masyarakat
Satria Mandala
Telkom University
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Neuroscience Nursing Physics Social Sciences Other

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Study of Arrhythmia Classification Algorithms on Electrocardiogram Using Deep Learning Arifin, Rezki Fauzan; Mandala, Satria
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12687

Abstract

Arrhythmia is a heart disease that occurs due to a disturbance in the heartbeat that causes the heart rhythm to become irregular. In some cases, arrhythmias can be life-threatening if not detected immediately. The method used to detect is electrocardiogram (ECG) signal analysis. To avoid misdiagnosis by cardiologists and to ease the workload, methods are proposed to detect and classify arrhythmias by utilizing Artificial Intelligence (AI). In recent years, there has been a lot of research on the detection of this disease. However, many of such studies are more likely to use machine learning algorithms in the classification process, and most of the accuracy results still do not reach optimal levels in general. Therefore, this study aims to classify arrhythmias using deep learning algorithms. There are several stages of performing arrhythmia detection, namely, preprocessing, feature extraction, and classification. The focus of this research is only on the classification stage, where the Long Short-Term Memory (LSTM) algorithm is proposed. After going through a series of experiments, the performance of the proposed algorithm is further analyzed to compare accuracy, specificity, and sensitivity with other machine learning algorithms based on previous research, with the aim of obtaining an optimal algorithm for arrhythmia detection. Based on the results of the study, the Long Short-Term Memory (LSTM) algorithm managed to outperform the performance of other machine learning algorithms with accuracy, specificity, and sensitivity results of 98.47%, 99.24%, and 97.67%, respectively.
Analisis Pengelolaan Dana Desa dan Pemberdayaan Masyarakat Petani di Kabupaten Maros Musfirah, Andi; Sobirin, Sobirin; Mandala, Satria
Jurnal Ilmiah Ecosystem Vol. 23 No. 3 (2023): Ecosystem Vol. 23 No 3, September - Desember Tahun 2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35965/eco.v23i3.3893

Abstract

Alokasi Dana Desa merupakan satu aspek penting dari sebuah desa untuk menjalankan program-program untuk mengsejahtarakan masyarakat di semua wilayahnya. Pemerintah desa diyakini lebih mampuh melihat prioritas kebutuhan masyarakat dibandingkan pemerintah kabupaten yang secara nyata memiliki ruang lingkup permasalahan yang lebih luas dan rumit. Tujuan dari penelitian ini adalah untuk mengetahui pengelolaan dana desa terhadap peningkatan pembangunan sumber daya desa. Penelitian ini menggunanakan jenis penelitian lapangan dengan metode penelitian deskriptif kuantitatif, cara yang digunakan dalam penelitian ini yaitu: observasi, kuesioner, wawancara, serta dokumentasi. Hasil penelitian menunjukkan bahwa Model pengembangan pelaksanaan penggunaan dana desa dan pemberdayaan Petani model di kembangkan berdasarkan peningkatan pengetahuan mengenai dana desa yang dilakukan dengan sosialisasi terhadap masyarakat Desa Tompobulu. Peningkatan pengetahuan akan memotivasi masyarakat Desa Tompobulu untuk aktif dan konsisten dalam setiap program dana desa. Peningkatan kualitas perencanaan dalam menyusun program dan rencana anggaran program dana desa dengan mempertimbangkan karakteristik sosial petani yang ada di Desa Tompobulu didampingi oleh tenaga ahli. Peningkatan pengetahuan terhadap program-program yang didanai dana desa akan meningkatkan kepedulian masyarakat Desa Tompobulu  untuk berpartisipasi dan melakukan pengawasan terhadap pelaksanaan program dana desa.Model pengembangan pelaksanaan penggunaan  dana  desa  ini  dapat  dijadikan referensi bagi upaya peningkatan efisiensi dan daya guna pemanfaatan dana desa pada daerah-daerah yang memiliki karakteristik yang serupa dengan Desa Tompobulu Desa dengan ciri sumber penghidupannya dominan dari sektor pertanian yang ekonominya relatif belum berkembang. Village Fund Allocation is an important aspect of a village to carry out programs to improve the welfare of the community in all its areas. It is believed that the village government is more able to see the priority needs of the community than the district government which actually has a wider and more complex scope of problems. The aim of this research is to determine the management of village funds towards increasing village resource development. This research uses field research with quantitative descriptive research methods, the methods used in this research are: observation, questionnaires, interviews, and documentation. The results showed that the development model for implementing the use of village funds and the farmer empowerment model was developed based on increasing knowledge about village funds which was carried out by outreach to the people of Tompobulu Village. Increased knowledge will motivate the people of Tompobulu Village to be active and consistent in every village fund program. Improving the quality of planning in preparing programs and budget plans for village fund programs by taking into account the social characteristics of farmers in Tompobulu Village accompanied by experts. Increasing knowledge of programs funded by village funds will increase the awareness of the people of Tompobulu Village to participate in and supervise the implementation of village fund programs. an area that has characteristics similar to Tompobulu Village A village with the characteristics of a dominant source of livelihood from the agricultural sector whose economy is relatively underdeveloped.
Histological and Molecular Evaluation of the Antiproliferative Activity of Allium ampeloprasum Water Extract Against Oral Mucosa Cell Line (Gingival Cancer) Alwan, Maryam Hameed; Hameed, Zainab; Mandala, Satria
HAYATI Journal of Biosciences Vol. 31 No. 5 (2024): September 2024
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.31.5.829-835

Abstract

Gingival carcinoma is a malignant neoplasm affecting the oral mucosa and is associated with significant morbidity and mortality. Allium ampeloprasum var. porrum water extracts have gotten a lot of attention because of their bioactive components, such as polyphenols, flavonoids, and alkaloids, which have a variety of pharmacological activities, including antiproliferative actions. This study aimed to evaluate the histological and molecular effects of Allium ampeloprasum (leek) water extract on the proliferation of the murine gingival cancer cell line. Histological evaluation was conducted to examine morphological changes induced by extract treatment. Molecular mechanisms underlying the observed histological changes were investigated using real-time polymerase chain reaction (PCR). Expression levels of key genes associated with cell proliferation and apoptosis were assessed. Histological findings revealed a dose-dependent decrease (100, 50, 25, 12.5, and 6.25 µg/ml) in cell density and altered cell shape in the treated cell line. Also, the percentage of inhibition for the oral mucosa cell line was high, with a significant P of 0.006, in the treated group compared to the control group. Additionally, water extract has an IC50 value of 61 g/ml. The P53 fold increment of gene expression is 0.6, which means the expression level in the experimental condition is 60% higher than the control. This study provides evidence for the potential antiproliferative activity of Allium ampeloprasum water extract on the oral mucosa cell line. The observed histological changes, coupled with the modulation of key genes involved in proliferation and apoptosis, suggest that leek water extract may have therapeutic implications in managing oral cancer.
Pengembangan IDS pada IoT Menggunakan Ensemble Learning Nadia Ariana; Satria Mandala; Mohd Fadzil Hasssan; Muhammad Qomaruddin; Bilal Ibrahim Bakri
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1113.2024

Abstract

The utilization of intrusion detection systems (IDS) can significantly enhance the security of IT infrastructure. Machine learning (ML) methods have emerged as a promising approach to improving the capabilities of IDS. The primary objective of an IDS is to detect various types of malicious intrusions with a high detection rate while minimizing false alarms, surpassing the capabilities of a firewall. However, developing an IDS for IOT poses substantial challenges due to the massive volume of data that needs to be processed. To address this, an optimal approach is required to improve the accuracy of data containing numerous attacks. In this study, we propose a novel IDS model that employs the Random Forest, Decision Tree, and Logistic Regression algorithms using a specialized ML technique known as Ensemble Learning. For this research, we used the BoT-IoT datasets as inputs for the IDS model to distinguish between malicious and benign network traffic. To determine the best model, we compared the performance metrics of each algorithm across different parameter combinations. The research findings demonstrate exceptional performance, with metric scores exceeding 99.995% for all parameter combinations. Based on these conclusive results, we deduce that the proposed model achieves remarkable success and outperforms other traditional ML-based IDS models in terms of performance metrics. These outcomes highlight the potential of our novel IDS model to enhance the security posture of IoT-based systems significantly.
APD-BayTM: Prediksi Indeks Kualitas Udara Jakarta Menggunakan Bayesian Optimized LSTM Raey Faldo; Satria Mandala; Mohd Shahrizal Sunar; Salim M. Zaki
JURNAL NASIONAL TEKNIK ELEKTRO Vol 13, No 2: July 2024
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v13n2.1183.2024

Abstract

The Air Quality Index (AQI) is a metric for evaluating air quality in a region. Jakarta holds the fifth position globally in terms of air pollution. Several studies have been performed to forecast pollution levels in Jakarta. However, existing studies exhibit limitations such as outdated datasets, lack of data normalization, absence of machine learning parameter setting, neglect of k-fold cross-validation, and a failure to incorporate deep learning algorithms for pollution detection. This study introduces an air quality detection system called APD-BayTM to address these issues. This proposed system leverages Long Short-Term Memory (LSTM) and uses Bayesian Optimization (BO) to enhance the performance of air pollution detection. The methodology used in this research involves four key steps: data preprocessing, LSTM model development, hyperparameter tuning through BO, and performance assessment using 5-fold cross-validation. APD-BayTM exhibits robust performance that is comparable to previous research outcomes. The LSTM model in APD-BayTM on the training dataset achieved average precision, recall, F1 score, and accuracy values of 93.29%, 91.41%, 91.89%, and 95.90%, respectively. These metrics improved on the test dataset, reaching 97.44%, 99.71%, 98.52%, and 99.34%, respectively. These findings show the robustness of APD-BayTM across datasets of varying sizes, encompassing both large and small datasets.
Penanggulangan Abrasi Melalui Penanaman Mangrove Menggunakan Media Bambu Muhajir, Humaidid; Malina, Asmi Citra; Assir, Andi; Mandala, Satria; Alimuddin, Ilham; Marmin, Hidayat; Indrayuni, Armi; Annas, Aswar
Jurnal Aplikasi dan Inovasi Iptek Vol 6 No Risdamas (2024): Jurnal Aplikasi dan Inovasi Iptek No. 6 Vol. Risdamas Desember, 2024
Publisher : Denpasar Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52232/jasintek.v6iRisdamas.200

Abstract

Bencana abrasi yang terjadi di Dusun Tulang Desa Barugaia Kabupaten Kepulauan Selayar telah menimbulkan beragam dampak buruk, yaitu; terkikisnya pantai sekitar 700 meter, tumbangnya 50 pohon kelapa, hilangnya demplot penetasan telur penyu, merusak sarana konservasi penyu, mengganggu pengembangbiakan penyu, hingga mengancam kehidupan masyarakat pesisir. Melalui dampak ekstrim tersebut, proses penanaman 4000 mangrove menggunakan media bambu diperlukan agar dapat mengurangi risiko abrasi yang terjadi dan tidak mengganggu proses pendaratan penyu. Penanaman mangrove dilakukan tepat dipesisir pantai yang terdampak abrasi, wadah tanaman berbahan dasar bambu yang telah diisi tanah secara padat, cara ini dilakukan agar mangrove dapat dipastikan tumbuh diatas pesisir pantai. Metode pengabdian yang dilakukan menggunakan model. Pertama sosialisasi kegiatan kepada masyarakat, Kedua pelatihan dan penyuluhan terkait teknologi tepat guna yang diberikan, Ketiga penerapan teknologi inovasi yang diberikan, dan Kempat melakukan pendampingan dan evaluasi keberhasilan kegiatan. Hasil penerapan teknologi inovasi penanaman mangrove menggunakan media bambu menunjukkan tanaman mangrove dapat menahan proses pasir di pesisir pantai terbawa arus, tanaman mangrove menjadi tempat perlindungan ikan, kepiting, dan hewan lainnya yang hidup dipesisir pantai, meningkatkan pengetahuan dan keterampilan masyarakat terkait ketahanan pesisir pantai dari dampak abrasi, dan menjadi penyangga pesisir pantai dari gelombang ekstrim dan angin kencang
Analisis pola perkembangan Kota Lewoleba berdasarkan morfologi ruang Kota Lewoleba Satria Mandala; Jumadil, Jumadil; Hasanuddin, Hasanuddin
Teknosains Vol 18 No 3 (2024): September-Desember
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/teknosains.v18i3.47246

Abstract

Abstract : The aim of this research is to analyze the development pattern of Lewoleba City regarding the spatial morphology of Lewoleba City, Lembata Regency. This type of research is quantitative using a descriptive observational research design. The population in this study is the entire community in the Lewoleba City Government, totaling 36,426 people. Sampling was carried out using a simple random sampling method. personal. To determine the population, it ranges from 5% to 15% of the total population. Data were analyzed regarding the analysis of Lewoleba City Development Patterns on the Spatial Morphology of Lewoleba City, Lembata Regency, research through qualitative descriptive descriptions. Validity tests are carried out to check construct validity and content validity. Construct validity is carried out by the author with the opinion of experts (evaluation experience). After the data is tabulated, then construct validity testing is carried out using factor analysis, namely by correlating between instrument product scores using the product moment correlation formula. The data analysis technique used Descriptive Statistics is used for hypothesis testing purposes to determine and describe the sample characteristics of each variable studied in this research. The results of the analysis show that by analyzing the development pattern of Lewoleba City regarding the spatial morphology of Lewoleba City, Lembata Regency, it can provide benefits to the local government in planning Lewoleba City in the future with sustainable development policies. The significant factors that influence are Land Use, Population Growth, Infrastructure, Road Network, Accessibility and Geographical Conditions. Key words: City Development Pattern, Spatial Morphology, Lewoleba City
Ensemble Deep Learning Study for Pest Detection in Strawberry Plants Nugroho, Kahargyan Ario; Mandala, Satria
Eduvest - Journal of Universal Studies Vol. 5 No. 6 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i6.50311

Abstract

This deep learning-based classification model was developed to recognize different types of pest infections in strawberry plants. The model aims to quickly identify pest symptoms, thus enabling efficient pest management in smart farming. This research uses an actual dataset containing images of strawberry leaves collected from smart farm trials. To expand the dataset, open data from platforms such as Kaggle were used, while images of infected leaves were obtained through web crawling with the help of Python libraries. The added data were converted to a uniform size, and PseudoLabeling was used to ensure stable learning on both training and testing datasets. The RegNet and EfficientNet models are selected as the main CNN-based models for iterative learning, with ensemble learning techniques to improve prediction accuracy. The proposed model aims to assist the early identification and treatment of pests on strawberry leaves during the early planting period, a crucial phase in the development of smart agriculture. It is hoped that this model can increase production in the agricultural industry and strengthen its competitiveness. Detecting early symptoms of plant diseases and pests is essential to prevent their development and minimize the damage caused. Although many methods have been developed using deep learning techniques, detecting early symptoms is still challenging due to the lack of datasets capable of training models against subtle changes in plants. Therefore, researchers built an automated data collection system to gather a large dataset of plant images and train ensemble models to detect diseases and pests of the target plants.
Studi Autoencoder Deep Learning pada Sinyal EKG Mochamad Reza, Dandi; Satria Mandala; Zaki, Salim M.; Ming, Eileen Su Lee
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1117.2023

Abstract

Arrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram (ECG) is commonly employed, as it can record the heart's electrical signals. However, ECGs may encounter interference from sources like electromagnetic waves and electrode motion. Several researchers have investigated the denoising of electrocardiogram signals for arrhythmia detection using deep autoencoder models. Unfortunately, these studies have yielded suboptimal results, indicated by low Signal-to-Noise Ratio (SNR) values and relatively large Root Mean Square Error (RMSE). This study addresses these limitations by proposing the utilization of a Deep LSTM Autoencoder to effectively denoise ECG signals for arrhythmia detection. The model's denoising performance is evaluated based on achieved SNR and RMSE values. The results of the denoising evaluations using the Deep LSTM Autoencoder on the AFDB dataset show SNR and RMSE values of 56.16 and 0.00037, respectively. Meanwhile, for the MITDB dataset, the corresponding values are 65.22 and 0.00018. These findings demonstrate significant improvement compared to previous research. However, it's important to note a limitation in this study—the restricted availability of arrhythmia datasets from MITDB and AFDB. Future researchers are encouraged to explore and acquire a more extensive collection of arrhythmia data to further enhance denoising performance.
Klasifikasi Multikelas Infark Miokard Berdasarkan Sinyal Phonokardiogram dengan Ensemble Learning Nia Madu Marliana; Satria Mandala; Hau, Yuan Wen; Yafooz, Wael M.S.
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1121.2023

Abstract

Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.
Co-Authors Abd. Rasyid Syamsuri Abdul Karim Adiwijaya Adly, Muhammad Ihsan Agus Alex Yanuar Akbar, Muh Aldebaran Bayu Nugroho Alwan, Maryam Hameed Andika Nugroho Putra Andreas Jonathan Silaban Annas, Aswar Ardian Rizal Arifin, Rezki Fauzan Ashydiki Malik Asmi Citra Malina, Asmi Citra Assir, Andi Azha Alvin Rahmansyah Bilal Ibrahim Bakri Budi Santosa Burchanuddin, Andi Eko Darwiyanto Endro Ariyanto Erwid Jadied Mustofa Erwid Mustofa Jadied Erwied M. Jadied Faiz Rofi Hencya Faizal Akbari Putra Fenty Alia Fikry, Maurice Fityanul Akhyar Hameed, Zainab Hasanuddin Hasanuddin Hidayatus Sholikhin Ilham Alimuddin, Ilham Indrayuni, Armi Irma Ruslina Defi Jumadil, Jumadil Juni, Juni Kusuma Adi Achmad Ledya Novamizanti Marmin, Hidayat Martin Halomoan Tamba Maurice Fikry Miftah Pramudyo Ming, Eileen Su Lee Mochamad Reza, Dandi Mohd Fadzil Hasssan Mohd Shahrizal Sunar Mohd Soperi Mohd Zahid Muhajir, Humaidid Muhammad Alif Akbar Muhammad Aniq Wafa Muhammad Hablul Barri Muhammad Ihsan Adly Muhammad Qomaruddin Muhammad Rafli Ramadhan Muhammad Yaumil Ihza Ihza Musfirah, Andi Nadia Ariana Nia Madu Marliana Niken Dwi Wahyu Cahyani Nugroho, Kahargyan Ario Putu Harry Gunawan Raey Faldo Rafael Sebastian Rafi Ullah Rafly Athalla Ramadhan, Yusril Rifqi Syafiq Hibatul Aziz Rino Andias Anugraha Rio Guntur Utomo Rita Purnamasari Rizal, Ardian Salim M. Zaki Sobirin Sobirin Sutiyo Suyanto Suyanto Tong Boon Tang Tora Fahrudin Wael M.S. Yafooz Wiyono Sutari Yafooz, Wael M.S. Yuan Wen Hau Yusril Ramadhan Zaki, Salim M. Zelmi Muhammad Adjel