Article Per Year (5 Year)
p-Index From 2021 - 2026
4.116
P-Index
This Author published in this journals
All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknik Komputer AMIK BSI JIKO (Jurnal Informatika dan Komputer) Faktor Exacta Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Sisfokom (Sistem Informasi dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTIKA - Jurnal Sistem Komputer Jurnal Manajemen Informatika Jurnal Informatika Global Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Informatika dan Rekayasa Elektronik Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer ILKOMNIKA: Journal of Computer Science and Applied Informatics Scientific Journal of Informatics Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Pengabdian Masyarakat Indonesia Jurnal Abdimas Bina Bangsa Jurnal Abdi Masyarakat Indonesia Ngabdimas : Pengabdian Pada Masyarakat KREATIF: Jurnal Pengabdian Masyarakat Nusantara Journal Of Artificial Intelligence And Software Engineering
Asep Syaputra
Sekolah Tinggi Teknologi Pagaralam
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

Published : 37 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 37 Documents
Search

 1   2   3   4 
PENGUJIAN EFEKTIVITAS ANTENA YAGI GRID EXTREME SEBAGAI PENGUAT SINYAL DI DESA PAJAR BULAN Asep Syaputra; Dedi Setiadi
Jurnal Informatika dan Rekayasa Elektronik Vol. 6 No. 2 (2023): Jire Nopember 2023
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v6i2.642

Abstract

Desa Pajar Bulan Kabupaten Lahat terletak di dataran tinggi yang di kelilingi banyak bukit dan pepohonan. Hal ini merupakan salah satu faktor yang menyebabkan sinyal yang dipancarkan oleh tower Base Tranceiver Station (BTS) yang berada ±22 km ke desa Pajar Bulan menjadi kurang baik. Oleh karena itu untuk meningkatkan kualitas jaringan wifi di desa Pajar Bulan dengan memanfaatkan antena yagi grid extreme sebagai alat penguat sinyal. Dalam penelitian ini menggunakan metode Prepare, Plan, Design, Implemant, Operate, Optimaze (PPDIOO), karena di dalam metode ini terdapat tahapan-tahapan yang dapat membantu dalam meningkatkan kualitas jaringan wifi dengan menggunakan antena yagi grid extreme di desa Pajar Bulan. Pada saat sebelum pemasangan antena yagi grid extreme, kekuatan sinyal wifi  yang didapat bernilai 2,3 Kbps yang berarti kekuatan sinyalnya dikisaran sinyal E atau EDGE (Enchance Data Rate for GSM Eolution) yang artinya transfer data yang ada terasa lambat. Pada pengukuran setelah menggunakan antena yagi grid extreme didapatkan kekuatan sinyal ddalam ruang tertutup dengan nilai -37 dBm dengan kecepatan 83 Mbps, akan tetapi ketika  jangkauan pengguna 25 meter dari antenna maka kekuatan sinyal wifi menjadi -81 dBm dengan kecepatan 20 Mbps. Sedangkan pengujian ketika antena di pasang di luar dengan tiang penyangga, kekuatan sinyal wifi yaitu -23 dBm dan kecepatan 77 Mbps, yang berarti sinyal berada pada kategori excellent atau sangat baik.
The Implementation of Support Vector Machine Method with Genetic Algorithm in Predicting Energy Consumption for Reinforced Concrete Buildings Asep Syaputra
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.v7i3.12516

Abstract

Accurate information on energy consumption is crucial for measuring energy efficiency and savings in buildings. It refers to the energy needed to power a building at a specific time. Energy savings can reduce costs and environmental impact by lowering greenhouse gas emissions. Obtaining precise energy consumption data is essential for all parties involved in building planning, construction, and management. Over the past decades, global energy consumption in buildings has consistently increased, with HVAC systems being a significant contributor. To tackle this problem, research developed a support vector machine model with genetic algorithms to accurately predict energy consumption in buildings. Two models were tested: a standard support vector machine and a genetic algorithm-integrated support vector machine. The test results revealed that the support vector machine model achieved an RMSE value of 2.6. Additionally, the genetic algorithm optimized the parameter C and selected the most relevant predictor variables, reducing the RMSE to 1.7 and utilizing only 3 predictor variables. In the subsequent stage, parameter optimization and function selection were performed to achieve an improved RMSE value of 1.537. This research aims to enhance energy consumption prediction for reinforced concrete buildings by combining SVM and Genetic Algorithm. SVM serves as the primary prediction model, while the Genetic Algorithm is employed to determine optimal SVM parameters and relevant features. Recent studies have demonstrated that this combination yields more accurate predictions compared to standard methods. It enables more efficient energy planning, reduced operational costs, and optimized resource utilization in reinforced concrete buildings. However, it's worth noting that this implementation may require substantial processing and resource utilization, depending on the dataset's size and complexity.
Klasifikasi Penyakit Daun pada Tebu dengan Pendekatan Algoritma K-Nearest Neighbors, Multilayer Perceptron dan Support Vector Machine Syaputra, Asep
Jurnal Ilmiah Informatika Global Vol. 15 No. 3: Desember 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i3.4856

Abstract

Sugarcane is a vital crop in Indonesia, serving as the primary raw material for sugar production. Unfortunately, leaf diseases in sugarcane often pose a serious threat, potentially causing significant economic losses. These diseases are typically characterized by leaf morphological changes, making early detection and accurate classification essential to prevent further spread. This study compares three algorithms for identifying sugarcane leaf diseases: K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Support Vector Machine (SVM). Each algorithm employs a different approach to recognize patterns and disease characteristics: SVM separates data by identifying the optimal hyperplane, KNN classifies based on the proximity of data to training data, while MLP, as an artificial neural network, can recognize more complex patterns. The deep learning model VGG16 was utilized for feature extraction from sugarcane leaf images to enhance classification accuracy. The dataset used comprises 8,200 images of sugarcane leaves, categorized into four classes: 2,050 images of Cercospora spot gray, 2,050 of common rust, 2,050 of northern blight, and 2,050 of healthy leaves. Each category was further divided into training and testing datasets in an 80:20 ratio, with 6,560 images for training and 1,640 images for testing. The results indicate that the MLP algorithm achieved the best performance, with accuracy, precision, and recall values of 97.4%. This establishes MLP as the most effective choice for classifying sugarcane leaf diseases.
Implementasi Teknologi IoT dalam Sistem Akuaponik dan Akuakultur Modern untuk Optimasi Pertumbuhan Ikan Lele Syaputra, Asep; Prawira, Nanda S.
ILKOMNIKA Vol 6 No 3 (2024): Volume 6, Nomor 3, Desember 2024
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v6i3.692

Abstract

Di era modern, sistem pertanian dan budidaya ikan berkembang pesat, termasuk akuaponik modern sebagai metode inovatif dan berkelanjutan yang mengintegrasikan budidaya ikan dan pertanian dalam satu sistem. Akuaponik modern efektif mengatasi keterbatasan lahan perkotaan, menjadi solusi efisien untuk produksi pangan. Penelitian ini bertujuan menerapkan sistem Internet of Things (IoT) pada akuaponik modern untuk meningkatkan pertumbuhan ikan lele secara terkontrol dan efisien. Sistem ini menggunakan sensor IoT, seperti sensor pH, TDS, dan suhu, untuk memantau kualitas air secara berkelanjutan. Pemantauan ini memastikan kondisi ideal untuk pertumbuhan ikan dan meningkatkan produktivitas tanaman, sehingga budidaya menjadi lebih optimal dan berkelanjutan. Hasil pengujian menunjukkan pH air sebesar 7,2, TDS 300 ppm, dan suhu air 28°C, yang semuanya berada dalam rentang optimal untuk mendukung pertumbuhan ikan lele sehat. Kondisi ini menciptakan lingkungan stabil yang ideal untuk keberhasilan sistem akuaponik.
Implementation of IoT Technology in Aquaponics and Modern Aquaculture Systems for Optimizing Catfish Growth Asep Syaputra; Nanda S.Prawira
Journal of Computer, Electronic, and Telecommunication (COMPLETE) Vol. 5 No. 2 (2024): December
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/complete.v5i2.642

Abstract

In the modern era, various systems of agriculture and aquaculture have evolved rapidly. One remarkable innovation is modern aquaponics, a method that is gaining recognition as a sustainable solution for food production. This system combines fish farming and agriculture into a unified and mutually beneficial approach. Modern aquaponics has proven to be effective in overcoming the constraints of urban land, providing a viable solution for both agriculture and aquaculture. For optimal results in fish farming, it is crucial to consistently monitor the conditions of fish growth and health to avoid the risk of crop failure. In response to this challenge, this study aims to implement an Internet of Things (IoT) system in modern aquaponics, focusing primarily on enhancing the growth of catfish in a controlled and efficient manner. This system employs a range of IoT sensors, including pH sensors, Total Dissolved Solids (TDS) sensors, and temperature sensors, to monitor the water quality within the aquaponics setup continuously. Such monitoring not only ensures optimal conditions for healthier fish but also increases plant productivity, thereby enhancing the overall sustainability and effectiveness of the cultivation process. The results of sensor testing revealed a pH value of 7.2, indicating that the water's acidity level is within a balanced and optimal range for supporting the health of catfish. TDS sensor readings showed a value of 300 ppm, suggesting that the concentration of dissolved particles is ideal for the well-being of the fish. Furthermore, temperature measurements from the DS18B20 sensor recorded a water temperature of 28°C, which falls within the optimal range for catfish growth (28–30°C). These conditions create a stable environment that supports the healthy growth of fish in the aquaponics system.
Implementation of Machine Learning Algorithms for Predicting Student Final GPA Using Multiclass Classification Models Sayaputra, Asep
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7015

Abstract

This study aims to predict students’ final Grade Point Average (GPA) and study duration by applying the Random Forest Regressor algorithm based on historical academic data. The dataset includes key variables such as semester GPA, socio-economic background, demographic information, study habits, and the complexity level of the enrolled courses. The regression analysis results indicate that the model's performance was suboptimal, with a Mean Squared Error (MSE) of 0.341 for GPA prediction and 3.831 for study duration estimation. Additionally, the negative R-squared (R²) values reflect the model's limited ability to explain data variability. As an alternative, a multi-class classification approach was implemented to categorize students into final GPA groups, including Cum Laude, Very Satisfactory, Satisfactory, and Adequate. At this stage, the model achieved a remarkably high accuracy of 99.8% with an error rate of only 0.03. These findings demonstrate that the classification approach is more effective than regression in predicting academic performance. This research contributes to the development of data-driven academic decision support systems. Future studies are recommended to explore feature optimization techniques and alternative algorithms to enhance overall prediction performance.
Eksplorasi Model LSTM dengan Autoregressive Integrated Moving Average (ARIMA) dan Bayesian Optimization untuk Peningkatan Akurasi Prediksi Ketahanan Pangan Jang Cik, Idi; Syaputra, Asep
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.2077

Abstract

Tujuan : Penelitian ini bertujuan untuk mengeksplorasi Model LSTM dengan Autoregressive Integrated Moving Average (ARIMA) dan Bayesian Optimization untuk Peningkatan Akurasi Prediksi Ketahanan Pangan Kota Pagar Alam. SIstem ini diharapkan mampu memprediksi secara akurat ketahanan pangan untuk proyeksi 5 tahun kedepan melalui data timeseries: Sumsel mengalami penurunan Indeks Ketahanan Pangan (IKP) yang berada pada batas level bawah <50 ARIMA dan LSTM dalam meramalkan Ketahanan Pangan Sumatera Selatan  menggunakan  dua  skenario  data:  basic LSTM dan ARIMA dan LSTM, ARIMA yang di optimasi dengan Bayes Optimization).  Hasil menunjukkan  bahwa  LSTM  menghasilkan visualisasi data yang tidak konsisten dengan turun dan naik data yang inbalancing sedangkan arima mampu memprediksi data dengan lebih balance. Kedua visualisasi tersebut lebih di optimasi dengan Bayesian Optimization sehingga menghasilkan data yang balance dan beriringan satu sama lain MAPE nya juga dibawah 10% sehingga menunjulan tingkat error data sangat sedikit. Temuan ini  menegaskan  bahwa  pemilihan  model  peramalan harus  disesuaikan  dengan  karakteristik  data. Hasil penelitian ini dapat menjadi menjadi   panduan   dalam   memilih   metode   prediksi ketahanan pangan sumatera selatan yang paling efektif untuk mendukung upaya mitigasi  kekurangan pangan. 
Co-Authors Asep Deden Rahmat S Barrorotul Azizah Barrorotul Azizah Buhori Muslim Buhori Muslim Dedi Setiadi Dhiniati, Fameira Didi Ardiansyah Edowinsyah Edowinsyah Edowinsyah Edownsyah Ego Winanda Eva Susilawati Eva Susilawati Febriansyah . Ferry Putrawansyah Jang Cik, Idi Nanda S. Prawira Rony, Zahara Tussoleha Sasmita Sasmita Tri Susanti Vike Itteridi Vike Itteridi Wenny Djuarni Yogi Isro Mukti, Yogi Isro