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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics JKTP: Jurnal Kajian Teknologi Pendidikan Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Journal of Engineering and Scientific Research (JESR) Cakrawala: Jurnal Pendidikan Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Komputasi
Helmy Fitriawan
Universitas Lampung
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Other

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Realization of Zigbee Wireless Sensor Networks for Temperature and Humidity Monitoring Helmy Fitriawan; Danny Mausa; Ahmad Surya Arifin; Agus Trisanto
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 2: EECSI 2015
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1069.134 KB) | DOI: 10.11591/eecsi.v2.779

Abstract

Many physical phenomena and processes in theenvironment must be monitored. This problem can be solved withan ad-hoc wireless sensor network (WSN), which consists of anumber of small and self-power sensing devices (nodes) that areconnected with each other using effective wireless networks. Thispaper reports development of an embedded wireless sensornetwork (WSN) prototype for environmental parametersmonitoring. The network itself consists of a coordinator or datagateway which wirelessly collect temperature and humidity dataover several sensor nodes. Each sensor node is developed from anarduino based microcontroller, Xbee wireless module based onZigbee/IEEE 802.15.4 standards, and temperature and humiditysensor devices. In the prototype system, both temperature andhumidity sensors are calibrated with accurate instruments to obtainprecise measurements. Its communication functionality is alsotested in various network topologies. The battery lifetime iscalculated to predict the energy consumption. The testing resultsshow that the system works well in terms of its functionality.
Pengembangan Media Pembelajaran Informatika Melalui E-Learning untuk Meningkatkan Berpikir Kreatif Siswa Rohimah Rohimah; Riswandi Riswandi; Helmy Fitriawan
JKTP: Jurnal Kajian Teknologi Pendidikan Vol 3, No 3 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um038v3i32020p330

Abstract

Abstrak: Tujuan penelitian ini adalah untuk mengembangkan media pembelajaran melalui e-learning yang memudahkan siswa kelas x di SMAN 1 Pagelaran dalam memahami konsep algoritma dan pemrograman yang selama ini menjadi masalah bagi siswa karena metode pembelajaran yang belum tepat dan belum memanfaatkan sumber belajar yang ada. Metode yang digunakan dalam penelitian ini adalah Research and Development (R&D) yang dikembangkan oleh Sugiyono, penelitian ini dilakukan hanya sampai pada 6 tahap yaitu: potensi dan masalah, pengumpulan data, desain  produk, validasi desain, perbaikan desain,  uji coba produk. Pada uji validasi ahli media mendapat nilai rata-rata 3,46  dengan keterangan media layak, dan pada hasil pengujian oleh ahli materi rata-rata 3,58 dikatakan bahwa materi layak untuk digunakan, pada uji kemenarikan elearning yang dilakukan kepada 10 responden terdiri dari siswa dan guru mendapat nilai rata-rata 88% menunjukkan klasifikasi bahwa media e-learning sangat menarik digunakan untuk meningkatkan berpikir kreatif siswa dalam mata pelajatan informatika pada  materi algoritma dan pemrograman.Abstract: The purpose of this research is to develop learning media through e-learning that makes it easy for students of class X at SMAN 1 Pagelaran to understand the concepts of algorithms and programming that have been a problem for students because of the learning methods that are not appropriate and do not utilize existing learning resources. The method used in this development is Research and Development (R&D) developed by Sugiyono, this research only reached 6 stages: potential and problems, data collection, product design, design validation, design improvement, product trials. In the media expert validation test the average value of 3.46 with media information is appropriate to use, and the results of testing by material experts on average 3.58 said that the material is suitable for use in the product interesting test conducted on 10 respondents consisting of students and teachers got an average value of 88% shows the classification e-learning media is very interesting to be used to improve students' creative thinking on informatics subjects on algorithmic and programming.
Pengembangan Video Pembelajaran Pada Materi Ikatan Kimia Untuk Mendukung Pembelajaran Daring Berbasis Advance Organizer Niken Widyastuti; Riswandi; Helmy Fitriawan
Cakrawala: Jurnal Pendidikan Vol 15 No 1 (2021)
Publisher : Universitas Pancasakti Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (368.057 KB) | DOI: 10.24905/cakrawala.v15i1.268

Abstract

The policy of learning from home due to the Covid 19 virus encourages teachers to create innovative learning media thatcan be accessed by students anywhere. Learning media is developed in accordance with learning objectives and studentneeds. The purpose of this research is to make learning videos to support advance organizer distance learning and tomeasure the attractiveness of learning videos that have been developed using the Dick and Carey model. The test subj ects inthis study were 39 students of class X of laboratory test analytical competence. Research instruments include observationsheets and interviews and attractiveness test questionnaires. The results of the attractiveness test showed a value of 3.45from a scale of 4.00. The conclusion of this research is that this learning video is attractive to use by students and supportadvance organizer distance learning
Penerapan Machine Learning Untuk Prediksi Masa Studi Mahasiswa di Perguruan Tinggi X Isna oktadiani; Helmy Fitriawan
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 17 No. 3 (2023)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v17n3.2529

Abstract

Universities play a role in producing quality resources from their graduate students, so that the quality and accreditation of tertiary institutions are things that need attention. One indicator of higher education accreditation is student graduation on time, so student graduation must be an important concern for tertiary institutions. Based on the results of the documentation, the percentage of students graduating on time is lower than students who are not completing their studies on time, therefore it is necessary to analyze the student's study period to overcome the study period that graduates are not on time using the machine learning method with the Naïve Bayes Classifier algorithm to predict student study period. The research method uses the Naïve Bayes Classifier algorithm method which is part of Artificial Intelligence (AI), which consists of preprocessing, input, process and output. because this method has high accuracy and can work better in real-world cases. The results of predicting the timeliness of student study time at college X with 3553 data using the Naïve Bayes Classifier algorithm method, using WEKA tools succeeded in predicting student study time with 70% data taining and 30% as random testing data with the system. Using 11 attributes, namely study program, GPA, mother's occupation, mother's income, entry period, father's occupation, father's income, route of entry, gender, and school of origin, obtained a percentage of precision value of 54.545%, recall value of 67.220%, and the accuracy level reaches 79.925% which is categorized as good, using the ROC curve calculation to form almost close to (0.1) with an AUC value of 0.844 which is categorized as very good. Based on the results of the percentage accuracy rate, ROC curve and AUC value, the Naïve Bayes Classifier predicts student graduation in the "Good" category
Lecturer Forecasting and Professor Acceleration using Bussiness Intelligence Bambang Sundari Tamtosutrisno; Helmy Fitriawan; Mardiana Mardiana
Journal of Engineering and Scientific Research Vol. 5 No. 1 (2023)
Publisher : Faculty of Engineering, Universitas Lampung Jl. Soemantri Brojonegoro No.1 Bandar Lampung, Indonesia 35141

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jesr.v5i1.120

Abstract

Universities must be allowed to compete and increase public accountability and competitiveness in order to enhance the quality of education. Human resources, such as professors and educational staff, are one of the supporting aspects. Lecturers are one of the most significant markers for gauging the viability and quality of academic programs and postsecondary institutions. Lecturer data evolves according to educational level, functional position and tenure (retirement) which need to be monitored and anticipated as data to help strategic planning for the next few years to improve the quality and accountability of a tertiary institution. Data sources that are validated and linked with legally authorized data sources are required to support business intelligence in the decision-making processes of policymakers and institutional leaders.
Simulasi Kinerja Web Server Pada Jaringan LAN (Local Area Network) Kampus Menggunakan NS2 (Network Simulator 2) Muhammad Iqbal; Helmy Fitriawan; Didik Kurniawan
Jurnal Komputasi Vol. 10 No. 2 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i2.3166

Abstract

Web server adalah komputer yang terhubung dalam jaringan atau internet yang memberikan informasi berupa teks, audio dan video. Untuk mencapai efisiensi rancangan dan tingkat kehandalan yang optimal, web server skala besar membutuhkan perhitungan performansi rancangan jaringan tersebut. Pada tugas akhir dibahas mengenai pemodelan dan simulasi rancangan web server pada jaringan LAN (Local Area Network) Universitas Lampung untuk mengetahui performansi web server pada jaringan LAN optimal yang dapat dicapai. Penelitian dilakukan dengan memodelkan client dan server pada perangkat lunak simulator.  Simulasi terhadap rancangan model web server pada jaringan LAN dilakukan dengan mengimplementasikan suatu skenario simulasi. Pemodelan dan simulsai ini akan memberikan pendekatan tingkat performansi web server pada jaringan LAN sebagai pertimbangan pengambilan kebijakan realisasi web server pada jaringan LAN. Berdasarkan hasil simulasi diperoleh rancangan web server pada jaringan LAN optimum adalah pada rancangan dengan jumlah node sebanyak 100 node.  Rancangan tersebut memberikan tingkat performansi rata-rata throughput yang diperoleh hampir mencapai nilai maksimum rata-rata throughput dengan nilai rata-rata delay dan jitter yang minimum serta pencapaian persentase packet loss yang paling rendah dibandingkan dengan simulasi menggunakan jumlah node lainnya
IDENTIFIKASI CALON MAHASISWA BARU DALAM PEMILIHAN PROGRAM STUDI SEBAGAI MODEL PENGEMBANGAN KERJASAMA DENGAN MKKS SE PROVINSI LAMPUNG Helmy Fitriawan; Bambang Hermanto; Hery Dian Septana; Nandi Haerudin
Jurnal Komputasi Vol. 10 No. 2 (2022)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v10i2.3182

Abstract

There are still many prospective students from the University of Lampung from various high school graduates and their equivalent who do not know which study program at the University of Lampung is good and suitable for them, so that when they are accepted and start lectures, it is not uncommon for students to feel that they have chosen the wrong major which results in curiosity. changing study programs late or even unable to complete college. This study aims to build a decision support system that displays recommendations for Study Programs at the University of Lampung that are in accordance with the interests and academic abilities or intelligence of prospective students. This system is expected to assist prospective students in choosing a study program that suits them, so as to reduce the risk of changing study programs, as well as being late for graduation or even not being able to graduate. The system development method that will be carried out in this research is Extreme Programming (XP) in order to build a system quickly and according to user needs which has four main stages, namely planning, designing, coding, and testing before releasing the system. Meanwhile, to determine recommendations based on the interests and intelligence of prospective students, they will use Holland's theoretical basis for interest, and the Intelligence Structure Test (IST) theory for intelligence
Artificial Neural Network Backpropagation Method for Predicting Soil Nutrient Content: Artificial Neural Network Backpropagation Method for Predicting Soil Nutrient Content Witaningsih Witaningsih; Sri Ratna Sulistiyanti; Mareli Telaumbanua; F X Arinto Setyawan; Helmy Fitriawan; Rita Anggraini
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 6 (2025): December 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i6.2424-2438

Abstract

Monitoring soil nutrient levels such as nitrogen (N), phosphorus (P), and potassium (K) is essential to support fertilizer efficiency and sustainable agricultural land management. However, commonly used laboratory-based analytical methods are time-consuming and costly. Therefore, alternative approaches that are more practical and efficient are needed. This study aimed to develop an Artificial Neural Network (ANN)-based system for predicting soil nutrient levels using soil physical parameters, namely pH, temperature, moisture content, and electrical resistance, as input variables. Data were collected from red-yellow podzolic soil subjected to different fertilization treatments. After normalization, the data were trained using an ANN model with four input nodes, two hidden layers (each consisting of five nodes), and one output node, employing the backpropagation algorithm and evaluating 27 combinations of activation functions. The training results showed coefficients of determination (R²) of 0.9642 for nitrogen, 1.0000 for phosphorus, and 0.9996 for potassium, with RMSE values of 0.0107, 10.5386, and 0.016457 and RRMSE values of 8.5048%, 0.79786%, and 1.581111%, respectively. During validation, R² values of 0.7218 (nitrogen), 0.6479 (phosphorus), and 0.6137 (potassium) were obtained. Nitrogen prediction exhibited good accuracy (RMSE 0.0222; RRMSE 15.54%), potassium prediction showed moderate accuracy (RMSE 0.2963; RRMSE 28.46%), while phosphorus prediction resulted in relatively high errors (RMSE 1066.77; RRMSE 80.98%), indicating the need for further model development.
Design and Implementation of an Artificial Neural Network Model for Soil Nitrogen Prediction Rita Anggraini; Sri Ratna Sulistiyanti; Helmy Fitriawan; FX Arinto Setyawan; Mareli Telaumbanua; Witaningsih Witaningsih
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 2 (2026): April 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i2.732-742

Abstract

The availability of nitrogen in soil is a crucial factor determining crop productivity. However, the measurement of total nitrogen (N-total) content requires considerable time and cost. Therefore, a fast, accurate, and easy prediction method is needed to support the agricultural development. This study aims to develop an Artificial Neural Network (ANN) model based on the backpropagation algorithm to identify soil N-total content using soil pH, moisture content, and soil resistance as input parameters. The model was trained using the trainbr training function with variations of logsig and tansig activation functions and hidden layer structures of 5–5, 8–8, and 12–12 to obtain the best configuration. The training results indicate that the tansig–tansig combination with 8–8 hidden layer structure achieved the highest performance, with a R2 training of 0.953 and a R2 testing of 0.911. The model was implemented in the form of a Graphical User Interface (GUI) application to facilitate field-level prediction. Validation using 40 testing data samples showed a classification accuracy of 70% and an R² value of 0.932 for nitrogen prediction. The model correctly classified 28 data samples out of the total 40 tested data. These results indicate that the proposed model is capable of predicting soil nitrogen content accurately and reliably.
Co-Authors Agus Trisanto Ahmad Surya Arifin Bambang Hermanto Bambang Sundari Tamtosutrisno Danny Mausa F X Arinto Setyawan Hery Dian Septana Isna oktadiani Mardiana Mardiana Mareli Telaumbanua Muhammad Iqbal Nandi Haerudin Niken Widyastuti Riswandi, Riswandi Rita Anggraini Rohimah Rohimah Setyawan, FX Arinto Siti Khodijah Sri Ratna Sulistiyanti Witaningsih Witaningsih