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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Advances in Applied Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal EECCIS Jurnal Informatika dan Teknik Elektro Terapan JURNAL PENGABDIAN KEPADA MASYARAKAT Science and Technology Indonesia Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Teknoinfo JCRS (Journal of Community Research and Service) Jurnal Teori dan Aplikasi Fisika Journal of Engineering and Scientific Research (JESR) TEKTAN Journal of Innovation and Technology Journal of Multidisciplinary Applied Natural Science Jurnal Rekayasa Lampung (JRL) Prosiding Seminar Nasional Hasil Penelitian Sains, Teknik dan Aplikasi Industri Fakultas Teknik Universitas Lampung Electrician : Jurnal Rekayasa dan Teknologi Elektro SNTE Journal of Digital Market and Digital Currency Nemui Nyimah Analit: Analytical And Environmental Chemistry
Sri Ratna Sulistiyanti
[Scopus ID: 36606902600] Department Of Electrical Engineering Faculty Of Engineering University Of Lampung Jl. Sumantri Brojonegoro 1, Bandar Lampung
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Design of an Earthquake Intensity Estimation System for Early Warning Trismahargyono Trismahargyono; Sri Ratna Sulistiyanti; Roniyus Marjunus
Jurnal Teori dan Aplikasi Fisika Vol. 9 No. 2 (2021): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v9i2.362

Abstract

Penggunaan Interpolasi Bilinier Pada Akuisisi Data Massa Muhammad Ifan Saputra; Sri Ratna Sulistiyanti; F.X. Arinto Setyawan
Jurnal Teori dan Aplikasi Fisika Vol. 12 No. 02 (2024): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v12i02.376

Abstract

The bilinear interpolation method is generally used to improve images that have noise. However, in this research the bilinear interpolation method will be used to determine the weight value of a digital scale designed with four load cell sensors. Data collection was carried out by placing loads at nine different points. The loads used are 2kg and 5kg. The results of this research are that the value of the point in the center or on the axis of the four load cells, namely points B, D, E, F, and H, has the same voltage as the actual value, namely for a 2kg weight of 4.31 mV. and for a weight of 5 kg it is 5.87 mV. Meanwhile, the other points, namely points A, C, G and I, have values ​​that deviate from the actual value by 0.36 mV or an error of 36%.   Keywords: Bilinear Interpolation, Digital Scales, Load Cell Sensors.
Rancang Bangun Mesin CNC Laser 4 Axis menggunakan Motor Stepper Tipe Nema 23 Terintergrasi Mach3 USB untuk Aplikasi Mesin Cutting Otomatis Hesti Wahyu Handani; Sri Ratna Sulistiyanti; Yanti Yulianti; Posman Manurung; Junaidi
Jurnal Teori dan Aplikasi Fisika Vol. 13 No. 02 (2025): Jurnal Teori dan Aplikasi Fisika
Publisher : Department of Physics, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtaf.v13i02.413

Abstract

Perancangan dan pembuatan mesin CNC Laser 4 Axis menggunakan motor stepper tipe nema 23 terintegrasi Mach3 USB untuk aplikasi mesin cutting otomatis telah dilakukan. Alat ini merupakan suatu alat laboratorium bidang manufaktur yang digunakan untuk memotong material berbahan akrilik secara otomatis dengan dimensi pemotongan mencapai 1000 mm x 2000 mm. Alat ini memiliki mata potong berupa laser dioda ukuran 40 watt yang mampu memotong lembaran akrilik dengan ketebalan 3 mm. Alat ini dikontrol menggunakan kontroler Mach3 board dan dikomunikasikan dengan software Mach3 menggunakan perintah berupa G-code. Alat ini mampu memotong lembaran akrilik ketebalan 3 mm dengan kecepatan maksimum 55 mm/menit. Untuk hasil pemotongan optimal, proses pemotongan akrilik dilakukan pada jarak laser terhadap akrilik yaitu sejauh 15 mm. Alat ini memiliki kesalahan relatif yaitu 0,27% dan deviasi sebesar 0,25 mm. Berdasarkan spesifikasi tersebut, mesin CNC Laser ini dapat diaplikasikan untuk mesin cutting otomatis untuk material berbahan dasar akrilik.
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.
ANALISIS PEMANFAATAN JEMBATAN GARAM KCl DAN NaCl TERHADAP LAJU KOROSI ELEKTRODA Zn PADA SEL VOLTA MENGGUNAKAN AIR LAUT SEBAGAI ELEKTROLIT Gurum Ahmad Pauzi; Arie Anjarwati; Ahmad Saudi Samosir; Sri Ratna Sulistiyanti; Wasinton Simanjuntak
Analit : Analytical and Environmental Chemistry Vol. 4, No. 02 October (2019) Analit : Analytical and Environmental Chemistry
Publisher : Jurusan Kimia FMIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/aec.v4i2.2019.p50-58

Abstract

Penelitian ini dilakukan untuk menganalisis pengaruh jembatan garam terhadap laju korosi elektroda Zn pada Sel volta. Pasang elektroda Cu (Ag)-Zn digunakan untuk menghasilkan tegangan dan arus dalam sel dengan elektrolit air laut. Variasi jembatan garam menggunakan agar yang dilarutkan dengan 0,1 mol NaCl, 0,1 mol KCl, 1 mol NaCl, dan 1 mol KCl. Sel volta terdiri dari 20 sel yang tersusun secara seri, masing-masing sel diisi ± 300 ml air laut. Sel volta terhubung ke beban LED 3 watt 12 volt selama satu hari, dan 30 hari. Hasil penelitian menunjukkan bahwa jembatan garam NaCl 1 mol menghasilkan karakteristik listrik yang lebih tinggi dan laju korosi yang lebih tinggi pada elektroda Zn.http://dx.doi.org/10.23960/aec.v4.i2.2019.p50-58
Analysis of Mill Motor Speed on the Sugar Value in Bagasse Using the Fuzzy Logic Method at the Sugar Factory of PT. Pratama Nusantara Sakti Ricky Rachman Nursa; Helmy Fitriawan; Sri Ratna Sulistiyanti
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.1130-1142

Abstract

The Indonesian sugar industry faces a serious challenge in the form of low efficiency in sugarcane milling, which is indicated by the high pol value in bagasse. This condition indicates that a considerable amount of sugar remains trapped in the bagasse, resulting in sugar losses and reduced productivity. One of the operational factors contributing to this phenomenon is the rotational speed of the mill motor, as non-optimal speed can affect the level of juice extraction and the amount of sugar remaining in the bagasse. Therefore, this study aims to analyze the effect of mill motor rotational speed on the pol value of bagasse and to optimize this parameter using the fuzzy logic method. The fuzzy system was designed to process machine variables (motor speed and motor load) as well as supporting factors (moisture content, temperature, service life, and harvesting age) through inference rules based on membership functions. Results show that most fuzzy predictions are consistent with the actual data from the quality control division, with a high level of accuracy indicated by an RRMSE of 7.84%, MAE of 0.0603, and MAPE of 3.34%. These findings demonstrate that fuzzy logic is capable of handling uncertainty and the complexity of variables in the milling process, while also providing a practical solution to reduce sugar losses, improve quality, and enhance the productivity of the national sugar industry.
Identifikasi Karakteristik Suhu Pada Kesehatan Baterai litium-ion Berbasis Citra Thermal Perdana Agung Nugraha; Sri Ratna Sulistiyanti; F.X. Arinto Setyawan; Helmy Fitriawan; Lukmanul Hakim
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 20 No. 1 (2026)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v20n1.2891

Abstract

Over the past few decades, the demand for environmentally friendly energy has led to an increase in the use of energy storage technologies such as batteries. One type of battery that is widely used is the lithium-ion battery because it has high durability, high energy density, and is lightweight. However, this battery is sensitive to extreme conditions such as high temperatures and excessive charging or discharging, which can affect battery health. This study aims to determine the health condition of lithium-ion batteries based on temperature characteristics from thermal images, as well as to evaluate the accuracy of a fuzzy logic system in predicting battery health status. The fuzzy logic system is used because it can handle uncertainty within varying temperature data ranges. The data used consists of 20 battery samples categorized into three groups: Healthy, Warning, and Unhealthy. The input parameters include the battery's operating temperature and the difference between the battery temperature and the ambient temperature. Evaluation was conducted using confusion matrices such as accuracy, precision, recall, and F1-score. The analysis results show that the fuzzy model has an accuracy of 84% and a precision rate of 84% for the Healthy category, 75% for the Warning category, and 93.75% for the Unhealthy category, as well as a recall evaluation of 91.30% for the Healthy category, 54.55% for the Warning category, and 93.75% for the Unhealthy category. These findings indicate that the fuzzy method is quite effective in monitoring battery health through temperature analysis.
KLASIFIKASI JENIS KAIN BERDASARKAN EKSTRAKSI FITUR TEKSTUR MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRICES (GLCM): CLASSIFICATION OF FABRIC TYPES BASED ON TEXTURE FEATURE EXTRACTION USING THE GRAY LEVEL CO-OCCURRENCE MATRICES (GLCM) METHOD Tiya Muthia; FX Arinto Setyawan; Helmy Fitriawan; Sri Ratna Sulistiyanti; Mutia Aini Lutfia
Jurnal Rekayasa Lampung Vol. 5 No. 1 (2026)
Publisher : Fakultas Teknik Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jrl.v5i1.113

Abstract

Klasifikasi jenis kain secara manual berdasarkan karakteristik tekstur melalui pengamatan dan perabaan dapat bersifat subjektif, sehingga diperlukan pendekatan otomatis berbasis citra. Penelitian ini bertujuan mengklasifikasikan lima jenis kain, yaitu drill, katun, polyester, satin, dan wool, berdasarkan karakteristik tekstur menggunakan metode Gray Level Co-Occurrence Matrix (GLCM) dan algoritma K-Nearest Neighbor (K-NN). Citra kain diperoleh menggunakan mikroskop digital yang terhubung dengan komputer. Tahapan pengolahan citra meliputi resizing dan konversi citra RGB menjadi grayscale. Ekstraksi fitur tekstur menggunakan enam fitur GLCM, yaitu dissimilarity, homogeneity, correlation, contrast, angular second moment (ASM), dan energy, dengan empat orientasi, yaitu 0°, 45°, 90°, dan 135°. Pengujian dilakukan menggunakan 250 citra data latih dan 50 citra data uji dengan variasi jarak piksel 1–5 dan nilai K=13. Hasil pengujian menunjukkan bahwa kinerja terbaik diperoleh pada jarak 4 piksel dengan akurasi 78%, precision 46%, recall 46%, dan F1-score 46%. Waktu komputasi tercepat diperoleh pada jarak 4 sebesar 0,044 detik, dengan rata-rata waktu pengujian sebesar 0,047 detik. Hasil penelitian menunjukkan bahwa kombinasi GLCM dan K-NN dapat digunakan untuk klasifikasi jenis kain berbasis karakteristik tekstur citra.
Co-Authors A S Samosir Achmad Yahya Teguh Panuju Adi Saputra Admi Syarif Afri Yudamson Afri Yudamson Ageng Sadnowo Ageng Sadnowo Repelianto Agus Trisanto Agus Wantoro Ahmad Pauzi, Gurum Ahmad Saudi Samosir Ahmad Saudi Samosir Anjarwati, Arie Arie Anjarwati Arie Setya Putra Arief, Khollaqul Dedyk Erryyanto dhika, eduar Dyah Indriana Kusumastuti Eko Efendi Eko Rismawan Endro Prasetyo Wahono F X Arinto Setyawan F.X. Arinto F.X. Arinto Setyawan Ferika Shaumi, Rahma Fitria Yunita Fitriwan, Helmy Frisky Volino Andreas Gurum Ahmad Pauzi Gurum Ahmad Pauzi Gusri Akhyar Ibrahim Haris Murwadi Helmy Fitriawan Helmy Fitriawan Helmy Fitriawan Hendro Utomo Herlinawati -, Herlinawati Herlinawati Herlinawati Herlinawati Herlinawati Herri Gusmedi Herti Utami Herti Utami Hesti Wahyu Handani Jimmy Lukita Junaidi Junaidi Junaidi Junaidi Junaidi Khairudin Khairudin Khairun Nisa Khollaqul Arief Komalasari, Agrianti Komarudin, M. Kris Sivam Kurnia Muludi Kurniawan, Dendi Luh Putu Ratna Sundari Lukmanul Hakim M Jerry Juliandr Suja M Said Hasibuan M Yusuf Tamtomi M. Dyan Susila Madi Hartono Mahfut Mardiana Mardiana Mardiyah, Luthfiyyatun Mareli Telaumbanua Marjunus, Roniyus Meizano Ardhi Muhammad Minhajjul Abidin Jaya Muhamad Komarudin Muhamad Komarudin Muhamad Komarudin Muhammad David Muhammad Ifan Saputra Muthia, Tiya Mutia Aini Lutfia Nadia Muthiati Nisa, Mia Abi Noer Sudjarwanto Nurul Hudayani Okta Ainita Pami Ruli Setiawan Pauzi, Gurum Ahmad Perdana Agung Nugraha Posman Manurung Quart Ferrina Rahmat, Rafli Dwi Rakhmat, Riko Ranny Dwidayanti Ricky Rachman Nursa Rita Anggraini Riza Muhida Rudi Darmawan Setyawan, F X Arinto Setyawan, FX Arinto Sony Ferbangkara Sri Purwiyanti Sri Purwiyanti Sri Purwiyanti Sri Wahyu Suciyati Sumadi Sumadi SUMADI SUMADI Surtono, Arif Suryadiwansa Harun Sutyarso Sutyarso Syafriadi Syafriadi Syaiful Alam Syaiful Alam Syaiful Alam Titin Yulianti Tiya Muthia Tiya Muthia Tiya Muthia Trismahargyono Trismahargyono ubaidah ubaidah Ubaidah, Ubaidah Umi Murdika Wahyu Eko Sulistiono Warsito . Warsono Warsono Wasinton Simanjuntak Wasinton Simanjuntak Wasinton Simanjuntak Wijaya, Agung Kusuma Winanti, Diki Danar Tri Winarto Winarto Witaningsih Witaningsih Y E Putra Yanti Yulianti Yanti Yulianti Yogi Aldino Yudi Eka Putra Yudi Eka Putra Yuli Darni Yuli Darni Yuli Darni