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All Journal EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Pseudocode Jurnal Pengabdian Kepada Masyarakat Sakai Sambayan Jurnal Informatika dan Teknik Elektro Terapan agriTECH Jurnal Kreativitas PKM Barometer International Journal of Aviation Science and Engineering International Journal of Electronics and Communications Systems Jurnal Inovasi Pembangunan Journal of Applied Science, Engineering and Technology (J. ASET) Malcom: Indonesian Journal of Machine Learning and Computer Science Seminar Nasional Insinyur Profesional (SNIP) Prosiding Seminar Nasional Hasil Penelitian Sains, Teknik dan Aplikasi Industri Fakultas Teknik Universitas Lampung Electrician : Jurnal Rekayasa dan Teknologi Elektro Jurnal Teknologi Riset Terapan Nemui Nyimah
Mulyani, Yessi
Universitas Lampung
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Penilaian Pembayaran Kredit dengan Logistic Regression dan Random Forest pada Home Credit Yulianti, Titin; Cahyana, Amanda Hasna; Komarudin, Muhamad; Mulyani, Yessi; Septama, Hery Dian
Jurnal Pseudocode Vol 11 No 2 (2024): Volume 11 Nomor 2 September 2024
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/pseudocode.11.2.79-88

Abstract

Global economic development has led to the high complexity of society's needs. Financial institutions are here to provide facilities to meet the increasingly complex needs of society. However, the existence of problem loans can be a serious threat so classification techniques in data mining are used to overcome this problem. This research develops a model that can predict customers' ability to make credit payments so that financial institutions can avoid problematic credit. In this research, the SMOTE resampling technique is used to see the effect of sampling in dealing with class imbalance and conducting credit assessments. The research results show that the model built using SMOTE has better AUC than the model without SMOTE. From the two machine learning algorithms, logistic regression and random forest, the results show that the random forest model with SMOTE has the best performance with an accuracy value of 90%, precision of 92%, recall of 88%, F1-score of 90%, and AUC value of 0.97. Based on the best model, ten important features were obtained that influence the process of assessing credit repayment capabilities, namely the normalized score from external data sources, the period for changing customer numbers, the number of previous installment payments, the customer's age, registration time, the period for applying for credit at the credit bureau, the period for changing identity documents, the time for updating information at the credit bureau, and the length of time the customer has worked. In addition, this research produces visualizations via dashboards that can be used to improve the process of assessing credit repayment capabilities. Keywords: Prediction; Logistic Regression; Random Forest; Credit; Repayment Capabilities.
PENERAPAN METODE RAPID APPLICATION DEVELOPMENT UNTUK SISTEM INFORMASI EVENT BERBASIS WEB PADA UNIVERSITAS LAMPUNG Rhomadhona, Nazmah Wulan; Muhammad, Meizano Ardhi; Wintoro, Puput Budi; Mulyani, Yessi
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5597

Abstract

Perguruan tinggi berperan penting dalam membentuk mahasiswa yang intelektual dan inovatif melalui kegiatan akademis dan non-akademis. Event seperti seminar, kuliah umum, kompetisi dan kegiatan lainnya dapat mendukung pencapaian tersebut dengan meningkatkan keterampilan dan pengetahuan mahasiswa. Namun, di Universitas Lampung, akses informasi mengenai event mendatang masih terbatas, biasanya hanya disebarkan melalui media sosial atau sesama mahasiswa saja. Penelitian ini bertujuan mengembangkan Sistem Informasi Event berbasis web untuk memudahkan akses informasi event kampus. Sistem dikembangkan dengan metode Rapid Application Development (RAD) selama 105 hari dalam dua iterasi dan memberikan fleksibilitas terhadap perubahan tanpa harus mengulangi proses dari awal. Sistem ini menghasilkan 10 fitur utama, dengan hasil pengujian menunjukkan bahwa seluruh fitur berfungsi dengan baik berdasarkan black box testing dan mencapai tingkat keberhasilan 100% dari 33 skenario. Selain itu, hasil User Experience Questionnaire (UEQ) dari 30 responden memberikan penilaian "excellent" pada kategori daya tarik, kejelasan, ketepatan, dan stimulasi, serta penilaian "good" pada kategori efisiensi dan kebaruan. Sistem ini diharapkan dapat meningkatkan aksesibilitas dan partisipasi mahasiswa dalam event-event di Universitas Lampung secara lebih efektif.
Systematic Comparison of Machine Learning Model Accuracy Value Between MobileNetV2 and XCeption Architecture in Waste Classification System Mulyani, Yessi; Kurniawan, Rian; Budi Wintoro, Puput; Komarudin, Muhammad; Mugahed Al-Rahmi, Waleed
International Journal of Aviation Science and Engineering - AVIA Vol. 4, No. 2 (December 2022)
Publisher : FTMD Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47355/avia.v4i2.70

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the MobileNetV2 architecture with the SGD optimizer and a learning rate of 0.1 has an accuracy of 86.07% and the model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
STUDY OF XCEPTION MACHINE LEARNING ARCHITECTURE IN WASTE CLASSIFICATION SYSTEM Mulyani, Yessi; Budi Wintoro, Puput; Komarudin, M.; Kurniawan, Rian
Journal of Applied Science, Engineering and Technology Vol. 2 No. 2 (2022): December 2022
Publisher : INSTEP Network

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

Abstract

Garbage generated every day can be a problem because some types of waste are difficult to decompose so they can pollute the environment. Waste that can potentially be recycled and has a selling value is inorganic waste, especially cardboard, metal, paper, glass, plastic, rubber and other waste such as product packaging. Various types of waste can be classified using machine learning models. The machine learning model used for classification of waste systems is a model with the Convolutional Neural Network (CNN) method. The selection of the CNN architecture takes into account the required accuracy and computational costs. This study aims to determine the best architecture, optimizer, and learning rate in the waste classification system. The model designed using the Xception architecture with the Adam optimizer and a learning rate of 0.001 has an accuracy of 87.81%.
Peningkatan Kemandirian Wanita Tani melalui Edukasi Kompos Berbasis Aplikasi Digital pada Kelompok Wanita Tani Bungsu Cantik Kota Bandar Lampung Marlina, Lina; Mulyani, Yessi; Efendi, Ujang; Ilim, Ilim
Jurnal Kreativitas Pengabdian Kepada Masyarakat (PKM) Vol 8, No 1 (2025): Volume 8 No 1 (2025)
Publisher : Universitas Malahayati Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33024/jkpm.v8i1.12642

Abstract

ABSTRAK KWT Bungsu Cantik merupakan salah satu kelompok wanita tani yang berada di kelurahan Penengahan Raya, Kota Bandar Lampung memiliki potensi sumberdaya manusia (SDM) yang dapat memiliki kontribusi terhadap keberlanjutan lingkungan melalui kegiatan pengolahan sampah. Namun saat ini kegiatan pengolahan sampah masih belum dikelola secara optimal karena masih kurangnya pemahaman mengenai pemilahan sampah, pembuatan sampah organik dan pemasaran produk kompos. Tujuan dari kegiatan pengabdian adalah membantu meningkatkan semangat dan kemampuan mitra untuk melakukan pemilahan sampah yang masih bisa dimanfaatkan kembali, membantu mengurangi permasalahan sampah yang terdapat di lingkungan mitra, dan memberikan pengetahuan kepada mitra mengenai aplikasi digital penjualan produk olahan dari sampah. Pelaksanaan PKM terdiri dari beberapa kegiatan. Kegiatan pertama adalah penyuluhan Penyuluhan Pemilahan Sampah Dan Pembuatan Kompos. Kedua, kegiatan Pemanfaatan Aplikasi Digital Dalam Pemasaran Produk. Terjadi peningkatan pengetahuan peserta setelah dilakukan PKM yang artinya kegiatan ini dapat memberikan manfaat bagi peserta sehingga peserta memahami pentingnya pengolahan sampah dan dapat membuat pupuk kompos yang bisa dimanfaatkan untuk pupuk pada tanaman perkarangan, kebun maupun untuk dijual. Peserta juga dapat menggunakan aplikasi Jual Beli Sampah sehingga memudahkan dalam pemasaran produk. Kata Kunci: Aplikasi Digital, Kelompok Wanita Tani, Kompos, Sampah  ABSTRACT KWT Bungsu Cantik is a group of women farmers in the Penengahan Raya sub-district, Bandar Lampung City, which has the potential for human resources (HR) who can contribute to environmental goals through waste processing activities. However, these activities are currently not managed optimally due to a lack of understanding regarding waste sorting, organic waste processing, and compost product marketing. The service activities aim to increase partner’s enthusiasm and ability to short reusable waste, reduce waste problems in the partner environment, and provide knowledge to partners regarding digital applications for marketing waste-processed products. The implementation of PKM consists of several activities. The first is education on Waste Sorting and Compost Making. The second focuses on using digital applications for product marketing. There was an increase in participants' knowledge after PKM was carried out, indicating this program benefits by enhancing their understanding of waste processing and compost production that can be used as fertilizer for yard plants, gardens, or sold. Additionally, participants are now able to use a Trash Buying and Selling application to facilitate product marketing.  Keywords: Compost, Digital Applications, Waste, Women's Farming Groups
Comparison Study of Convolutional Neural Network Architecture in Aglaonema Classification Mulyani, Yessi; Septiangraini, Dzihan; Muhammad, Meizano Ardhi; Nama, Gigih Forda
International Journal of Electronics and Communications Systems Vol. 2 No. 2 (2022): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v2i2.13694

Abstract

Convolutional Neural Network (CNN) is very good at classifying images. To measure the best CNN architecture, a study must be done against real-case scenarios. Aglaonema, one of the plants with high similarity, is chosen as a test case to compare CNN architecture. In this study, a classification process was carried out on five classes of Aglaonema imagery by comparing five architectures from the Convolutional Neural Network (CNN) method: LeNet, AlexNet, VGG16, Inception V3, and ResNet50. The total dataset used is 500 image data, with the distribution of training data by 80 percent and test data by 20 percent. The segmentation process is performed using the Grabcut algorithm by separating the foreground and background. To build a model for CNN architecture using Google Colab and Google Drive storage. The results of the tests carried out on five classes of Aglaonema images obtained the best accuracy, precision, and recall results on the Inception V3 architecture with values of 92.8 percent, 93 percent, and 92.8 percent. The CNN architecture has the highest level of accuracy in classifying aglaonema plant types based on images. This study seeks to close research gaps, contribute to the field of research, and serve as a platform for primary prevention research.
Development of Lampung Script Characters Recognition Model using TensorFlow Muhammad, Meizano Ardhi; Martinus, Martinus; Nurhartanto, Adhi; Mulyani, Yessi; Djausal, Gita Paramita; Achmad, Deni; Ferbangkara, Sony
International Journal of Electronics and Communications Systems Vol. 3 No. 2 (2023): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v3i2.19878

Abstract

In the face of cultural erosion, particularly the dwindling proficiency in deciphering Lampung characters, this research pioneers an innovative approach to cultural preservation. The Lampung character recognition model was developed using TensorFlow, a robust computer vision and machine learning framework. Convolutional Neural Networks (CNN) are integrated to enhance the image processing capabilities. The research employs the Design Science Research methodology, emphasizing problem identification, solution objectives, design and development, demonstration, evaluation, and communication. The dataset, comprising 3900 instances, is meticulously collected and features diverse Lampung script writing. Through preprocessing and classification, the model undergoes training with an 80:10:10 split for training, validation, and test data. The architecture includes CNN layers with ReLu activation functions, and transfer learning is employed using the MobileNet V2 network model. Demonstrating commendable performance, the model achieves an accuracy spectrum of 0.652 to 0.998. The research not only underscores the viability of the TensorFlow model but also establishes a foundation for future explorations in preserving Lampung cultural heritage. This intersection of advanced machine learning and cultural preservation signifies a promising synergy, ensuring the enduring legacy of Lampung characters amid societal and technological transformations.
Analisis Akurasi dan Optimalisasi Dataset untuk Klasifikasi Tanaman Aristolochia acuminata dengan Algoritma CNN Ferbangkara, Sony; Mulyani, Yessi; Mardiana, Mardiana; Pratama, Rama Wahyu Ajie; Putri, Renatha Amelia Manggala; Rafi'syaiim, Muhammad Afif
Jurnal Teknologi Riset Terapan Vol. 3 No. 1 (2025): Januari
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/jatra.v3i1.5014

Abstract

Purpose: Purpose: Aristolochia acuminata is a rare plant species of significant conservation value. However, the accurate classification of its parts, such as leaves, stems, and twigs, remains a challenge. This study aimed to develop a reliable classification model to support conservation efforts using Convolutional Neural Network (CNN) technology. Methodology/approach: A digital dataset was systematically collected from various parts of Aristolochia acuminata, forming the foundation for training a CNN-based classification model. To evaluate the model performance and determine the optimal training parameters, three experimental scenarios were conducted using 10, 100, and 200 training epochs. The impact of each training duration on the classification accuracy was analyzed. Results: The model trained with 200 epochs achieved the highest accuracy, outperforming those trained with 10 epochs (68.89%) and 100 epochs (86.67%). This suggests that a longer training period enables the model to learn the visual features of each plant part better, leading to improved classification performance. Conclusion: The results confirm the effectiveness of CNN in classifying the components of Aristolochia acuminata. Using 200 training epochs allowed for deeper feature learning without overfitting, proving optimal in this context. Limitations: This study was limited by the dataset size and the number of classes involved. Further expansion of the dataset and class categories could improve the generalizability of the model. Contribution: This study contributes to plant conservation technology by demonstrating how CNN and structured dataset collection can be applied to classify rare plant species, providing a valuable tool for biodiversity preservation.
ANALISIS DAFTAR PEMILIH TETAP PADA HASIL REKAPITULASI KPU BERDASARKAN USIA MENGGUNAKAN ALGORITMA K-MEANS (STUDI KASUS : KOTA BANDAR LAMPUNG) Fajriansyah, Gilang; Nama, Gigih Forda; Mulyani, Yessi
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 15 No. 1 (2021)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v15n1.2147

Abstract

Pada pemilihan umum di Kota Bandar Lampung, masih banyak yang masuk ke dalam golongan putih (golput). Minimnya pengetahuan masyarakat mengenai tata cara pemilu dan pentingnya menggunakan hak pilih mereka terutama pemilih pemula, dewasa dan usia lanjut menjadi faktor rendahnya penggunaan hak pilih. Kurang efektifnya pendekatan sosialisasi dari panitia penyelenggara kepada masyarakat, tetapi jika harus melakukan sosialisasi secara acak dan menyeluruh ke semua daerah akan menghabiskan dana yang besar. Dari latar belakang tersebut bertujuan untuk menerapkan Teknik data mining dengan metode clustering dengan menggunakan algoritma K-Means dan memanfaatkan tools data mining RapidMiner 9.2 terhadap data yang ada untuk memperoleh informasi mengenai daerah mana yang banyak terdapat pemilih muda, dewasa dan lansia. Penelitian ini mengelompokan data DPT dari Kecamatan Langkapura, Rajabasa dan Kemiling. Data di kelompokan berdasarkan usia dan daerah. Algoritma yang digunakan untuk meng-cluster adalah K-Means, dengan menggunakan metode CRISP-DM (Cross Industry Standart for Data Mining). Hasil cluster dianalisa berdasarkan Kelurahan dan Kecamatan. Hasil analisa cluster Kecamatan Langkapura usia muda berjumlah 10167, usia dewasa berjumlah 9527,lansia berjumlah 4821 orang. Kecamatan Rajabasa usia muda berjumlah 12557, dewasa berjumlah 10930 dan lansia berjumlah 5097. Kecamatan Kemiling pada usia muda, dewasa dan lansia berjumlah 19442, 19086 dan 9394.
Pengembangan Aplikasi Jual Beli Sampah Daur Ulang Menggunakan Framework Multiplatform Ragil Saputra, Ardi; Mulyani, Yessi
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 17 No. 2 (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.v17n2.2480

Abstract

Environmental pollution caused by waste is increasingly worrying if there is no effort to overcome it. The lack of behavior in managing recyclable waste in society is one of the biggest factors in waste pollution in Indonesia. There are many recycling waste management facilities in Indonesia, but the location of waste management facilities is unevenly distributed, and there are price differences at each location, making the community less interested in managing recyclable waste. In this study, a buying and selling system for recyclable waste was developed based on Android devices as a buying and selling tools and a web application for managing user buying and selling data. The focus of this research is to build a buying and selling system for recyclable waste by integrating a multi-platform framework, namely using the Laravel framework for the web admin side and using the Flutter framework for the android application side. Then, to analyze the review of the users for the android application, this study used the System Usability Scale (SUS). The result of this study is an Android-based buying and selling application for recyclable waste and a web admin application for managing the data of the Android application. From the SUS analysis, the application obtained a score of 70, which is acceptable, with the note that the application built is already good, but still needs to be improved for transactions using an electronic wallet (e-wallet).
Co-Authors Adhi Nurhartanto, Adhi Ageng Sadnowo Repelianto Agus Haryanto Agustina, Indria Ardi Ragil Saputra Arifudin, M. Bagus br Ginting, Simparmin Budi Wintoro, Puput Cahyana, Amanda Hasna Deni Achmad Djausal, Gita Paramita Dzihan Septiangraini Efendi, Ujang Eliza Hara Fajriansyah, Gilang Filya, Kwinny Intan Gigih Forda Nama Gilang Fajriansyah Gita Paramitha Djausal Gunawan, Charles Gusti, Khalid Surya Halim Abdillah Sholeh Helmy Fitriawan Herti Utami Hery Dian Saptama Hery Dian Septama Hery Dian Septama Hilmi Hermawan Huda, Zulmiftah Ilim, Ilim Irza Sukmana Jaya, Winaldi Putra Kesuma, Yunita Khalid Surya Gusti Komarudin, M. Laksana, Muhammad Fajar lina marlina, lina M. Bagus Arifudin Mahendra Pratama Manzi, Satria Berliano MARDIANA Mardiana Mardiana Mareli Telaumbanua Martinus Martinus Martinus, Martinus Meizano Ardhi Muhammad Meizano Ardi Muhamad Mona Arif Muda Mugahed Al-Rahmi, Waleed Muhamad Komarudin Muhamad Komarudin Muhamad Komarudin Muhammad Amin Muhammad Komarudin Muhammad Komarudin Muhammad, Meizano Ardhi Nanda Sazqiah Nyoman Herman Ardike Panji Kurniawa Pratama, Rama Wahyu Ajie Puput Budi Wintoro Puput budi wintoro Puput Budi Wintoro, Puput Budi Putri, Renatha Amelia Manggala Rafi'syaiim, Muhammad Afif Ragil Saputra, Ardi Reza Dwi Permana Rhomadhona, Nazmah Wulan Rian Kurniawan Rian Kurniawan Satrio, Muhamad Septiangraini, Dzihan Shalihah , Atiqah Hanifah Sony Ferbangkara Sugeng Triyono Titin Yulianti Trisya Septiana Umi Murdika Wahyu Aji Pulungan Wahyu Eko Sulistiono Waleed Mugahed Al-Rahmi Wijaya , Aldo Wulan Rahma Izzati