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All Journal Jurnal Edukasi Universitas Jember Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ilmiah Universitas Batanghari Jambi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Bisma: Jurnal Bisnis dan Manajemen Martabe : Jurnal Pengabdian Kepada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL AKUNTANSI KEUANGAN DAN MANAJEMEN Jurnal Tekinkom (Teknik Informasi dan Komputer) Journal of Soft Computing Exploration Studi Ilmu Manajemen dan Organisasi Jurnal Abdimas Ekonomi dan Bisnis Transekonomika : Akuntansi, Bisnis dan Keuangan Perwira Journal of Science and Engineering (PJSE) Reviu Akuntansi, Manajemen, dan Bisnis PENA ABDIMAS : Jurnal Pengabdian Masyarakat Journal of Advances in Information Systems and Technology Indonesian Journal of Informatic Research and Software Engineering Jurnal Pemberdayaan Ekonomi eProceedings of Management Journal of Student Research Exploration Journal of Information System Exploration and Research Recursive Journal of Informatics IJEB JPM JER Jurnal Akuntansi dan Governance Andalas Media Penelitian dan Pengembangan Kesehatan Jurnal Ekonomi, Manajemen, Akuntansi Jurnal Abdi Negeri
Budi Prasetiyo, Budi
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Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization Dasril, Yosza; Muslim, Much Aziz; Hakim, M. Faris Al; Jumanto , Jumanto; Prasetiyo, Budi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.3060

Abstract

The credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining LightGBM with PSO for large data from the Lending Club Dataset which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.
Deep Learning Model Implementation Using Convolutional Neural Network Algorithm for Default P2P Lending Prediction Nikmah, Tiara Lailatul; Jumanto, Jumanto; Prasetiyo, Budi; Fitriani, Nina; Muslim, Much Aziz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26366

Abstract

Peer-to-peer (P2P) lending is one of the innovations in the field of fintech that offers microloan services through online channels without intermediaries. P2P  lending facilitates the lending and borrowing process between borrowers and lenders, but on the other hand, there is a threat that can harm lenders, namely default.  Defaults on  P2P  lending platforms result in significant losses for lenders and pose a threat to the overall efficiency of the peer-to-peer lending system. So it is essential to have an understanding of such risk management methods. However, designing feature extractors with very complicated information about borrowers and loan products takes a lot of work. In this study, we present a deep convolutional neural network (CNN) architecture for predicting default in P2P lending, with the goal of extracting features automatically and improving performance. CNN is a deep learning technique for classifying complex information that automatically extracts discriminative features from input data using convolutional operations. The dataset used is the Lending Club dataset from P2P lending platforms in America containing 9,578 data. The results of the model performance evaluation got an accuracy of 85.43%. This study shows reasonably decent results in predicting p2p lending based on CNN. This research is expected to contribute to the development of new methods of deep learning that are more complex and effective in predicting risks on P2P lending platforms.
Application go-sport as a solution to search information on facilities, places, partners, and sports events for students Rofik, Rofik; Anggraini, Tasya Fitria; Prasetiyo, Budi; KA, Cecep Bagus Suryadinata
Journal of Student Research Exploration Vol. 1 No. 2: July 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v1i2.164

Abstract

Sport is a physical and mental activity that is beneficial for people to maintain the body and develop the quality of health. This makes exercise an activity that needs to be done for everyone to maintain their stamina. However, the lack of information about places, facilities, partners, and sports events is a strong reason in terms of reducing student motivation in carrying out sports activities themselves. The purpose of this research is none other than to design an application that can help students get all sports information. These things are none other than to foster a strong desire to do sports activities. Through technology smartphone which has been owned by the wider community, this research creates a solution by designing an application called "Go-Sport". This study uses the "Design Thinking" method, which focuses on finding and understanding user needs to obtain an optimal solution in the form of the results of the features to be made. From this research, a design or prototype of the "Go-Sport" application was produced which is ready to be implemented and tested on users.
Consumer Acceptance Analysis of Purchase Interest Using Live Features on The Marketplace with Technology Acceptance Model (TAM) Method Case Study : Shopee Jhonatan, Edward; Prasetiyo, Budi
Journal of Advances in Information Systems and Technology Vol 5 No 2 (2023): October
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v5i2.61455

Abstract

The development of technology has become one of the instant lifestyles of the community because it has high mobility in carrying out daily activities. The use of the internet for commercial transactions is known as electronic commerce (e-Commerce). In 2021 Shopee launched a new feature before other competitors had the Shopee live feature. This research is more focused on analyzing buyers using the Shopee live feature on the Shopee marketplace using the Technology Acceptance Model (TAM). The purpose of this study is to analyze how the influence of consumers in buying interest using the Shopee live feature and to determine the effect of each variable used. This type of research is quantitative research. The research sample was 255 UNNES student respondents from 2015-2019. The data analysis method used is descriptive analysis using SEM with SmartPLS 3.0 software. The data that has been obtained is done data cleaning. It was found that the valid data were 203 people. The results of the research based on what has been done on the outer model, there are three indicators that are deleted, namely PEOU 2, PEOU 4, and PU 5, because the results obtained are still below the set value <0.7. In the inner model research, there is one hypothesis that is rejected, namely the t-statistics test, namely PEOU > BI because this hypothesis has a value less than the standard value of the t-test so it is rejected. However, there are six accepted hypotheses, namely PE > PU, PE > PEOU, PE > BI, PT > PU, PT > BI, and PU > BI. Based on the results of this analysis, the six variables have factors that influence the purchase intention using the Shopee live feature.
Factors Influencing Student Intentions in Using the Mobile Legends Bang-Bang Game Using the UTAUT 2 and DeLone McLean Models Nelly, Fredy Kusuma; Prasetiyo, Budi
Journal of Advances in Information Systems and Technology Vol 5 No 2 (2023): October
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jaist.v5i2.67179

Abstract

Online game users have increased every year following technological developments. This makes game developers develop their products even better so that they can play games on smartphones or commonly called mobile games. The increasing number of mobile game users is the backdrop for intense competition between game genres. However, one of the most frequently accessed games is Mobile Legends Bang-Bang. Then make the Mobile Legends game popular with various groups of students. Seeing this phenomenon, researchers are interested in researching Semarang State University students who play the online game Mobile Legend Bang-Bang because on campus there are many students who play together and form teams so that many E-Sport competitions are held. The UTAUT 2 and Delone & Mclean methods determine the factors that influence students' intentions to play the Mobile Legends Bang-Bang game. The data source in this study was taken from the results of online questionnaires so as to produce 316 respondent data after the screening process was carried out. Data processing uses SmartPLS V3 to test the outer and inner models. The results of this study indicate that of the ten hypotheses proposed, four hypotheses are not accepted. Namely, social influences on behavioral intentions, habits on behavioral intentions, system quality on behavioral intentions, and service quality on behavioral intentions. This means that social influence, habits, system quality, and service quality have no significant effect on behavioral intentions. The results of the analysis show that user intentions influence user behavioral in using the Mobile Legends Bang-Bang game.
Analysis of the Stacking Ensemble Learning Model of Categorical Boosting and Naïve Bayes Algorithms for Crop Selection Based on Soil Characteristics Maulana, Ilham; Prasetiyo, Budi
Journal of Information System Exploration and Research Vol. 3 No. 2 (2025): July 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i2.604

Abstract

This study aims to develop a machine learning model for selecting crop types based on soil characteristics, using the Categorical Boosting and Naïve Bayes algorithms as base learners. Next, an ensemble learning technique using a stacking approach was applied to improve the performance of the base model that was built. This was done to analyze and compare the performance results of each ensemble model that was carried out. Model performance was evaluated using evaluation metrics including precision, recall, f1-score, and accuracy. The results of this study indicate that the stacking ensemble model with Random Forest as the meta learner can provide better performance compared to other ensemble models. This model achieved a precision of 98.85337%, a recall of 99.84848%, an F1-score of 99.84844%, an accuracy of 99.84848%, and a model training time of 78.61110 seconds. Based on these results, this study is expected to provide tangible contributions and new knowledge in plant selection classification based on soil characteristics, thereby aiding in the precise and efficient determination of suitable plant types.
Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction Jumanto, Jumanto; Rofik, Rofik; Sugiharti, Endang; Alamsyah, Alamsyah; Arifudin, Riza; Prasetiyo, Budi; Muslim, Much Aziz
Journal of Information Systems Engineering and Business Intelligence Vol. 10 No. 1 (2024): February
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.10.1.38-50

Abstract

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics.  Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability.   Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech
OPTIMALISASI POTENSI ECO TRAVEL HUTAN KERTAS PADA DESA KUTAMEKAR KARAWANG MELALUI PENGEMBANGAN PRODUK ZERO WASTE Fitria Rahmani, Hani; Prasetiyo, Budi; Ardila Rahma, Rana; Sugiharto, Muhammad
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 6, No 12 (2023): Martabe : Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v6i12.4276-4282

Abstract

Bidang pariwisata merupakan salah satu bidang industri terbesar di dunia dan telah memberikan kontribusi yang sangat besar terhadap ekonomi global, termasuk di Indonesia. Karawang merupakan sebuah kabupaten di Jawa Barat yang berdasarkan RKPD Kabupaten karawang 2023 mendeskripsikan bahwa terdapat permasalahan yang di alami Kab. Karawang yaitu salah satunya sarana dan prasarana pengelolaan sampah maupun dalam pengelolaan lingkungan yang belum memadai. Berdasarkan permasalahan yang di alami Kab. Karawang, maka program PKM dibuat untuk membantu mengatasi permasalahan tersebut dengan optimalisasi potensi eco travel dan pengembangan produk zero waste sehingga mampu meningkatkan potensi pariwisata dan bisa mengurangi jumlah limbah yang ada di Kab Karawang. Hal tersebut didukung juga oleh Indikator Kinerja Utama Kabupaten Karawang yaitu meningkatnya kontribusi sektor pariwisata dan indeks kualitas lingkungan hidup yang memadai. Pengabdian ini bertujuan membantu mitra dalam mengembangkan kemampuan berwirausaha di sektor sustainable development dan memberi pemahaman tentang eco travel dan penguasaan limbah pada pengelolaan wisata. Kegiatan ini mendukung transformasi pendidikan tinggi melalui indikator kinerja utama (IKU), adapun IKU yang akan di capai oleh pengabdian saat ini adalah dosen berkegiatan di luar kampus dan mahasiswa mendapatkan pengalaman di luar kampus.
EKSPLORASI PENGARUH CITRA RUMAH SAKIT TERHADAP KEPUASAN PASIEN: TINJAUAN SISTEMATIS LITERATUR Prasetiyo, Budi; Ramadhian, M. Arief Rahman; Hurriyati, Ratih; Dirgantari, Puspo Dewi; Widjajanta, Bambang
Media Penelitian dan Pengembangan Kesehatan Vol. 35 No. 1 (2025): MEDIA PENELITIAN DAN PENGEMBANGAN KESEHATAN
Publisher : Poltekkes Kemenkes Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34011/jmp2k.v35i1.2371

Abstract

The primary challenge in measuring patient satisfaction lies in its conceptual and operational complexity. Patient satisfaction is often regarded as a subjective concept, influenced by individual expectations, experiences, and perceptions of the healthcare services received. This literature review aims to examine and summarize the impact of hospital image on patient satisfaction based on studies conducted in various countries across multiple continents. The PRISMA approach was employed in this systematic literature review, with articles collected from Google Scholar, Crossref, and PubMed databases. The articles selected were published between 2010-2024.  An in-depth analysis was conducted on 11 selected articles.  The results of the review indicate that studies on hospital image and patient satisfaction often incorporate other variables such as service quality, loyalty, and trust. Research from diverse regions, including Southeast Asia, the Middle East, Europe, and the Americas, demonstrates that hospital image significantly influences patient satisfaction. However, findings in Indonesia reveal a contrasting result, where hospital image does not affect patient satisfaction. These differences reflect unique cultural contexts, such as the importance of interpersonal relationships in Iran, ethnic diversity in Malaysia, or the high value placed on healthcare quality in Taiwan.  This review further underscores the importance of collaborative efforts among governments, hospital administrators, healthcare professionals, and marketers to enhance sustainable service quality and meet patients' evolving needs.
Modernisasi Alat Utama Sistem Senjata TNI dalam Mendukung Tugas TNI AL Prasetiyo, Budi; Riesnandar, Edi; Nendya, Bima
Jurnal Ilmiah Universitas Batanghari Jambi Vol 24, No 3 (2024): Oktober
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v24i3.5648

Abstract

This study analyzes the modernization of TNI AL weaponry systems to support maritime defense tasks in Indonesian jurisdictional waters. Using the Analytic Network Process (ANP) method, the research identifies the challenges and obstacles faced, as well as determining modernization priorities. The ANP results show that modernizing the Indonesian Navy Warship (KRI) is the top priority, followed by the development of Fast Missile Boats (KCR), the modernization of the Maritime Air Defense System, and the procurement of new submarines. Additionally, the Indonesian Navy faces both conventional military threats, such as naval attacks from other countries, and asymmetric threats, including piracy and maritime terrorism. To address these threats, the Indonesian Navy needs to develop advanced technologies, including cyber security and monitoring technologies to prevent illegal activities at sea. The study also finds that modernization efforts face various obstacles, requiring the improvement of maintenance facilities and intensive training. The use of ANP and NVivo helps formulate an effective modernization strategy to enhance operational readiness and the maritime defense capabilities of the Indonesian Navy.
Co-Authors Afrizal Rizqi Pranata, Afrizal Rizqi Ahmad Roziqin, Ahmad Aisy, Salsabila Rahadatul Aji Purwinarko, Aji Alamsyah - Amidi Amidi, Amidi Anggraini, Tasya Fitria Anggyi Trisnawan Putra Ardila Rahma, Rana Aziz, Alif Abdul Azura, Amberia Narfi Bachtiar, Muhammad Irgi Bambang Widjajanta, Bambang Bayuaji, Hibatullah Zamzam Tegar Beta Noranita Biyantoro, Arell Saverro D.W, Made Bagus Paramartha Deske W. Mandagi Didimus Tanah Boleng Dinova, Dony Benaya Endang Sugiharti, Endang Fachrezi, Farhan Rifa Fadhilah, Muhammad Syafiq Fadlil, Affan Fajriati, Nafa Fata, Muhamad Nasrul Fata, Muhamad Nasrul Ferninda, Varin Fikri Mohamad Rizaldi Fitria, Yunita Fitriana, Jevita Dwi Hakim, Ade Anggian Hakim, M Faris Al Hakim, M. Faris Al Hakim, Roshan Aland Hani Fitria Rahmani Ilham Maulana Jhonatan, Edward Jumanto Jumanto , Jumanto Jumanto Jumanto, Jumanto Jumanto Unjung KA, Cecep Bagus Suryadinata Korina, Nanda Putri Leo nardo Lestari , Apri Dwi Lestari, Apri Dwi Lestari, Fitri Duwi Lintang, Irendra M. Faris Al Hakim Makrina Tindangen Maulidia Rahmah Hidayah, Maulidia Rahmah Much Aziz Muslim Muhammad Sugiharto Mukhlisin, Ahmad Munahefi, Detalia Noriza Mustaqim, Amirul Muzayanah, Rini Naufal Zuhdi, Hamzah Ndruru, Toni Krisman Nelly, Fredy Kusuma Nendya, Bima Nicko, Robertus Nikmah, Tiara Lailatul Nina Fitriani, Nina Ningsih, Maylinna Rahayu Nisa, Intan Khairun Niswah Baroroh Partini, Emilia Paundra, Fajar Pertiwi, Dwika Ananda Agustina Pradana, Fadli Dony PRASETYO, ERWIN Pratama, Muhammad Hasbi Puspo Dewi Dirgantari Rachmawati, Eka Yuni Rachmawati, Eka Yuni Rahmat Gernowo Ramadhian, M. Arief Rahman Ratih Hurriyati Riesnandar, Edi Ristiawati, Monika Riza Arifudin Robianty, Nenden Sondari Rofik Rofik, Rofik S.Pd. M Kes I Ketut Sudiana . Sadid, Moh Naufal Salsabila, Malika Putri Saparina, Iska Ayu Saputra, Angga Riski Dwi Satriawan, Grace Yudha Satrio Ardiansyah, Adi Seivany, Ravenia Septian, M Rivaldi Ali Subhan Subhan Sulastri, Ai Syaharani, Reisya Triyadi, Indra Vember, Hilda Wahyu, Aufa Azfa Walean, Ronny H. Yahya Nur Ifriza Yosza Dasril Yulia Nur Hasanah