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Budi Prasetiyo, Budi
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Pengaruh E-Service Innovation Dan E-Service Quality Terhadap Customer Satisfaction Aplikasi Mytelkomsel Di Kota Bandung Nisa, Intan Khairun; Prasetiyo, Budi
eProceedings of Management Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Management

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

Abstract

AbstrakPenelitian ini dilatarbelakangi oleh semakin tingginya tingkat konsumsi masyarakat terhadap suatu produk ataumerek sehingga perusahaan dituntut untuk menjaga kepuasan konsumen. Telkomsel sebagai penyedia jasa jugadituntut untuk menjaga kepuasan pelanggan dengan cara menjaga kualitas pelayanan atau inovasi dalampelayanan. Sampel yang diambil adalah 100 responden yang menggunakan kartu Telkomsel dan membeli paketdata aktif. Analisis Regresi Berganda digunakan sebagai teknik analisis data dengan bantuan sistem pengolahandata SPSS. Hasil penelitian menunjukkan bahwa inovasi layanan elektronik dan kualitas layanan elektronikmemiliki pengaruh positif dan signifikan terhadap kepuasan pelanggan terhadap aplikasi MyTeam. Variabelvariabel mediator seperti efficiency dan responsibility memainkan peran penting dalam hubungan antara inovasidan kualitas layanan elektronik dan kepuasan pelanggan. Kesimpulan dari penelitian ini dapat digunakan sebagaibahan masukan bagi Telkomsel untuk meningkatkan kualitas layanan aplikasi MyTelkomsel dan mempertahankanloyalitas pelanggan. Kata kunci: Inovasi E-service, Kualitas E-service, Kepuasan Pelanggan, MyTelkomsel, Kota Bandung
Pengaruh Key Opinion Leaders dan Electronic Word of Mouth terhadap Purchase Decision pada Huisaeng Dimsum D.W, Made Bagus Paramartha; Prasetiyo, Budi
EKOMA : Jurnal Ekonomi, Manajemen, Akuntansi Vol. 4 No. 5: Juli 2025
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/ekoma.v4i5.10737

Abstract

Di era digital, Key Opinion Leader (KOL) dan Electronic Word of Mouth (e-WOM) menjadi faktor penting dalam memengaruhi keputusan pembelian konsumen. Penelitian ini bertujuan untuk menganalisis pengaruh KOL dan e-WOM terhadap keputusan pembelian produk Huisaeng Dimsum secara parsial maupun simultan. Metode yang digunakan adalah kuantitatif deskriptif dengan teknik purposive sampling, melibatkan 385 responden Gen Z dan Milenial awal di Bandung. Analisis data dilakukan dengan regresi linear berganda. Hasil penelitian menunjukkan bahwa KOL (77,9%) dan e-WOM (78,9%) berada pada kategori baik serta berpengaruh signifikan terhadap keputusan pembelian (76,3%). Kesimpulannya, KOL dan e-WOM memiliki kontribusi positif dalam membentuk minat dan keyakinan konsumen untuk membeli produk Huisaeng Dimsum.
STACKING ENSEMBLE, XGBOOST DAN SMOTE UNTUK EFISIENSI ENERGI PADA FRAUD CREDIT CARD Bayuaji, Hibatullah Zamzam Tegar; Prasetiyo, Budi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2409

Abstract

Extreme class imbalance in credit card fraud detection datasets often leads machine learning models to fail in recognizing minority fraud cases. This study proposes a Stacking Ensemble approach combined with Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek Links, employing XGBoost, LightGBM, and Random Forest as base learners and XGBoost as a meta-learner. Using the Kaggle Credit Card Fraud Detection dataset (class ratio 1:492), the method was evaluated with Recall, F1-Score, AUC-PR, and AUC-ROC, while CodeCarbon was integrated to measure energy consumption and carbon emissions during model training. Experimental results show that the proposed ensemble improves Recall of fraud detection by up to 6% compared to single models, achieves stable F1-Scores of 0.92 (SMOTE) and 0.91 (SMOTE-Tomek), and records an AUC-PR above 0.90. Furthermore, CodeCarbon tracking indicates that SMOTE models produce slightly lower carbon emissions (0.62 gCO₂) than SMOTE-Tomek (0.68 gCO₂), highlighting a trade-off between detection accuracy and energy efficiency. These findings emphasize that integrating ensemble learning with oversampling techniques not only enhances fraud detection performance but also provides transparent insights into the environmental impact of machine learning models.
PERANCANGAN UI/UX APLIKASI PENCARI PEKERJAAN FREELANCE MENGGUNAKAN METODE DESIGN THINKING Sadid, Moh Naufal; Prasetiyo, Budi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2395

Abstract

The global shift in work patterns over the past decade has significantly increased the number of freelancers, including in Indonesia, where nearly one-third of the 145.79 million workforce engage in freelance work. This trend is driven by limited formal job opportunities, high qualification requirements, lack of work experience among job seekers, and restricted access to job vacancy information. Freelancing has become an adaptive alternative amid high unemployment rates among the productive-age population. This situation highlights the need for innovative digital platforms that connect freelancers with the job market through optimal user interface (UI) and user experience (UX) design. This study applies the design thinking method to develop the UI/UX of a freelance job search application called Jobble, addressing freelancers’ needs such as interest-based job searches, automatic CV generation, direct client communication, and skill-enhancing training. The resulting UI/UX design facilitates access to relevant job opportunities. A usability test using the System Usability Scale (SUS) achieved a score of 81.53, categorized as “Good,” indicating that Jobble’s UI/UX design meets user needs and has strong potential as a digital solution for freelancers in Indonesia.
Sentiment analysis of user comments on the shopeepay feature in the shopee application: Evaluation of accuracy with k-nearest neighbors (KNN) algorithm Lestari, Fitri Duwi; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 3 No. 1 (2025): January 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i1.392

Abstract

This research analyzes Shopeepay user reviews on the Shopee app using the K-Nearest Neighbor (KNN) algorithm with TF-IDF weighting and a Cosine Similarity matrix. Data was collected through web scraping 500 reviews from the Google PlayStore and labelled into positive, neutral, and negative sentiments. The process includes literature study, data collection, labelling, text preprocessing, word weighting, and sentiment classification using KNN. Results show an accuracy range of 86%-91%, with Precision, Recall, and F1-Score as evaluation metrics. The findings indicate that convenience, trust, and risk significantly affect users' interest in Shopeepay, especially during the Covid-19 pandemic. A Word Cloud was also used to visualize common terms in the reviews, providing insights for Shopee to enhance Shopeepay based on user feedback.
Optimising SVM models in text mining to see the sentiments and user complaints of DANA mobile application through play store reviews Biyantoro, Arell Saverro; Prasetiyo, Budi
Journal of Student Research Exploration Vol. 3 No. 2 (2025): July 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/josre.v3i2.396

Abstract

Dana is a mobile electronic wallet application available for download on Google Play Store. Users can rate and comment on this application directly through the review section on the platform. By utilizing these user reviews, research can be conducted to identify the main complaints experienced by Dana application users. This research uses Support Vector Machine (SVM) sentiment analysis to classify reviews and Latent Dirichlet Allocation (LDA) to map negative comment topics. LDA extracts several representative words or tokens that are grouped to form specific themes. The findings show that the most common sources of user complaints are related to transaction issues, premium features, and app updates. These insights can provide valuable input for developers to improve the overall quality and user experience of the Dana app.
Penguatan Kelembagaan Bumdes Desa Cokro Melalui Reorganisasi Dan Penataan Struktur Organisasi Amidi, Amidi; Prasetiyo, Budi; Munahefi, Detalia Noriza; Ardiansyah, Adi Satrio
PENA ABDIMAS : Jurnal Pengabdian Masyarakat Vol 6 No 2 (2025): Juli 2025
Publisher : LPPM Universitas Pekalongan

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

Abstract

Village-Owned Enterprises (BUMDes) play a strategic role in strengthening economic independence and optimizing local village potential. However, in various regions, including Cokro Village, Tulung District, Klaten Regency, several BUMDes have experienced institutional stagnation, resulting in the suboptimal functioning of their organizations. One such example is BUMDes Tirta Kencana, which had been inactive due to weak organizational structure and management. This community service activity aimed to reactivate the BUMDes through an institutional strengthening strategy based on reorganization and organizational structure arrangement. The implementation methods included problem identification, community socialization and forums, managerial training, reorganization deliberations, development of institutional documents, also mentoring and evaluation. The activity was carried out using a participatory-collaborative approach that actively involved the community in every stage. The results showed an increase in community awareness of the importance of village institutions, the formation of a new and more functional organizational structure, the preparation of standard operating procedures (SOPs) and annual work plans, as well as an improvement in the managerial capacity of the administrators, as reflected in the increase in average understanding scores from 56.3 to 80.1 (an increase of 42%). These findings confirm that a participatory-collaborative approach is effective in reactivating inactive BUMDes while building community commitment to program sustainability.  
Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings Rofik, Rofik; Hakim, Roshan Aland; Unjung, Jumanto; Prasetiyo, Budi; Muslim, Much Aziz
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3566

Abstract

Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.
Peningkatan Akurasi Klasifikasi Algoritma C 4.5 Menggunakan Teknik Bagging pada Diagnosis Penyakit Jantung Prasetyo, Erwin; Prasetiyo, Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020752379

Abstract

Perkembangan teknologi yang begitu pesat menjadikan kebutuhan akan suatu informasi semakin meningkat, sehingga keakuratan suatu informasi menjadi suatu hal yang sangat penting, Terutama keakuratan informasi yang dibutuhkan dalam memprediksi penyakit dalam bidang medis. Dalam proses pengumpulan suatu informasi dibutuhkan metode tertentu, sehingga informasi yang telah diproses menjadi sebuah pengetahuan menggunakan suatu metode tertentu disebut dengan penambangan data atau istilah lainnya adalah data mining. Umumnya data mining digunakan untuk memprediksi suatu penyakit yang bersumber dari data rekam medis pasien, khususnya penyakit jantung. Data penyakit jantung diambil dari dataset UCI Machine Learning Repository. Tujuan dari penulis melakukan penelitian ini yaitu untuk mengetahui penerapan teknik bagging pada algoritma C4.5, mengetahui hasil akurasi dalam algoritma C4.5, dan membandingkan tingkat akurasi dari penerapan teknik bagging pada algoritma C4.5. Dataset yang diklasifikasikan dengan algoritma C4.5 memperoleh akurasi sebesar 72,98%. Hasil akurasi ini dapat ditingkatkan dengan menerapkan teknik bagging menghasilkan akurasi sebesar 81,84%, sehingga terjadi peningkatan akurasi sebesar 8,86%  dari penerapan teknik bagging pada Algoritma C4.5. AbstractThe quick development of technology makes the need for information increase, so that the accuracy of the information becomes a very important thing, especially the accuracy of the information needed in predicting diseases in the medical field. In the process of gathering information certain methods are needed, so information that has been processed into knowledge using a certain method is called data mining or other terms is data mining. Data mining is generally used to predict a disease originating from patient medical record data, especially heart disease. Heart disease data is taken from the UCI Machine Learning Repository dataset. The purpose of the authors conducting this research is to determine the application of bagging techniques on the C4.5 algorithm, determine the accuracy of the results in the C4.5 algorithm, and compare the level of accuracy of the application of bagging techniques on the C4.5 algorithm. The dataset classified by the C4.5 algorithm obtained an accuracy of 72.98%. The results of this accuracy can be improved by applying bagging techniques resulting in an accuracy of 81.84%, resulting in an increase in accuracy of 8.86% from the application of bagging techniques in the C4.5 Algorithm.
Optimasi Algoritma Naive Bayes dengan Diskritisasi K-Means pada Diagnosis Penyakit Jantung Fajriati, Nafa; Prasetiyo, Budi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 3: Juni 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106510

Abstract

Penyakit jantung iskemik adalah salah satu jenis penyakit kardiovaskular dengan jumlah penderita yang besar dan menjadi penyebab utama kematian di dunia. Disamping itu, penyakit jantung juga menyebabkan kerugian ekonomi. Diagnosis penyakit jantung pada tahap awal dapat membantu mengurangi risiko kematian dan tingginya biaya perawatan akibat penyakit jantung. Diagnosis penyakit merupakan proses penting yang harus dilakukan secara akurat agar tidak terjadi kesalahan diagnosis. Data mining dapat diterapkan untuk meningkatkan akurasi dan mengurangi jumlah kesalahan diagnosis. Salah satu teknik data mining adalah klasifikasi. Naïve Bayes merupakan algoritma klasifikasi yang memiliki kemampuan yang cukup baik untuk membangun model pengklasifikasi. Pada penelitian ini, dilakukan klasifikasi penyakit jantung menggunakan algoritma Naïve Bayes. Dataset yang digunakan yaitu Cleveland heart disease dataset dari UCI Machine Learning Repository. Untuk meningkatkan akurasi klasifikasi menggunakan algoritma Naive Bayes, atribut kontinu pada dataset diubah menjadi atribut diskrit dengan diskritisasi K-means. Diskritisasi K-means mengubah nilai setiap atribut kontinu menjadi kategori-kategori diskrit berupa cluster sejumlah k yang terbentuk dari proses algoritma K-means. Hal tersebut dilakukan karena algoritma Naïve Bayes menunjukkan kemampuan klasifikasi yang lebih baik apabila menggunakan data masukan berupa diskrit dibanding kontinu. Hasil akurasi yang diperoleh dari algoritma Naïve Bayes tanpa menerapkan diskritisasi K-means pada Cleveland heart disease dataset adalah 86,89%, sedangkan hasil akurasi yang diperoleh dari algoritma Naïve Bayes dengan menerapkan diskritisasi K-means pada Cleveland heart disease dataset adalah 88,52%. Berdasarkan perbandingan akurasi yang dihasilkan, dapat diketahui adanya peningkatan akurasi sebesar 1,63%. Hal tersebut menunjukkan bahwa diskritisasi K-means berperan dalam mengoptimalkan kinerja algoritma Naïve Bayes sehingga menghasilkan akurasi yang lebih baik. Abstract Ischemic heart disease is a type of cardiovascular disease with a large number of sufferers and is the leading cause of death in the world. In addition, heart disease also causes economic losses. Diagnosing heart disease early can help reduce the risk of death and the high costs of treatment for heart disease. Diagnosis of the disease is an important process that must be carried out accurately to avoid misdiagnosis. Data mining can be applied to improve accuracy and reduce the number of misdiagnoses. One of the data mining techniques is classification. Naïve Bayes is a classification algorithm that has a fairly good ability to build a classifier model. In this study, heart disease was classified using the Naïve Bayes algorithm. The dataset used is the Cleveland heart disease dataset from the UCI Machine Learning Repository. To improve classification accuracy using the Naive Bayes algorithm, continuous attributes in the dataset are changed to discrete attributes using K-means discretization. K-means discretization changes the value of each continuous attribute into discrete categories in the form of k clusters formed from the K-means algorithm process. This is done because the Naïve Bayes algorithm shows a better classification ability when it uses discrete rather than continuous input data. The accuracy results obtained from the Naïve Bayes algorithm without applying the K-means discretization to the Cleveland heart disease dataset are 86.89%, while the accuracy results obtained from the Nave Bayes algorithm by applying the K-means discretization to the Cleveland heart disease dataset are 88.52%. . Based on the comparison of the resulting accuracy, it can be seen that there is an increase in accuracy of 1.63%. This shows that K-means discretization plays a role in optimizing the performance of the Naïve Bayes algorithm to produce better accuracy.
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