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All Journal Jurnal Teknologi dan Manajemen Informatika SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JATI (Jurnal Mahasiswa Teknik Informatika) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat IJIIS: International Journal of Informatics and Information Systems JPMB: Jurnal Pemberdayaan Masyarakat Berkarakter Jurnal Teknologi Informasi dan Komunikasi Journal of Applied Data Sciences Nusantara Hasana Journal International Journal for Applied Information Management Naratif : Jurnal Nasional Riset, Aplikasi dan Teknik Informatika Jurnal Rekayasa Informasi CSRID Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Saturnus: Jurnal Teknologi dan Sistem Informasi International Journal of Information Engineering and Science Journal of Digital Market and Digital Currency Journal of Current Research in Blockchain International Journal Research on Metaverse
Bangkit, Jeffri Prayitno
Universitas AMIKOM Purwokerto
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analyzing Key Factors Influencing Employee Resignation Through Decision Tree Modeling and Class Balancing Techniques Saputra, Jeffri Prayitno Bangkit; Hidayat, Muhammad Taufik
International Journal of Informatics and Information Systems Vol 8, No 2: March 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i2.259

Abstract

Employee resignation poses a significant challenge to organizational stability and workforce planning. This study aims to analyze the key factors influencing employee resignation by developing an interpretable predictive model using the Decision Tree algorithm. The analysis is conducted on the IBM HR Analytics dataset, which includes 1,470 employee records with diverse demographic, behavioral, and job-related attributes. To address the issue of class imbalance—where resignation cases are underrepresented—the Synthetic Minority Over-sampling Technique (SMOTE) is applied to enhance model sensitivity and balance. After a comprehensive data preprocessing phase, including feature selection and label encoding, the Decision Tree model is trained with a limited depth to reduce overfitting and maintain interpretability. The model achieves an accuracy of 77%, with a recall of 0.80 and an F1-score of 0.77 for the resignation class. Feature importance analysis identifies stock option level, job satisfaction, monthly income, relationship satisfaction, and job involvement as the most influential predictors. These findings provide actionable insights for human resource practitioners seeking to implement targeted and data-driven employee retention strategies. The study highlights the practical value of interpretable machine learning models in human capital analytics.
PELATIHAN TEKNIS APLIKASI SCREEN TIME & LOCATION TRACKER GUNA MEMANTAU DAN MELACAK PENGGUNAAN GADGET PADA ANAK Fiby Nur Afiana; Jeffri Prayitno Bangkit Saputra; Farah Setyaningsih; El Syafangatun Aulia Difa
Nusantara Hasana Journal Vol. 4 No. 1 (2024): Nusantara Hasana Journal, Juny 2024
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v4i1.1133

Abstract

Staring at a gadget screen too often can have a negative impact on a child's development and health. The consequence or impact of excessive use of gadgets in children is that it worsens the child's motor function abilities and increases the risk of early obesity. Daily screen time or known as daily screen time is a screen time management setting that allows parents to manage application usage. The problem that often occurs in some of the Association of Guardians of SDN 3 Purwanegara students is that on average they are working mothers, either office workers or traders who cannot supervise their children full twenty-four (24) hours. Sometimes after school they don't know where their children are, how long they use their gadgets, and they can't control what applications they access. The aim of this supervision is to protect children from the negative effects of using technology. Especially for working mothers who cannot supervise 24-hour gadget use and monitor their children's location. So that children can avoid bad things that can affect the future. Currently there are many Screen Time & Location Tracker applications that are applications to help parents manage their children's use of technology which can be controlled remotely. One of them is Find My Kids and Google Family Link. This allows parents to set a daily screen time limit to get notifications when that time has been reached and also helps parents know where their child is.
Analisis Kepuasan Pengguna Terhadap Aplikasi Dolan Banyumas Menggunakan Metode End User Computing Satisfaction (EUCS) dan DeLone and McLean Fadillah, Septiya Nur; Hidayah, Debby Ummul; Saputra, Jeffri Prayitno Bangkit
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 3 (2024): October 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.3.2024.344-366

Abstract

Technology has an important role in modern life, including in the tourism sector. The Dolan Banyumas application, which was launched by the Banyumas district Youth, Sports, Culture and Tourism Department, is one of the innovations to improve the regional tourism experience. The Dolan Banyumas application was created to make it easier for travelers to find a guide to enjoy the beauty of Banyumas with complete facilities. This research aims to identify problem points experienced by users and propose potential improvements for the application. Apart from that, this research also aims to determine the level of user satisfaction with the Dolan Banyumas application and examine several factors that have an influence on user satisfaction. This research uses the End User Computing Satisfaction (EUCS) and DeLone and McLean methods. The population in this study were users of the Dolan Banyumas application in the Banyumas district, with 82 respondents being the research sample. Data analysis was carried out using PLS-SEM with Microsoft Excel tools for demographic data and SmartPLS for statistical analysis. The research results show that the Dolan Banyumas application has several problem points that need to be improved, such as content, accuracy, format, ease of use, system quality and service quality variables which show a low level of satisfaction. However, the timeliness and information quality variables show a good level of satisfaction. So it can be concluded that the factors that influence user satisfaction with the Dolan Banyumas application use the End User Computing Satisfaction (EUCS) and DeLone and McLean methods, namely information quality and timeliness.
Pengenalan Digital Marketing Bagi Siswa PKBM Kartika di Kecamatan Pengadegan Kabupaten Purbalingga Priambodo, Aji; Metris, Diksi; Wakhidah, Esti Nur; Prakoso, Riyan Dwi Yulian; Saputra, Jeffri Prayitno Bangkit
Abdimas Galuh Vol 6, No 1 (2024): Maret 2024
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v6i1.13283

Abstract

Pusat Kegiatan Belajar Masyarakat (PKBM) adalah lembaga pendidikan nonformal yang berfokus pada pemberdayaan masyarakat melalui program pembelajaran, khususnya bagi siswa yang telah putus sekolah dan berkekurangan secara finansial. Salah satu program utama yang ditawarkan oleh PKBM Kartika adalah pelatihan digital marketing, yang bertujuan untuk meningkatkan potensi siswa dan mengembangkan kreativitas. Tujuan dari kegiatan ini adalah agar siswa PKBM dapat mengembangkan kemampuan mereka dalam memanfaatkan platform digital seperti WhatsApp Business dan Instagram Ads untuk kebutuhan bisnis. Dalam pengabdian masyarakat ini, diberikan pengenalan mengenai digital marketing kepada siswa PKBM, memberikan pengetahuan dan keterampilan yang relevan dengan bisnis digital saat ini, dengan fokus pada tips dan optimalisasi WhatsApp Business dan Instagram Ads. Dengan pengetahuan dan keterampilan yang diperoleh, diharapkan siswa PKBM dapat menjadi lebih kompeten dalam mengelola bisnis sendiri atau meningkatkan kemampuan untuk dapat bersaing di dunia kerja.
Modeling the Impact of Holidays and Events on Retail Demand Forecasting in Online Marketing Campaigns using Intervention Analysis Saputra, Jeffri Prayitno Bangkit; Kumar, Aayush
Journal of Digital Market and Digital Currency Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i2.9

Abstract

This study explores the impact of holidays and events on retail demand forecasting using intervention analysis within a SARIMAX model framework. Retail demand forecasting is critical for inventory management and supply chain optimization. Traditional forecasting models often struggle to account for irregular events like holidays, leading to inaccuracies. This study aims to address these limitations by incorporating holidays and events as exogenous variables in the forecasting model. The dataset, consisting of retail sales records across multiple product categories, was preprocessed to handle missing values and standardize date formats. Binary indicators for state holidays and school holidays were created, along with temporal features like the day of the week and hour of the day. The stationarity of the time series was confirmed using the Augmented Dickey-Fuller (ADF) test, with a statistic of -48.67066391486136 and a p-value of 0.0. The SARIMAX model (1, 1, 1)x(1, 1, 1, 24) was developed and evaluated. The model achieved an Akaike Information Criterion (AIC) of 363321.861 and a Bayesian Information Criterion (BIC) of 363375.269. Key coefficients included the state holiday variable at 0 (p-value: 1.000000) and the school holiday variable at 165.2158 (p-value: 0.919689), though neither were statistically significant. Diagnostic checks revealed significant non-normality and heteroscedasticity in the residuals. Forecasting accuracy was assessed using Mean Absolute Error (MAE: 8057.069376036054) and Mean Squared Error (MSE: 809008799.3517022). The Mean Absolute Percentage Error (MAPE) was not computable due to division by zero. Visualizations comparing forecasted versus actual demand highlighted the model’s strengths in capturing general trends and seasonal patterns but indicated challenges in accurately predicting demand during holidays and events. The study underscores the importance of incorporating holidays and events into demand forecasting models and suggests further refinement and the inclusion of additional variables for improved accuracy. Future research should explore alternative modeling approaches and validate findings across multiple datasets to enhance the generalizability and robustness of the forecasting tools.
Analysis of Blockchain Transaction Patterns in the Metaverse Using Clustering Techniques Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
Journal of Current Research in Blockchain Vol. 1 No. 1 (2024): Regular Issue June
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i1.10

Abstract

This study investigates the application of various clustering techniques on a metaverse transaction dataset to identify patterns and groupings. The clustering algorithms evaluated include K-Means, DBSCAN, Gaussian Mixture Model (GMM), Mean Shift, Spectral Clustering, and Birch. The performance of these algorithms is assessed using three metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. Among these algorithms, K-Means demonstrated the best overall performance, achieving the highest Silhouette Score (0.4702) and Calinski-Harabasz Index (151946.29), as well as the lowest Davies-Bouldin Index (0.6600), indicating well-defined and compact clusters. DBSCAN, while flexible, showed lower performance with a Silhouette Score of 0.1673, a Davies-Bouldin Index of 1.0084, and a Calinski-Harabasz Index of 4231.19. GMM achieved a Silhouette Score of 0.2453, a Davies-Bouldin Index of 1.3626, and a Calinski-Harabasz Index of 23011.20. Spectral Clustering had a Silhouette Score of 0.1668, a Davies-Bouldin Index of 2.0986, and a Calinski-Harabasz Index of 11830.24. Birch achieved a Silhouette Score of 0.2363, a Davies-Bouldin Index of 1.4967, and a Calinski-Harabasz Index of 21375.76. Mean Shift could not provide valid performance metrics. Visualizations, including histograms, box plots, and count plots, provided additional insights into the distribution of numerical features and cluster characteristics. This study highlights the need for tailored clustering approaches and suggests future research directions in hybrid models as well as the impact of feature selection and scaling methods on clustering outcomes.
The Impact of Market Activity on Property Valuations in Digital Real Estate Through a Quantitative Analysis of Bidding and Sales Dynamics Saputra, Jeffri Prayitno Bangkit; Putri, Nadya Awalia
International Journal Research on Metaverse Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i2.11

Abstract

This study investigates the impact of market activities, specifically the number of bids and sales, on property prices in digital real estate markets. With the rise of virtual environments and digital assets, understanding the factors that drive property valuations in these markets has become increasingly important. Utilizing a dataset of 2,000 property transactions, this research employs correlation and regression analyses to explore how competitive bidding and sales frequency influence prices. The results indicate a significant positive correlation (r=0.38r = 0.38r=0.38) between the number of bids a property receives and its final sales price, suggesting that properties attracting more bids are perceived as more valuable, leading to higher prices. The regression analysis further supports this, showing that each additional bid is associated with an increase of 6.63×10216.63 \times 10^{21}6.63×1021 in the sales price (p
Co-Authors Aji Priambodo Andri Laksono Arifah Budi Hidayah Bernarte, Racidon P Dede Ari Putragani Dendi Putra Prakoso Diya Ulhaq Jauhari Budiarto Dominic Dinand Aristo Dwi Puji Lestari, Dwi Puji El Syafangatun Aulia Difa El Syafangatun Aulia Difa Fadil Tazakka Pribadi Fadillah, Septiya Nur Farah Setyaningsih Farah Setyaningsih Fariz Nur Fikri Zaki Fiby Nur Afiana Fiby Nur Afiana Ford Lumban Gaol, Ford Lumban Hani, Nurul Harjanto Prabowo Hertono, Gatot Fatwanto Hidayah, Debby Ummul Hidayat, Muhammad Taufik Nur Ika Romadoni Yunita Ito Setiawan Iwan Haryatno Khalidy, Shodiq Kumar, Aayush Kusuma, Rahayu Linda Lestari, Putri Vidia M Afrian Maulana Metris, Diksi Muhamad Awiet Wiedanto Prasetyo Muhammad Taufik Hidayat Nurhidayah, Isnaeni Nurul Afifah Prakoso, Riyan Dwi Yulian Putri Awaliatuz Zahra Putri, Nadya Awalia Rahayu, Silvia Anggun Ramadhani, Sekar Ajeng Retno Waluyo Riyanto Riyanto Satria Dwi Nurwicaksana Setyaningsih, Gustin Sigit Setoyoko Taqwa Hariguna Tarwoto, Tarwoto Toni Anwar Trias Bratakusuma Ummul Hidayah, Debby Vidia Alma Cyrilla Wahyu Latifatun Wakhidah, Esti Nur Waluyo, Retno Wisnu Muamar Zamora, Hanenda Putri Zanuar Rifai Zulfa Aulia2