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All Journal JURNAL ILMIAH MATEMATIKA DAN TERAPAN JURNAL EDUCATION AND DEVELOPMENT JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Informasi Sains dan Teknologi (ISAINTEK) Gema Wisata Nawasena: Jurnal Ilmiah Pariwisata Mutiara: Jurnal Penelitian dan Karya Ilmiah Jurnal Penelitian Teknologi Informasi dan Sains International Journal of Mechanical, Industrial and Control Systems Engineering PESHUM Jurnal Ilmiah Manajemen Dan Kewirausahaan Berbakti
Agung Yuliyanto Nugroho
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Pelatihan Penggunaan Peta Digital bagi Pemandu Wisata Agung Yuliyanto Nugroho
BERBAKTI: Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 03 (2026): ISSUE FEBRUARI
Publisher : PT. Mifandi Mandiri Digital

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

Abstract

Pemandu wisata memiliki peran penting dalam memberikan pengalaman terbaik bagi wisatawan dengan menyediakan informasi yang akurat dan layanan yang berkualitas. Seiring dengan perkembangan teknologi, penggunaan peta digital menjadi keterampilan yang diperlukan untuk meningkatkan efisiensi dan profesionalisme dalam industri pariwisata. Namun, masih banyak pemandu wisata lokal yang kurang familiar dengan teknologi ini. Oleh karena itu, pelatihan penggunaan peta digital menjadi langkah strategis dalam meningkatkan kemampuan mereka. Artikel ini membahas pelaksanaan pelatihan yang dirancang untuk membekali pemandu wisata dengan keterampilan teknis dalam menggunakan peta digital. Pelatihan ini mencakup pemahaman dasar sistem informasi geografis (GIS), penggunaan aplikasi peta digital seperti Google Maps dan OpenStreetMap, serta penerapan dalam navigasi dan perencanaan perjalanan wisata. Metode yang digunakan dalam pelatihan meliputi ceramah, demonstrasi langsung, serta latihan berbasis studi kasus untuk memastikan pemahaman yang optimal. Hasil pelatihan menunjukkan peningkatan keterampilan peserta dalam mengoperasikan peta digital secara efektif, terutama dalam mencari lokasi, menavigasi rute, dan memberikan rekomendasi kepada wisatawan. Selain itu, pelatihan ini juga meningkatkan kepercayaan diri peserta dalam memberikan layanan berbasis teknologi. Meskipun terdapat beberapa tantangan seperti keterbatasan akses internet dan kesulitan adaptasi bagi peserta yang kurang familiar dengan teknologi, secara keseluruhan, pelatihan ini memberikan dampak positif terhadap kualitas layanan pemandu wisata lokal. Diharapkan, dengan adanya pelatihan yang berkelanjutan, pemandu wisata dapat terus meningkatkan kompetensi mereka dan berkontribusi dalam pengembangan industri pariwisata yang lebih modern dan berbasis teknologi.
Penerapan Prediksi untuk Klasifikasi Penerima Beasiswa Berprestasi pada SMK Islam Pemalang Berdasarkan Algoritma K-Nearest Neighbor Agung Yuliyanto Nugroho; Tundo; Riolandi Akbar
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 3 (2025): JULI-SEPTEMBER 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i3.3848

Abstract

This research aims to help Pemalang Islamic SMK in identifying outstanding students and predicting potential scholarship recipients, by utilizing the algorithm, K-Nearest Neighbor (K-NN) in determining students who have the potential to receive scholarships. This research used 100 student data involving attributes such as report card grades, academic achievement, parental responsibilities, parental salary, and participation in organizations. Meanwhile, the testing process is carried out by adding 6 data on potential scholarship recipients to be predicted. The data is then processed and normalized before being applied to the K-NN algorithm. The K-NN steps involve determining the K parameter (number of nearest neighbors), calculating the Euclidean distance, sorting the distance results, and selecting the majority category as a prediction for the new object class. The research results show that the application of the K-NN algorithm with K=3 is successful in providing predictions of outstanding students by considering relevant attributes. This process is carried out with the help of JAVA programming to calculate and analyze data. The research conclusion shows that the K-NN algorithm can be used as an effective prediction tool for classification to determine students who excel and are worthy of receiving scholarships. This research contributes to increasing efficiency and accuracy in the selection of outstanding scholarship recipients in the school environment with an accuracy of 83.33%.
K-Means Clustering dalam Dunia Konveksi: Pengelompokan Cerdas untuk Optimalisasi Stok Agung Yuliyanto Nugroho
Jurnal Informasi, Sains dan Teknologi Vol. 5 No. 02 (2022): Desember: Jurnal Informasi Sains dan Teknologi
Publisher : Politeknik Negeri FakFak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/isaintek.v5i02.231

Abstract

The garment industry faces challenges in grouping diverse goods based on their characteristics, which can affect the efficiency of the production process and inventory management. This study aims to apply the K-Means Clustering algorithm in garment goods classification to improve business process management and optimization. The K-Means algorithm, as one of the popular clustering methods, is used to group garment goods data based on features such as size, color, fabric type, and product model. This method begins with the selection of relevant features from the dataset obtained from the garment industry. Furthermore, the K-Means algorithm is implemented to determine the optimal number of clusters using the elbow score and silhouette methods. The clustering results are analyzed to evaluate the extent to which the algorithm can form homogeneous and business-relevant groups of goods. The results of this study indicate that the K-Means Clustering algorithm is effective in grouping garment goods into several categories that are consistent with business patterns and needs. The application of this method results in a better understanding of goods grouping that can improve production efficiency and facilitate inventory management. This study contributes to the best practices in the use of the K-Means algorithm in the convection sector and shows the potential of this method in supporting data-driven decision making. Keywords:,
Spatial Statistical Analysis for Poverty Mapping Using Machine Learning: Spatial Statistical Analysis for Poverty Mapping Using Machine Learning Agung Yuliyanto Nugroho; Puji Sarwono
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17883

Abstract

Poverty is a multidimensional problem influenced not only by economic factors but also by spatial dimensions such as geographic location, accessibility, and environmental characteristics. This study aims to analyze spatial patterns of poverty and develop a poverty prediction model using a geospatial data-based machine learning approach. The data used comes from a combination of open sources such as the Central Statistics Agency (BPS), Landsat satellite imagery, and regional infrastructure data. The methods used include spatial autocorrelation analysis (Moran's I) to identify poverty clustering patterns, Local Indicators of Spatial Association (LISA) to detect poverty hotspots, and Random Forest and Gradient Boosting models to predict poverty levels based on environmental, social, and economic variables. The results show that poverty has a significant spatial pattern, where areas with high poverty rates tend to cluster in areas with low infrastructure access and high population density. The machine learning model demonstrated better prediction accuracy than the traditional linear regression approach, with an R² value reaching 0.87 and a lower prediction error rate (RMSE). These findings emphasize the importance of integrating spatial analysis and machine learning technology in understanding the dynamics of poverty geographically. This research contributes to the development of spatial data analysis methods in the context of public policy, particularly in supporting more targeted poverty alleviation intervention planning. The mapping results can serve as a basis for local governments in identifying priority areas, allocating resources, and designing data-driven development policies. Thus, this approach offers an innovative solution towards more efficient and evidence-based decision-making in poverty alleviation in Indonesia.
Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis: Application of the KMeans Clustering Algorithm in E-Commerce Transaction Pattern Analysis Agung Yuliyanto Nugroho
JURNAL ILMIAH MATEMATIKA DAN TERAPAN Vol. 22 No. 1 (2025)
Publisher : Program Studi Matematika, Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/2540766X.2025.v22.i1.17884

Abstract

In the era of digital transformation, e-commerce platforms have become a major driver of economic activity, generating vast amounts of transaction data every day. Analyzing these data can provide valuable insights into customer behavior, purchasing trends, and business performance. This study aims to apply the K-Means clustering algorithm to identify and analyze transaction patterns in e-commerce systems. The research focuses on developing an efficient data-driven approach to segment customers based on their transactional attributes, such as purchase frequency, transaction value, and product category preferences. The methodology involves several stages: data preprocessing, including cleaning and normalization; feature selection based on relevant transactional indicators; and the application of the K-Means clustering algorithm to group customers into clusters with similar characteristics. The Elbow Method was used to determine the optimal number of clusters. Data were processed using the Python programming language and libraries such as Scikit-learn and Pandas. The results reveal that K-Means effectively segments e-commerce customers into distinct groups that reflect their purchasing patterns—ranging from high-value loyal customers to occasional buyers. Each cluster presents unique behavioral profiles that can be interpreted for targeted marketing strategies. The clustering outcome provides useful insights for customer relationship management (CRM), inventory optimization, and personalized product recommendations. In conclusion, the application of the K-Means algorithm demonstrates significant potential in uncovering hidden patterns within large-scale e-commerce transaction data. The findings support the use of mathematical and computational models in improving decision-making processes in digital commerce. Future research is recommended to enhance cluster accuracy by integrating hybrid algorithms or deep learning-based segmentation approaches.
PENGARUH PROGRAM FCS DAN KERJA SAMA LUAR NEGERI TERHADAP MINAT MAHASISWA BARU DI STIPRAM YOGYAKARTA Moch. Nur Syamsu; Agung Yuliyanto Nugroho
Jurnal Education and Development Vol 14 No 2 (2026): Vol 14 No 2 Mei 2026
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v14i2.7848

Abstract

This study aims to analyze the influence of the Foreign Case Study (FCS) Program and international collaboration on the interest of new students in choosing the Tourism Study Program at the Ambarrukmo Tourism College (STIPRAM). The internationalization of tourism higher education is an important strategy in increasing institutional competitiveness amidst global competition, so an empirical study is needed to measure the effectiveness of the implemented international programs. This study uses a quantitative approach with a survey method on new students of the Tourism Study Program at STIPRAM. Data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) through SmartPLS software. The results show that the FCS Program has a positive and significant effect on the interest of new students, with a path coefficient value of 0.41 and a p-value <0.05. In addition, international collaboration also has a positive and significant effect, with a line coefficient of 0.47 and a p-value <0.05. Simultaneously, the two exogenous variables are able to explain 63% of the variation in new students' interest, indicating that the research model has strong explanatory power. These findings demonstrate that international experiential learning programs and global institutional networks play a role in shaping prospective students' perceptions and interests in tourism education. This research provides an empirical contribution to the development of tourism management research, particularly regarding the internationalization strategies of higher education institutions. Practically, the results can serve as a basis for tourism institution managers in designing and optimizing international programs to increase the interest and quality of new students.
PENGARUH PEMANFAATAN SISTEM INFORMASI AKADEMIK BERBASIS WEB DAN MOBILE TERHADAP KEPUASAN DAN KINERJA MAHASISWA (STUDI PADA STMIK MULTICOM KOTAMOBAGU Supit Mamuayat; Agung Yuliyanto Nugroho; Fitly Tjong Mamuaya
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 2 (2026): April 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i2.6119

Abstract

This study aims to examine the effect of the utilization of web- and mobile-based academic information systems on student satisfaction and academic performance at Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Multicom Kotamobagu. The rapid advancement of information technology has encouraged higher education institutions to adopt digital systems to enhance the efficiency, accessibility, and quality of academic services. However, the effectiveness of such systems in improving student satisfaction and performance still requires empirical validation. This research employs a quantitative approach using a survey method. The population consists of active students at STMIK Multicom Kotamobagu, while the sample is determined using purposive sampling, focusing on students who have utilized the academic information system. Data were collected through structured questionnaires using a Likert scale. The data analysis technique applied in this study is Structural Equation Modeling based on Partial Least Squares (SEM-PLS), which allows for the simultaneous examination of relationships among variables. The independent variable in this study is the utilization of the academic information system, while the dependent variables include student satisfaction and academic performance. The findings reveal that the utilization of web- and mobile-based academic information systems has a positive and significant effect on student satisfaction. Furthermore, the system utilization also significantly influences student performance, both directly and indirectly through satisfaction as a mediating variable. These results indicate that an accessible, user-friendly, and responsive academic information system can enhance user experience and support improved academic outcomes. This study provides practical implications for higher education institutions in optimizing digital academic systems. Improving system quality, usability, and real-time information delivery is essential to enhance student satisfaction and performance sustainably.
Co-Authors Annisa Fikria Shimbun Dedi Prayitno Ferat Kristanto Fitly Tjong Mamuaya Herra Herryani Moch Nur Syamsu Moch. Nur Syamsu Nur Hamid Sutanto Nur rohman Puji Sarwono Rhisty Frida Utami Riolandi Akbar Rus Bintoro Suherli, Eli Supit Mamuayat Syamsu Rijal Syamsu Rijal Tundo, Tundo