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All Journal Teknika: Jurnal Sains dan Teknologi Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Agribisnis Terpadu Informatika Pertanian J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika Amerta Nutrition Jurnal Aplikasi Statistika & Komputasi Statistik Jurnal Abdi Insani Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) Journal of Artificial Intelligence and Digital Business PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS JUSTE (Journal of Science and Technology) Journal of Information Technology and its Utilization
Budiaji, Weksi
Universitas Sultan Ageng Tirtayasa
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Hierarchical clustering algorithm-dendogram using Euclidean and Manhattan distance Mukhtar, Mukhtar; Majahar Ali, Majid Khan; Arina, Faula; Wicaksono, Agung Satrio; Ikhsan, Aulia; Budiaji, Weksi; Abdullah, Syarif; Pertiwi, Dinda Dwi Anugrah; Zidny, Robby; Oktarisa, Yuvita; Sukarna, Royan Habibie
Jurnal Teknika Vol 20, No 1 (2024): Available Online in June 2024
Publisher : Faculty of Engineering, Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjst.v20i1.23187

Abstract

This paper presents the outcomes of a research experiment on the drying process of seaweed. There are numerous approaches to clustering data, such as partitioning and the Hierarchical Clustering Algorithm (HCA). The HCA has been implemented in binary tree structures to visualize data clustering. We conducted a comparative analysis of the four primary methodologies utilized in HCA, namely: 1) single linkage, 2) complete linkage, 3) average linkage, and 4) Ward's linkage. Clustering validation is widely recognized as a crucial issue that significantly impacts the effectiveness of clustering algorithms. Clustering validation can be identified, such as internal and external validation. Internal clustering validation, in particular, holds significant importance in the realm of data science. With this article, the main goal is to do an empirical evaluation of the traits that a representative set of internal clustering validation indices, namely Connectivity, Dunn, and Silhouette, show. In this paper, the HCA applies two distance functions between Euclidean and Manhattan distances to analyze the entanglement function and internal validity.
Applied Machine Learning DBSCAN for Identifying Clusters of Micro and Small Industries Wijaya, Ferdian Bangkit; Budiaji, Weksi; Wicaksono, Agung Satrio
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.515

Abstract

This study aims to identify clustering patterns of sub-districts in Serang District based on village participation in Micro and Small Industry (MSI) activities using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, a machine learning method in Unsupervised Learning. Secondary data from the Statistics Indonesia (BPS) on Potentials of Villages in Serang District for 2024 was used, covering 29 sub-districts and 15 MSI sector variables. Data preprocessing involved Min-Max Scaler normalization and Principal Component Analysis (PCA) to address sparsity and multicollinearity. DBSCAN parameter optimization was done through simulations of epsilon values (0–1) and MinPts (1–10), validated with the Silhouette Score and Davies-Bouldin Index. The optimal configuration of epsilon=0.3 and MinPts=1 resulted in seven clusters with no noise, and a Davies-Bouldin Index of 0.620, indicating good separation. Spatial analysis revealed meaningful cluster distribution, with comprehensive industry clusters in the central region and specialized clusters in peripheral areas. These findings provide a basis for formulating MSI development policies in Serang District, highlighting the importance of data preprocessing techniques in sparse data analysis for evidence-based decision-making.
Analisis Efisiensi Produksi Usahatani Cabai Merah Keriting (Studi Kasus Di Kecamatan Baros, Kabupaten Serang) Pratiwi, Widya; Pancawati, Juwarin; Budiaji, Weksi; Mulyati, Sri; Sulaeni, Sulaeni
JURNAL AGRIBISNIS TERPADU Vol 18, No 1 (2025): Jurnal Agribisnis Terpadu
Publisher : Jurusan Agribisnis Fakultas Pertanian Universitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33512/jat.v18i1.31260

Abstract

Produktivitas cabai merah keriting di Kecamatan Baros masih berfluktuasi dan cenderung belum maksimal yang diduga disebabkan oleh penggunaan faktor produksi yang yang belum optimal sehingga mempengaruhi tingkat efisiensi produksi. Tujuan dari penelitian ini adalah untuk menganalisis faktor-faktor produksi dalam usahatani cabai merah keriting dengan mengidentifikasi faktor yang mempengaruhi produksi cabai merah keriting serta menganalisis efisiensi penggunaan faktor-faktor produksi dari aspek teknis, harga dan ekonomi. Penelitian ini dilakukan di Kecamatan Baros pada bulan bulan Januari sampai dengan Februari 2024. Metode penelitian yang digunakan yaitu kuantitatif deskriptif dan penentuan daerah ditetapkan secara secara purposive dengan pertimbangan petani di Kecamatan Baros yang menanam cabai merah keriting pada musim tanam Agustus hingga Desember 2023. Jumlah populasi petani yang memenuhi kriteria tersebut yaitu 35 orang sehingga menggunakan teknik pengambilan sampel jenuh. Analisis data menggunakan fungsi produksi Coubb Douglass, fungsi produksi Stochastic Frontier dan analisis efisiensi. Hasil dari penelitian ini secara simultan menunjukkan bahwa faktor produksi lahan, benih, pupuk kandang, Pupuk NPK Mutiara, Pestisida Demolish, dan tenaga kerja berpengaruh secara bersama-sama terhadap produksi. Secara parsial, faktor produksi yang berpengaruh signifikan hanya luas lahan dan pupuk NPK Mutiara, sedangkan faktor produksi benih, pupuk kandang, pestisida Demolish pestisida dan tenaga kerja tidak berpengaruh signifikan terhadap produksi. Hasil analisis efisiensi menunjukkan bahwa usahatani cabai merah keriting di Kecamatan Baros belum efisien secara teknis serta tidak efisien secara harga dan ekonomi.
Web Scraping Analysis of Job Platform Adoption in Banten's Manufacturing Sector Wijaya, Ferdian Bangkit; Budiaji, Weksi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2282

Abstract

This study addresses the critical disconnect between Indonesia's manufacturing sector and the digitally native Gen Z workforce, focusing on Banten Province, a region with high youth unemployment despite its industrial concentration. We quantitatively assess the digital presence of manufacturing companies on key recruitment platforms, including LinkedIn, Jobstreet, and the government's Karirhub portal, to quantify this gap. Using web scraping techniques with Python, company profile data was systematically collected and analyzed. The findings reveal a limited digital footprint, with overall company presence recorded at 43.24% on LinkedIn, 42.70% on Karirhub, and a notably lower 32.97% on Jobstreet. Significant disparities exist across subsectors; consumer-facing industries like Food, Beverage, and Automotive show high digital engagement, while sectors such as Textiles, Electronics, and Non-metallic Minerals lag considerably. Notably, the Tobacco industry was found exclusively on the government's Karirhub platform. This confirmed digital divide hinders effective talent acquisition and limits job seekers' access to credible information. We conclude that a strategic imperative exists for manufacturers to enhance their digital recruitment strategies. This is crucial not only for attracting Gen Z talent but also for aligning with Indonesia’s national digitalization agenda to reduce unemployment.
Kontributor dan Faktor Utama Kerawanan Pangan pada Daerah 3T: Studi Kasus di Kabupaten Seram Bagian Timur M Indra Rumasukun; Kamaruddin Kamaruddin; Weksi Budiaji; Suman Sangadji; Miranda H Hadijah; Juni La Djumat
JUSTE (Journal of Science and Technology) Vol. 5 No. 1 (2024): JUSTE (Journal of Science and Technology)
Publisher : LLDIKTI WIlayah XII Ambon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51135/

Abstract

Penelitian ini bertujuan untuk mengidentifikasi faktor utama yang menyebabkan kerawanan pangan di wilayah tertinggal, terdepan, dan terluar (3T), dengan studi kasus di Kabupaten Seram Bagian Timur, Provinsi Maluku. Dengan pendekatan deskriptif kuantitatif dan menggunakan data primer serta sekunder dari tahun 2023, penelitian ini menganalisis aspek ketersediaan, distribusi, konsumsi pangan, serta ketahanan pangan rumah tangga. Hasil analisis menunjukkan bahwa meskipun terdapat potensi besar dalam produksi pangan lokal seperti sagu, ikan laut, dan protein hewani, kerawanan pangan masih tinggi akibat distribusi yang terbatas, infrastruktur lemah, serta rendahnya diversifikasi konsumsi pangan. Skor Pola Pangan Harapan (PPH) dan tingkat konsumsi energi per kapita juga berada di bawah standar ketahanan pangan nasional. Rekomendasi diarahkan pada peningkatan konektivitas antarwilayah, optimalisasi komoditas lokal, serta edukasi konsumsi gizi seimbang.
COMBINING SUPERVISED AND UNSUPERVISED METHODS IN TOURISM VISITOR DATA Weksi Budiaji; Vebriana Vebriana; Juwarin Pancawati
Journal of Information Technology and Its Utilization Vol 5 No 1 (2022)
Publisher : Sekolah Tinggi Multi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56873/jitu.5.1.4659

Abstract

Combining supervised and unsupervised method can assist in the data analysis process. This research aims to apply a supervised method, i.e. Poisson regression, that is followed by an unsupervised method, namely cluster analysis of the visitors in a tourism dataset. The samples were taken 80 persons purposively from the visitors of the Flower Garden X in Serang Regency, Banten Province. The dataset consists of the number of visits, travel cost, income/ stipend per month, gender, age, distance from the place of origin, and perception, which is formed by 11 questions of facilities and services. The Poisson regression was applied in the 30, 40, and 50 bootstrap samples resulted in the perception as the significant features. Then, medoid-based cluster analysis, i.e. pam and simple k-medoids, in the perception dataset was applied. They compared simple matching and cooccurrence distances and were validated via medoid-based shadow value. It grouped the visitors into five clusters as the most suitable number of clusters. The combined methods of supervised and unsupervised provided the cleanliness as the important indicator. The improvement of the tourism object had to be focus on the cleanliness aspect.
Unsupervised YouTube Video Segmentation of “Bendera One Piece” Content Using Medoid-Based Clustering with Statistical Significance Testing Budiaji, Weksi; Kumenap, Patricia; Delano, M Fabian; Wijaya, Ferdian; Riyanto, Rifqi
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.639

Abstract

The curse of dimensionality and sparsity are well-documented phenomena in applied statistics where the data’s dimensionality (number of features) far outnumbers the observations. This work aims to present an integrated applied statistics framework to distill semantic structure from high-dimensional data by combining pre-processing, dimensionality reduction via principal component analysis, medoid-based clustering (partitioning around medoids and simple k medoids), and a modified Statistical Significance Clustering (SigClust) test for validation and inference in the context of viral media. In this case study, we demonstrate an approach that segments and interprets YouTube videos from the lens of the Indonesian viral media “Bendera One Piece” through its user commentary. The PCA-based dimensionality reduction helped resolve the curse of dimensionality, where the first principal component alone explained 80% of the variance in text-based features and captured a dominant socio-political pattern. Internal validation and the SigClust test agreed on the presence of a statistically significant three-cluster solution that could be labelled as the audiences of “Pop-Culture Enthusiasts”, “Cautious Observers”, and “Political Protesters”. The study presents the utility of integrating established statistical methods with a modified validation step for high-dimensional text data analysis and pattern recognition.
The Digital Frontline: A Thematic Analysis of User Grievances and Satisfaction Drivers for Indonesian Public Service Apps Bangkit Wijaya, Ferdian; Budiaji, Weksi; Priyantama Ramadhan Bagaskara, Rafly; Ainun Tazkia, Zilda; Dwi Anugrah Pertiwi, Dinda
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2025 No. 1 (2025): Proceedings of 2025 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2025i1.738

Abstract

This research assesses Indonesia's digital public service ecosystem by analyzing 50 mobile applications from a wide range of state agencies. Using a computational content analysis of metadata and user reviews from the Google Play Store, this study presents a dual-faceted evaluation. First, a thematic analysis of negative reviews (1-2 stars) reveals that user grievances are overwhelmingly dominated by foundational issues, such as login/access problems, slow performance, and technical glitches, rather than a lack of advanced features. Second, a corresponding analysis of positive reviews (5 stars) identifies that user satisfaction is primarily driven by high-quality features, ease of use, and overall application reliability. Quantitative findings show significant performance disparities across institutional categories, with Ministrydeveloped apps receiving the lowest average user satisfaction. An Importance-Performance Quadrant Analysis further uncovers a critical paradox: many high-download, mandatory apps suffer from low user ratings, indicating a clear disconnect between enforced adoption and usercentric quality. The research concludes that enhancing digital public services requires a strategic shift from feature proliferation to foundational reliability. Ensuring robust core functionalities is paramount to building citizen trust and achieving a successful digital transformation.
Co-Authors Adi Susanto Ahmad Luthfi Nur Fauzi Ainun Tazkia, Zilda Amaliah, inti Arina, Faula Bangkit Wijaya, Ferdian Delano, M Fabian Dwi Anugrah Pertiwi, Dinda Edo Ahmad Solahudin Herjayanto, Muh. Ikhsan, Aulia Juni La Djumat Juwarin Pancawati, Juwarin Kamaruddin Kamaruddin Kumenap, Patricia M Indra Rumasukun Majahar Ali, Majid Khan Miranda H Hadijah Moch Suryana Muhamad Iqbal Muttaqin Mukhtar, Mukhtar Mulyati, Sri Nada Thalia Permata Adriani Nur ‘Aida Nur Fitria Nur Fitria, Nur Oktarisa, Yuvita Pancawati, Juwarin Pertiwi, Dinda Dwi Anugrah Pratiwi, Widya Priyantama Ramadhan Bagaskara, Rafly Rifqi Ahmad Riyanto Riyanto, Rifqi Robby Zidny Saleh , Khaerul Setiawan Sariyoga Sri Fajriah Suherna Suherna Sukarna, Royan Habibie Sulaeni, Sulaeni Suman Sangadji Syarif Abdullah Syifa Alfiah Vebriana Vebriana Wicaksono, Agung Satrio WICAKSONO, AGUNG SATRIO Wijaya, Ferdian Wijaya, Ferdian Bangkit