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All Journal SINTECH (Science and Information Technology) Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jusikom: Jurnal Sistem Informasi Ilmu Komputer Journal of Information Systems and Informatics Cyber Security dan Forensik Digital (CSFD) Journal of Dinda : Data Science, Information Technology, and Data Analytics International Journal Software Engineering and Computer Science (IJSECS) Jurnal Locus Penelitian dan Pengabdian Journal of Computers and Digital Business Journal of Intelligent Software Systems Journal Of Artificial Intelligence And Software Engineering
Bambang Purnomosidi Dwi Putranto
Universitas Teknologi Digital Indonesia
Aerospace Engineering Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Mathematics

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Implementation of Artificial Intelligence-Based Face Recognition in Web Application Development Muammar Muammar; WIDYASTUTI ANDRIYANI; Bambang Purnomosidi D.P
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2312

Abstract

Traditional web login systems using usernames and passwords have weaknesses in terms of security and are vulnerable to hacking attacks. Therefore, this research proposes the development of a web login system with a face recognition authentication method using artificial intelligence to overcome that problem. This project involves several main steps. First, a dataset of facial images will be collected, including variations in lighting conditions, poses, and facial expressions. Furthermore, the facial recognition model will train using the Haar Cascade Classifier and Local Binary Pattern algorithms. The trained model will be integrated into the web application using the appropriate programming language and framework, such as Python with Django. The web login interface will allow users to enter login information and take pictures of their faces. The system will use face detection and feature extraction techniques to extract facial features from uploaded images. These features will compare with the stored dataset of facial images using the trained facial recognition model. It is successful when there is a match, and the user will be granted access to the web application. To improve security, testing, and evaluation of the developed system will be carried out to measure its performance, including using metrics such as accuracy, precision, recall, and F1-score. All vulnerabilities of security or limitations identified during the evaluation will be addressed and fixed. Implementation will provide a more secure and user-friendly login experience. By utilizing the advances in facial recognition technology and artificial intelligence, this study aims a contribution to improving web authentication systems and protecting user data from unauthorized access
Integration of Constraint-based Mining in Frequent Closed Itemset Mining using CEG&REP Approach Bambang Purnomosidi Dwi Putranto; Yuli Astuti; Muhammad Haries; Wiwi Widayani; Ali Impron; Rikie Kartadie
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2315

Abstract

Frequent Closed Itemset Mining is an important approach in discovering hidden patterns inlarge-scale data. The CEG&REP (Concurrent Edge Prevision and Rear Edge Pruning)algorithm has previously been proven to improve the efficiency of the pattern mining processthrough parallel edge projection mechanisms and selective pruning of sequence graphstructures. However, the search space exploration can still be very large when the datasetcontains many items, high sequence lengths, or complex pattern variations. This research is animprovement of CEG&REP through the integration of constraint-based mining, namely theapplication of various types of constraints that can direct the mining process only to relevantpatterns. Three main types of constraints are introduced: temporal constraints (time-basedconstraints), length constraints (pattern length constraints), and item constraints (itemexistence or attribute constraints). This integration allows the pruning process to occur earlier,reducing the exploration of irrelevant branches, and improving the quality of the resultingpatterns. This approach aims to make CEG&REP more adaptive, efficient, and suitable forvarious application domains such as user activity logs, IoT sensor data, retail transactions, andbioinformatics analysis.
Comparison of Self-Organizing Maps and K-means Algorithms in Grouping Divorce Cases in Yogyakarta City Priyo Purnomo; Domy Kristomo; Widyastuti Andriyani; Bambang Purnomosidi Dwi Putranto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2309

Abstract

The Religious Court of Yogyakarta serves as the primary judicial body that processes, examines, and renders decisions on civil cases involving adherents of Islam at the initial legal level. The court manages approximately 900 cases annually, with marital dissolution proceedings constituting the predominant category. Categorizing these cases according to marital age and divorce causation is crucial for illustrating the distinctive patterns of divorce within the jurisdiction, thereby informing governmental initiatives promoting family wellness education. For the purpose of data clustering, a comparative analysis was conducted between self-organizing maps (SOM) and K-means methodologies. The research utilized secondary data from 2021, comprising 45 entries of concluded divorce cases. The internal silhouette validation metric established two as the optimal cluster quantity for both analytical approaches. The distribution and attributes of clusters remained largely consistent across both models. Evaluation using the standard deviation ratio revealed superior clustering performance by the SOM method when applied to the divorce case dataset. The analytical results demonstrated that cluster 1 encompassed 11 neighborhoods characterized by elevated divorce rates, while cluster 2 contained 34 regions exhibiting lower to moderate divorce frequencies. The principal determinants shaping divorce case characteristics in Yogyakarta City were identified as "ongoing conflicts and arguments," "abandonment by one spouse," and "financial circumstances."
CLASSIFICATION OF OIL PALM FRUIT CROSS-SECTIONS USING HSV FEATURE EXTRACTION AND GAUSSIAN NAÏVE BAYES Teguh Junian Kuswanto; WIDYASTUTI ANDRIYANI; Rikie Kartadie; Bambang Purnomosidi D.P; Danny Kriestanto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2310

Abstract

Accurate identification of oil palm fruit varieties is essential for supporting breeding programs and optimizing seed quality in plantation operations. Manual approaches often lead to inconsistencies due to the high visual similarity among fruit types, particularly between dura and tenera. This study proposes an automatic classification model for oil palm fruit cross-sections using HSV-based color feature extraction combined with a Gaussian Naïve Bayes classifier. A dataset of 186 cross-sectional fruit images was used, consisting of 90 training samples and 96 testing samples representing the dura, pisifera, and tenera varieties. The methodology includes preprocessing, segmentation, HSV feature extraction, model training, and performance evaluation through a confusion matrix. Experimental results show that the proposed model achieves an accuracy of 85%, with misclassifications primarily occurring in the tenera class due to its close resemblance to the dura variety. Compared to Linear Discriminant Analysis (LDA), the proposed approach demonstrates faster computation time and competitive accuracy. These findings indicate that Gaussian Naïve Bayes, supported by HSV feature descriptors, provides an efficient solution for lightweight and cost-effective digital classification of oil palm fruit varieties
Benchmarking Konfigurasi Apache Spark Single-Node: Studi Format, Partitioning, dan Caching pada Dataset Log Sintetis Syahrur Ro'uf; Bambang Purnomosidi Dwi Putranto
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.1015

Abstract

Studi benchmarking Apache Spark umumnya dilakukan pada klaster multi-node berskala komersial, sehingga panduan konfigurasi untuk mode single-node (local mode) yang lazim dipakai peneliti dengan sumber daya terbatas masih jarang diverifikasi secara empiris dan terbuka. Penelitian ini mengisi kesenjangan tersebut dengan mengevaluasi pipeline data lima tahap (ingest, cleanse, transform, aggregate, dan sink) menggunakan Apache Spark 4.1 pada mesin single-node (4 core, memori driver 4 GB) untuk mengolah dataset log transaksional sintetis 5 juta rekaman (424 MB) yang meniru karakteristik data akses terbuka. Empat eksperimen terkendali dilakukan: (E1) format penyimpanan CSV versus Parquet, (E2) strategi shuffle partitioning, (E3) in-memory caching untuk beban kerja iteratif, dan (E4) profiling waktu eksekusi per tahap. Pada lingkungan uji tersebut, Parquet memberikan percepatan baca 4,03× dengan rasio kompresi 2,92× dibanding CSV; jumlah partisi optimal adalah 4, sesuai jumlah core CPU; in-memory caching menghasilkan percepatan kumulatif 2,22× pada empat kueri berulang; dan pipeline end-to-end mencapai throughput 26.130 rekaman per detik nilai yang spesifik terhadap mesin uji dengan tahap sink Parquet terpartisi sebagai bottleneck dominan (75,1% total waktu). Kontribusi bersifat konfirmatoris-empiris: memverifikasi secara terkendali panduan konfigurasi yang terdokumentasi namun jarang diukur pada konteks single-node, sekaligus menyediakan kode sumber dan generator dataset secara terbuka untuk mendukung reproducibility.
Penerapan K-Means Clustering, DBSCAN, dan K-Modes untuk Segmentasi Mahasiswa Baru Berdasarkan Asal Sekolah dan Wilayah Geografis (Studi Kasus: STIKES Guna Bangsa Yogyakarta) Muhammad Thoif Junaidi; Bambang Purnomosidi Dwi Putranto
Jurnal Locus Penelitian dan Pengabdian Vol. 5 No. 8 (2026): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v5i8.6017

Abstract

Perguruan tinggi menghadapi tantangan dalam merumuskan strategi promosi tepat sasaran karena belum memahami pola sebaran mahasiswa baru berdasarkan asal sekolah dan wilayah geografis, menyebabkan alokasi anggaran promosi tidak efisien dan kerja sama dengan sekolah asal tidak terarah. Penelitian ini bertujuan menerapkan K-Means, DBSCAN, dan K-Modes untuk segmentasi mahasiswa baru serta membandingkan kinerja ketiga metode dalam menyelesaikan permasalahan strategi promosi di STIKES Guna Bangsa Yogyakarta. Menggunakan 634 data mahasiswa baru dengan variabel tingkat pendidikan, jurusan, kabupaten, dan provinsi, metode meliputi label encoding dan normalisasi Min-Max untuk K-Means dan DBSCAN, sedangkan K-Modes bekerja langsung pada data kategorikal. Jumlah klaster optimal ditentukan menggunakan metode elbow (k=4), DBSCAN dengan ?=0,45 dan MinPts=8, serta evaluasi menggunakan Silhouette Coefficient dan Davies-Bouldin Index. Hasil menunjukkan K-Means membentuk empat klaster: Nusa Tenggara dengan SMA IPA (29,5%), Jawa dengan SMK kesehatan (24,6%), Jawa dengan SMA IPS (26,5%), dan luar Jawa dengan MA (19,4%). DBSCAN mengidentifikasi 53 mahasiswa (8,36%) sebagai noise, sementara K-Modes menghasilkan centroid berupa kategori aktual dengan performa lebih rendah (Silhouette: 0,193; DBI: 11,978). K-Means unggul secara metrik (Silhouette: 0,412; DBI: 1,024) dan menjawab inefisiensi anggaran dengan panduan prioritas wilayah. K-Modes unggul dalam interpretabilitas centroid untuk kerja sama sekolah terarah. DBSCAN unggul mendeteksi profil unik yang terlewatkan untuk memperluas jangkauan promosi. Ketiga metode bersifat komplementatif dan menjembatani kesenjangan antara data mentah dengan kebutuhan strategi promosi kampus secara operasional.
Co-Authors Agung Wilis Nurcahyo Ahmad Bilal Almagribi Ali Impron Almagribi, Ahmad Bilal Andre Argisitawan Andriyani, Widyastuti Anwarudin Anwarudin Arma Fauzi Bagas Triaji Cucut Hariz Pratomo Danny Kriestanto Dommy Kristomo Domy Kristomo Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Hamdani Hamdani Hendra Maryanto Heri Muhrial I Nyoman Oka Semadi Ivónia Fátima Ruas da Silva Kuindra Iriyanta Moh. Abdul Kholik Muammar Muammar Muhammad Guntara Muhammad Haries Muhammad Najib Muhammad Thoif Junaidi Nenen Isnaeni Nugroho, Muhammad Agung Priyo Purnomo Rikie Kartadie Robertus Saptoto Robertus Saptoto Siti Khomsah, Siti Sri Redjeki Syahrur Ro'uf Teguh Junian Kuswanto Wiwi Widayani Yuli Astuti