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All Journal JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Intelligent Software Systems
Muhammad Haries
Universitas Islam Kebangsaan Indonesia
Computer Science & IT

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PERBANDINGAN KINERJA TEKNOLOGI NEAR FIELD COMMUNICATION PADA PERANGKAT ANDROID MENGGUNAKAN METODE MANN WHITNEY Muhammad Haries
Journal of Information System Management (JOISM) Vol. 4 No. 2 (2023): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2023v4i2.982

Abstract

Penelitian membahas perbandingan kinerja 2 ponsel pintar Android dalam membaca tag Near Field Communication. Perangkat pertama merupakan Samsung J7 2016 (ponsel kelas menengah) menggunakan modul NFC NXP PN544 dan perangkat kedua merupakan Sony Xperia Z Ultra (ponsel kelas atas) menggunakan modul NFC NXP PN65N. Studi kasus dilakukan dengan menerapkan teknologi NFC pada pendataan barang. Penelitian ini bertujuan mengetahui kinerja kedua perangkat ponsel pintar Android yang berbeda kelas apakah mempunyai perbedaan melakukan pembacaan tag NFC dalam pendataan barang berdasarkan jarak dan waktu. Hasil data dari pembacaan tag NFC diuji untuk melihat apakah terdapat perbedaan signifikan atau tidak. Untuk sampel jarak pengujian yang diuji pada dua perangkat adalah 5 mm, 10 mm, dan 15 mm dengan pembacaan 15 tag NFC. Hasil data berdasarkan pengujian waktu dan jarak dari kedua ponsel pintar Android yang telah diuji menggunakan metode uji statistik uji Mann Whitney. Melalui metode tersebut diketahui pada pembacaan tag dengan jarak 5 mm didapatkan nilai signifikansi sebesar .152, kemudian pada jarak 10 mm didapatkan nilai signifikansi sebesar .013, serta pada jarak 15 mm didapatkan nilai signifikansi sebesar .002, dan dari ketiga pengujian tersebut membuktikan pada jarak 5 mm tidak terdapat perbedaan signifikan dalam pembacaan tag NFC, sedangkan pada jarak 10 mm dan 15 mm terdapat perbedaan signifikan dalam pembacaan tag NFC.
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.
LACM-Tree: Exact Similarity Search via Learning-Enhanced Clustered Metric Trees with Distance-Table Compression Techniques Ali Impron; Linda Sutriani; Adi Kusjani; Agung Wilis Nurcahyo; Fadhlih Girindra Putra; Muhammad Haries
Journal of Intelligent Software Systems Vol 5, No 1 (2026): July 2026
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v5i1.2816

Abstract

Unmanned aerial vehicle (UAV)-based agricultural and forestry monitoring generates large volumes of imagery, creating a need for efficient similarity search over the high-dimensional embeddings produced by deep learning models. Classical metric indexing methods such as the M-tree, Slim-tree, and the Clustered Metric Tree (CM-tree) provide exact search with support for dynamic operations, but their performance degrades in high dimensions because the quadratic size of the pairwise distance table erodes the effective node capacity. This paper identifies distance-table compression as the key enabler for extending the CM-tree to high-dimensional embeddings. The core contribution is the Compressed Distance Table (CDT): 8-bit quantization of the pairwise distance table combined with an error-margin pruning rule that provably preserves result completeness (Theorem 1). Under an iso-memory configuration in which the per-node table budget is held constant, CDT halves the total distance-table footprint and reduces node accesses by up to 53% at n = 20,000 (and 61% on skewed data) while maintaining recall of exactly 1.000, verified by an auditing protocol that found zero unsafe prunings across more than 24,000 quantized pruning decisions. Three lighter-weight extensions—cost-oriented pivot selection (LPS), density-adaptive splitting (DAS), and multi-pivot bound tightening (MBT)—are evaluated in a controlled component-wise ablation that serves as a diagnostic study of learning-augmented metric trees. The ablation shows that these components are not additive: DAS in particular degrades performance through split-induced fragmentation, and the mechanism of this negative interaction is analyzed in detail. All results are obtained from a complete open prototype with directly measured distance computations and node accesses, on datasets up to 20,000 objects and 256 dimensions. The findings position distance-table compression—rather than learned heuristics—as the most robust path toward exact, dynamic, memory-efficient metric indexing for embedding workloads.
Co-Authors Agung Wilis Nurcahyo Ali Impron Bambang Purnomosidi Dwi Putranto Fadhlih Girindra Putra Linda Sutriani Rikie Kartadie Wiwi Widayani Yuli Astuti