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All Journal JIKO (Jurnal Informatika dan Komputer) Innovation in Research of Informatics (INNOVATICS) Journal of Intelligent Software Systems Jurnal Pengabdian Masyarakat dan Riset Pendidikan
Ali Impron
Universitas Muhammadiyah Sampit
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Pemberdayaan Mahasiswa Melalui Kegiatan Literasi Digital Bersama Dosen: Meningkatkan Kompetensi Digital Sivitas Akademika Moh. Solehuddin; Ansori; Bernardus Agus Rukiyanto; Ali Impron; Muhammad Husnur Rofiq
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5520

Abstract

Pengabdian ini bertujuan untuk mengetahui peran kegiatan literasi digital yang dilaksanakan secara kolaboratif antara dosen dan mahasiswa dalam meningkatkan kompetensi digital sivitas akademika. Pelaksanaan kegiatan dilakukan melalui beberapa tahapan, meliputi tahap persiapan dan analisis kebutuhan, penyusunan materi, sosialisasi dan pengenalan program, pelatihan dan pendampingan intensif, implementasi, serta evaluasi dan refleksi bersama. Hasil kegiatan menunjukkan bahwa program ini memberikan dampak positif terhadap peningkatan kompetensi digital sivitas akademika. Program ini tidak hanya meningkatkan pemahaman mahasiswa mengenai makna dan peran strategis teknologi dalam pembelajaran dan penelitian, tetapi juga membentuk pola pikir yang lebih kritis, reflektif, dan bertanggung jawab dalam memanfaatkannya. Peningkatan keterampilan praktis terlihat dari kemampuan mahasiswa dalam mengakses, mengelola, serta memanfaatkan berbagai platform digital untuk mendukung aktivitas akademik, yang turut diiringi dengan tumbuhnya rasa percaya diri dalam menggunakan teknologi. Selain itu, proses pendampingan mendorong kemampuan berpikir kritis dalam menyaring informasi, memilih sumber yang kredibel, serta menghasilkan karya ilmiah yang lebih berkualitas. Interaksi akademik antara dosen dan mahasiswa juga menjadi lebih aktif dan partisipatif melalui pemanfaatan media digital. Secara keseluruhan, kegiatan ini berkontribusi dalam membentuk kebiasaan positif dalam penggunaan teknologi serta mendorong terciptanya budaya akademik yang adaptif, inovatif, dan berkelanjutan dalam menghadapi dinamika era digital.
Enhancing YOLOv5s with Attention Mechanisms for Object Detection in Complex Backgrounds Environment Ali Impron; Dina Lestari; Linda Sutriani; Syadza Anggraini; Randi Rizal
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16833

Abstract

Enhancing performance for object detection in complex environments is essential for real-world applications that represent complexities, such as stacking objects in the same location or environment. Models for detecting objects developed to this day still have difficulties in detecting objects with environments that have complex backgrounds. The reason is that the model often experiences a decrease in accuracy when the object to be detected is occlusion by other objects and is small in size. Therefore, in this study, a model improvement method was carried out in detecting objects in a complex environment. The algorithm used in this study is YOLOv5s. Optimization is carried out by adding a CBAM (Convolutional Block Attention Module) attention mechanism layer which is integrated with the C3 layer (C3CBAM) in the backbone of the YOLOv5s model architecture. In addition, a P2 feature map is also added to the architecture head. The optimization results carried out were quite satisfactory, namely there was an increase in the precision value by 1.6 %, at mAP@0.5 an increase of 1.4 %, and also mAP@50-95 increased by 0.1%. This proves that the enhancement method applied to YOLOv5s in this study can improve the performance of the model. However, with the addition of the attention mechanism layer, it turns out that it can increase the computational load. Therefore, for future research, a method can be applied to reduce computing load, one of the methods is knowledge distillation.
Analisis Pola Konsumsi Energi Listrik Rumah Tangga Berbasis Simulasi IoT Menggunakan Model Hybrid LSTM-Attention Ali Impron
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1922

Abstract

Pengelolaan energi listrik rumah tangga menjadi tantangan penting seiring meningkatnya kebutuhan energidan keterbatasan sumber daya. Penelitian ini mengusulkan pendekatan berbasis simulasi IoT untuk menganalisis pola konsumsi energi, mendeteksi anomali, dan memberikan rekomendasi efisiensi energi tanpa perangkat fisik, menggunakan model hybrid LSTM-Attention. Dataset simulasi (14.400 sampel) dibangun denganEnergyPlus, divalidasi terhadap data riil, dan diolah untuk mengevaluasi performa model. Hasil menunjukkan akurasi 96%, recall 0.95 untuk anomali, dan F1-score 0.96, melampaui baseline LSTM (91.5%). Mekanisme attention memprioritaskan power_usage_per_hour (bobot 0.47), meningkatkan deteksi anomali. Rekomendasi seperti penjadwalan ulang dan penggantian perangkat menghasilkan penghematan energi 20-40%. Dengan waktu pelatihan 1,5 jam pada Google Colab, pendekatan ini menawarkan solusi skalabel dan hemat biaya untuk pengelolaan energi berkelanjutan, dengan potensi pengujian riil dan peningkatan model di masa depan.
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 Ansori Bambang Purnomosidi Dwi Putranto Dina Lestari Fadhlih Girindra Putra Linda Sutriani Linda Sutriani Moh. Solehuddin Muhammad Haries Muhammad Husnur Rofiq Randi Rizal Rikie Kartadie Rukiyanto Rukiyanto, Rukiyanto Syadza Anggraini Wiwi Widayani Yuli Astuti