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All Journal OPSI International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF)
Nur Indrianti
Jurusan Teknik Industri UPN “Veteran” Yogyakarta
Computer Science & IT Industrial & Manufacturing Engineering

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PENGEMBANGAN SISTEM INFORMASI UNTUK MENDUKUNG KEBIJAKAN SEKTOR INDUSTRI MENUJU PEMBANGUNAN YANG BERKELANJUTAN Indrianti, Nur
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 5 (2008): Information System And Application
Publisher : Jurusan Teknik Informatika

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

Abstract

Naskah ini membahas strategi alternatif untuk mengurangi kecurangan atau pengelakan pajak dalam konteks pembangunan yang berkelanjutan pada sektor industri melalui penerapan kebijakan pajak dan subsidi. Analisis kualitatif telah dilakukan terhadap sistem informasi untuk administrasi perpajakan, yang disarankan berdasarkan pada kemitraan antara pemerintah dengan supplier. Sistem informasi yang diusulkan dikembangkan berdasarkan mekanisme cross-check informasi guna mengurangi kecurangan pajak. Selain ketersediaan informasi yang akurat, sistem informasi yang diusulkan juga mengarah kepada fleksibilitas dalam pengelolaan kebijakan sehingga kebijakan dapat selalu diarahkan kepada sasaran yang telah ditetapkan. Sistem yang diusulkan juga dapat lebih efisien karena dapat mengurangi biaya kepatuhan pembayaran pajak baik bagi pemerintah maupun pembayar pajak.
Optimizing LPG distribution: A hybrid particle swarm optimization and genetic algorithm for efficient vehicle routing and cost minimization Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Abdul-Rashid, Salwa Hanim; Kuncoro, Andreas Mahendro; Liestyana, Yuli
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1837

Abstract

This paper aims to develop an optimized solution for the Vehicle Routing Problem (VRP), tailored explicitly for Liquid Petroleum Gas (LPG) distribution, with a focus on minimizing transportation costs and enhancing delivery reliability. The critical role of LPG as an essential public infrastructure commodity, widely utilized for cooking and heating, makes its efficient and reliable distribution a significant logistical challenge due to the strict adherence to delivery time windows, heterogeneous fleets, multi-trip scenarios, and intricate loading and unloading requirements. To address these complexities, this study proposes a novel hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) that uniquely integrates multi-trip routing, time windows, and heterogeneous vehicle fleet management into a single optimization framework. The dual-phase optimization strategy leverages the exploratory capability of PSO and the solution-refining power of GA, resulting in high-quality, feasible solutions. Validation against real-world data involving VRP instances with 88 and 40 stations demonstrates the model’s practical impact, achieving reductions of up to 4.56% in transportation costs compared to existing operational routes. This research makes a significant contribution to interdisciplinary domains, including logistics optimization, sustainability, and energy distribution, by offering a robust and scalable model that comprehensively addresses complex, real-world VRP constraints.
Feature-based classification of sugarcane quality using the K-nearest neighbor algorithm Indrianti, Nur; Iqbal, Muhammad; Rustamaji, Heru Cahya; Ferriyan , Andrey; Mulyono , Panut; Ananta, Moh. Ais
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.16000

Abstract

The rapid advancement of artificial intelligence has enabled practical, data-driven approaches to agricultural quality assessment. However, many existing methods rely on complex sensor systems that are costly and difficult to deploy in the field. This study proposes a lightweight and interpretable K-Nearest Neighbor (KNN) model for non-destructive evaluation of sugarcane milling feasibility using five easily measurable physical attributes: relative distance ratio, internode length, mean diameter, circumference, and weight per centimeter. Samples with Brix less than 16 are categorized as not feasible for milling, while Brix equal to or greater than 16 are classified as possible. A dataset of 1,889 Bululawang samples collected in Malang, East Java, Indonesia, was evaluated across twenty-two scenarios that varied the train-test split, normalization method, distance metric, and neighborhood size. The optimal configuration, consisting of an 80:20 split, Standard normalization, the Minkowski distance metric, and k=75, achieved an accuracy of 78%. The findings confirm that physical measurements can serve as effective predictors of sugarcane quality and support data-driven inspection and sustainable resource utilization in line with SDGs 2, 9, and 12.
Non-destructive classification of sugarcane milling feasibility using deep learning: A comparative study of VGG19 and ResNet50 Indrianti, Nur; Leuveano, Raden Achmad Chairdino; Rustamaji, Heru Cahya; Ferriyan, Andrey; Mulyono, Panut; Wijaya, Bayu Prasetya
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2236

Abstract

Assessing sugarcane quality is crucial for ensuring both economic value and processing efficiency in sugar production. Conventional approaches, such as refractometer-based Brix measurements, are destructive, labor-intensive, and unsuitable for large-scale or rapid field evaluations. This study proposes a non-destructive deep learning framework for classifying sugarcane internodes into two quality categories (< 16 °Bx and ≥16 °Bx) to address existing limitations. Two convolutional neural network architectures, VGG19 and ResNet50, were evaluated utilizing a defined transfer learning and data augmentation methodology. Because of its residual connections, which enable deeper and more stable feature learning, ResNet50 consistently outperformed VGG19, achieving the highest accuracy of 78.85% on the Luar2_Putih dataset. This comparative finding demonstrates that modern residual-based networks provide superior robustness for subtle visual classification tasks in agricultural imaging, while also validating the stability of the proposed two-phase training framework. The study advances AI-driven non-destructive quality assessment by offering a scalable, field-deployable solution that supports sustainable, efficient sugarcane processing in line with the UN Sustainable Development Goals (SDG 2, 9, 12, and 13).
Co-Authors Abdul-Rashid, Salwa Hanim Ananta, Moh. Ais Andrey Ferriyan, Andrey Ferriyan , Andrey Heru Cahya Rustamaji Kuncoro, Andreas M Leuveano, Raden Achmad Chairdino Muhammad Iqbal Mulyono , Panut Panut Mulyono Wijaya, Bayu Prasetya Yuli Liestyana Yunto Satrio