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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Journal of Electrical Technology UMY Jurnal Sistem dan Manajemen Industri Journal of Robotics and Control (JRC) Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Nasional Teknik Elektro dan Teknologi Informasi The Indonesian Journal of Computer Science
Dharmawan, Dhimas Arief
Program Studi Informatika, Universitas Pembangunan Nasional Veteran Yogyakarta, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Transportation Other

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Implementation of GLCM for Features Extraction and Selection of Batik Images Dhimas Arief Dharmawan; Latifah Listyalina
Journal of Electrical Technology UMY Vol 2, No 1 (2018)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.2128

Abstract

Batik is a craft that has high artistic value and has been a part of Indonesian culture (especially Java) for a long time. Batik cloth in Indonesia has various types of batik textures, batik cloth colors, and batik fabric patterns that reflect the regional origins of the batik cloth. Regarding the image of batik, the texture feature is an important feature because the ornaments on the batik cloth can be seen as different texture compositions. Besides batik motifs, also influenced by the shape characteristics that become parts of each batik motif. This research will add insight and knowledge to understand batik patterns based on the characteristics of batik motifs, namely texture. There are five batik motifs used, namely inland solo batik, semarang coastal batik, sidhomukti batik, parangklithik batik, and sidhodrajat batik. Initially preprocessing is done by cropping and grayscalling. Of the five image motifs, a cropping process is carried out for each motif. The next step is feature extraction. The features of GLCM were selected in this study. From the features contained in the GLCM, in this study four features were chosen, namely contrast, energy, correlation, and homogeneity. The final step is the selection or selection of features. The result of the feature selection of the four features carried out feature extraction are energy and homogeneity.
Deep-RIC: Plastic Waste Classification using Deep Learning and Resin Identification Codes (RIC) Latifah Listyalina; Yudianingsih Yudianingsih; Adjie Wibowo Soedjono; Evrita Lusiana Utari; Dhimas Arief Dharmawan
Telematika Vol 19, No 2 (2022): Edisi Juni 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i2.7419

Abstract

In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in the training phase of the deep learning architecture DenseNet-121 to identify the resin code of each plastic waste image and classify it into the appropriate class. The training phase is run for 100 epochs, and at each epoch, the cross-entropy loss function is calculated, which expresses the performance of the deep learning architectures in classifying plastic waste images. In the next stage, a trained deep learning architecture is used to classify the plastic waste images from the test set. Classification performance in the test set is also expressed as the cross-entropy loss function value. In addition, the accuracy value has also been calculated, which shows the percentage of the number of plastic waste images successfully classified correctly to the total number of plastic waste images in the test set, which the best accuracy is equal to 85%.
Conv-Tire: Tire Condition Assessment using Convolutional Neural Networks Latifah Listyalina; Irawadi Buyung; Agus Qomaruddin Munir; Ikhwan Mustiadi; Dhimas Arief Dharmawan
Telematika Vol 19, No 3 (2022): Edisi Oktober 2022
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v19i3.7697

Abstract

Purpose: In this study, the authors designed an algorithm based on convolutional neural networks that can automatically assess tire quality.Design/methodology/approach: The proposed algorithm is built through several stages as follows. In the first stage, the tire images, which are the input of the designed algorithm, are acquired. Further, the acquired images are divided into two sets, namely training and testing sets. The training set contains tire images used in the training phase of several convolutional neural networks (CNN) architectures such as ResNet-50, MobileNetV2, Inception V3, and DenseNet-121. The training phase is carried out in a number of epochs, and at each epoch, the cross entropy loss function will be calculated which expresses the performance of the CNN architecture in classifying tire images. For this reason, the training stage requires a label or reference that shows the feasibility of the tires displayed in each image.Findings/result: In the testing phase, trained CNN architectures are used to classify tire images from the test set. Classification performance in the test set is also expressed in terms of cross-entropy loss function value. In addition, the accuracy value has also been calculated which shows the percentage of the number of tire images that are successfully classified correctly to the total number of tire images in the test set, namely the DenseNet-121 model has the best accuracy of 92.62%.Originality/value/state of the art: Given the high accuracy achieved by our algorithm, this work can be used as a reference by other researchers, specifically to benchmark their tire quality classification methods developed in the future.
Retinal Vessel Segmentation to Support Foveal Avascular Zone Detection Dhimas Arief Dharmawan
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9645

Abstract

Purpose: This study aims to perform retinal vessel segmentation to support foveal avascular zone detection. Methodology: The proposed approach consists of a multi-stage image processing approach, including preprocessing, image quality enhancementt, and segmentation of retinal blood vessel using matched filter and length filter techniques.Findings: The proposed framework has achieved remarkable results with an average sensitivity, specificity, and accuracy of 77.99%, 86.43%, and 85.24%, respectively.Value: This achievement has the potential to significantly enhance the accuracy and efficiency of detecting and diagnosing medical conditions related to the retina, improving the quality of life for countless individuals.
Deteksi Kanker Serviks Otomatis Berbasis Jaringan Saraf Tiruan LVQ dan DCT Dhimas Arief Dharmawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 3 No 4: November 2014
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (159.334 KB)

Abstract

Cervical cancer has became the common women disease in the world. Mostly, cervical cancer has been already known lately, because it is very dificult to detect this in early stage. In this work, a computer based software using Learning Vector Quantization (LVQ) has been designed as the early cervical cancer detection aid tool. There are six methods before the detection is performed, namely preprocessing, contrast stretching, median filtering, morphology operation, image segmentation, and Discrete Cosine Transform based feature extraction. In tihis work, 73 cervical cell images that consist of 50 normal images and 23 cancer images are used. 35 normal images and 14 cancer images are used to train the LVQ. Then, 23 normal images and 9 cancer images are used in the testing process. Our results show 88,89 % cancer image can be detected correctly (sensitivity), 100 % normal image can be detected corerctly (specificity), and 95,83 % for overall detection (accuracy).
Balancing Inventory Management: Genetic Algorithm Optimization for A Novel Dynamic Lot Sizing Model in Perishable Product Manufacturing Leuveano, Raden Achmad Chairdino; Asih, Hayati Mukti; Ridho, Muhammad Ihsan; Darmawan, Dhimas Arief
Journal of Robotics and Control (JRC) Vol 4, No 6 (2023)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v4i6.20667

Abstract

In Indonesia, the significant role of perishable products in food wastage has placed the country fourth globally in household food waste. Managing inventory for such products, with their short shelf life and stringent safety standards, emphasizes the need for efficient lot sizing planning. This study introduces a novel Dynamic Lot-Sizing (DLS) model, addressing perishable products and inventory constraints across multiple products, periods, and varying demands. The model aims to optimize production quantity and binary production, minimizing overall system costs. Employing a Genetic Algorithm (GA), this research solves the DLS model under constrained and unconstrained inventory capacities. Real-case data from a bread manufacturing company validates the model, while sensitivity analysis examines perishability's impact on the solution and model performance. The DLS-GA model not only reduces system costs but also effectively considers product perishability, offering optimal production plans.
Optimizing lot sizing model for perishable bread products using genetic algorithm Hayati Mukti Asih; Raden Achmad Chairdino Leuveano; Dhimas Arief Dharmawan
Jurnal Sistem dan Manajemen Industri Vol. 7 No. 2 (2023): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v7i2.7172

Abstract

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.
Implementasi Deep Classifier untuk Diagnosis Penyakit Glaukoma pada Citra Retina Mata Dharmawan, Dhimas Arief; Leuveano, Raden Achmad Chairdino; Suryotomo, Andiko Putro; Tahya, Michel Pierce; Sani, Sayang
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3378

Abstract

Penerapan deep learning untuk diagnosis glaukoma dari gambar retina merupakan bidang yang berkembang pesat dalam pencitraan medis. Penelitian ini menyelidiki keampuhan model pembelajaran mendalam dengan menggunakan dua set data uji yang berbeda: DRISHTI-GS dan ORIGA, yang menjelaskan potensi dan tantangan dalam tugas medis yang kritis ini. Dalam kasus dataset DRISHTI-GS, model deep learning menunjukkan kinerja yang bervariasi di seluruh zaman. Epoch awal menunjukkan akurasi yang rendah dan kehilangan yang tinggi, tetapi peningkatan yang signifikan terjadi antara epoch 40 dan 70, mencapai akurasi sekitar 96% pada epoch 100. Hal ini menunjukkan potensi deep learning dalam mendiagnosis glaukoma dari gambar retina DRISHTI-GS. Sebaliknya, dataset ORIGA menunjukkan kemajuan yang lebih konsisten. Model ini terus meningkatkan akurasi, mencapai 97,54% pada epoch 80, dengan penurunan kerugian yang terjadi secara bersamaan, yang mengindikasikan konvergensi yang kuat. Hal ini menggarisbawahi kemahiran model dalam diagnosis dataset ORIGA, menyoroti janji klinisnya. Singkatnya, penelitian ini menunjukkan kelayakan deep learning untuk diagnosis glaukoma dari gambar retina, dengan hasil yang menjanjikan pada dataset DRISHTI-GS dan ORIGA.
A genetic algorithm approach to green vehicle routing: Optimizing vehicle allocation and route planning for perishable products Asih, Hayati Mukti; Leuveano, Raden Achmad Chairdino; Dharmawan, Dhimas Arief; Ardiansyah, Ardiansyah
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1784

Abstract

This paper introduces a novel approach to the Green Vehicle Routing Problem (GVRP) by integrating multiple trips, heterogeneous vehicles, and time windows, specifically applied to the distribution of bakery products. The primary objective of the proposed model is to optimize route planning and vehicle allocation, aiming to minimize transportation costs and carbon emissions while maximizing product quality upon delivery to retailers. Utilizing a Genetic Algorithm (GA), the model demonstrates its effectiveness in achieving near-optimal solutions that balance economic, environmental, and quality-focused goals. Empirical results reveal a total transportation cost of Rp. 856,458.12, carbon emissions of 365.43 kgCO2e, and an impressive average product quality of 99.90% across all vehicle trips. These findings underscore the capability of the model to efficiently navigate the complexities of real-world logistics while maintaining high standards of product delivery. The proposed GVRP model serves as a valuable tool for industries seeking sustainable and cost-effective distribution strategies, with implications for broader advancements in supply chain management.
Optimizing lot sizing model for perishable bread products using genetic algorithm Asih, Hayati Mukti; Leuveano, Raden Achmad Chairdino; Dharmawan, Dhimas Arief
Jurnal Sistem dan Manajemen Industri Vol. 7 No. 2 (2023): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v7i2.7172

Abstract

This research addresses order planning challenges related to perishable products, using bread products as a case study. The problem is how to effi­ci­ently manage the various bread products ordered by diverse customers, which requires distributors to determine the optimal number of products to order from suppliers. This study aims to formulate the problem as a lot-sizing model, considering various factors, including customer demand, in­ven­tory constraints, ordering capacity, return rate, and defect rate, to achieve a near or optimal solution, Therefore determining the optimal order quantity to reduce the total ordering cost becomes a challenge in this study. However, most lot sizing problems are combinatorial and difficult to solve. Thus, this study uses the Genetic Algorithm (GA) as the main method to solve the lot sizing model and determine the optimal number of bread products to order. With GA, experiments have been conducted by combining the values of population, crossover, mutation, and generation parameters to maximize the feasibility value that represents the minimal total cost. The results obtained from the application of GA demonstrate its effectiveness in generating near or optimal solutions while also showing fast computational performance. By utilizing GA, distributors can effectively minimize wastage arising from expired or perishable products while simultaneously meeting customer demand more efficiently. As such, this research makes a significant contri­bution to the development of more effective and intelligent decision-making strategies in the domain of perishable products in bread distribution.
Co-Authors Adjie Wibowo Soedjono Agus Qomaruddin Munir Andiko Putro Suryotomo ARDIANSYAH ARDIANSYAH Asih, Hayati Mukti Evrita Lusiana Utari Evrita Lusiana Utari, Evrita Lusiana Hayati Mukti Asih Ikhwan Mustiadi Irawadi Buyung, Irawadi Latifah Listyalina Latifah Listyalina Leuveano, Raden Achmad Chairdino Listyalina, Latifah Michel Pierce Tahya Raden Achmad Chairdino Leuveano Raden Achmad Chairdino Leuveano Ridho, Muhammad Ihsan Sani, Sayang Sayang Sani Tahya, Michel Pierce Yudianingsih Yudianingsih Yudianingsih, Yudianingsih