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All Journal Prosiding KOMMIT Jurnal Simetris Prosiding SNATIF Jurnal Transformatika JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Devotion: Journal of Research and Community Service Jurnal Pengabdian Literasi Digital Indonesia
Fikri Budiman
Universitas Muria Kudus
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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DESAIN INTEGRASI DATA ANTAR DATABASE EPIDEMIOLOGI UNTUK MENDUKUNG PUSAT DATA KESEHATAN DENGAN MENGGUNAKAN SOA WEBSERVICE Budiman, Fikri; Sudaryanto N, Slamet; Muslih, Muslih
Prosiding SNATIF 2015: Prosiding Seminar Nasional Teknologi dan Informatika
Publisher : Prosiding SNATIF

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Abstract

Abstrak Monitoring data wabah penyakit pada wilayah tertentu (epidemiologi) memerlukan dukungan data yang terintegrasi dari setiap unit surveilans yang terkait. Integrasi data antar unit surveilans (puskesmas,poliklinik,rumah sakit) harus dikelola dan disdesain dengan baik sehingga memungkinkan pimpinan dan para analis kesehatan untuk memperoleh, mengintegrasikan, menganalisis dan memonitoring data (kasus penyakit) dari sumber data yang berbeda. Sumber data tersebut bersumber dari sistem yang heterogen, dalam hal ini sumber data tersebut adalah unit surveilans. Untuk memudahkan penegelolaan data surveilans tersebut akan di desain data center dalam model data warehouse epidemiologi sehingga membentuk sistem surveilans terpadu. Permasalahan yang dihadapi adalah berkaitan dengan interoperabilitas, yaitu kemampuan untuk mengintegrasikan dan mensinkronisasi data yang bersumber dari sistem yang berbeda platform (heterogen). Dengan demikian diperlukan suatu metodologi pengintegrasian data dalam model XML kedalam data warehouse epidemiologi. Sejak XML menjadi standar untuk pertukaran data melalui internet, terutama didalam komunikasi B2B dan B2C maka membutuhkan integrasi data XML kedalam sistem data warehouse. Didalam penelitian ini menjelaskan desain model integrasi antar unit surveilans pada Dinkes Kab. Grobogan. Metode yang digunakan adalah XML Web Services, yaitu metode yang dapat mengintegrasikan aplikasi dan pertukaran data dalam format XML (Extensible Markup Language). Pertukaran data dalam format XML menggunakan teknologi SOAP (Simple Object Acces Protocol) dan WSDL (Web Services Description Language) serta menggunakan library NuSOAP. Kata kunci:Integrasi, Epidemilogi, Sinkronisasi, XML,SOA, Web Services, NuSOAP
DESAIN DATABASE E-SUPERMUSEUM BATIK INDONESIA Budiman, Fikri
Prosiding KOMMIT 2012
Publisher : Prosiding KOMMIT

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Abstract

E-Supermuseum adalah sebuah model start-up berupa creative digital berbasis web yang menggabungkan supermarket dan museum online. E-supermuseum menerapkan model bisnis yang memuat museum maya batik Indonesis, dan dikelilingi oleh toko-toko maya yang menjual beraneka ragam batik khas Indonesia hasil produksi dari Unit Usaha Kecil / home industry. Metode pengembangan e-Supermuseum menggunakan metode model waterfall yang logisprespektif umum. Kualitaspengembangan produk website dengan model waterfall ditekankan berdasarkan pada tuntutan kebutuhan pemakai. Pemakai disini adalah pengelola website, pengrajin, dan pengunjung website. Pada tahapan pengembangan, rancangan dan desain database merupakan bagian proses yang sangat penting. Dengan desain database yang benar maka input dan output hasil implementasi akan sesuai dengan kebutuhan pemakai.
Conditional Matting For Post-Segmentation Refinement Segment Anything Model Susanto, Al Birr Karim; Soeleman, Moch Arief; Budiman, Fikri
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9024

Abstract

Segment Anything Model (SAM) is a model capable of performing object segmentation in images without requiring any additional training. Although the segmentation produced by SAM lacks high precision, this model holds interesting potential for more accurate segmentation tasks. In this study, we propose a Post-Processing method called Conditional Matting 4 (CM4) to enhance high-precision object segmentation, including prominent, occluded, and complex boundary objects in the segmentation results from SAM. The proposed CM4 Post-Processing method incorporates the use of morphological operations, DistilBERT, InSPyReNet, Grounding DINO, and ViTMatte. We combine these methods to improve the object segmentation produced by SAM. Evaluation is conducted using metrics such as IoU, SAD, MAD, Grad, and Conn. The results of this study show that the proposed CM4 Post-Processing method successfully improves object segmentation with a SAD evaluation score of 20.42 (a 27% improvement from the previous study) and an MSE evaluation score of 21.64 (a 45% improvement from the previous study) compared to the previous research on the AIM-500 dataset. The significant improvement in evaluation scores demonstrates the enhanced capability of CM4 in achieving high precision and overcoming the limitations of the initial segmentation produced by SAM. The contribution of this research lies in the development of an effective CM4 Post-Processing method for enhancing object segmentation in images with high precision. This method holds potential for various computer vision applications that require accurate and detailed object segmentation.
Message Hiding Using the Least Significant Bit Method with Shifting Hill Cipher Security Mahendra, Syafrie Naufal; Budiman, Fikri
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.9321

Abstract

Technological developments go hand in hand with advances in digital messaging. In protecting the confidentiality of the message, it is necessary to double secure the data. This security can be done with a combination of steganography and cryptographic techniques. Steganography algorithm which is a technique for hiding messages well, one of which is Least Significant Bit (LSB). The LSB algorithm is a simple method because it only converts the value of the last bit in a message with the inserted message bit, which is a convenience of the LSB algorithm, but it becomes vulnerable to message theft attacks if not combined with other algorithms for security. So it is necessary to increase security. This research developed a combination method of LSB algorithm for steganography technique with Hill Cipher algorithm for cryptographic technique, Hill Cipher was developed with shifting (shifting) 2 (two) characters. With the development of this method, hackers will find it difficult to crack messages, and is expected to improve the performance of the algorithm in affecting image quality and travel time in running the algorithm. The results of this study will be tested using several evaluation tools MSE, PSNR, BER, CER, AE, and Entropy. With the development of this method, hackers will find it difficult to decipher messages, and from the results of this experiment has been able to improve the performance of the algorithm in maintaining image quality and can shorten travel time in running the algorithm.
Optimizing Classification Algorithms Using Soft Voting: A Case Study on Soil Fertility Dataset Kamarudin, Fatkhurridlo Pranoto; Budiman, Fikri; Winarno, Sri; Kurniawan, Defri
Jurnal Teknologi Informasi dan Pendidikan Vol 16 No 2 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i2.800

Abstract

Classification algorithms are crucial in developing predictive models that identify and classify soil fertility levels based on relevant attributes. However, optimizing classification algorithms presents a major challenge in enhancing the accuracy and effectiveness of these models. Therefore, this research aims to optimize the classification algorithm in soil fertility analysis using ensemble learning techniques, specifically Soft Voting Ensemble. This research method is designed to understand soil fertility levels in modern agriculture by comparing the performance of various classification algorithms and ensemble approaches. Using a dataset from the Purwodadi Department of Agriculture, this study examines the optimization of algorithm parameters such as Random Forest, Gradient Boosting, and Support Vector Machine (SVM) and the implementation of Soft Voting Ensemble. Before applying Soft Voting Ensemble, each algorithm was evaluated with the following results: Random Forest achieved an accuracy of 90.93%, precision of 91.08%, recall of 90.33%, and F1-Score of 90.70%; Gradient Boosting achieved an accuracy of 91.53%, precision of 91.19%, recall of 91.56%, and F1-Score of 91.38%; SVM achieved an accuracy of 88.91%, precision of 89.66%, recall of 87.45%, and F1-Score of 88.54%. After implementing Soft Voting Ensemble, the accuracy improved to 91.63%, with an average precision of 91.21%, recall of 91.77%, and F1-Score of 91.49%. This study divided the data into 80% for training data and 20% for testing data. These findings indicate that the Soft Voting Ensemble has the potential to enhance agricultural productivity and sustainability.
Peningkatan Fitur Ekstraksi Berbasis Discrete Wavelet Transform dan Principal Component Analysis Pada Pengenalan Citra Batik Sugiarto, Edi; Budiman, Fikri; Muslih, Muslih; Arifin, Zaenal; Fahmi, Amiq; Hendriyanto, Novi
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5613

Abstract

Pengenalan pola batik menjadi penting karena batik sebagai warisan budaya bangsa perlu dilestarikan kepada generasi ke generasi. Salah satu upaya untuk memperkenalkan pola batik ini yaitu dengan memperkenalkan keragaman motif atau polanya. Penelitian ini bertujuan untuk mengoptimalisasi metode fitur ekstraksi dengan menggunakan metode Discrete Wavelet Transform (DWT) dan Principal Component Analysis (PCA) untuk mereduksi hasil fitur ekstraksi yang diperoleh dari DWT berdasarkan fitur-fitur yang memiliki korelasi yang baik. Tahapan dilakukan dengan menggunakan 310 data berupa citra batik yang terdiri dari 7 motif dengan komposisi 240 untuk data training dan 70 untuk data testing. Pada tahap fitur ekstraksi dengan menambahkan metode PCA pada DWT mampu mereduksi fitur dari 20 menjadi 5 fitur. Selanjutnya fitur tersebut diuji dengan melakukan klasifikasi menggunakan metode KNN dan SVM. Hasil dari klasifikasi dapat dibuktikan bahwa dengan menggunakan metode PCA dan DWT pada tahap fitur ekstraksi mampu meningkatkan klasifikasi hingga 5%.
Optimasi Analisis Kesuburan Tanah dengan Pendekatan Soft Voting Ensemble Budiman, Fikri; Awaludin, Yoga Mahendra
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 14, No 2 (2023): JURNAL SIMETRIS VOLUME 14 NO 2 TAHUN 2023
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v14i2.11285

Abstract

Penelitian ini mengusulkan Optimasi Algoritma Decison Tree, K-Nearest Neighbor dan Support Vector Machine menggunakan Metode Soft Voting dalam konteks kesuburan tanah pertanian. Hal ini didasari karena untuk klasifikasi kesuburan tanah masih dilakukan secara manual dan hal itu memungkinkan adanya suatu kesalahan dalam proses klasifikasi. Studi ini sangat relevan karena kesuburan tanah merupakan aspek kunci dalam pertanian yang dapat mempengaruhi hasil panen dan kualitas produk. Metode penelitian ini melibatkan pengumpulan data unsur hara dari Dinas Pertanian Kabupaten Grobogan dan analisis sistematis dengan Algoritma Decision Tree, K-Nearest Neighbor dan SVM kemudian diterapkan Metode Optimasi Soft Voting untuk meningkatkan akurasi. Hasil penelitian menunjukan bahwa Metode Optimasi Soft Voting mampu mengatasi masalah kesuburan tanah dengan meningkatkan akurasi klasifikasi. Nilai akurasi dari Algoritma Decision Tree sebesar 88,7%, nilai akurasi K-Nearest Neighbor sebesar 86,7, nilai akurasi SVM sebesar 90,1%,  dan akurasi penggabungan algoritma dengan Optimasi Soft Voting sebesar 90,4. Penelitian ini  penting dalam bidang pertanian karena membantu petani dan ahli pertanian dalam mengambil keputusan mengenai klasifikasi kesuburan tanah. Dengan meningkatkan pemahaman penggunaan teknologi melalui optimalisasi algoritma,  diharapkan  produktivitas pertanian dan ketahanan pangan meningkat.
Analisa Optimasi Grid Search pada Algoritma Random Forest dan Decision Tree untuk Klasifikasi Stunting Rahmayani, Ririt Sheila Tina; Budiman, Fikri
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6128

Abstract

Stunting is a serious problem that is of global concern because of its significant impact on the health and growth of children under five. This condition occurs due to long-term malnutrition. In Indonesia, nutritional problems are still common, including stunting which affects children's growth and development. In this regard, data mining has an important role in facing this challenge. Therefore, the aim of this research is to optimize stunting classification using Decision Tree and Random Forest algorithms optimized with Grid Search. This optimization was carried out to increase the accuracy of the two algorithms and identify algorithms that are superior in determining stunting. The dataset used consists of 10,000 toddler data with important attributes related to health conditions. The analysis results show that the initial Decision Tree model has an accuracy of 70.2%. After optimization using Grid Search, the accuracy of the Decision Tree model increased significantly to 82.8%. Meanwhile, the initial Random Forest model achieved an accuracy of 77.9%, and after optimization with Grid Search, its accuracy increased even higher compared to Decision Tree, namely 84.1%. This increase reflects the effectiveness of optimization in increasing the model's ability to classify stunting more accurately. This research provides important insights into the effectiveness of both algorithms in identifying stunting and emphasizes the importance of optimization to improve classification accuracy, which can support appropriate interventions for the well-being of future generations.
Optimasi Klasifikasi Stunting Balita dengan Teknik Boosting pada Decision Tree Hastuti, Nanda Tri; Budiman, Fikri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27913

Abstract

Malnutrition in the growth of small children is known as stunting. Currently, nutrition is still a serious problem that needs to be addressed, especially the nutrition of children under five. Considering the target prevalence rate (14%) in 2024 and how dangerous stunting is in Indonesia, this stunting problem needs to be addressed. The purpose of this research is to optimize the decision tree algorithm in stunting classification using boosting technique optimization. The boosting techniques used are AdaBoost, XGBoost, and Gradient Boosting methods. The boosting technique was chosen because it can improve classifier performance by combining multiple models that are learned sequentially, resulting in more effective predictions. This research uses infant data from Kaggle, which has a total of 10,000 data points, 8 attributes, and 2 classes. Based on the results of this study, decision tree optimization using the XGBoost method achieved the best results with accuracy of 83.8%, precision of 82%, recall of 83.8%, and F1-score of 81.2%, which shows great potential in improving the classification of stunted infants. The boosting technique is the best choice compared to other techniques. Based on the results of this study, the boosting technique can accurately predict and demonstrate a high level of precision in handling stunting classification.
Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition Sugiarto, Edi; Budiman, Fikri; Fahmi, Amiq
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2019

Abstract

Batik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%.
Co-Authors A.A. Ketut Agung Cahyawan W Abi Senoprabowo Agus Winarno, Agus Arief Soeleman, M Arifudin, Agus Asih Rohmani, Asih Awaludin, Yoga Mahendra Bao Sumantoro, Yeremia Joen Candra Irawan Edi Sugiarto Fahmi Amiq Fudhlatina, Dina Hastuti, Nanda Tri Kamarudin, Fatkhurridlo Pranoto Karis Widyatmoko Kurniawan, Defri Mahendra, Syafrie Naufal Moch Arief Soeleman, Moch Arief Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Noor Hasyim Novi Hendriyanto, Novi Rahmawan P, Elkaf Rahmayani, Ririt Sheila Tina S Nurhendratno Sri Winarno Supriyono Asfawi Susanto, Al Birr Karim Yuventius Tyas Catur Pramudi Zaenal Arifin