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All Journal IJID (International Journal on Informatics for Development) Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Internet of Things and Artificial Intelligence Journal
Riwanto, Yudha
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Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Data Search Process Optimization using Brute Force and Genetic Algorithm Hybrid Method Riwanto, Yudha; Nuruzzaman, Muhammad Taufiq; Uyun, Shofwatul; Sugiantoro, Bambang
IJID (International Journal on Informatics for Development) Vol. 11 No. 2 (2022): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2022.3743

Abstract

High accuracy and speed in data search, which are aims at finding the best solution to a problem, are essential. This study examines the brute force method, genetic algorithm, and two proposed algorithms which are the development of the brute force algorithm and genetic algorithm, namely Multiple Crossover Genetic, and Genetics with increments values. Brute force is a method with a direct approach to solving a problem based on the formulation of the problem and the definition of the concepts involved. A genetic algorithm is a search algorithm that uses genetic evolution that occurs in living things as its basis. This research selected the case of determining the pin series by looking for a match between the target and the search result. To test the suitability of the method, 100-time tests were conducted for each algorithm. The results of this study indicated that brute force has the highest average generation rate of 737146.3469 and an average time of 1960.4296, and the latter algorithm gets the best score with an average generation rate of 36.78 and an average time of 0.0642.
Performance Analysis of Genetic Algorithms and KNN Using Several Different Datasets Riwanto, Yudha; Atika, Enda Putri
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.767

Abstract

This research aims to increase the accuracy of the classification of mango, corn, and potato leaf types using an approach involving feature selection with a genetic algorithm (Genetic Algorithm), classification with K-Nearest Neighbors (KNN), and image processing in the HSV color space (Hue, Saturation). , Value). The dataset used consists of more than 1500 image samples for each type of leaf, with a total of 10 tests carried out. The research process begins with processing leaf images in HSV color space to extract more representative color information. Next, a genetic algorithm is applied to select the most relevant features from the processed image. The selected features are then used as input for the KNN model in the classification process. The test results show that the proposed method can achieve a classification accuracy of 87,9%. This shows that the combination of image processing in the HSV color space, feature selection using a genetic algorithm, and classification with KNN can improve performance in recognizing leaf types. This research makes significant contributions to the field of image processing and classification and shows the potential of using genetic algorithms for feature selection in pattern recognition applications.
Performance Comparison Analysis on Weather Prediction using LSTM and TKAN Wardhana, Ajie Kusuma; Riwanto, Yudha; Rauf, Budi Wijaya
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.808

Abstract

The development of machine learning methods in the last few decades has shown great potential in various predictive applications, including in domains such as financial prediction, medical diagnosis, and big data analysis. One of the most widely used methods in prediction tasks is Long Short-Term Memory (LSTM). LSTM has become popular because of its ability to handle time series data by retaining relevant information in the long term and the ability to forget irrelevant information through the forget-gate mechanism. However, along with the development of technology and the need to improve accuracy and efficiency, new methods such as the Kolmogorov Arnold Network (KAN)  have emerged. KAN was then developed into the Temporal Kolmogorov Arnold Network (TKAN), which was designed to match or even surpass the performance of LSTM. The TKAN architecture has produced significant improvements in the management of both new and historical information. Because of this capability, TKAN can excel in multi-step predictions, demonstrating a clear advantage over conventional models such as LSTM and GRU, particularly in the context of long-term forecasting. This research goal is to give insight into the comparison of both the TKAN and LSTM models for weather prediction using model loss and mean absolute error evaluation (MAE). The model for both LSTM and TKAN achieved 0.09 and 0.11 for model loss and 0.08 and 0.96 for MAE.
Pengembangan Website dan Konten Karang Taruna Rukun Agawe Santosa Ngijo Bantul sebagai Optimalisasi Media Digital: Development of Web Platforms and Digital Content for Karang Taruna Rukun Agawe Santosa Ngijo Bantul as Digital Media Optimization Yanuar Risca Pratiwi, Inggrid; Riwanto, Yudha; Wardhana, Ajie Kusuma; Ningrum, Fauzia Anis Sekar; Fikri, Muhammad Ainul
Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Vol. 10 No. 1 (2025): December
Publisher : Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jindeks.v10i1.9093

Abstract

Kegiatan ini bertujuan untuk mengoptimalkan pemanfaatan media digital sebagai sarana informasi, publikasi, dan komunikasi organisasi kepemudaan melalui pengembangan website serta pelatihan manajemen konten bagi Karang Taruna Rukun Agawe Santosa (RAS) Dusun Ngijo, Kabupaten Bantul, Daerah Istimewa Yogyakarta. Permasalahan utama yang dihadapi Karang Taruna RAS meliputi keterbatasan media publikasi kegiatan dan rendahnya kemampuan pengurus dalam mengelola informasi secara digital. Kegiatan dilaksanakan melalui empat tahapan, yaitu observasi dan pengembangan, sosialisasi dan pelatihan, implementasi teknologi dan evaluasi. Website yang dikembangkan memiliki fitur-fitur utama yaitu Manajemen Agenda, Keuangan, Inventaris Perlengkapan, dan Broadcast WhatsApp. Hasil sosialisasi dan pelatihan ini telah berhasil mengembangkan dan menyerahkan website untuk Karang Taruna yang fungsional dan dapat diakses melalui internet kapan saja dan di mana saja. Dari sisi sumber daya manusia, para pengurus Karang Taruna RAS telah menerima transfer pengetahuan melalui pelatihan manajemen konten dan terbukti mampu mengelola website secara mandiri, termasuk mempublikasikan beberapa konten awal pasca-pelatihan. Berdasarkan hasil pengujian UAT terhadap website oleh pengurus dan anggota Karang Taruna RAS  didapatkan nilai rata-rata 97,8%. Hal ini menunjukkan peningkatan kemampuan digital yang signifikan dalam pengelolaan informasi organisasi. Luaran kegiatan ini mencakup website dan buku panduan penggunaan website (manual book) yang diberikan kepada Karang Taruna RAS.
Co-Authors Atika, Enda Putri Bambang Sugiantoro Fikri, Muhammad Ainul Ningrum, Fauzia Anis Sekar Nuruzzaman, Muhammad Taufiq Rauf, Budi Wijaya Shofwatul ‘Uyun Wardhana, Ajie Kusuma Yanuar Risca Pratiwi, Inggrid