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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Teknologi dan Kejuruan: Jurnal teknologi, Kejuruan dan Pengajarannya Jurnal Pendidikan Sains Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Pendidikan (Teori dan Praktik) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Ilmiah Flash JITK (Jurnal Ilmu Pengetahuan dan Komputer) JTAM (Jurnal Teori dan Aplikasi Matematika) SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer EDUMATIC: Jurnal Pendidikan Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Community Development Journal: Jurnal Pengabdian Masyarakat Letters in Information Technology Education (LITE) Indonesian Journal of Instructional Media and Model Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Pembelajaran Indonesia (JPPI) Bulletin of Community Engagement Indonesian Journal of Innovation Studies Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia JP (Jurnal Pendidikan) : Teori dan Praktik JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM)
Triyanna Widiyaningtyas
Universitas Negeri Malang
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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UTILIZATION OF INFORMATION TECHNOLOGY TO SUPPORT THE LEGALITY OF UMKM DM-THOYIBA Triyanna Widiyaningtyas; Sujito Sujito; Soenar Soekopitojo; Budi Wibowotomo; Adam Ramadhani P; Ahmad Farobi
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 4 No. 3 (2023): Volume 4 Nomor 3 Tahun 2023
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v4i3.17527

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) berperan penting dalam mendorong pertumbuhan dan pembangunan ekonomi di banyak negara. Di Indonesia, DM-THOYIBA, sebagai salah satu UMKM yang sedang berkembang, telah mendapatkan pengakuan atas produk dan layanannya yang berkualitas tinggi. Namun, seperti banyak UMKM lainnya, DM-THOYIBA menghadapi tantangan dalam menghadapi kerumitan dalam mendapatkan izin dan memastikan legalitasnya. Kegiatan ini berfokus untuk menggali potensi teknologi informasi dalam memperkuat legitimasi perizinan DM-THOYIBA. Studi ini menggunakan pendekatan metode campuran, menggabungkan wawancara kualitatif dengan pelaku usaha UMKM DM-THOYIBA, termasuk analisis data kuantitatif mengenai waktu pengajuan izin dan tingkat keberhasilan. Data kualitatif memberikan wawasan tentang tantangan saat ini yang dihadapi oleh DM-THOYIBA dalam proses perolehan izin, sedangkan analisis kuantitatif menyoroti potensi peningkatan efisiensi yang dapat dicapai melalui adopsi teknologi informasi. Dalam kegiatan ini pemanfaatan teknologi informasi sebagai sarana untuk mendukung legitimasi perizinan DM-THOYIBA dimana dengan terbitnya NIB (nomor Induk Berusaha), Pengajuan PIRT, Halal dan Hak Cipta Merek. Dengan merangkul solusi digital dan menjalin kemitraan yang kuat, UMKM dapat meningkatkan kepatuhan mereka terhadap persyaratan hukum, mendorong pertumbuhan, dan berkontribusi pada pembangunan ekonomi Indonesia yang berkelanjutan. Selain itu, wawasan studi ini dapat berfungsi sebagai landasan untuk upaya serupa di sektor UMKM lainnya yang berupaya memanfaatkan teknologi untuk kepatuhan terhadap peraturan dan keunggulan operasional.
Educational Data Mining: Multiple Choice Question Classification in Vocational School Sucipto Sucipto; Didik Dwi Prasetya; Triyanna Widiyaningtyas
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 23 No 2 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i2.3499

Abstract

Data mining on student learning outcomes in the education sector can overcome this problem. This research aimed to provide a solution for selecting quality multiple choice questions (MCQ) using the results of students’ mid-semester exams in vocational high schools using a Data Mining approach. The research method used was the Cross-Industry Standard Process for Machine Learning (CRISP-ML) model. Steps to assess the accuracy of analyzing the difficulty level of questions based on student profile data and midterm test results. The data used in this research were the findings of basic computer tests on mid-term exams in mathematics disciplines at vocational high schools. This research used several classification algorithms, including SVM, Naive Bayes, Random Forest, Decision Three, Linear Regression, and KNN. The results of evaluating the classification
A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS Arif Mudi Priyatno; Triyanna Widiyaningtyas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5015

Abstract

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.
Augmented Rice Plant Disease Detection with Convolutional Neural Networks Hairani, Hairani; Widiyaningtyas, Triyanna
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 8 No 1 (2024): February 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v8i1.21168

Abstract

The recognition and classification of rice plant diseases require an accurate system to generate classification data. Types of rice diseases can be identified in several ways, one of which is leaf characterization. One method that has high accuracy in identifying plant disease types is Convolutional Neural Networks (CNN). However, the rice disease data used has unbalanced data which affects the performance of the method. Therefore, the purpose of this research was to apply data augmentation to handle unbalanced rice disease data to improve the performance of the Convolutional Neural Network (CNN) method for rice disease type detection based on leaf images. The method used in this research is the CNN method for detecting rice disease types based on leaf images. The result of this research was the CNN method with 100 epochs able to produce an accuracy of 99.7% in detecting rice diseases based on leaf images with a division of 80% training data (2438 data) and 20% testing data (608 data). The conclusion is that the CNN method with the augmentation process can be used in rice disease detection because it has very high accuracy.
Digitalisasi Produk UMKM Berbasis E-Katalog untuk Meningkatkan Komersialisasi Pemasaran di Lingkungan Komunitas UMKM PADUKA Irianto, Wahyu Sakti Gunawan; Widiyaningtyas, Triyanna; Sujito, Sujito; Habibi, Muhammad Afnan; Syah, Abdullah Iskandar; Abdul Hadi, Afif; Fuadi, Ahmad
Bulletin of Community Engagement Vol. 3 No. 2 (2023): Bulletin of Community Engagement
Publisher : CV. Creative Tugu Pena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51278/bce.v3i2.826

Abstract

The Indonesian economy has traditionally relied heavily on the vital contributions of Micro, Small, and Medium Enterprises (MSMEs) to support economic growth and national development. MSMEs not only create jobs but also drive economic growth, especially after economic crises. This community engagement focuses on the Kasembon Subdistrict in Malang Regency and involves the PADUKA MSME Community. In this program, the introduction of an E-Catalog is presented as a solution to improve the marketing of MSME products. The results show that the implementation of the E-Catalog has enhanced the efficiency of product marketing, facilitated access to product information, and provided benefits to consumers. However, challenges related to technology adaptation and diverse understandings need to be addressed. Continuous support and capacity-building in the form of training and mentoring are necessary to ensure the sustainability of this program. This initiative underscores the significant potential of technology in advancing MSMEs and emphasizes the need for a sustainable approach to drive local economic growth. Keywords: E-Catalog, Community Engagement, Economic Growth, Product Digitalization
Analisis Perbandingan Pearson Correlation dan Cosine Similarity pada Rekomendasi Musik berbasis Collaborative Filtering Yuniardini, Fatma; Widiyaningtyas, Triyanna
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.v8i2.27781

Abstract

Advances in digital technology have revolutionized the world of music, making access to various genres and musicians easier and unlimited, but users still have difficulty finding music that suits their tastes. This research aims to analyze and compare the performance of the pearson correlation and cosine similarity methods on personal music recommendations based on Collaborative Filtering, with a focus on Item-Based Filtering, measured using Mean Absolute Error (MAE) and Root Mean Squared Error  (RMSE). The dataset utilized comprises public metal music ratings from Amazon, sourced from Kaggle, totaling 19,065 samples. The k-Nearest Neighbors (KNN) algorithm was employed for recommendation prediction. The research steps included data collection, pre-processing to address missing values, duplicates, normalization, and outlier detection, followed by prediction using the KNN algorithm, and accuracy measurement using MAE and RMSE. Evaluation results indicated that Pearson Correlation produced an MAE of 0.066538 and an RMSE of 0.086698, while cosine similarity yielded an MAE of 0.066559 and an RMSE of 0.086709. These findings suggest that pearson correlation is more effective in capturing linear relationships within the rating data, leading to recommendations that are more relevant and aligned with user preferences. Pearson correlation considers the variability in each user's ratings, resulting in more accurate recommendations that align with individual rating patterns.
Perbandingan Cosine Similarity dan Mean Squared Difference dalam Rekomendasi Buku Fiksi berbasis Item Rosydah, Lucyta Qutsyaning; Widiyaningtyas, Triyanna
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.v8i2.27783

Abstract

The need for recommendations is increasingly crucial in the digital era, especially with the abundance of fiction book data from e-book platforms and digital libraries. This study aims to evaluate the effectiveness of item-based collaborative filtering using cosine similarity and Mean Squared Difference (MSD) metrics for book recommendations. The knowledge discovery in databases method was applied, encompassing data selection, pre-processing, transformation, data mining, and evaluation. The dataset includes 100,000 user ratings obtained from Kaggle's "Book Recommendation Dataset." Our findings show that the Mean Absolute Error for MSD is 0.152307, slightly better than cosine similarity at 0.152406. The Root Mean Squared Error for MSD is lower at 0.185551, compared to cosine similarity's 0.185636. However, Cosine Similarity is more efficient in processing time, with 0.50 seconds compared to 0.59 seconds for MSD. Understanding these metrics is crucial, as they reveal differences in accuracy and efficiency in book recommendation. The results indicate that MSD performs better in the accuracy of fiction book recommendations compared to cosine similarity, making it more suitable for applications prioritizing recommendation precision, while Cosine is more efficient for large data processing.
Analisis Metode Collaborative Filtering menggunakan KNN dan SVD++ untuk Rekomendasi Produk E-commerce Tokopedia Hazizah, Chalista Yulia; Widiyaningtyas, Triyanna
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.v8i2.27793

Abstract

The rapid development of internet technology has driven increased adoption of e-commerce, yet companies face challenges in enhancing users' shopping experiences. To assist users in finding products that match their preferences, relevant recommendation analysis is crucial. This research compares the effectiveness of K-Nearest Neighbors (KNN) and Singular Value Decomposition Plus Plus (SVD++) algorithms for e-commerce product recommendations using the Tokopedia Product Reviews dataset from Kaggle, which contains 40,893 reviews. The study includes data collection and preprocessing steps such as removing duplicates, replacing missing values with the average, and normalizing ratings. KNN and SVD++ are then applied to predict ratings using cosine similarity and factor matrices. Evaluation using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) shows that SVD++ outperforms KNN, achieving a lower MAE of 0.161176 and RMSE of 0.185252, compared to KNN's MAE of 0.163964 and RMSE of 0.197045. This indicates that SVD++ is more effective in delivering accuracy and capturing data complexity. The findings highlight the potential to enhance recommendation effectiveness in e-commerce, improving user satisfaction by efficiently matching products to preferences.
Si Pelabuhanna: Game Edukasi Pengenalan Buah-Buahan Mengandung Vitamin A menggunakan Metode Forward Chaining Kurniawan, Rizky Rizaldi; Widiyaningtyas, Triyanna
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.v8i2.27837

Abstract

Providing education in a fun way about fruits that contain vitamin A is important because one of the important benefits of vitamin A is for human vision. The purpose of this study to develop the game Si Pelabuhanna and apply the method of forward chaining to determine the eligibility of players to rise to level 2. Using the Game Development Life Cycle (GDLC) development method with stages used initiation, pre-production, production, testing, and release. Our findings are in the form of Si Pelabuhanna games that have a play menu, material, and information and apply the forward chaining method. The Si Pelabuhanna Game can be used in elementary school children's subjects where the material is about fruits containing vitamin A. Application of forward chaining method by specifying variables to be used to create rules. Rules are used to determine if a player is eligible to advance to level 2. Testing using simulation by testing one by one rule after being applied in the game. Based on the test obtained an accuracy value of 100%. This means that the forward chaining method is successfully applied to determine whether a player is eligible to rise to level 2 in a Si Pelabuhanna game.
Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning Purnawansyah, Purnawansyah; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna; Haviluddin, Haviluddin; Raja, Roesman Ridwan; Darwis, Herdianti; Nafalski, Andrew
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.333

Abstract

Network congestion arises from factors like bandwidth misallocation and increased node density leading to issues such as reduced packet delivery ratios and energy efficiency, increased packet loss and delay, and diminished Quality of Service and Quality of Experience. This study highlights the potential of deep learning and ensemble learning for network congestion analysis, which has been less explored compared to packet-loss based, delay-based, hybrid-based, and machine learning approaches, offering opportunities for advancement through parameter tuning, data labeling, architecture simulation, and activation function experiments, despite challenges posed by the scarcity of labeled data due to the high costs, time, computational resources, and human effort required for labeling. In this paper, we investigate network congestion prediction using deep learning and observe the results individually, as well as analyze ensemble learning outcomes using majority voting, from data that we recorded and clustered using K-Means. We leverage deep learning models including BPNN, CNN, LSTM, and hybrid LSTM-CNN architectures on 12 scenarios formed out of the combination of level datasets, normalization techniques, and number of recommended clusters and the results reveal that ensemble methods, particularly those integrating LSTM and CNN models (LSTM-CNN), consistently outperform individual deep learning models, demonstrating higher accuracy and stability across diverse datasets. Besides that, it is preferably recommended to use the QoS level dataset and the combinations of 3 clusters due to the most consistent evaluation results across different configurations and normalization strategies. The ensemble learning evaluation results show consistently high performance across various metrics, with accuracy, Matthews Correlation Coefficient, and Cohen's Kappa values nearing 100%, indicates excellent predictive capability and agreement. Hamming Loss remains minimal highlighting the low misclassification rates. Notably, this study advances predictive modeling in network management, offering strategies to enhance network efficiency and reliability amidst escalating traffic demands for more sustainable network operations.
Co-Authors - Ardiansyah, - Abdul Hadi, Afif Adam Ramadhani P Adiba Qonita Ahmad Farobi Ahmad Fuadi Aji P Wibawa Aji Prasetya Wibawa Ali, Waleed Annas Gading Pertiwi Arif Mudi Priyatno Aya Shofia Mufti Bambang Nurdewanto Bintang Romadhon Binti Afifah Brilliant, Muhammad Zidan Budi Wibowotomo Darwis, Herdianti Dasuki, Moh. Didik Dwi Prasetya Ega Gefrie Febriawan Elta Sonalitha Fadhlullah, Aufar Faiq Fadli Hidayat, M. Noer Falah, Moh Zainul Fitriyah Fitriyah Fitriyah Fitriyah Gading Pertiwi, Annas Gamma Fitrian Permadi Hairani Hairani Haviluddin Haviluddin Hazizah, Chalista Yulia Heru Wahyu Herwanto I Made Wirawan Imansyah, Pranadya Bagus Indriana, Poppy Kornelius Kamargo/Irawan Dwi Wahyono Kornelius Kamargo Kurniawan, Rizky Rizaldi M. Ardhika Mulya Pratama M. Zainal Arifin Martin Indra Wisnu Prabowo Maryani, Sri Moh Zainul Falah Moh. Robieth Alfan Alhamid Mohamad Yusuf Kurniawan Muhammad Afnan Habibi Muhammad Firman Aji Saputra Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Rizki Irwanto Mulki Indana Zulfa, Mulki Indana Mulya Pratama, M. Ardhika Nafalski, Andrew Nazhiroh Tahta Arsyillah Nurhidayati Pindo Tutuko Poppy Indriana Purnawansyah Purnawansyah Qonita, Adiba Raja, Roesman Ridwan Rendy Yani Susanto Rhomdani, Rohmad Wahid Rizal, Muhammad Fatkhur Rosydah, Lucyta Qutsyaning Saifudin, Ilham Satria Putra Pratama Setiadi Cahyono Putro Shandy Krisnawan Sihombing, Wesly M Soenar Soekopitojo Soraya Norma Mustika Suastika Yulia Riska Sucipto Sucipto Sucipto Sucipto Sujito Sujito Syaad Patmanthara Syah, Abdullah Iskandar Syamsul Arifin Utomo Pujianto Wahyu Caesarendra Wahyu Sakti Gunawan Wahyu Sakti Gunawan Irianto Wibawa, Aji P Wisnu Prabowo, Martin Indra Yogi Dwi Mahandi Yuniardini, Fatma