Garuda - Garba Rujukan Digital

Deep Learning Approaches for Multi-Label Incidents Classification from Twitter Textual Information Sherly Rosa Anggraeni; Narandha Arya Ranggianto; Imam Ghozali; Chastine Fatichah; Diana Purwitasari
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.31-41

Background: Twitter is one of the most used social media, with 310 million active users monthly and 500 million tweets per day. Twitter is not only used to talk about trending topics but also to share information about accidents, fires, traffic jams, etc. People often find these updates useful to minimize the impact. Objective: The current study compares the effectiveness of three deep learning methods (CNN, RCNN, CLSTM) combined with neuroNER in classifying multi-label incidents. Methods: NeuroNER is paired with different deep learning classification methods (CNN, RCNN, CLSTM). Results: CNN paired with NeuroNER yield the best results for multi-label classification compared to CLSTM and RCNN. Conclusion: CNN was proven to be more effective with an average precision value of 88.54% for multi-label incidents classification. This is because the data we used for the classification resulted from NER, which was in the form of entity labels. CNN immediately distinguishes important information, namely the NER labels. CLSTM generates the worst result because it is more suitable for sequential data. Future research will benefit from changing the classification parameters and test scenarios on a different number of labels with more diverse data. Keywords: CLSTM, CNN, Incident Classification, Multi-label Classification, RCNN

Pengukuran Usability pada Website Kampus Akademi Komunitas Negeri Pacitan Menggunakan System Usability Scale (SUS) Gramandha Wega Intyanto; Narandha Arya Ranggianto; Vika Octaviani
Walisongo Journal of Information Technology Vol 3, No 2 (2021): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2021.3.2.9549

Pada dunia pendidikan website memiliki peran penting dalam memberikan informasi terkait dengan instansi pemilik website dan juga media promosi. Salah satu website pada lingkungan pendidikan yaitu website kampus Akademi Komunitas Negeri Pacitan. Pengunjung atau pengguna juga memiliki pengaruh besar terhadap website. Beberapa tinjauan tersebut yang mendorong kami dalam penganalisaan usability website kampus Akademi Komunitas Negeri Pacitan, yang mana pengukuran dan analisa menggunakan System Usability Scale (SUS) yang dilakukan uji validitas yang menyatakan valid dengan hasil Rhitung Rtabel pada item kuesioner dan uji Reliabilitas dengan hasil 0.637 yang menyatakan reliabel. Hasil Skor pada website kampus Akademi Komunitas Negeri Pacitan yaitu 60,75, dimana mendapat predikat D, kategori OK, tingkat penerimaan (feel) pengunjung yaitu margin low dan sudut pandang Net Promoter Score (NPS) berdasarkan skor SUS dinyatakan bahwa berpotensi detractor. Hasil ini menjabarkan bahwa dianggap belum efektif, efisien dan memuaskan bagi pengguna/pengunjung serta belum usable.

Pemberdayaan PKMB Rumah Pintar Melalui Penerapan Aplikasi “ROOMPI” untuk Meningkatkan Layanan dan Literasi Bagi Masyarakat Desa Karangharjo, Kabupaten Jember Alif Auliya, Yudha; Fadah, Isti; Baihaqi, Yustri; Arya Ranggianto, Narandha; Budi Yuswanto, Istatuk
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 4 No 2 (2024)
Publisher : Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31967/jpm.v4i2.1232

Karangharjo is a constituent village situated in the Silo District, under the Jember Regency of the East Java Province. Statistically, a significant number of individuals residing in rural regions have not attained schooling up to the junior high school or senior high school level. In 2018, the Community Learning Center (PKBM) Rumah was founded with the aim of ensuring universal access to education. Rumah Pintar provides three comprehensive services: the coordination of educational programs for packages A, B, and C, provision of basic and advanced literacy services, and establishment of community reading gardens. Two priority issues have been successfully addressed. The initial concern pertains to the administration of the PKBM Rumah Pintar services, which at present remains manual and lacks support for remote learning. The second concern regarding the human resources at Rumah Pintar is the insufficient proficiency in soft skills related to information technology. For the purpose of addressing the issue, the application "ROOMPI" has been created. The objective of this application is to enhance the effectiveness, efficiency, and accessibility of services for the general population. One approach to address the second issue is to enhance the capabilities of human resources in the areas of service management and efficient information technology management. The testing results acquired utilizing the SUS approach yielded a score of 85, which falls within the acceptable range. Furthermore, the assessment findings indicate that 83% of users express satisfaction, and the ROOMPI program greatly boosts learning motivation

Implementasi Pemasaran Digital Menggunakan ShopeeFood untuk Meningkatkan Penjualan (Studi Kasus Rumah Es Nina) Afandi, Khoirunnisa; Arief, M. Habibullah; Ranggianto, Narandha Arya; Fadhil, Martiana Kholila
Abdimas Indonesian Journal Vol. 4 No. 2 (2024)
Publisher : Civiliza Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59525/aij.v4i2.515

The current digital marketing trend is experiencing significant transformation. Micro and Small and Medium Enterprises (MSMEs) need a marketing strategy for a product using a digital platform to increase business excellence and optimize business to reach a broader range of consumers. This community service aims to evaluate MSMEs such as Nina's Ice House in Jember Regency in optimizing the use of ShopeeFood as a marketing platform. The method used in this community service activity is qualitative descriptive analysis. The service activity scheme goes through needs analysis, implementation, and evaluation. This digital marketing empowerment was done by interviewing and training on the ShopeeFood application with MSME sellers of Nina's Ice House in Jember Regency. The expected results of this service are increased knowledge ownership, sales, and visibility of Nina's Ice House products. This service supports business sustainability and provides insight for other MSME players in utilizing digital marketing to strengthen competitiveness.

Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture Saputra, Muhammad Andryan Wahyu; Novtahaning, Damar; Narandha Arya Ranggianto; Dwi Wijonarko
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5054

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.

MULTI-DOCUMENT SUMMARIZATION USING A COMBINATION OF FEATURES BASED ON CENTROID AND KEYWORD Ranggianto, Narandha Arya; Purwitasari, Diana; Fatichah, Chastine; Sholikah, Rizka Wakhidatus
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 21, No. 2, July 2023
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v21i2.a1195

Summarizing text in multi-documents requires choosing important sentences which are more complex than in one document because there is different information which results in contradictions and redundancy of information. The process of selecting important sentences can be done by scoring sentences that consider the main information. The combination of features is carried out for the process of scoring sentences so that sentences with high scores become candidates for summary. The centroid approach provides an advantage in obtaining key information. However, the centroid approach is still limited to information close to the center point. The addition of positional features provides increased information on the importance of a sentence, but positional features only focus on the main position. Therefore, researchers use the keyword feature as a research contribution that can provide additional information on important words in the form of N-grams in a document. In this study, the centroid, position, and keyword features were combined for a scoring process which can provide increased performance for multi-document news data and reviews. The test results show that the addition of keyword features produces the highest value for news data DUC2004 ROUGE-1 of 35.44, ROUGE-2 of 7.64, ROUGE-L of 37.02, and BERTScore of 84.22. While the Amazon review data was obtained with ROUGE-1 of 32.24, ROUGE-2 of 6.14, ROUGE-L of 34.77, and BERTScore of 85.75. The ROUGE and BERScore values outperform the other unsupervised models.

Perbandingan Performa Algoritma Random Tree, K-NN, dan A-NN untuk Deteksi Serangan DDoS pada Software Defined Network (SDN) Akbar Pandu Segara; Muhammad Andryan Wahyu Saputra; Narandha Arya Ranggianto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8387

Software-Defined Networks (SDNs) with a centralized architecture are vulnerable to Distributed Denial of Service (DDoS) attacks, which can cause widespread network service failures. This study aims to compare the performance of three Machine Learning algorithms—K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN), and Random Tree—in detecting DDoS attacks in an SDN environment. The DDoS-SDN dataset, consisting of 104,345 rows and 23 columns, was used with a data split of 70% for training and 30% for testing. Evaluation was conducted using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that ANN achieved the best performance with an accuracy of 96.85%, precision of 94.35%, recall of 97.79%, F1-score of 96.04%, and AUC of 0.994, followed by K-NN with an accuracy of 88.89% and Random Tree with the lowest accuracy of 86.49%. The superiority of ANN is attributed to its ability to capture complex non-linear patterns, perform automatic feature extraction, and adapt to the heterogeneity of data from the 22 features used. These findings indicate that ANN is the optimal choice for implementing a real-time DDoS attack detection system in an SDN environment, providing a strong foundation for the development of intelligent and adaptive Machine Learning-based network security systems

Abstractive and Extractive Approaches for Summarizing Multi-document Travel Reviews Ranggianto, Narandha Arya; Purwitasari, Diana; Fatichah, Chastine; Sholikah, Rizka Wakhidatus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5170

Travel reviews offer insights into users' experiences at places they have visited, including hotels, restaurants, and tourist attractions. Reviews are a type of multidocument, where one place has several reviews from different users. Automatic summarization can help users get the main information in multi-document. Automatic summarization consists of abstractive and extractive approaches. The abstractive approach has the advantage of producing coherent and concise sentences, while the extractive approach has the advantage of producing an informative summary. However, there are weaknesses in the abstractive approach, which results in inaccurate and less information. On the other hand, the extractive approach produces longer sentences compared to the abstractive approach. Based on the characteristics of both approaches, we combine abstractive and extractive methods to produce a more concise and informative summary than can be achieved using either approach alone. To assess the effectiveness of abstractive and extractive, we use ROUGE based on lexical overlaps and BERTScore based on contextual embeddings which it be compared with a partial approach (abstractive only or extractive only). The experimental results demonstrate that the combination of abstractive and extractive approaches, namely BERT-EXT, leads to improved performance. The ROUGE-1 (unigram), ROUGE-2 (bigram), ROUGE-L (longest subsequence), and BERTScore values are 29.48%, 5.76%, 33.59%, and 54.38%, respectively. Combining abstractive and extractive approach yields higher performance than the partial approach.

Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture Saputra, Muhammad Andryan Wahyu; Novtahaning, Damar; Narandha Arya Ranggianto; Dwi Wijonarko
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5054

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.

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