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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Sinus Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Dulang: Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics
Anggiratih, Endang
Master Program Of Computer Science; FMIPA UGM, Yogyakarta
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Ship Identification on Satellite Image Using Convolutional Neural Network and Random Forest Endang Anggiratih; Agfianto Eko Putra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.37461

Abstract

Ship identification on satellite imagery can be used for fisheries management, monitoring of smuggling activities, ship traffic services, and naval warfare. However, high-resolution satellite imagery also makes the segmentation of the ship difficult in the background, so that to handle it requires reliable features so that it can be identified adequately between large vessels, small vessels and not ships. The Convolutional Neural Network (CNN) method, which has the advantage of being able to extract features automatically and produce reliable features that facilitate ship identification. This study combines CNN ZFNet architecture with the Random Forest method. The training was conducted with the aim of knowing the accuracy of the ZFNet layers to produce the best features, which are characterized by high accuracy, combined with the Random Forest method. Testing the combination of this method is done with two parameters, namely batch size and a number of trees. The test results identify large vessels with an accuracy of 87.5% and small vessels with an accuracy of not up to 50%.
Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning Endang Anggiratih; Sri Siswanti; Saly Kurnia Octaviani; Arum Sari
Jurnal Ilmiah SINUS Vol 19, No 1 (2021): Vol. 19 No. 1, Januari 2021
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v19i1.526

Abstract

The level of rice productivity is influenced by several inhibiting factors, for example disease attack in rice plants. The slow and inappropriate treatment of rice plant can make the crop failure so that rice production and farmers' income decrease. The symptoms of rice disease are difficult to distinguish, especially in severe symptoms. Collaboration with other fields, especially computer science, is needed to classify diseases automatically so that the farmers can take action for plant treatment and the spread of disease can be controlled quickly. The classification of diseases based on images requires the best features/characteristics so that the disease can be classified. In this research, Deep Learning method, especially Convolutional Neural Network with EfficientNet B3 architecture, can extract features very well. In this research, the classification of brown spot and bacterial leaf disease by applying EfficientNet B3 with transfer learning reached 79.53% accuracy and 0.012 loss/error.
Network Slicing Using FlowVisor for Enforcement of Bandwidth Isolation in SDN Virtual Networks Vivi Monita; Wisnu Wendanto; Endang Anggiratih
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26533

Abstract

Software-defined networking (SDN) is becoming increasingly popular because of features such as programming control, embedded monitoring, fine-grained control, flexibility, support for many tenants, and scalability. Problems with the prior design, known as the conventional network, include the need to configure each network device individually, decentralized control, and a persistent issue with tenant enforcement for multitenant support. Tenants are unable to administer their networks without disturbing their neighbours. In this research, network slicing on SDN will ensure tenant isolation using FlowVisor and an SDN controller. Flowspace, which is part of FlowVisor capable of implementing network isolation, is for isolation in this research. Multitenancy is supported in SDN via the network slicing technique. Two types of renters were employed, and two testing procedures connectivity and functionality were run to meet the research objectives. This research produced several findings, including that all hosts were correctly linked, and the connection was achieved without turning on FlowVisor. The host function can only send and receive data from hosts with the same tenant. The research results show that FlowVisor can be applied for isolation enforcement. As a result of each tenant utilising their slice of the network without being interrupted by other slices, this research finds that utilising FlowVisor to construct Flowspace can segment the network to allow multitenancy. Expanding the number of slices for more study and testing in a real-world setting is possible.
LOOK ALIKE-SOUND ALIKE PREDICTION AS A TOOL FOR PATIENT SAFETY anggiratih, endang
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 19, No 1 (2025): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.103210

Abstract

Report from the WHO that one of the highest causes of medication errors is Look Alike – Sound Alike (LASA) drugs, leading to errors in receiving information about the drugs, which of course will affect patient safety. Efforts to reduce medication errors have been widely implemented, such as conducting medication training, managing medications, and storing and labeling medications. However, all of that leads to human error, so the utilization of technology is needed to address this issue. The technology expected to help reduce medication errors is the utilization of artificial intelligence (AI). AI is designed for automation processes and systems that can learn independently, allowing the causes of medication errors such as LASA to be learned by the system and predicted automatically. Deep learning is a part of AI that works by providing solutions accurately and automatically. The Recurrent Neural Networks (RNN) algorithm is one of the deep learning methods that has been proven accurate in predictions based on previous research studies. In this study, LASA predictions were made using RNN with the aim of serving as an aid to reduce medication errors, thereby ensuring patient safety. The accuracy achieved is 99% for training and 81% for testing.
Digitalization of double entry bookkeeping and marketing of Wonogiri Cashew Farmers Group towards globalization Suwasono, Heru; Yulianto, Bagas Dwi; Anggiratih, Endang
Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Vol. 10 No. 1 (2025): February 2025
Publisher : University of Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/abdimas.v10i1.14622

Abstract

This service focuses on digitizing bookkeeping and marketing in the cashew industry. Despite existing for over 20 years and has not developed. The team aims to address this issue by providing bookkeeping training for businesses, making their transactions more visible and allowing for a better understanding of their financial results. Currently, business revenue is often mixed with personal funds, leading to the perception of small profits. The team offers marketing training to help businesses stay up to date with industry trends. These training sessions are conducted to improve the industry's overall knowledge and skills. The outcomes of the service include the creation of a web platform for industry players, a discussion group on WhatsApp, and the introduction of bookkeeping through physical books or Excel. These tools are crucial for expanding product reach and enabling better financial management, helping businesses plan for growth and make informed decisions regarding loans and other financial matters.
Implementation of IndoBERT for Sentiment Analysis of the Constitutional Court's Decision Regarding the Minimum Age of Vice Presidential Candidates Setiawan, Very Dwi; Iswavigra, Dwi Utari; Anggiratih, Endang
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.26320

Abstract

Purpose: This study aims to analyze the effectiveness of the IndoBERT model for sentiment analysis of Indonesian anguage YouTube comments related to the legal Court’s ruling on the minimum age of vice presidential candidates for 2024. While previous research applied conventional machine learning methods, this study addresses the challenge of understanding nuanced public opinion using a language-specific transformer model. Methods: A dataset of 23,796 YouTube comments was collected using the YouTube Data API in January 2025. The comments underwent extensive preprocessing including normalization, case folding, text cleansing, symbol removal, stopword elimination, and stemming. Sentiment labels (positive, negative, neutral) were assigned through a lexicon based approach. Three models IndoBERT, BERT, Support Vector Machine (SVM), and Random Forest were trained and tested using an 80% and 20% split. Model result was evaluated with accuracy, precision, recall, and F1-score metrics. Result: IndoBERT achieved the maximum result with 95% accuracy, outperforming BERT 92%, SVM 88%, and Random Forest 85%. This confirms IndoBERT’s superior ability to capture contextual nuances in Indonesian sentiment analysis compared to other models. Novelty: This research demonstrates the advantage of transformer based models, particularly IndoBERT, in analyzing complex Indonesian social media texts. The findings support the use of IndoBERT for automated sentiment monitoring to inform government and media responses. Future work could extend to broader discourse analysis across diverse public sectors.
EDUKASI PENGENALAN TEKNOLOGI INFORMASI DAN KOMPUTER PADA SISWA SDN 1 TRAYU BOYOLALI Praningki, Tutus; Yulianto, Bagas Dwi; Anggiratih, Endang
Jurnal Pengabdian Masyarakat Polmanbabel Vol. 5 No. 02 (2025): DULANG : Jurnal Pengabdian Kepada Masyarakat
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33504/dulang.v5i02.617

Abstract

The increasingly significant role of Information Technology (IT) is expected to have a positive impact on various aspects of life. Therefore, it is essential for young children to learn IT effectively. However, the lack of opportunities for children to develop their potential in using computer applications poses a barrier to enhancing their technological skills. As a concrete step, this community service program focuses on IT education for students at SD Negeri Trayu 1 Banyudono. The aim of this program is to improve students' knowledge and skills in learning computer software, particularly Microsoft Word and Microsoft PowerPoint, as well as to introduce them to computer hardware, thereby equipping them with the necessary tools to effectively utilize technology in the future.
LITERASI DIGITAL: MEMBANGUN KARAKTER ANAK DI ERA DIGITAL DI BA AISYIYAH DUWET KECAMATAN BAKI KABUPATEN SUKOHARJO Dwi Setiawan, Very; Utari Iswavigra, Dwi; Anggiratih, Endang; Mar’atullatifah, Yulaikha; Mursalim, Mursalim; Rahmasari, Yunita
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 8, No 8 (2025): MARTABE : JURNAL PENGABDIAN KEPADA MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v8i8.%p

Abstract

Era digital menempatkan anak-anak dalam lingkungan teknologi yang intensif, sehingga literasi digital menjadi fondasi penting untuk pembentukan karakter yang bijak dan bertanggung jawab. Penelitian Pengabdian kepada Masyarakat (PkM) di BA Aisyiyah Duwet, Sukoharjo, bertujuan mengatasi kesenjangan pemahaman literasi digital di kalangan guru dan orang tua serta dampaknya pada karakter anak. Metode deskriptif kualitatif dengan observasi, wawancara, dokumentasi, dan pelatihan tatap muka melibatkan 40 peserta. Program mencakup sosialisasi, pelatihan intensif, pendampingan, dan evaluasi pre-test dan post-test. Hasil menunjukkan peningkatan signifikan pada semua aspek literasi digital peserta, seperti pemahaman konsep literasi digital naik dari 45% ke 85%, kemampuan media sosial produktif dari 50% ke 80%, dan keterampilan desain konten dari 30% ke 75%. Peningkatan ini mencerminkan keberhasilan program dalam membangun etika digital dan penggunaan teknologi yang sehat. PkM ini berhasil mengubah pola pikir peserta menjadi lebih positif terhadap teknologi sebagai alat pendidikan karakter.
Network Slicing Using FlowVisor for Enforcement of Bandwidth Isolation in SDN Virtual Networks Monita, Vivi; Wendanto, Wisnu; Anggiratih, Endang
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26533

Abstract

Software-defined networking (SDN) is becoming increasingly popular because of features such as programming control, embedded monitoring, fine-grained control, flexibility, support for many tenants, and scalability. Problems with the prior design, known as the conventional network, include the need to configure each network device individually, decentralized control, and a persistent issue with tenant enforcement for multitenant support. Tenants are unable to administer their networks without disturbing their neighbours. In this research, network slicing on SDN will ensure tenant isolation using FlowVisor and an SDN controller. Flowspace, which is part of FlowVisor capable of implementing network isolation, is for isolation in this research. Multitenancy is supported in SDN via the network slicing technique. Two types of renters were employed, and two testing procedures connectivity and functionality were run to meet the research objectives. This research produced several findings, including that all hosts were correctly linked, and the connection was achieved without turning on FlowVisor. The host function can only send and receive data from hosts with the same tenant. The research results show that FlowVisor can be applied for isolation enforcement. As a result of each tenant utilising their slice of the network without being interrupted by other slices, this research finds that utilising FlowVisor to construct Flowspace can segment the network to allow multitenancy. Expanding the number of slices for more study and testing in a real-world setting is possible.
Co-Authors Agfianto Eko Putra Arum Sari Bagas Dwi Yulianto Heru Suwasono Iswavigra, Dwi Utari Mar’atullatifah, Yulaikha Mursalim Mursalim Rahmasari, Yunita Saly Kurnia Octaviani setiawan, very dwi Sri Siswanti Utari Iswavigra, Dwi Vivi Monita Wendanto, Wisnu Wisnu Wendanto