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All Journal Jurnal Ilmu Komputer dan Informasi Proceeding of the Electrical Engineering Computer Science and Informatics ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Repositor Journal of Integrated Agribusiness Literatus JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Nusantara Journal of Computers and its Applications The Indonesian Journal of Computer Science
Syadza Anggraini
Universitas Muhammadiyah Sampit
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS Khairiyyah Nur Aisyah; Syadza Anggraini; Novi Nur Putriwijaya; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.612 KB) | DOI: 10.21609/jiki.v12i2.757

Abstract

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.
Improvement of Cluster Importance Algorithm with Sentence Position for News Summarization Nur Hayatin; Gita Indah Marthasari; Syadza Anggraini
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (704.002 KB) | DOI: 10.11591/eecsi.v5.1624

Abstract

Text summarization is one of the ways to reduce large document dimension to obtain important information from the document. News is one of information which usually has several sub-topics from a topic. In order to get the main information from a topic as fast as possible, multi-document summarization is the solution, but sometimes it can create redundancy. In this study, we used cluster importance algorithm by considering sentence position to overcome the redundancy. Stages of cluster importance algorithm are sentence clustering, cluster ordering, and selection of sentence representative which will be explained in the subsections below. The contribution of this research was to add the position of sentence in the selection phase of representative sentence. For evaluation, we used 30 topics of Indonesian news tested by using ROUGE-1, there were 2 news topics that had different ROUGE-1 score between using cluster importance algorithm by considering sentence position and using cluster importance. However, those 2 news topics which used cluster importance by considering sentence position have a greater score of Rouge-1 than the one which only used cluster importance. The use of sentence position had an effect on the order of sentence on each topic, but there were only 2 news topics that affected the outcome of the summary.
Peringkasan Multi Dokumen Berita Dengan Pemilihan Kalimat Utama Berbasis Algoritma Cluster Importance Dengan Mempertimbangkan Posisi Kalimat Syadza Anggraini; Nur Hayatin; Gita Indah Marthasari
Jurnal Repositor Vol 2 No 1 (2020): Januari 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v2i1.161

Abstract

Peringkasan teks merupakan salah satu cara untuk mengurangi suatu dimensi dokumen yang besar untuk mendapatkan informasi penting dari dokumen tersebut. Berita adalah salah satu informasi yang biasanya dalam satu topik memiliki beberapa sub topik. Untuk dapat mengambil informasi penting dari satu topik secara cepat, peringkasan multi dokumen berita dapat menjadi solusi. Namun, peringkasan multi dokumen dapat menimbulkan redundansi. Oleh sebab itu, penelitian ini menerapkan algoritma cluster importance dengan mempertimbangkan posisi kalimat untuk mengatasi redundansi tersebut. Penelitian ini menggunakan 30 topik berita berbahasa Indonesia, dimana tiap topiknya terdiri dari 5 sub topik berita. Dari 30 topik berita yang diuji menggunakan Rouge-1, dimana terdapat 2 topik berita yang memiliki nilai Rouge-1 berbeda antara yang menggunakan algoritma cluster importance ditambah posisi kalimat dengan yang hanya menggunakan algoritma cluster. Namun dari 2 topik berita tersebut, nilai Rouge-1 yang menggunakan cluster importance ditambah posisi kalimat memiliki nilai yang lebih besar daripada yang hanya menggunakan cluster importance. Penggunaan posisi kalimat memiliki pengaruh terhadap urutan bobot kalimat pada setiap topiknya, namun hanya 2 topik berita yang berpengaruh terhadap hasil ringkasan.
Pengukuran Kemiripan berbasis Leksikal dan Semantik untuk Perangkingan Dokumen Berbahasa Arab Syadza Anggraini; Diana Purwitasari; Agus Zainal Arifin
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 4 No 2 (2022): Volume 4, Nomor 2, Agustus 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v4i2.495

Abstract

Hasil pencarian relevan pada sistem temu kembali informasi tergantung pengukuran kemiripan antara query dan dokumen berdasarkan bobot kata query terhadap dokumen yang akan dirangking. Namun, perhitungan kemiripan menggunakan bobot kata dimungkinkan adanya lafal kata yang berbeda tetapi memiliki makna sama. Hasil dokumen pencarian teks berbahasa Arab akan dipengaruhi kemampuan pengguna yang beragam dalam memahami bahasa tersebut. Oleh karena itu diusulkan pengukuran kemiripan secara leksikal untuk mengatasi lafal kata yang beda serta juga menggunakan kemiripan secara semantik untuk mengenali kata dengan makna sama. Penggabungan perhitungan kemiripan leksikal dan semantik dilakukan berdasarkan bobot kata (secara leksikal) yang digabungkan dengan word embedding (secara semantik). Hasil dari uji coba dilakukan pada 2900 kitab berbahasa Arab Maktabah Syamilah menunjukkan keunggulan dengan rata-rata f-measure tertinggi dibandingkan metode lainnya yaitu 66.7% pada keseluruhan query, serta 65.2% dan 69% pada short query dan long query. Short query adalah frekuensi jumlah kata di dalam query yang berjumlah 1-2 kata sedangkan long query adalah frekuensi jumlah kata di dalam query yang berjumlah lebih dari 2 kata. Short query dan long query berpeluang me-retrieve dokumen yang tidak relevan. Hasil retrieve dokumen yang tidak relevan disebabkan karena rendahnya kemiripan antar kata di dalam suatu query akibat pemilihan kata yang kurang tepat. Pemilihan kata-kata query membutuhkan penguasaan pengguna yang tidak hanya mampu mengolah query dalam bahasa Arab, tetapi juga dapat memahami konteks dokumen yang akan dicari.
PERINGKASAN TEKS MULTI-DOKUMEN BERDASARKAN METODE SENTENCE EXTRACTION DAN WORD SENSE DISAMBIGUATION Khairiyyah Nur Aisyah; Syadza Anggraini; Agus Zainal Arifin
NJCA (Nusantara Journal of Computers and Its Applications) Vol 4, No 1 (2019): Juni 2019
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v4i1.89

Abstract

Memahami makna utama yang terkandung dalam beberapa dokumen tentu tidak mudah dan membutuhkan waktu yang cukup lama. Menanggapi masalah tersebut, penelitian terkait peringkasan teks dokumen secara otomatis menjadi perhatian khusus dalam beberapa tahun terakhir. Penelitian ini mengusulkan metode peringkasan teks multi-dokumen yang dapat meningkatkan relevansi antar kalimat dengan menggunakan metode sentence extraction  dan word sense disambiguation. Metode sentence extraction yang digunakan didasarkan pada sentence distribution dan part of speech (POS) tagging. Berdasarkan pengujian peringkasan teks dengan metode yang diusulkan, nilai rata-rata ROUGE-1 adalah 0,712, 0,163, 0,247 pada recall, precision,  f-measure secara berurutan. Sedangkan hasil pengujian peringksan teks multi-dokumen tanpa menggunakan word sense disambiguation mendapatkan nilai rata-rata ROUGE-1 sebesar 0,685, 0,139, 0,216 pada recall, precision, f-measure secara berurutan. Hasil penelitian menunjukkan bahwa penggunaan metode sentence extraction dan word sense disambiguation pada peringkasan teks multi-dokumen dapat meningkatkan kualitas hasil peringkasan teks.
Kecerdasan Buatan dalam Aspek Deforestasi dan Keberlanjutan Perkebunan: Pendekatan Bibliometrik Sutriani, Linda; Impron, Ali; Saragih, Veny Betsy; Anggraini, Syadza; Suraji, Suraji
LITERATUS Vol 6 No 2 (2024): Jurnal Ilmiah Internasional Sosial Budaya
Publisher : Neolectura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37010/lit.v6i2.1583

Abstract

This study examines the application of Artificial Intelligence (AI) in addressing deforestation and promoting sustainability in plantations using a bibliometric approach. Deforestation, a critical global issue, results from agricultural expansion, plantation development, and land-use changes, leading to significant environmental degradation. AI has been proposed as a powerful tool to monitor and manage deforestation more effectively, offering solutions such as satellite imagery analysis and predictive models. Through a bibliometric analysis spanning the last decade (2013–2023), this study uses VOSviewer to visualize co-citation networks, identifying key research trends and clusters related to AI in deforestation and plantation sustainability. The findings reveal that research is concentrated in regions like Indonesia and Brazil, where AI technologies like machine learning are employed to predict deforestation and enhance resource management. Emerging research areas include the integration of AI with the Internet of Things (IoT) and blockchain for improved data management and sustainability practices. This analysis provides insights into the growing role of AI in mitigating deforestation and offers recommendations for future research, including addressing ethical challenges and regulatory frameworks to further enhance sustainable plantation management.
Sertifikasi Indikasi Geografis Kopi: Pendekatan Studi Bibliometrik Saragih, Veny Betsy; Barus, Riantri; Yanti, Chicka Willy; Anggraini, Syadza
Journal of Integrated Agribusiness Vol 6 No 2 (2024): Journal of Integrated Agribusiness
Publisher : Jurusan Agribisnis, Fakultas Pertanian, Perikanan dan Kelautan Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jia.v6i2.5589

Abstract

Geographical indication certification of coffee is growing due to the demand for "specialty" coffee based on geographic origin. According to coffee lovers, the taste of coffee varies, depending on where the coffee is produced. As demand for geographic indication certified coffee increased, the research that related to this topic also developed. To find out the extent of research that has been carried out regarding the topic, this bibliometic research was conducted. The purpose of this research is to map research on geographical indication certification of coffee based on keywords and research titles so that gaps and novelties related to the topic are obtained. The type of research used is a quantitative descriptive method. The analysis used is bibliometric analysis with the Vosviewers software tool. Based on the analysis results, it was obtained from the network visualization mapping that there were 6 clusters originating from 3032 terms with 50 keywords that appeared at least 10 times. In research on geographical indication certification, the 3 most keywords that appeared were coffee, Indonesia, geographical indications. From the mapping visualization results, it is known that the latest research topics studied are signs, Arabica coffee, coffee farmers, factors, indicators, Indonesian coffee, Robusta coffee, quality, and West Java. The average publication related to these items was published in 2020-2021. From the results of the visualization mapping, the density of research topics related to coffee items in cluster 1, Indonesian items in cluster 4, and geographical indication items in cluster 2 have been widely researched, while topics in clusters 3, 5 and 6 have not yet been widely researched. These items are signs, coffee farmers, indicators, value, quality, coffee farmers, Robusta coffee, quality, and West Java.
Analisis Sentimen Masyarakat Kalimantan Tengah Terhadap Perkebunan Kelapa Sawit Menggunakan TF-IDF dan Support Vector Machine Kurniawan Tri Putra; Syadza Anggraini; Linda Sutriani; Suraji Suraji; Ali Impron
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 5 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss5pp1115-1123

Abstract

The transformation towards Society 5.0 has had a significant impact on the rapid growth of data available worldwide, both useful and less directly beneficial, known as big data. This phenomenon provides opportunities for researchers to leverage big data as a valuable source of information, provided it is processed and analyzed using appropriate methods. One of the rapidly growing applications is sentiment analysis, which extracts insights from text data, such as that gathered from social media platforms. This study applies the TF-IDF feature extraction technique and the SVM (Support Vector Machine) classification method to perform sentiment analysis on Twitter text data. The results of the research show that the model built using the combination of TF-IDF and SVM achieved an accuracy of 86%, with precision, recall, and F1-Score values of 85% each. These findings indicate that the application of TF-IDF with SVM provides optimal performance in sentiment analysis, considering the word frequency within documents, and makes a significant contribution to processing big data for more accurate and effective sentiment analysis
Penerapan Machine Learning Pada Kelapa Sawit: Analisis Bibliometrik Anggraini, Syadza; Saragih, Veny Betsy; Sutriani, Linda
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4224

Abstract

The advancement of machine learning-based technology spread widely, especially in oil palm. Oil palm has become a main source of domestic products because of its high production and a leading commodity where it cannot be separated from the use of machine learning. However, the potential of machine learning has not yet been identified specifically through bibliography aspects where those aspects are needed for future research. The main objective of this research is to analyze trends of machine learning utilization and potential topics in oil palm by using bibliometric analysis to obtain year distribution, author productivity, citation, and keyword co-occurrence. As a result, the highest peak number of publications is 2023 where the most cited authors are Haohuan Fu and Weijia Li. Then, the most used algorithms are deep learning, ANN, SVM, RF and CNN based on the occurrences while the tree detection and counting topic has the highest citation articles. The result indicates that scientific interest in the study of this research benefits as a starting point for future works.
Co-Authors Agus Zainal Arifin Chicka Willy Yanti Diana Purwitasari Dini Adni Navastara, Dini Adni Gita Indah Marthasari Impron, Ali Khairiyyah Nur Aisyah Khairiyyah Nur Aisyah, Khairiyyah Nur Kurniawan Tri Putra Linda Sutriani Novi Nur Putriwijaya Nur Hayatin Rarasmaya Indraswari Riantri Barus, Riantri Suraji Suraji suraji suraji Veny Betsy Saragih