Garuda - Garba Rujukan Digital

Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging Abdullah, Moch. Zawaruddin; Fatichah, Chastine
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 4, No 2 (2018): July-December
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

News Feature Scoring (NeFS) merupakan metode pembobotan kalimat yang sering digunakan untuk melakukan pembobotan kalimat pada peringkasan dokumen berdasarkan fitur berita. Beberapa fitur berita diantaranya seperti word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), dan kemiripan kalimat terhadap judul. Metode NeFS mampu memilih kalimat penting dengan menghitung frekuensi kata dan mengukur similaritas kata antara kalimat dengan judul. Akan tetapi pembobotan dengan metode NeFS tidak cukup, karena metode tersebut mengabaikan kata informatif yang terkandung dalam kalimat. Kata-kata informatif yang terkandung pada kalimat dapat mengindikasikan bahwa kalimat tersebut penting. Penelitian ini bertujuan untuk melakukan pembobotan kalimat pada peringkasan multi-dokumen berita dengan pendekatan fitur berita dan informasi gramatikal (NeFGIS). Informasi gramatikal yang dibawa oleh part of speech tagging (POS Tagging) dapat menunjukkan adanya konten informatif. Pembobotan kalimat dengan pendekatan fitur berita dan informasi gramatikal diharapkan mampu memilih kalimat representatif secara lebih baik dan mampu meningkatkan kualitas hasil ringkasan. Pada penelitian ini terdapat 4 tahapan yang dilakukan antara lain seleksi berita, text preprocessing, sentence scoring, dan penyusunan ringkasan. Untuk mengukur hasil ringkasan menggunakan metode evaluasi Recall-Oriented Understudy for Gisting Evaluation (ROUGE) dengan empat varian fungsi yaitu ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4. Hasil ringkasan menggunakan metode yang diusulkan (NeFGIS) dibandingkan dengan hasil ringkasan menggunakan metode pembobotan dengan pendekatan fitur berita dan trending issue (NeFTIS). Metode NeFGIS memberikan hasil yang lebih baik dengan peningkatan nilai untuk fungsi recall pada ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4 secara berturut-turut adalah 20,37%, 33,33%, 1,85%, 23,14%.Â Â Â News Feature Scoring (NeFS) is a sentence weighting method that used to weight the sentences in document summarization based on news features. There are several news features including word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), and sentences resemblance to the title. The NeFS method is able to select important sentences by calculating the frequency of words and measuring the similarity of words between sentences and titles. However, NeFS weighting method is not enough, because the method ignores the informative word in the sentence. The informative words contained in the sentence can indicate that the sentence is important. This study aims to weight the sentence in news multi-document summarization with news feature and grammatical information approach (NeFGIS). Grammatical information carried by part of speech tagging (POS Tagging) can indicate the presence of informative content. Sentence weighting with news features and grammatical information approach is expected to be able to determine sentence representatives better and be able to improve the quality of the summary results. In this study, there are 4 stages that are carried out including news selection, text preprocessing, sentence scoring, and compilation of summaries. Recall-Oriented Understanding for Gisting Evaluation (ROUGE) is used to measure the summary results with four variants of function; ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4. Summary results using the proposed method (NeFGIS) are compared with summary results using sentence weighting methods with news feature and trending issue approach (NeFTIS). The NeFGIS method provides better results with increased value for recall functions in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 respectively 20.37%, 33.33%, 1.85%, 23.14%.Â

Improvisasi Teknik Oversampling MWMOTE Untuk Penanganan Data Tidak Seimbang Saputra, Pramana Yoga; Abdullah, Moch Zawaruddin; Kirana, Annisa Puspa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2811

Imbalance data is a condition which there is a distinction in the quantity of data that results withinside the majority class (classes with very many members) and minority class (classes with very few members). It can complicate the classification process since the machine learning algorithm method is designed to classify already balanced data. The oversampling process technique is used to resolve data imbalance by applying synthetic data to the minority class in such a manner that it has the same volume of data as the majority class. MWMOTE is an oversampling technique that generates synthetic data based on members of the minority class clusters that are close to the majority class. This approach is capable of generating synthetic data well. The resulting synthesis data remains in the nearby majority region and too dense on the border of the cluster. It is hence permitting the resulting synthetic data to go into the majority class classification. This study is objectives to improve the process of generating synthetic data on MWMOTE so that the resulting data is extensively dispensed withinside the minority class. The outcomes of the test show that the proposed method is capable of enhancing the classification performance for KNN and C4.5 Decision Tree classification sequentially by 0.46% and 0.96% compared to MWMOTE

Sistem Informasi PT Bintang Sidoraya Dengan Peramalan Penjualan Menggunakan Metode Statistical Parabolic Projection Amalia, Eka Larasati; Abdulullah, Moch. Zawaruddin; Attariq, Muhammad Daffa
Jurnal Buana Informatika Vol 12, No 2 (2021): Jurnal Buana Informatika Volume 12 - Nomor 2 - Oktober 2021
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v12i2.4649

Abstract. PT Bintang Sidoraya Information Systems with Sales Forecasting Using Statistical Parabolic Projection Method. The problem that often occurs in companies is the sales prediction in the future period based on data and information in the previous period. These predictions will affect the decisions taken by management for stock availability for the coming period. Due to the demand for goods shipping from around all major cities in Indonesia, sufficient stock availability is needed to minimize the possibility of losing customers. This research was conducted to build an information system application to record data and accompanied by forecasting features using the Statistical Parabolic Projection method. The result of this research is an information system that successfully predicts sales that can facilitate the stock availability calculation for the future period.Keywords: PT Bintang Sidoraya, information systems, Statistical Parabolic ProjectionÂ Abstrak. Permasalahan yang sering terjadi pada perusahaan ialah prediksi penjualan di periode yang akan datang berdasarkan data dan informasi pada periode sebelumnya. Prediksi tersebut akan berpengaruh terhadap keputusan yang diambil oleh manajemen untuk berapa persediaan stok periode yang akan datang. Karena permintaan pengiriman barang yang hampir mencakupi seluruh kota besar di Indonesia, diperlukan persediaan stok yang cukup untuk meminimalkan terjadinya potensi kehilangan pelanggan. Penelitian ini dilakukan untuk membangun aplikasi sistem informasi untuk melakukan perekapan data dan disertai fitur peramalan menggunakan metode Statistical Parabolic Projection. Hasil dari penelitian ini ialah sebuah sistem informasi yang berhasil melakukan prediksi penjualan yang dapat mempermudah penentuan jumlah stok pada periode mendatang.Kata kunci: PT Bintang Sidoraya, information systems, Statistical Parabolic Projection

Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging Abdullah, Moch. Zawaruddin; Fatichah, Chastine
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 4, No 2 (2018): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v4i2.1251

News Feature Scoring (NeFS) merupakan metode pembobotan kalimat yang sering digunakan untuk melakukan pembobotan kalimat pada peringkasan dokumen berdasarkan fitur berita. Beberapa fitur berita diantaranya seperti word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), dan kemiripan kalimat terhadap judul. Metode NeFS mampu memilih kalimat penting dengan menghitung frekuensi kata dan mengukur similaritas kata antara kalimat dengan judul. Akan tetapi pembobotan dengan metode NeFS tidak cukup, karena metode tersebut mengabaikan kata informatif yang terkandung dalam kalimat. Kata-kata informatif yang terkandung pada kalimat dapat mengindikasikan bahwa kalimat tersebut penting. Penelitian ini bertujuan untuk melakukan pembobotan kalimat pada peringkasan multi-dokumen berita dengan pendekatan fitur berita dan informasi gramatikal (NeFGIS). Informasi gramatikal yang dibawa oleh part of speech tagging (POS Tagging) dapat menunjukkan adanya konten informatif. Pembobotan kalimat dengan pendekatan fitur berita dan informasi gramatikal diharapkan mampu memilih kalimat representatif secara lebih baik dan mampu meningkatkan kualitas hasil ringkasan. Pada penelitian ini terdapat 4 tahapan yang dilakukan antara lain seleksi berita, text preprocessing, sentence scoring, dan penyusunan ringkasan. Untuk mengukur hasil ringkasan menggunakan metode evaluasi Recall-Oriented Understudy for Gisting Evaluation (ROUGE) dengan empat varian fungsi yaitu ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4. Hasil ringkasan menggunakan metode yang diusulkan (NeFGIS) dibandingkan dengan hasil ringkasan menggunakan metode pembobotan dengan pendekatan fitur berita dan trending issue (NeFTIS). Metode NeFGIS memberikan hasil yang lebih baik dengan peningkatan nilai untuk fungsi recall pada ROUGE-1, ROUGE-2, ROUGE-L, dan ROUGE-SU4 secara berturut-turut adalah 20,37%, 33,33%, 1,85%, 23,14%.Â Â Â News Feature Scoring (NeFS) is a sentence weighting method that used to weight the sentences in document summarization based on news features. There are several news features including word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), and sentences resemblance to the title. The NeFS method is able to select important sentences by calculating the frequency of words and measuring the similarity of words between sentences and titles. However, NeFS weighting method is not enough, because the method ignores the informative word in the sentence. The informative words contained in the sentence can indicate that the sentence is important. This study aims to weight the sentence in news multi-document summarization with news feature and grammatical information approach (NeFGIS). Grammatical information carried by part of speech tagging (POS Tagging) can indicate the presence of informative content. Sentence weighting with news features and grammatical information approach is expected to be able to determine sentence representatives better and be able to improve the quality of the summary results. In this study, there are 4 stages that are carried out including news selection, text preprocessing, sentence scoring, and compilation of summaries. Recall-Oriented Understanding for Gisting Evaluation (ROUGE) is used to measure the summary results with four variants of function; ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4. Summary results using the proposed method (NeFGIS) are compared with summary results using sentence weighting methods with news feature and trending issue approach (NeFTIS). The NeFGIS method provides better results with increased value for recall functions in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 respectively 20.37%, 33.33%, 1.85%, 23.14%.Â

Sentence Extraction Based on Sentence Distribution and Part of Speech Tagging for Multi-Document Summarization Agus Zainal Arifin; Moch Zawaruddin Abdullah; Ahmad Wahyu Rosyadi; Desepta Isna Ulumi; Aminul Wahib; Rizka Wakhidatus Sholikah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8431

Automatic multi-document summarization needs to find representative sentences not only by sentence distribution to select the most important sentence but also by how informative a term is in a sentence. Sentence distribution is suitable for obtaining important sentences by determining frequent and well-spread words in the corpus but ignores the grammatical information that indicates instructive content. The presence or absence of informative content in a sentence can be indicated by grammatical information which is carried by part of speech (POS) labels. In this paper, we propose a new sentence weighting method by incorporating sentence distribution and POS tagging for multi-document summarization. Similarity-based Histogram Clustering (SHC) is used to cluster sentences in the data set. Cluster ordering is based on cluster importance to determine the important clusters. Sentence extraction based on sentence distribution and POS tagging is introduced to extract the representative sentences from the ordered clusters. The results of the experiment on the Document Understanding Conferences (DUC) 2004 are compared with those of the Sentence Distribution Method. Our proposed method achieved better results with an increasing rate of 5.41% on ROUGE-1 and 0.62% on ROUGE-2.

AUTOMATIC DETERMINATION OF SEEDS FOR RANDOM WALKER BY SEEDED WATERSHED TRANSFORM FOR TUNA IMAGE SEGMENTATION Moch Zawaruddin Abdullah; Dinial Utami Nurul Qomariah; Lafnidita Farosanti; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Tuna fish image classification is an important part to sort out the type and quality of the tuna based upon the shape. The image of tuna should have good segmentation results before entering the classification stage. It has uneven lighting and complex texture resulting in inappropriate segmentation. This research proposed method of automatic determination seeded random walker in the watershed region for tuna image segmentation. Random walker is a noise-resistant segmentation method that requires two types of seeds defined by the user, the seed pixels for background and seed pixels for the object. We evaluated the proposed method on 30 images of tuna using relative foreground area error (RAE), misclassification error (ME), and modified Hausdroff distances (MHD) evaluation methods with values of 4.38%, 1.34% and 1.11%, respectively. This suggests that the seeded random walker method is more effective than exiting methods for tuna image segmentation.

Rancang Bangun Sistem Informasi Akuntansi Berbasis Website menggunakan Framework Laravel Moch Zawaruddin Abdullah; Mungki Astiningrum; Yuri Ariyanto; Dwi Puspitasari; Atiqah Nurul Asri
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 18, No 1 (2020): Desember 2020
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v18i1.11313

Perjalanan UKM di Indonesia, tak terkecuali kelompok UKM Ron Tuwuh dan Prohandji tidak semudah yang dibayangkan. Salah satu kendala yang dihadapi adalah pengelolaan keuangan untuk bisnis. Pada setiap bisnis yang dilakukan memerlukan menejemen keuangan agar roda kehidupan bisnis dapat berjalan dengan baik. Fakta dilapangan, pegiat UKM masih mempunyai kesulitan dalam proses pencatatan keuangan baik dari pemasukan dan pengeluaran. Sebagian besar UKM tidak memperhatikan transaksi keluar-masuk keuangan dan tanpa perhitungan atau pencatatan yang jelas, sehingga menimbulkan ketidakstbilan keuangan yang ada pada usaha. Dari kendala keuangan yang terjadi pada pelaku usaha UKM dan minimnya pengetahuan akan keuangan. Maka penelitian ini bertujuan untuk merancang sistem informasi akuntansi berbasis website untuk UKM khususnya pada kelompok UKM Ron Tuwuh dan Prohandji. Sistem informasi ini dikembangkan menggunakan framework Laravel untuk memenuhi kebutuhan standar pengelolaan informasi keuangan sehingga pencatatan keuangan tersistem dengan baik dan meminimalisir resiko tak terkontrolnya data keuangan.

Sistem Informasi PT Bintang Sidoraya Dengan Peramalan Penjualan Menggunakan Metode Statistical Parabolic Projection Eka Larasati Amalia; Moch. Zawaruddin Abdulullah; Muhammad Daffa Attariq
Jurnal Buana Informatika Vol. 12 No. 2 (2021): Jurnal Buana Informatika Volume 12 - Nomor 2 - Oktober 2021
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v12i2.4649

Abstract. PT Bintang Sidoraya Information Systems with Sales Forecasting Using Statistical Parabolic Projection Method. The problem that often occurs in companies is the sales prediction in the future period based on data and information in the previous period. These predictions will affect the decisions taken by management for stock availability for the coming period. Due to the demand for goods shipping from around all major cities in Indonesia, sufficient stock availability is needed to minimize the possibility of losing customers. This research was conducted to build an information system application to record data and accompanied by forecasting features using the Statistical Parabolic Projection method. The result of this research is an information system that successfully predicts sales that can facilitate the stock availability calculation for the future period.Keywords: PT Bintang Sidoraya, information systems, Statistical Parabolic Projection Abstrak. Permasalahan yang sering terjadi pada perusahaan ialah prediksi penjualan di periode yang akan datang berdasarkan data dan informasi pada periode sebelumnya. Prediksi tersebut akan berpengaruh terhadap keputusan yang diambil oleh manajemen untuk berapa persediaan stok periode yang akan datang. Karena permintaan pengiriman barang yang hampir mencakupi seluruh kota besar di Indonesia, diperlukan persediaan stok yang cukup untuk meminimalkan terjadinya potensi kehilangan pelanggan. Penelitian ini dilakukan untuk membangun aplikasi sistem informasi untuk melakukan perekapan data dan disertai fitur peramalan menggunakan metode Statistical Parabolic Projection. Hasil dari penelitian ini ialah sebuah sistem informasi yang berhasil melakukan prediksi penjualan yang dapat mempermudah penentuan jumlah stok pada periode mendatang.Kata kunci: PT Bintang Sidoraya, information systems, Statistical Parabolic Projection

Feature-based POS tagging and sentence relevance for news multi-document summarization in Bahasa Indonesia Moch Zawaruddin Abdullah; Chastine Fatichah
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3275

Sentence extraction in news document summarization determines representative sentences primarily by employing the news feature known as news feature score (NeFS). NeFS can achieve meaningful sentences by analyzing the frequency and similarity of phrases while neglecting grammatical information and sentence relevance to the title. The presence of instructive content is indicated by grammatical information carried by part of speech (POS). POS tagging is the process of giving a meaningful tag to each term based on qualified data and even surrounding words. Sentence relevance to the title is intended to determine the sentence's level of connectivity to the title in terms of both word-based and meaning-based similarity, primarily for news documents in Bahasa Indonesia. In this study, we present an alternative sentence weighting method by incorporating news features, POS tagging, and sentence relevance to the title. Sentence extraction based on news features, POS tagging, and sentence relevance is introduced to extract the representative sentences. The experiment results on the 11 groups of Indonesian news documents are compared with the news features scores with the grammatical information approach method (NeFGIS). The proposed method achieved better results. The increasing f-score rate of ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 sequentially are 1.84%, 3.03%, 3.85%, 2.08%.

Perangkat Desa Melek Digital dan Kreatif: Pelatihan Pengembangan Konten Digital Desa Ngijo Kabupaten Malang Cahya Rahmad; Arwin Datumaya Wahyudi Sumari; Annisa Puspa Kirana; Moch. Zawaruddin Abdullah; Septian Enggar Sukmana
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 4, No 1 (2021): Januari 2021
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v4i1.140

Potensi pariwisata, kekayaan sumber daya pertanian, dan lokasi strategis yang dimiliki oleh Desa Ngijo Kecamatan Karangploso Kabupaten Malang perlu mendapatkan perhatian pada perubahan informasi terkini di desa tersebut. Desa Ngijo telah memiliki website sebagai media penyampaian informasi, namun kendala yang muncul adalah pengembangan konten di dalam webiste tersebut, para perangkat desa yang bekerja di desa tersebut masih merasakan kesulitan untuk mengembangkan konten yang tepat. Pelatihan yang dilaksanakan ini bertujuan untuk mengatasi permasalahan tersebut. Perangkat yang dipakai pada pelatihan ini adalah Canva sehingga memudahkan perangkat desa karena dapat dipakai langsung di smartphone setiap perangkat desa. Pelatihan ini dilaksanakan dengan protokol kesehatan dan mendapatkan hasil 90% peserta pelatihan ini menyatakan pelatihan ini sangat bermanfaat.

Title

Found 28 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 28 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 28 Documents
Search