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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) SEIKO : Journal of Management & Business Jiko (Jurnal Informatika dan komputer) Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Ilmiah Edunomika (JIE) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimas Galuh : Jurnal Pengabdian Kepada Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Brilliance: Research of Artificial Intelligence Jurnal Inovasi Masyarakat Ranah Research : Journal of Multidisciplinary Research and Development

Yan Puspitarani

Universitas Widyatama

Author-ID : 7085551

Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Nursing Public Health Social Sciences Transportation Other

Published : 21 Documents Claim Missing Document

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Indonesian Information Extraction : Challenges and Opportunities Yan Puspitarani
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 8 No 1 (2021): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v8i1.710

Information extraction is part of natural language processing, aiming to find, retrieve, or process information. The data source for information extraction is text. Text cannot be separated from people's daily lives. Through text, a lot of confidential information can be obtained. To produce information, the unstructured text will be converted into structured data. There are many approaches that researchers take to this process. Most of the studies are in English. Therefore, this paper will present current research trends, challenges, and information extraction opportunities using Indonesian.

Pemanfaatan Optical Character Recognition Dan Text Feature Extraction Untuk Membangun Basisdata Pengaduan Tenaga Kerja Yan Puspitarani; Yenie Syukriyah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 4 (2020): Agustus 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

The examination of complaints of labor violations is part of the main activity of the labor inspection section within the Department of Manpower. Monitors will examine companies that are considered to have violated labor laws based on a letter of complaint sent by the relevant union organization or legal aid agency. The easy way to communicate at this time, making the submission of complaint letters can be directly sent in the form of images through electronic media such as whatsapp or email. This makes it difficult for administrative staff to recapitulate incoming complaints because they have to read and enter data manually into the system. Therefore, this research was conducted to create a system that utilizes OCR technology and text feature extraction to be able to input complaints data automatically. This research resulted in a prototype of letter input and a database of letter storage that can be further utilized for Data Mining and Business Intelligent. OCR implementation is done by using the Tesseract library while the text feature selection utilizes the Natural Language Toolkit (NLTK) library. The results of testing of the prototype showed an accuracy of 66.7% of the OCR results and 91.67% of the manually typed letters.

SENTIMEN ANALYSIS TERHADAP NILAI KEPERCAYAAN SEBUAH ONLINE SHOP DI INSTAGRAM Yan Puspitarani
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 2 No. 1 (2015)
Publisher : Universitas Widyatama

[id]AbstrakInstagram menjadi tempat yang menarik untuk memasarkan produk. Dengan modal foto dan caption yang menarik, online shop akan mendapatkan followers yang kemungkinan besar tertarik untuk membeli produk mereka. Akan tetapi, seiring dengan mudahnya membuat akun Instagram, tingkat kepercayaan pembeli terhadap online shop tersebut menjadi hal yang paling penting. Oleh karena itu, diperlukan review terhadap online shop tersebut. Salah satu caranya adalah dengan memanfaatkan sentimen analysis terhadap komentar-komentar pada foto dan caption di akun Instagram online shop yang bersangkutan. Model sistem untuk proses review menggunakan sentiment analysis pun diusulkan pada paper ini. Proses sentiment akan menggabungkan pendekatan lexicon dan machine learning. Data yang akan digunakan adalah komentar-komentar terhadap foto dan caption beberapa online shop di Instagram.Kata Kunci : sentiment analysis, lexicon analysis, machine learning for sentiment [en]AbstractInstagram became an attractive place to market the product. With attractive photos and captions, online shop will gain followers who are interested in buying their products. However, Instagram account can create easily so the level of buyer confidence to shop online has become the most important thing. Therefore, the review of the online shop is required. The review can be used by sentiment analysis to the comments of photos and captions in the online shop Instagram account. A model system for the review process using sentiment analysis was proposed in this paper. Sentiment process will combine lexicon and machine learning approaches. Data will be used in this study are comments on photos and captions several online shop on Instagram.Keywords: sentiment analysis, lexicon analysis, machine learning for sentiment

IMAGE PROCESSING BASED TILAPIA SORTATION SYSTEM USING NA Sukenda Sukenda; Ari Purno Wahyu; Benny Yustim; Sunjana Sunjana; Yan Puspitarani
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 7 No. 1 (2020)
Publisher : Universitas Widyatama

Tilapia has a value of export quality and is imported from America and Europe, tilapia is cultivated in freshwater, the largest tilapia producing areas are Java and Bali for the export market in the Middle East, value fish with a size of 250 grams / head (4 fish / kg ) in their intact form is in great demand. According to news circulating, fish of this size in the Middle East are ordered to meet the consumption of workers from Asia. the fish classification process is a very difficult process to find the quality value of the fish to be sold to meet export quality. Fish classification techniques can use the GLCM technique (Gray Level Oc-Currance Matrix) classification using images of fish critters with the GLCM method.The fish image data is analyzed based on the value of Attribute, Energy, Homogenity, Correlation, Contrash, from the attribute the density data matrix is ??generated for each. Fish image data and displayed in the form of a histogram, the data from the GLCM results are then classified with the Naive Bayes algorithm, from the results of the classification of data taken from 3 types of tilapia from the types of gift, Red, and Blue.

Penerapan Natural Language Processing (NLP) di bidang pendidikan Fitrah Rumaisa; Yan Puspitarani; Ai Rosita; Azizah Zakiah; Sriyani Violina
Jurnal Inovasi Masyarakat Vol. 1 No. 3 (2021): Jurnal Inovasi Masyarakat
Publisher : LP2M Universitas Widyatama

NLP adalah cabang dari kecerdasan buatan (AI) yang berhubungan dengan melatih komputer untuk memahami, memproses, dan menghasilkan bahasa. Salah satu implementasi NLP yang sangat penting adalah penerapannya di dunia pendidikan. NLP adalah proses yang efektif untuk membantu siswa dalam proses pembelajaran. Menerapkan NLP dalam lingkungan pendidikan tidak hanya membantu dalam mengembangkan proses bahasa yang efektif, tetapi juga penting untuk meningkatkan prestasi akademik. Beberapa penerapan NLP di dunia pendidikan adalah Peringkasan Teks dan Paraphrasing, Tanya Jawab, Chatbot (feedback dari pendidik), Evaluasi Ejaan dan Grammar

MODELING OF DIGITALIZATION AND VISUALIZATION OF LABOR COMPLAINTS USING OCR, FEATURE EXTRACTION AND BUSINESS INTELLIGENCE Yan Puspitarani; Sukenda Sukenda; Ari Purno Wahyu; Benny Yustim; Sunjana Sunjana
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 7 No. 2 (2021)
Publisher : Universitas Widyatama

The handling of labor complaints is one of the performance factors for Disnakertrans. This performance is related to the decisions that must be taken to determine policies so that violations of labor norms can be reduced. Making this decision will be easier if the data on complaints can be recapitulated quickly and accurately. This recapitulation will be a tool to monitor the leadership in seeing the progress report on the status of incoming complaints. However, the manual administrative process makes the recapitulation slower. Therefore, this study will model the complaint reporting digitization system by utilizing OCR and information extraction as well as modeling the visualization of the results of the recapitulation using Business Intelligence. With the creation of this model, it is hoped that the performance of Disnakertrans in resolving labor complaints will be more effective

DECISION SUPPORT SYSTEM FOR DETERMINING STUDENT ELIGIBILITY TO PARTICIPATE OF MBKM IN THE INFORMATICS STUDY PROGRAM USING AHP AND TOPSIS Yan Puspitarani; Fitrah Rumaisa; Sriyani Violina; Feri Sulianta; Ai Rosita
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 8 No. 1 (2023)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v8i1.156

To be able to have mastery of various sciences that are useful for experience in the world of work, students are encouraged through the MBKM program. Most students of the Informatics Study Program will choose an internship scheme and independent study in applying for the MBKM. However, the increasing number of students' interest in this program requires the Informatics Study Program to be more selective in analyzing the eligibility of students to take part in the MBKM program it has chosen. For this reason, a decision support system is needed that can assist the study program in determining and assessing the eligibility of these students. This study created a DSS modeling using the Analytical Hierarchy Process (AHP) method and the Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) using 8 criteria.

Penerapan Clustering Terhadap Segmentasi Zonasi Gangguan Layanan Pelanggan Dengan Menggunakan Analisis RFM Nurhadi; Puspitarani, Yan
TEMATIK Vol 9 No 1 (2022): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2022
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v9i1.900

The service area and the wide distribution of customers often require quite a long time in terms of handling disturbances, which can reduce the level of customer trust and loyalty to using the network services provided. Customer segmentation by region can help make it easier to analyze disruptions that occur to customer service in customer area zoning so that customer trust and loyalty can be maintained. This research was conducted to understand the characteristics of disturbances that occur in several customer zoning areas. In this study, RFM (Recency, Frequency, Monetary) analysis was performed. Clustering using the K-Means modeling technique, and also performance checking using Cluster Distance Performance to determine the performance of each cluster performed. The results of this study indicate that K-Means Clustering with a value of K=2 is the with the smallest Davies Bouldin value. It means that K=2 has the best performance when compared to other clusters formed.

Phrase Detection's Impact on Sentiment Analysis of Public Opinion and online Media Toward Political Figures Muhammad Irsa Nurodin; Yan Puspitarani
Jurnal Riset Informatika Vol. 6 No. 2 (2024): March 2024
Publisher : Kresnamedia Publisher

Public opinion of political figures and policy significantly impacts general elections. Sentiment analysis, as a method to comprehend opinion and emotion in texts, requires the step of text preprocessing to improve data quality. However, textual data often encounters irrelevant words and ambiguous language. These conditions can impact the accuracy of sentiment analysis. Given the significance of precisely interpreting public opinion toward political figures, these issues may result in biased or inaccurate sentiment analysis outcomes. Irregular punctuation or unclear language can disturb the text's intended context, compromising sentiment analysis quality. Additionally, irrelevant words can obscure the focus of the analysis, causing fundamental changes in the original text's meaning. This research focuses on the impact of a specific preprocessing technique, namely Phrase Detection with N-Gram, on sentiment analysis of political figures. By applying this method, the study aims to detail the effects of using Bigram, Trigram, and Unigram on the quality of sentiment analysis, particularly in the context of political figures on Twitter and online media articles. This research indicates that using Bigram in Phrase Detection provides more significant results than Trigram and Unigram for most political figures at Twitter, with the highest accuracy score of 88,23%. Sentiment analysis of articles in online media also indicates various results depending on the type of N-Gram. The results indicate that using N-gram phrase detection can influence the accuracy of sentiment analysis, and the resulting accuracy values are pretty high.

Penentuan Kecenderungan Opini Publik di Media Sosial dan Berita Online Mengenai Tokoh Politik di Indonesia Menggunakan Opinion Mining Yan Puspitarani; Adi Purnama
Ranah Research : Journal of Multidisciplinary Research and Development Vol. 6 No. 4 (2024): Ranah Research : Journal Of Multidisciplinary Research and Development (Mei 202
Publisher : Dinasti Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38035/rrj.v6i4.884

Tokoh politik akan berusaha sebaik mungkin untuk mencari dukungan masyarakat agar dapat dipilih mewakili rakyat sebagai anggota DPR, kepala daerah, bahkan presiden. Popularitas tokoh politik ini dapat terlihat dari tingkat dukungan masyarakat. Salah satu cara untuk melihat tingkat dukungan masyarakat terhadap para tokoh politik ini dapat tercermin dari jumlah pendapat yang diungkapkan di media sosial. Selain itu, untuk menarik perhatian masyarakat, para tokoh ini seringkali menggunakan sosialisasi melalui artikel berita online. Pendapat di media sosial dan artikel berita online tentang para tokoh politik ini dapat mengindikasikan preferensi masyarakat dan pendekatan media massa terhadap tokoh-tokoh tertentu dengan memanfaatkan teknologi big data. Penelitian ini akan menggunakan opinion mining dan visualisasi Business Intelligence untuk menganalisis kecenderungan masyarakat terhadap para tokoh politik tersebut.

Co-Authors Adi Purnama Ai Rosita Andriyani, Fitri Ari Purno Wahyu Wibowo Azhar, Muhammad Rifqi Azizah Zakiah Benny Yustim Fitrah Rumaisa Juliadi, Revin Novian Muhammad Irsa Nurodin NURHADI Rosita, Ai Rumaisha, Fitrah Sriyani Violina Sukenda Sukenda Sulianta, Feri Sunjana * Sunjana Sunjana Widyanto, Adi Yenie Syukriyah Zakaria, Ai Rosita Zakiah, Azizah

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