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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris Jurnal technoscientia Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informatika dan Rekayasa Perangkat Lunak JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Intelligent Software Systems Journal of Public Health Concerns Eduvest - Journal of Universal Studies Journal Of Artificial Intelligence And Software Engineering
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia, Yogyakarta
Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Other

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Sentiment Analysis of X Platform on Viral 'Fufufafa' Account Issue in Indonesia Using SVM Suryanto, Suryanto; Andriyani, Widyastuti
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.104158

Abstract

In this study, we conducted a comprehensive sentiment analysis of users on the social media platform X concerning the viral controversy surrounding the KasKus account known as “Fufufafa.” This issue attracted widespread attention and sparked varied reactions within the online community. To gain insights into public opinion on the topic, we utilized the Support Vector Machine (SVM) method, a widely recognized machine learning algorithm for classification tasks. The data for this research was gathered from various posts, comments, and public discussions on platform X, which were pre-processed to filter out irrelevant information, such as spam, unrelated topics, and non-informative content. After cleaning the data, user sentiments were categorized into three primary classes: positive, negative, and neutral. The SVM model was then trained and tested using a labeled dataset to accurately predict user sentiments based on the textual content of their interactions.
Analisis Sentimen pada Ulasan Produk dengan SVM dan Word2Vec ANDRIYANI, WIDYASTUTI; Astuti, Yuli; Wisesa, Bradika Almandin; Hengki, Hengki
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 1 (2025): Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i1.1498

Abstract

Analisis sentimen adalah salah satu cabang pemrosesan bahasa alami (NLP) yang bertujuan untuk mengidentifikasi opini dalam teks. Penelitian ini mengusulkan model analisis sentimen dengan menggunakan kombinasi Word2Vec sebagai teknik representasi fitur dan Support Vector Machine (SVM) sebagai algoritma klasifikasi. Dataset yang digunakan adalah Amazon Customer Reviews, dengan 500 ribu sampel ulasan produk yang dilabeli sebagai sentimen positif atau negatif. Model yang diusulkan dibandingkan dengan baseline seperti Naive Bayes dan Logistic Regression, yang menggunakan representasi fitur berbasis TF-IDF.Hasil evaluasi menunjukkan bahwa SVM dengan Word2Vec menghasilkan akurasi 91.3\%, precision 90.8\%, recall 92.1\%, dan F1-score 91.4\%, lebih unggul dibandingkan model baseline. Grafik Precision-Recall Curve dan ROC Curve memperkuat temuan bahwa Word2Vec memberikan representasi fitur yang lebih informatif, yang secara signifikan meningkatkan performa SVM dalam tugas klasifikasi teks.Penelitian ini membuktikan efektivitas kombinasi Word2Vec dan SVM untuk analisis sentimen pada dataset besar dan kompleks. Pendekatan ini relevan untuk berbagai domain, seperti e-commerce dan analisis opini di media sosial, serta membuka peluang untuk pengembangan lebih lanjut menggunakan model berbasis transformer.
Application of Extreme Programming Methods in the Design and Building of the Nusantara Capital Sentiment Analysis System Said, Famidin; Kristomo, Domy; Andriyani, Widyastuti
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14617

Abstract

Information about the capital city of the archipelago (IKN) in the digital era serves as a platform for individuals to express views on development, policies, and socio-economic impacts. Such information often contains personal emotional expressions, categorized as negative, neutral, or positive sentiments. This study aims to design a sentiment analysis system to evaluate public opinions regarding IKN. The system utilizes Google NLP services, which offer sentiment measurement features for analyzed text, and web scraping techniques to automate data collection from online sources. The development process employs the Laravel framework and follows the Extreme Programming approach, which ensures work efficiency. Sentiment analysis is conducted using the Support Vector Machine (SVM) method, achieving an accuracy rate of 95%. The system is designed to be web-based, ensuring accessibility across devices, including smartphones and computers. The results demonstrate that this sentiment analysis system can help individuals, organizations, and governments gain deeper insights into public perspectives on IKN. Furthermore, it serves as a valuable tool for strategic decision-making and policy evaluation related to IKN development. Future research may explore expanding the data sources and integrating more advanced analytical techniques to improve system performance.
Software Testing in E-Commerce: A Comparison Between Manual and Automated Testing Using Katalon Studio and Python Rakly, Brian Duen; Andriyani, Widyastuti
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): March
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6448

Abstract

Software testing is a crucial element in software development to ensure quality and reliability. This study compares manual and automated testing using tools like Katalon Studio and Python. Manual testing is effective for scenarios requiring human judgment, such as user experience (UX) evaluation. In contrast, automated testing is more efficient for routine and repetitive tasks, reducing human error and speeding up the process. This study evaluates the effectiveness, efficiency, and costs of both methods in the context of e-commerce software testing. The results indicate that manual testing is superior in detecting defects before release, while automated testing is more cost-effective and time-efficient for repetitive testing. This guide assists developer for selecting the appropriate testing method based on their project needs.
Fruit Image Classification Using Naive Bayes Algorithm with Histogram of Oriented Gradients (HOG) Feature Extraction Saputra, Andika Jodhi; Andriyani, Widyastuti
Journal of Artificial Intelligence and Software Engineering Vol 5, No 1 (2025): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i1.6536

Abstract

A classification system using Naïve Bayes algorithm was developed to distinguish between fresh and rotten fruits, specifically apples, bananas and oranges. This research utilized a dataset consisting of 13,599 images and applied the Histogram of Oriented Gradients (HOG) technique for feature extraction, followed by model training and evaluation. The results showed that the Naïve Bayes algorithm achieved an accuracy of 87%, with the highest precision in the fresh apple class (0.9792) and the highest recall in the rotten apple class (0.9843). The rotten banana class showed a balanced performance with the highest F1-score of 0.9085. Although there were some misclassifications, especially in the rotten citrus fruit category, this study shows that image processing techniques have great potential and are reliable for assessing fruit quality based on visual characteristics.
Multi-Area OSPF Analysis Using Virtual Link and GRE Tunnel Huda, Miftahul; Andriyani, Widyastuti
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.1710

Abstract

This study discusses the implementation and analysis of network performance using multi-area OSPF (Open Shortest Path First) with the application of Virtual Link and GRE Tunnel. OSPF is a dynamic routing protocol that is often used in large networks due to its ability to find the shortest path quickly and efficiently. However, on large networks, the use of OSPF in a single area can increase routing overhead and slow down convergence times. Therefore, multi-area OSPF is a solution by limiting the spread of routing information only to related areas. This study uses an experimental method with the PNETLab simulator running five Cisco routers. The test was carried out by measuring QoS parameters such as throughput, packet loss, jitter according to TIPHON standards and OSPF convergence time using iPerf3 and Wireshark software. The results show that multi-area OSPF with Virtual Link has a more stable performance than GRE Tunnel in terms of jitter and convergence time, namely the average convergence time of Virtual Link is 24.1166 seconds while GRE Tunnel is 28.6144 seconds. Nonetheless, GRE Tunnel shows lower packet loss at large data sizes. This study provides practical guidance for network professionals in optimizing multi-area OSPF configurations.
PENERAPAN MADM DENGAN METODE SAW UNTUK MENENTUKAN TARGET PROMOSI BERDASARKAN ASAL JURUSAN DI SEKOLAH SUTRISNO, B. T.; Andriyani, Widyastuti
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 2 (2020): JURNAL SIMETRIS VOLUME 11 NO 2 TAHUN 2020
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i2.4784

Abstract

Penerimaan Mahasiswa Baru (PMB) adalah proses yang sangat penting untuk merekrut mahasiswa baru, di mana jumlah mahasiswa sangat penting untuk operasional dan perencanaan pengembangan Perguruan Tinggi Swasta (PTS). Sebagai bagian dari PTS di Yogyakarta, Fakultas Kesehatan Universitas Jenderal Achmad Yani Yogyakarta berusaha meningkatkan jumlah mahasiswanya, dimana salah satunya dengan mengoptimalkan penenttuan target promosi. Model Multiple Attribute Decision Making (MADM) dengan metode Simple Additive Weighting (SAW) dapat menghasilkan urutan peringkat yang dapat digunakan untuk menentukan keputusan terkait target promosi berdasarkan asal jurusan pada calon siswa di sekolah menengah, sehingga pembuat kebijakan Penerimaan Mahasiswa di Fakultas Kesehatan Universitas Jenderal Achmad Yani Yogyakarta dapat menentukan target promosi yang sesuai untuk meningkatkan jumlah mahasiswa baru.
Pengembangan Sistem Rekomendasi Pembimbing Tugas Akhir Menggunakan Teknik Content Based Filtering Astuti, Femi Dwi; Andriyani, Widyastuti
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 2 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i2.1599

Abstract

Pemilihan dosen pembimbing untuk tugas akhir merupakan tahapan penting bagi mahasiswa di jenjang pendidikan tinggi. Proses ini membutuhkan kecocokan antara bidang keahlian dosen dan topik penelitian mahasiswa, dengan tetap memperhatikan kendala seperti kapasitas pembimbing yang tersedia. Meninjau profil dosen dan rekam jejak penelitian secara manual dapat menjadi proses yang lambat dan kurang efisien. Untuk mengatasi tantangan tersebut, penelitian ini merancang sebuah sistem rekomendasi pembimbing tugas akhir dengan pendekatan Content-Based Filtering. Sistem ini memanfaatkan metode Term Frequency-Inverse Document Frequency (TF-IDF) untuk menilai relevansi istilah dalam abstrak penelitian, serta Cosine Similarity untuk mengukur kedekatan antara topik mahasiswa dan bidang penelitian dosen. Berdasarkan hasil pengujian, sistem mampu memberikan rekomendasi dosen pembimbing secara akurat berdasarkan kesamaan tertinggi dari judul proyek akhir yang diajukan, sehingga mempercepat dan meningkatkan ketepatan proses pemilihan. Sistem ini menjadi solusi praktis bagi mahasiswa dalam menentukan pembimbing yang sesuai dengan kebutuhan akademiknya, serta berpotensi meningkatkan kualitas proses bimbingan. Ke depan, penelitian lebih lanjut dapat mengembangkan integrasi dengan pendekatan penyaringan kolaboratif atau model rekomendasi hibrida guna menyempurnakan hasil rekomendasi yang dihasilkan.
TEMPERATURE SENSOR DATA QUALITY ASSESSMENT IN MANUFACTURING ENVIRONMENT USING HAMPEL FILTER AND QSD P.D.P., Bambang; Andriyani, Widyastuti; Dahlan, Akhmad
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2003

Abstract

In the Industry 4.0 era, integrated temperature sensors in system production become source main data for taking decisions. However, the quality of the data produced often influenced by noise, missing values, and disturbing anomalies accuracy of analytical processes. Research This proposes a monitoring pipeline designed data quality For environment manufacturing based on the Internet of Things (IoT), with focus on usage Hampel Filter and Quality Score Delta (QSD) methods. Hampel Filter is used for detecting and handling outliers in temperature data in a way adaptive, while QSD is used for measure dynamics change data quality from time to time. Architecture system built with using Apache Kafka for data ingestion, InfluxDB For time-series storage, and Grafana for real-time visualization. Case study performed on temperature sensor data from the conveyor motor, and the results show that method. This capable detect degradation data quality in general proactive. Findings show potential big in increase reliability industrial monitoring system as well as support maintenance predictive data- based. Research This give contribution significant in developing modular and adaptive approach for management data quality in the manufacturing sector.
DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE Laksono, Triyan Agung; Andriyani, Widyastuti; Putra, Fadhlih Girindra; Ruas da silva, Ivonia Fatima; widayani, Wiwi
Journal of Intelligent Software Systems Vol 4, No 1 (2025): Juli 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i1.2005

Abstract

This research develops an approach for clustering digital activity locations based on Twitter geospatial data with the aim of supporting business intelligence spatiotemporal . By utilizing the Twitter Geospatial Data dataset containing more than 14 million tweets geo-tagged from the United States, this study implements and compares the DBSCAN and K- Means algorithms to identify spatial and temporal patterns of Twitter user activity. The research process begins with the data pre -processing stage using the Knowledge Discovery Database (KDD), followed by the implementation of the clustering algorithm , and ending with the integration of the results into the dashboard.business intelligence using Power BI . The results show that DBSCAN is able to detect irregular clusters that follow geographic patterns and population density, while K- Means produces a division of the region into three main clusters (West Coast, Central Region, and East Coast) with different temporal activity patterns. Integration of clustering results into a BI dashboard produces actionable business insights , such as identification of digital activity hotspots , optimal time for content delivery, geographic segmentation for marketing strategies, and temporal activity patterns for campaign scheduling. This research contributes to the development of an integrated spatiotemporal analysis pipeline to support data-driven decision making.
Co-Authors Akhmad Dahlan Asyahri Hadi Nasyuha B.T. Sutrisno Bagas Triaji Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Brahmana, Ivanna Beru Cucut Hariz Pratomo Danny Kriestanto, Danny Dian Tri Wiyanti Domy Kristomo, Domy Duen Rakly, Brian Eny Retna Ambarwati Femi Dwi Astuti Femi Dwi Astuti Fika Pratiwi, Fika Hamdani Hamdani Hendra Hengki Hengki Heri Muhrial Herwantono, Herwantono Istichomah Istichomah, Istichomah Kuindra Iriyanta Laksono, Triyan Agung Miftahul Huda Murgi Handari, Murgi Nugroho, Daniel C.A. Nugroho, Muhammad Agung P.D.P., Bambang Pangestika , Elza Qorina Pereira, Elisabet da Conceição Pujianto Pujianto Purnomosidi D.P, Bambang Purnomosidi Dwi Putranto, Bambang Putra, Fadhlih Girindra Rakly, Brian Duen Reni Tri Lestari Riadinata, Riadinata Rifky Lana Rahardian Rikie Kartadie Roh Bintang Jaya, Mabrur Ruas da silva, Ivonia Fatima Said, Famidin Saputra, Andika Jodhi Saryanto Saryanto Sipayung, Hotma Sadariahta Suningrat, Nining Suryanto Suryanto Taufik Ismail Totok Suprawoto Tri Andi, Tri Wijayanti, Agnes Erida Wiwi Widayani, Wiwi Yohanni Syahra Yuli Astuti