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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 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 Sistem Komputer dan Informatika (JSON) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia Journal of Dinda : Data Science, Information Technology, and Data Analytics COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Computers and Digital Business Journal of Intelligent Software Systems Journal of Public Health Concerns Eduvest - Journal of Universal Studies Journal Of Artificial Intelligence And Software Engineering CYBER-Techn(Jurnal Informatika dan Industri) INOVTEK Polbeng - Seri Informatika BHAKTI JIVANA Quality
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia
Religion Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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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Multi-Area OSPF Analysis Using Virtual Link and GRE Tunnel Miftahul Huda; Widyastuti Andriyani
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.
Studi Eksploratif Pipeline Multilayer Perceptron pada Dataset Sintetik Berlabel Deterministik: Implikasi Metodologis untuk Klasifikasi Hipertensi Ivónia Fátima Ruas da Silva; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.991

Abstract

Hipertensi merupakan penyakit kardiovaskular dengan prevalensi tinggi dan menjadi penyebab utama mortalitas global, sehingga deteksi dini menjadi kebutuhan klinis yang krusial. Namun, pengembangan model deep learning pada konteks sumber daya terbatas sering terkendala ketersediaan dataset berskala besar. Gap penelitian yang diidentifikasi adalah belum tersedianya studi eksploratif yang secara eksplisit menguji kelayakan pipeline Multilayer Perceptron (MLP) sederhana pada dataset berukuran sangat kecil dengan dokumentasi reproducible. Penelitian ini bertujuan mendemonstrasikan pipeline MLP end-to-end pada dataset sintetik 150 sampel dengan sembilan fitur biometrik dan gaya hidup. Setelah one-hot encoding dan normalisasi Min-Max, dimensi input menjadi 15 neuron. Arsitektur MLP terdiri atas tiga hidden layer (64-32-16, ReLU) dan output sigmoid, dilatih 100 epoch menggunakan optimizer Adam (learning rate 0,001; batch size 16) dengan early stopping. Evaluasi pada test set (n = 30) memperoleh akurasi 90,00%, presisi 85,00%, recall 100%, F1-score 91,90%, dan AUC-ROC 0,91, dengan tiga false positive teridentifikasi sebagai kasus borderline pre-hypertension. Kontribusi penelitian terletak pada penyajian artefak reproducible—dataset sintetik, kode preprocessing, dan visualisasi diagnostik—sebagai baseline pedagogis untuk institusi berketerbatasan data. Keterbatasan utama, yaitu sifat deterministik label yang berpotensi menimbulkan circular reasoning pada fitur tekanan darah, didokumentasikan eksplisit sebagai catatan validitas internal.
Decision Support System for Business Location Selection using the AHP Method Based on Android Hizkia Hendra Rianingsih; Widyastuti Andriyani
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/z5xfh608

Abstract

The selection of a strategic business location is a crucial factor in the success of an enterprise. However, the decision-making process for choosing a location often involves various complex criteria that require careful analysis. This study aims to develop an Android-based Decision Support System (DSS) to assist entrepreneurs in selecting an optimal business location using the Analytical Hierarchy Process (AHP) method. AHP is chosen for its ability to break down complex problems into a hierarchy and quantitatively evaluate each criterion. The system is designed to allow users to easily access and operate the DSS through mobile devices. The criteria used in the location selection process include accessibility, cost, demographics, competition, security, infrastructure, regulations, and growth potential. Each criterion is weighted using pairwise comparisons, enabling objective and comprehensive assessments. The Android-based implementation allows for flexibility, enabling users to make decisions efficiently anytime and anywhere. System testing results indicate that the application can provide accurate business location recommendations based on the desired criteria, and it features a user-friendly interface. This Android-based DSS is expected to be a practical solution for supporting business decision-making processes quickly and accurately.
Pengembangan Sistem Rekomendasi Pembimbing Tugas Akhir Menggunakan Teknik Content Based Filtering Femi Dwi Astuti; Widyastuti Andriyani
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.
Analisis Sentimen pada Ulasan Produk dengan SVM dan Word2Vec WIDYASTUTI ANDRIYANI; Yuli Astuti; Bradika Almandin Wisesa; 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.
Implementation of Artificial Intelligence-Based Face Recognition in Web Application Development Muammar Muammar; WIDYASTUTI ANDRIYANI; Bambang Purnomosidi D.P
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2312

Abstract

Traditional web login systems using usernames and passwords have weaknesses in terms of security and are vulnerable to hacking attacks. Therefore, this research proposes the development of a web login system with a face recognition authentication method using artificial intelligence to overcome that problem. This project involves several main steps. First, a dataset of facial images will be collected, including variations in lighting conditions, poses, and facial expressions. Furthermore, the facial recognition model will train using the Haar Cascade Classifier and Local Binary Pattern algorithms. The trained model will be integrated into the web application using the appropriate programming language and framework, such as Python with Django. The web login interface will allow users to enter login information and take pictures of their faces. The system will use face detection and feature extraction techniques to extract facial features from uploaded images. These features will compare with the stored dataset of facial images using the trained facial recognition model. It is successful when there is a match, and the user will be granted access to the web application. To improve security, testing, and evaluation of the developed system will be carried out to measure its performance, including using metrics such as accuracy, precision, recall, and F1-score. All vulnerabilities of security or limitations identified during the evaluation will be addressed and fixed. Implementation will provide a more secure and user-friendly login experience. By utilizing the advances in facial recognition technology and artificial intelligence, this study aims a contribution to improving web authentication systems and protecting user data from unauthorized access
Designing a Digital Marketing Strategy Through Keyword Weighting on Ten Main Competitors of PT Oemah Solution Indonesia with the TF-IDF Approach Yani Aji Susilo; WIDYASTUTI ANDRIYANI
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2313

Abstract

Information on the PT Oemah Solution Indonesia website is currently not easily accessible to internet service users, because it still uses conventional methods in terms of product marketing, in addition to that, the optimization of the PT Oemah Solution Indonesia website has so far only focused on adding website content without involving keyword elements as part of the search optimization element with the implication that the search results are not optimal, the use of internet media and search engines to increase pagerank to become number one in the search index should be implemented in order to help in marketing and can increase company turnover. Analyzing keywords competitor PT Oemah Solution Indonesia website with TFIDF method as a form of digital marketing strategy of PT Oemah Solution Indonesia website. The highest TF-IDF value, URL https://bamai.uma.ac.id/.../software-membuat-aplikasi android/ gets the highest TF value at number 1 and IDF at number 109,531 and TF-IDF value 1 for android keyword, the second order is for URL https://www.slimfaq.com/hugaf/...mobile gets TF value 1 and IDF value 109 and TF-IDF with a value of 1 with website keyword. From the results of the research that has been done it can be recommended for the preparation of company web content, android and website keywords must be involved in the preparation of content material, including in the creation of articles, service descriptions, and other important elements. In addition, the use of keyword variations of more than 2 words can also increase visibility in online searches. Optimizing the results of digital marketing strategies, collaboration between the marketing team and the content development team is necessary. TF-IDF analysis must be translated into content that is engaging and meaningful to the target audience. Integration with SEO (Search Engine Optimization) techniques is necessary to ensure content is easily found by Google
DIGITAL ACTIVITY LOCATION CLUSTERING BASED ON TWITTER GEOSPATIAL DATA FOR SPATIOTEMPORAL BUSINESS INTELLIGENCE Triyan Agung Laksono; Widyastuti Andriyani; Fadhlih Girindra Putra; Ivonia Fatima Ruas da silva; Wiwi widayani
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.
Comparison of Self-Organizing Maps and K-means Algorithms in Grouping Divorce Cases in Yogyakarta City Priyo Purnomo; Domy Kristomo; Widyastuti Andriyani; Bambang Purnomosidi Dwi Putranto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2309

Abstract

The Religious Court of Yogyakarta serves as the primary judicial body that processes, examines, and renders decisions on civil cases involving adherents of Islam at the initial legal level. The court manages approximately 900 cases annually, with marital dissolution proceedings constituting the predominant category. Categorizing these cases according to marital age and divorce causation is crucial for illustrating the distinctive patterns of divorce within the jurisdiction, thereby informing governmental initiatives promoting family wellness education. For the purpose of data clustering, a comparative analysis was conducted between self-organizing maps (SOM) and K-means methodologies. The research utilized secondary data from 2021, comprising 45 entries of concluded divorce cases. The internal silhouette validation metric established two as the optimal cluster quantity for both analytical approaches. The distribution and attributes of clusters remained largely consistent across both models. Evaluation using the standard deviation ratio revealed superior clustering performance by the SOM method when applied to the divorce case dataset. The analytical results demonstrated that cluster 1 encompassed 11 neighborhoods characterized by elevated divorce rates, while cluster 2 contained 34 regions exhibiting lower to moderate divorce frequencies. The principal determinants shaping divorce case characteristics in Yogyakarta City were identified as "ongoing conflicts and arguments," "abandonment by one spouse," and "financial circumstances."
CLASSIFICATION OF OIL PALM FRUIT CROSS-SECTIONS USING HSV FEATURE EXTRACTION AND GAUSSIAN NAÏVE BAYES Teguh Junian Kuswanto; WIDYASTUTI ANDRIYANI; Rikie Kartadie; Bambang Purnomosidi D.P; Danny Kriestanto
Journal of Intelligent Software Systems Vol 4, No 2 (2025): Desember 2025
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v4i2.2310

Abstract

Accurate identification of oil palm fruit varieties is essential for supporting breeding programs and optimizing seed quality in plantation operations. Manual approaches often lead to inconsistencies due to the high visual similarity among fruit types, particularly between dura and tenera. This study proposes an automatic classification model for oil palm fruit cross-sections using HSV-based color feature extraction combined with a Gaussian Naïve Bayes classifier. A dataset of 186 cross-sectional fruit images was used, consisting of 90 training samples and 96 testing samples representing the dura, pisifera, and tenera varieties. The methodology includes preprocessing, segmentation, HSV feature extraction, model training, and performance evaluation through a confusion matrix. Experimental results show that the proposed model achieves an accuracy of 85%, with misclassifications primarily occurring in the tenera class due to its close resemblance to the dura variety. Compared to Linear Discriminant Analysis (LDA), the proposed approach demonstrates faster computation time and competitive accuracy. These findings indicate that Gaussian Naïve Bayes, supported by HSV feature descriptors, provides an efficient solution for lightweight and cost-effective digital classification of oil palm fruit varieties
Co-Authors Akhmad Dahlan Andre Argisitawan Anwarudin Anwarudin Arif Setiadi, Rizki Arma Fauzi Asyahri Hadi Nasyuha B.T. Sutrisno Bagas Triaji Bambang P.D.P Bambang P.D.P. Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Brahmana, Ivanna Beru Brian Duen Rakly Cucut Hariz Pratomo D P, Bambang Purnomosidi Danny Kriestanto Danny Kriestanto, Danny Dian Tri Wiyanti Dommy Kristomo Domy Kristomo Domy Kristomo, Domy Duen Rakly, Brian Dwi Wibowo Eny Retna Ambarwati Fadhlih Girindra Putra Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Femi Dwi Astuti Fika Pratiwi Firman Noor Hasan Hamdani Hamdani Hendra Hengki Hengki Heri Muhrial Herwantono, Herwantono Hizkia Hendra Rianingsih Hizkia Hendra Rianingsih Irfan Setiawan Istichomah Istichomah Ivónia Fátima Ruas da Silva Kuindra Iriyanta Miftahul Huda MILASARI, LISA ASTRIA Muammar Muammar Muhammad Agung Nugroho Muhammad Ali Sofian Murgi Handari Nenen Isnaeni Nugroho, Daniel C.A. Nurohman, Muhamad Pangestika , Elza Qorina Prisilia Talakua Priyo Purnomo Pujianto Pujianto Purnomosidi D.P, Bambang Purnomosidi Dwi Putranto, Bambang Purnomosidi, Bambang Rajie Al Qadri Anwar Rakly, Brian Duen Reni Tri Lestari Retantyo Wardoyo Riadinata Riadinata Rifky Lana Rahardian Rikie Kartadie Robertus Saptoto Roh Bintang Jaya, Mabrur Said, Famidin Saputra, Andika Jodhi Saryanto Saryanto Sipayung, Hotma Sadariahta Siti Khomsah, Siti Sri Redjeki Suningrat, Nining Suryanto Suryanto Taufik Ismail Teguh Junian Kuswanto Totok Suprawoto Tri Andi, Tri Triyan Agung Laksono Triyan Agung Laksono Wibowo, Gunturari Wijayanti, Agnes Erida Wiwi Widayani Yani Aji Susilo Yohanni Syahra Yuli Astuti