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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science (JOINTECS) JIKO (Jurnal Informatika dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) SKANIKA: Sistem Komputer dan Teknik Informatika
Anik Vega Vitianingsih
Universitas Dr. Soetomo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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Spatial analysis model for traffic accident-prone roads classification: a proposed framework Anik Vega Vitianingsih; Nanna Suryana; Zahriah Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp365-373

Abstract

The classification method in the spatial analysis modeling based on the multi-criteria parameter is currently widely used to manage geographic information systems (GIS) software engineering. The accuracy of the proposed model will play an essential role in the successful software development of GIS. This is related to the nature of GIS used for mapping through spatial analysis. This paper aims to propose a framework of spatial analysis using a hybrid estimation model-based on a combination of multi-criteria decision-making (MCDM) and artificial neural networks (ANNs) (MCDM-ANNs) classification. The proposed framework is based on the comparison of existing frameworks through the concept of a literature review. The model in the proposed framework will be used for future work on the traffic accident-prone road classification through testing with a private or public spatial dataset. Model validation testing on the proposed framework uses metaheuristic optimization techniques. Policymakers can use the results of the model on the proposed framework for initial planning developing GIS software engineering through spatial analysis models.
Analisis Spasial Untuk Klasifikasi Pengembangan Tempat Penampungan Sementara Menggunakan Metode Jaringan Syaraf Tiruan Luqman Hakim; Anik Vega Vitianingsih; Gita Indah Marthasari; Kresna Arief Nugraha; Anastasia Lidya Maukar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 1 (2022): Februari 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (376.78 KB) | DOI: 10.29207/resti.v6i1.3713

Abstract

Garbage is a problem that needs an in-depth study in urban areas because the development of an area has consequences on increasing population density, facilities and infrastructure, public services, and other aspects that impact increasing the volume of waste. The distribution of temporary waste shelters (TPS) in each area is still insufficient to accommodate the volume of waste, and its availability is inadequate. The purpose of this study is to model spatial data through spatial analysis using artificial intelligence methods in classifying the development of integrated temporary shelter locations (TPST) and regional integrated temporary shelters (TPST Regions) by utilizing Web-based technology (Geographical Information System (Web-GIS). The Artificial Neural Network method with the Backpropagation algorithm is used for the spatial analysis process based on the parameters of the population, the amount of organic and inorganic waste, the amount of industrial waste, and the volume of the TPST and Regional TPST capacity. The spatial analysis results using the Artificial Neural Network method obtained an accuracy value of 7171.02%. The results of this study can be the basis for Department of Environment and Cleanliness policies for the development of TPST and TPST areas with information coverage at the village level.
POTENTIAL CUSTOMER ANALYSIS USING K-MEANS WITH ELBOW METHOD Fitri Marisa; Arie Restu Wardhani; Wiwin Purnomowati; Anik Vega Vitianingsih; Anastasia L Maukar; Erri Wahyu Puspitarini
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 2 (2023): SEPTEMBER 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v7i2.911

Abstract

This study aims to obtain cluster data of potential customers using the K-Means clustering approach supported by the elbow method to determine the correct number of clusters. The data sample that was processed was 100 customer data from a minimarket containing three criteria (gender, age, and purchase retention). The number of initial clusters is determined as 5 and then processed by calculating K-Means. The calculation of the SSE value in the K-Means process produces the lowest SSE value, and the sharpest elbow angle graph visualization is in cluster 4. So, it can be stated that the best number of clusters in this K-Means calculation is four (4) which are used as material for further analysis. Then the analysis results of four (4) clusters state that potential customers are those with high purchase retention, consisting of female customers who dominate in the three (3) clusters. Most potential female customers are customers with an age range above 35 years. Meanwhile, customers with less potential are spread across each cluster with varied gender and age but are not dominant. Thus, this knowledge can be used as a consideration for the management in determining the right promotion strategy.
Sistem Pendukung Keputusan Penerimaan Bantuan Non Tunai Menggunakan Metode AHP Dan WP Nurhaba Djiha; Anik Vega Vitianingsih; Mochammad Syaiful Riza; Anastasia Lidya Maukar; Seftin Fitri Ana Wati
JOINTECS (Journal of Information Technology and Computer Science) Vol 8, No 1 (2024)
Publisher : Universitas Widyagama Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31328/jointecs.v8i1.5222

Abstract

Saat ini proses penentuan penerima Bantuan Pangan Non Tunai (BPNT) masih kurang obyektif dan belum maksimal sehingga menimbulkan ketidakpuasan di kalangan warga desa. Tujuan dari penelitian ini adalah menggunakan metode AHP dan WP untuk mengembangkan sistem pendukung keputusan bagi penerima bantuan non tunai (BPNT). Bobot kriteria ditentukan dengan menggunakan metode AHP, dan nilai setiap alternatif dihitung dengan menggunakan metode WP. Parameter yang digunakan makan sebanyak dalam sehari, biaya pengobatan, pendapatan per bulan, sumber penerangan, bahan bakar memasak, fasilitas buang air besar, konsumsi jenis makanan, luas lantai, jenis dinding, sumber air minum, tabungan, jenis lantai, pembelian pakaian, pendidikan kepala rumah tangga dan sebanyak 20 data uji yang akan di olah dalam melakukan perangkingan. Akurasi sebesar 80% dicapai pada pengujian validasi dengan confusion matriks dan 20 kumpulan data alternatif, menunjukkan bahwa metode WP dapat menghasilkan rekomendasi alternatif yang paling optimal. Hasil penelitian ini dapat membantu pemerintah desa dalam menentukan penerima BPNT yang layak sesuai dengan kriteria yang digunakan.
SISTEM MONITORING PASSENGER EXIT SURVEY BERBASIS WEB MENGGUNAKAN MODEL PENGEMBANGAN PERANGKAT LUNAK WATERFALL Halimatul Rofidah; Anik Vega Vitianingsih; Pamudi Pamudi; Litafira Syahadiyanti; Alda Raharja
Journal of Information System Management (JOISM) Vol. 8 No. 1 (2026): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2026v8i1.2873

Abstract

Perkembangan teknologi informasi telah mendorong digitalisasi pada instansi pemerintah, termasuk Badan Pusat Statistik (BPS) untuk meningkatkan efisiensi dan transparansi pengelolaan data survei. BPS Kabupaten Sidoarjo masih melakukan monitoring kegiatan Passenger Exit Survey (PES) secara manual melalui file spreadsheet dan grup komunikasi, sehingga menyulitkan proses pemantauan target petugas, dan evaluasi progres kegiatan. Tujuan penelitian untuk mengimplementasikan sistem monitoring survei berbasis web di BPS Kabupaten Sidoarjo guna mendukung pengawasan kegiatan PES secara terstruktur dan terpadu. Model pengembangan perangkat lunak yang digunakan adalah Waterfall dengan model evaluasi sistem menggunakan System Usability Scale (SUS). Data penelitian mencakup kebutuhan monitoring target negara, pencapaian petugas, dokumentasi survei, kategori negara, kehadiran petugas, serta pelaporan capaian triwulan. Cakupan informasi dalam sistem meliputi fitur dashboard, daftar target, monitoring pencapaian petugas, dokumentasi survei, visualisasi data dalam bentuk tabel, peta, serta laporan capaian survei.  Hasil pengujian sistem menunjukkan nilai rata-rata SUS yaitu 80,45 dengan kategori dapat diterima dengan baik untuk mendukung proses monitoring kegiatan survei.
ANALISIS SENTIMEN ULASAN PRODUK SPAREPART MOTOR DI E-COMMERCE MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM) Reza Ega Resnanda; Dwi Cahyono; Anik Vega Vitianingsih
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3608

Abstract

This study was motivated by the increasing use of e-commerce in Indonesia, which highlights the importance of analyzing customer reviews as a basis for evaluating product and service quality. This study aims to analyze the sentiment of reviews of Honda motorcycle spare parts at the Ducks Garage store on the Tokopedia platform using the Support Vector Machine (SVM) algorithm. The dataset used consists of 2.537 reviews obtained through web scraping techniques and processed through text preprocessing stages, including data cleaning, normalization, tokenization, stopword removal, and stemming. Sentiment labelling was carried out into three classes, namely positive, negative, and neutral, with lexicon-based and feature weighting using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Data distribution imbalance was handled using the Synthetic Minority Over-Sampling Technique (SMOTE) method. The SVM model was tested using three types of kernels, namely Linear, Polynomial, and Radial Basis Function (RBF). The test results showed that the RBF kernel produced the best performance with an accuracy of 92.79%, followed by the Linear kernel at 89.89% and the Polynomial kernel at 72.57%. The conclusion of this study shows that the application of SVM with SMOTE data balancing is effective in classifying the sentiment of e-commerce product reviews and can be used to support data-driven business decisions based on customer data.
Co-Authors Alda Raharja Anastasia L Maukar Anastasia Lidya Maukar Arie Restu Wardhani Dwi Cahyono Erri Wahyu Puspitarini Fitri Marisa Gita Indah Marthasari Halimatul Rofidah Kresna Arief Nugraha Litafira Syahadiyanti Luqman Hakim Mochammad Syaiful Riza Nanna Suryana Nurhaba Djiha Pamudi Pamudi Reza Ega Resnanda Seftin Fitri Ana Wati Wiwin Purnomowati Zahriah Othman