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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Infotech Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH MATEMATIKA DAN TERAPAN IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jiko (Jurnal Informatika dan komputer) SEINASI-KESI METIK JURNAL Jurnal Muara Sains, Teknologi, Kedokteran dan Ilmu Kesehatan Technologia: Jurnal Ilmiah Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics JIKA (Jurnal Informatika) International Journal of Global Operations Research Journal La Multiapp Jurnal Locus Penelitian dan Pengabdian Jurnal Informatika Polinema (JIP) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Wina Witanti
Jurusan Teknik Informatika, Universitas Jenderal Ahmad Yani
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Other

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PRIMARY QUERY ANALYSIS ON SQL DATABASE RESTRUCTURING IN GEOGRAPHIC INFORMATION SYSTEMS Ilyas, Ridwan; Witanti, Wina; Syarafina, Fildzah
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8565

Abstract

Database restructuring is a crucial process aimed at enhancing data management and access efficiency by modifying the existing data structure. This research focuses on improving a Geographic Information System (GIS) for taxation by migrating and restructuring an inefficient and redundant database. The study conducts a comparative performance evaluation of the old and restructured databases using benchmarking tests with varying numbers of threads and ramp-ups. The results reveal a significant increase in average throughput (24.60%) following the restructuring, indicating a substantial improvement in the database's data processing capacity. However, there is also an average increase in response time (21.65%), suggesting a trade-off between enhanced throughput and slower response times. This increase in response time indicates that while the system can handle more data, it requires more time to process each query. Overall, the restructured database demonstrates enhanced performance and efficiency, though further optimization is necessary to achieve consistent throughput across different workloads and to mitigate the increased response times
PREDIKSI BANTUAN OPERASIONAL RAUDHATUL ATHFAL DI TINGKAT KABUPATEN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE – REGRESSION Fauzan, Ariq; Witanti, Wina; Rakhmat Umbara, Fajri
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12505

Abstract

Bantuan Operasional Pendidikan Raudhatul Athfal (BOPRA) merupakan bantuan yang diberikan oleh pemerintah untuk membiayai personalia dan nonpersonalia agar dapat meningkatkan kegiatan belajar mengajar sekolah tingkat RA. RA (Raudhatul Athfal) merupakan salah satu perogram pendidikan berbasis agama Islam dikelola oleh Kementerian Agama. Dana BOPRA dihitung berdasarkan jumlah siswa yang terdaftar di DAPODIK (Data Pokok Pendidikan) dari setiap tahunnya akan dikalkulasikan per-triwulan dengan persentase 30%, 40%, dan 30% setiap persentase dibagi kedalam empat bulan yang nantinya akan diterima RA setiap bulannya. Untuk memprediksi dana yang akan diterima di masa mendatang diperlukan data historis yang di olah menggunakan machine learning. Machine learning merupakan metode yang dapat memproses data historis dalam jumlah yang besar untuk melakukan prediksi data dengan lebih akurat. Penggunaan pada algoritma Support Vector Regression (SVR) digunakan untuk melakukan prediksi nilai variable kontinu dengan prinsip yang sama dengan Support Vector Machine (SVM), dengan menerapkan algoritma SVR dapat membantu instansi kementerian agama dan sekolah – sekolah tingkat RA di kabupaten dalam pengambilan keputusan untuk memanfaatkan dana bantuan pendidikan yang akan diterima. Hasil dari prediksi BOPRA dengan menggunakan algoritma SVR sangat bagus digunakan dengan nilai parameter terbaik gamma 0.1, C 0.01, epsilon 1.8 menghasilkan RMSE 0.173 dan MAPE 3,74%.
Klasifikasi Citra Pada Wayang Kulit Menggunakan Convolutional Neural Network Nurhasanah, Wulandari; Witanti, Wina; Ashaury, Herdi
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3700

Abstract

This research aims to develop a Convolutional Neural Network (CNN)-based shadow puppet image classification system by utilizing the ResNet-18 architecture, which is known to be efficient in handling image data and has a high level of accuracy. The system is designed to classify the Punakawan characters in shadow puppets, namely Bagong, Gareng, Petruk, and Semar, which are part of Indonesia's cultural heritage. In addition, this study also compares the performance of ResNet-18 with two other architectures, namely MobileNetV2 and DenseNet121. The dataset used consists of 2,148 images, which were obtained through live shooting and online searches. The images were processed using augmentation techniques and divided in a ratio of 70:15:15 for training, validation, and testing. The model was trained using optimal hyperparameters, such as learning rate 0.001 and batch size 32, to evaluate the performance of the three architectures. The evaluation results showed that the ResNet-18 architecture, as the main focus of the research, achieved an overall accuracy of 93.90%, with precision, recall, and F1-score of 94% each. In comparison, MobileNetV2 produced the highest validation accuracy of 96%, with better performance in generalization, while DenseNet121 produced a validation accuracy of 95%. This result confirms that although MobileNetV2 has the best performance in shadow puppet image classification, ResNet-18 still shows excellent results with simpler complexity, so it can be an efficient solution for the implementation of Punakawan shadow puppet classification system.
Prediction Of Asteroid Hazard Distance Through Earth's Orbit Using K-Neirest Neighbor Method Firdaus, Syahrul; Witanti, Wina; Melina; Hadiana, Asep Id
International Journal of Global Operations Research Vol. 6 No. 2 (2025): International Journal of Global Operations Research (IJGOR), May 2025
Publisher : iora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/ijgor.v6i2.373

Abstract

The National Aeronautics and Space Administration (NASA) is the U.S. government agency that is responsible forspace program. NASA observes objects in space, including asteroids. Asteroids are small, rocky objects that orbit thesun with irregular shapes and are also called planetoids. The Government agencies observe space objects includingasteroids. In terms of the infinite number of objects in space that will cross Earth's orbit, prediction is needed todetermine the danger and its level when they are crossing Earth's orbit. Prediction is a process to know what willhappen in the future which is aimed to find out the approximate asteroids that will cross the earth in the future. In thisstudy, data mining classification techniques and the K-Nearest Neighbor algorithm are used to create a predictionsystem for the threat of asteroids while crossing the earth. Classification is a grouping by classifying items intodesignated class labels, building a classification model from the data set, building a model that is used to predict futuredata. To determine the distance of the asteroid's threat throughout the earth, data mining classification techniques andthe K-Nearest Neighbor algorithm are used. The results are 57.71% accuracy, 54.89% precision, 81.42% recall, and47.45% missclassification rate.
COMBINATION OF MULTI-VIEW LEARNING AND DEEP REINFORCEMENT LEARNING TO IMPROVE WEBSITE PHISING DETECTION Hasbia, Muhamad; Witanti, Wina; Abdillah, Gunawan
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9811

Abstract

Phishing is one of the most common and dangerous forms of cyberattacks, where perpetrators attempt to obtain sensitive information by masquerading as trustworthy entities. Traditional detection methods often fail to anticipate new attacks due to the dynamic nature of phishing. This research proposes an adaptive phishing detection system that combines Multi-Kernel Learning (MKL) and Deep Q-Network (DQN) approaches. MKL is utilized to integrate features from URL structure, domain metadata, and webpage content into a rich multi-view representation, while DQN enhances the model's adaptability through a reward-based learning mechanism. This combination was chosen because MKL effectively captures feature variations from different sources, while DQN excels at handling rapidly changing attack patterns. The dataset consists of 11,056 entries with 32 features, divided in an 80:20 ratio for training and testing. Moreover, evaluation is performed using a 5-Fold Cross Validation method to ensure result stability, and hyperparameter exploration is conducted to obtain the best configuration. Evaluation results show that the system achieves an accuracy of 96.34%, precision of 95.8%, recall of 97.85%, F1-score of 96.73%, and AUC of 0.98. These results demonstrate that the MKL-DQN approach is highly effective in accurately and adaptively detecting phishing
Forecasting Stock Returns Using Long Short-Term Memory (LSTM) Model Based on Inflation Data and Historical Stock Price Movements Prasetyo, Nur Faid; Witanti, Wina; Hadiana, Asep Id
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6422

Abstract

The stock market is crucial for economic growth and development, offering profit opportunities that attract investors worldwide. However, its inherent volatility necessitates the inclusion of macroeconomic indicators like inflation, which can affect stock valuation and investor behavior. This study explores predicting stock returns using a Long Short-Term Memory (LSTM) model by incorporating inflation data, historical stock price movements, and calculated returns as input features. The dataset was split into 80% for training and 20% for testing, with hyperparameter tuning conducted using the RMSprop optimizer under varying batch sizes and epoch settings. Experimental results show that the configuration using RMSprop with a batch size of 8 and 200 epochs delivered the best performance, achieving a Root Mean Squared Error (RMSE) of 0.0167 and a Mean Absolute Percentage Error (MAPE) of 25.89%. These results represent a significant improvement over alternative configurations and previous benchmarks. This study underscores the importance of including inflation as a predictive variable, enhancing the model's accuracy. The findings highlight the relevance of incorporating macroeconomic factors into stock return forecasting, providing valuable insights for investors and financial analysts seeking data-driven strategies in decision-making processes.
Principal Component Analysis (PCA) Untuk Meningkatkan Hasil Klasterisasi Penjualan Video Game Menggunakan Algoritma K-Means Nurputra, Windi Raihan; Witanti, Wina; Komarudin, Agus
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4151

Abstract

Perkembangan industri video game yang pesat menciptakan tantangan dalam menganalisis dan memahami pola penjualannya secara global. Penelitian ini bertujuan untuk menerapkan metode Principal Component Analysis (PCA) sebagai tahap pra-pemrosesan untuk mereduksi dimensi data sebelum dilakukan proses klasterisasi menggunakan algoritma K-Means. Dataset yang digunakan berasal dari situs Kaggle dengan jumlah data sebanyak 64.000 entri dan 11 atribut penjualan. Proses dimulai dari tahap preprocessing data, kemudian dilakukan PCA untuk menyederhanakan dimensi, dan selanjutnya data diklasterkan menggunakan K-Means. Evaluasi dilakukan dengan metode Silhouette Coefficient untuk mengetahui seberapa baik klaster yang terbentuk. Hasil dari penelitian menunjukkan bahwa kombinasi PCA dan K-Means mampu mengelompokkan data penjualan video game secara lebih efisien dan terstruktur, serta memberikan visualisasi yang membantu dalam pengambilan keputusan strategis. Penelitian ini juga dikembangkan dalam bentuk aplikasi desktop berbasis Python dengan antarmuka grafis untuk memudahkan pengguna dalam melakukan proses klasterisasi secara interaktif.
Klasterisasi Data Penjualan Toko Perak J-Maskus Mengguanakan Algoritma HDBSCAN Rusmana, Hendri Diana; Witanti, Wina; Abdillah, Gunawan
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i7.4162

Abstract

Di era digital, analisis data penjualan menjadi kunci pengambilan keputusan bisnis. Toko Perak J-Maskus menghadapi tantangan dalam mengelola stok akibat ketidakseimbangan persediaan dan permintaan. Penelitian ini mengelompokkan produk berdasarkan pola penjualan menggunakan algoritma HDBSCAN, yang dipilih karena kemampuannya mengidentifikasi cluster dengan kepadatan bervariasi dan mendeteksi outlier. Dataset terdiri dari 5.765 transaksi (2022–2024) dengan atribut produk dan jumlah terjual. Hasil eksperimen menunjukkan parameter optimal min_samples=5 dan min_cluster_size=5 dengan silhouette score 0.6507 (struktur menengah), menghasilkan 206 cluster. Visualisasi t-SNE mengonfirmasi distribusi cluster yang terpisah jelas. Temuan ini dapat digunakan untuk strategi manajemen stok, seperti identifikasi produk laris dan pengurangan overstock.
Prediksi Tingkat Keparahan Diabetes Melitus Menggunakan Support Vector Machine (SVM) dengan Kernel Polinomial dan RBF Pradana, Muhammad Rifqi; Witanti, Wina; Komarudin, Agus
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4357

Abstract

Diabetes melitus merupakan penyakit kronis dengan prevalensi tinggi yang memerlukan diagnosis dini dan penanganan yang akurat guna memitigasi risiko komplikasi serius. Penelitian ini berfokus pada pengembangan model prediktif untuk menentukan tingkat keparahan diabetes melitus, mengimplementasikan algoritma Support Vector Machine (SVM) dengan membandingkan kinerja fungsi kernel Polinomial dan Radial Basis Function (RBF). Dataset yang dimanfaatkan terdiri dari 100.000 entri data pasien, yang dikategorikan ke dalam dua kelas tingkat keparahan penyakit. Sebelum memasuki fase klasifikasi, serangkaian tahap pra-pemrosesan data diterapkan, termasuk penanganan outlier, untuk memastikan kualitas data dan meningkatkan robustnya model. Metodologi penelitian mencakup studi literatur mendalam, implementasi proses klasifikasi, serta pengujian sistem menggunakan pendekatan black box testing untuk memvalidasi fungsionalitas dan akurasi. Hasil eksperimen secara konsisten menunjukkan bahwa SVM dengan kernel RBF memberikan performa superior dibandingkan dengan SVM yang menggunakan kernel Polinomial, tercermin dari tingkat akurasi klasifikasi yang lebih tinggi secara signifikan. Oleh karena itu, sistem klasifikasi berbasis SVM yang dikembangkan ini berpotensi besar untuk mendukung diagnosis awal dan penentuan otomatis tingkat keparahan diabetes melitus dengan tingkat keakuratan yang kredibel. Kontribusi penelitian ini diharapkan dapat menjadi referensi berharga bagi pengembangan lebih lanjut sistem pendukung keputusan klinis dalam domain kesehatan, khususnya terkait manajemen diabetes.
Analisis Sentimen Menggunakan Metode IndoBERT Pada Ulasan Aplikasi Zoom Menggunakan Fitur Ekstrasi GloVe Andhika, Firza Rafiandi; Witanti, Wina; Nurul Sabrina, Puspita
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 2 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/g38fxb78

Abstract

This research aims to conduct a sentiment analysis on Zoom application reviews by testing whether combining features from IndoBERT and GloVe can improve classification accuracy. The methodology begins with the collection of 5000 reviews from the Google Play Store, which then undergo a pre-processing stage. To address class imbalance, the Random Oversampling technique was applied. Features are extracted using IndoBERT for contextual meaning and GloVe for global semantic meaning, and their vectors are then combined through concatenation. The balanced dataset is divided into 80% training data and 20% testing data to train and evaluate the model. The test results show that the combined model achieved an overall accuracy of 91%, with a high precision value for the positive class (0.97) and a high recall value for the neutral class (0.95). For comparison, a model using only IndoBERT achieved 90% accuracy. Based on these results, it can be concluded that the combination of IndoBERT and GloVe is an effective and reliable approach for sentiment analysis, with its advantage lying in a richer feature representation due to the integration of global and contextual semantic information.
Co-Authors Adzani, Nadhif Nurul Fajri Afifah, Hira Nur Andhika, Firza Rafiandi Asendra, Irfan Ashaury, Herdi Asrul Budiman, Zillan Taufiq Dewi, Wulan Dian Nursantika Edvin Ramadhan Eka Purnama Rijaludin, Muhamad Fahrezi, Rizal Febrian Faiza Renaldi, Faiza Fauzan, Ariq Firdaus, Syahrul Ghozali, Mohamad Aditya Muttaqin Gunawan Abdillah, Gunawan Hadiana, Asep Id Hardianto, Mohamad Aris Hasbia, Muhamad Kasyidi, Fatan Kiki Hidayat, Kiki Komarudin, Agus Komarudin, Agus Liesdiannisa, Mutiara Virgi Lusy Sundary, Lusy Melina Nabilah, Mitamufida Kusuma Nabilah, Mitamufida Kusuma Nugraha, Faisal Okta Nugraha, Faisal Okta Nurhakim, Riri Qorib Nurhasanah, Wulandari Nurputra, Windi Raihan Nurul Sabrina, Puspita Pinasty, Syarifudin Yoga Pradana, Muhammad Rifqi Prasetyo, Nur Faid Putra, Eddie Krishna Putri, Ika Rahmah Rakhmat Umbara, Fajri Ridwan Ilyas Rusmana, Hendri Diana Saputri, Intan Vidia Syarafina, Fildzah Trianggara, Dhani Umbara, Fajri Rakhmat Wahidin, Farhan Raihan Wahyudiputra, Helmy Fauzan