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All Journal International Journal of Electrical and Computer Engineering Information Technology and Telematics Dinamik Jurnal Ilmiah Dinamika Teknik Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Proceeding SENDI_U Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Informatika Upgris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Jurnal Komputer Terapan IJIS - Indonesian Journal On Information System JURNAL ILMIAH INFORMATIKA JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informasi dan Komputer JURNAL MAHAJANA INFORMASI Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Infotek : Jurnal Informatika dan Teknologi MEANS (Media Informasi Analisa dan Sistem) Journal of Applied Data Sciences Advance Sustainable Science, Engineering and Technology (ASSET) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal Rekayasa elektrika
Wiwien Hadikurniawati
Universitas Stikubank
Religion Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Sistem Pendukung Keputusan Pemilihan Perumahan Menggunakan WASPAS Huda, Azis Fatchul; Hadikurniawati, Wiwien
MEANS (Media Informasi Analisa dan Sistem) Volume 7 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/means.v7i2.2102

Abstract

Housing is one of the basic human needs and is an important factor in increasing human dignity. This is a very dominant problem in human survival to carry out all its activities. Prospective buyers are not easy in choosing housing, because hasty decision making will not provide the satisfaction expected by prospective buyers. In determining residential housing requires consideration to get a dwelling that fits your expectations, because everyone has different abilities. This study aims to create a decision support system for selecting the best housing in accordance with the wishes and needs using the WASPAS method with housing selection criteria including developer, price, land area, building area, distance to the city center. The recommendation from the selection criteria for the Bukit Semarang Jaya Metro developer using the WASPAS method is the Bukit Kencana Jaya housing estate with a Qi value = 0.872. Bukit Kencana Jaya has the highest value because it has a low price where the price criteria have the highest percentage weight. The combination of price advantage and weight advantage gives Bukit Kencana Jaya a high value.
Sistem Pakar Diagnosa Penyakit Sapi Menggunakan Metode Case Based Reasoning (CBR) Gunawan, Doni Triyoga; Hadikurniawati, Wiwien
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 1 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Hewan ternak merupakan salah satu potensi besar bagi Indonesia. Di berbagai daerah terutama pedesaan banyak masyarakat yang memiliki hewan ternak, salah satunya adalah sapi. Sapi banyak dipilih karena pakan yang mudah untuk didapatkan, pemanfaatan daging dan kotoran, susu, serta harga jual yang relatif tinggi. Namun demikian, meskipun banyak keuntungan yang akan didapatkan, para peternak sapi juga harus memastikan kesehatan sapi dengan lebih baik. Hal ini karena saat ini banyak kasus penyakit pada hewan ternak, baik yang dapat menular maupun tidak. Berdasarkan hal tersebut, peneliti ingin membangun sebuah sistem pakar yang dapat digunakan untuk mendiagnosa penyakit sapi berdasarkan pemilihan gejala guna mengetahui penanganan untuk penyakit tersebut. Sistem pakar yang akan dibangun menggunakan metode Case Based Reasoning (CBR) yang diimplementasikan pada sebuah website. Case Based Reasoning mengambil keputusan untuk kasus baru berdasarkan solusi kasus-kasus lampau yang pernah terjadi. Berdasarkan hasil confusion matrix terhadap hasil Sistem Pakar Diagnosa Penyakit Sapi Menggunakan Metode Case Based Reasoning (CBR) dapat ditentukan accuracy sebesar 92,11% dan misclassification (Error) rate sebesar 7,89%. Hasil akurasi dan error rate dari perhitungan ini menunjukkan bahwa metode Metode Case Based Reasoning (CBR) dapat digunakan untuk Diagnosa Penyakit Sapi dengan kualitas akurasi yang sangat baik.
AHP-COPRAS untuk Pemeringkatan Ketersediaan Fasilitas Kesehatan di Indonesia Wibisono, Setyawan; Hadikurniawati, Wiwien; Almin, Imam Husni Al
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 1 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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Abstract

In handling Covid-19, health resources are one of the factors that play a very important role in reducing the death rate. For this reason, we offer a study on the topic of ranking the availability of health resources in handling the Covid-19 pandemic in provinces in Indonesia using the AHP (Analytical Hierarchy Process) and COPRAS (Complex Proportional Assessment) hybrid methods. The use of the pairwise comparison matrix as a method for testing the validity of the weights for each criterion produces a weight value of 0.363760164 for the criteria for the number of doctors per population and the criteria for the number of nurses per population, a weight value of 0.1588353 for beds per 1000 people, a weight value of 0, 075333696 for the number of hospitals per population, and a weight value of 0.038310676 when going to the hospital. This ranking system places DKI Jakarta first with a utility value of 100%, while the second rank is the Special Region of Yogyakarta with a utility value of 63.59. There is a considerable gap compared to other provinces in terms of the availability of health resources in handling the Covid-19 pandemic. The availability of health facilities in DKI Jakarta is quite far when compared to other provinces in terms of the availability of health resources in handling the Covid-19 pandemic. DKI Jakarta remains the area with the most excellent health facilities.
Social Network Analysis untuk Pemeringkatan Popularitas Makanan Cepat Saji Menggunakan Metode PSI Wibisono, Setyawan; Hadikurniawati, Wiwien; Lestariningsih, Endang; Wahyudi, Eko Nur; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 1 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Abstract

This research aims to rank the popularity of fast-food brands in Indonesia based on Twitter conversations using the Preference Selection Index (PSI) method and validate the results with COPRAS and AHP-COPRAS methods. Data were obtained by crawling Twitter from April 21, 2023, to April 28, 2023. Seven well-known brands, such as KFC, MCD, PizzaHut, Hokben, Solaria, JCo, and Richeese, were evaluated as alternatives using eight criteria through Social Network Analysis. The criteria were categorized into advantageous and disadvantageous, and preference values were calculated using PSI. After normalizing the decision matrix, calculations were performed for preference variation and overall preference values. Alternatives were ranked based on the preference selection index, and the results were validated with COPRAS and AHP-COPRAS. The results revealed significant differences in rankings between the PSI method and others. The alternative that received the highest rank changed from A2 (COPRAS and AHP-COPRAS) to A3 (PSI). This emphasizes the importance of choosing the right method for brand ranking, as it can influence decision-making. Method validation through result comparison with other methods provides additional insights into the reliability of the PSI method in the context of this research.
Optimalisasi Model Klasifikasi Diabetes Menggunakan Ensemble Learning Adaboost, Gradient Boosting, dan XGBoost Wibisono, Setyawan; Hadikurniawati, Wiwien; Yulianton, Heribertus; Lestariningsih, Endang; Cahyono, Taufiq Dwi
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 9 No. 2 : Tahun 2024
Publisher : LPPM UNIKA Santo Thomas

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Abstract

Diabetes mellitus adalah penyakit kronis yang memengaruhi jutaan orang secara global dan membutuhkan metode diagnosis dini untuk mencegah komplikasi. Penelitian ini bertujuan untuk mengoptimalkan prediksi diabetes dengan membandingkan tiga metode ensemble learning: AdaBoost, Gradient Boosting, dan XGBoost. Dataset yang digunakan adalah Diabetes Health Indicators, yang menggabungkan indikator kesehatan seperti tekanan darah, kolesterol, dan kebiasaan gaya hidup. Tahapan penelitian meliputi pemrosesan data, pengembangan model, serta eval_uasi performa menggunakan metrik akurasi, presisi, recall, F1-score, dan AUC (Area Under the Curve). Hasil menunjukkan bahwa Gradient Boosting unggul dalam akurasi dan AUC, menandakan kemampuan yang lebih baik dalam mendeteksi diabetes secara konsisten dibandingkan dengan dua metode lainnya. AdaBoost memperlihatkan keseimbangan yang baik antara presisi dan recall, menjadikannya cocok untuk skenario yang memerlukan pengendalian kesalahan positif dan negatif secara proporsional. Sementara itu, XGBoost menawarkan efisiensi pemrosesan yang optimal dengan performa yang kompetitif. Gradient Boosting direkomendasikan untuk aplikasi klinis yang membutuhkan akurasi tinggi, sedangkan AdaBoost dapat menjadi alternatif ketika keseimbangan prediksi menjadi prioritas. Penelitian ini berkontribusi dalam pengembangan alat prediksi diabetes yang lebih akurat, efektif, dan dapat diterapkan di sektor kesehatan untuk mendukung upaya deteksi dini.
A Dual-Fusion Hybrid Model with Attention for Stunting Prediction among Children under Five Years Hadikurniawati, Wiwien; Hartomo, Kristoko Dwi; Sembiring, Irwan; Arthur, Christian
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.831

Abstract

Malnutrition remains a persistent global health challenge, especially among children under five. Traditional assessment methods often rely on static anthropometric measures, which are limited in capturing complex growth patterns. This study aims to develop a robust classification model for predicting the nutritional status of children under five years old, addressing the critical public health challenge of stunting. The model contributes to the growing need for accurate, data-driven early detection systems in child health monitoring by introducing a hybrid framework that combines deep learning and classical machine learning techniques. The proposed approach integrates automatically extracted features from a One-Dimensional Convolutional Neural Network (1D-CNN) with classical anthropometric indicators. These combined features are processed through an additive attention mechanism, highlighting the most informative attributes. The attention-weighted representation is then classified using an ensemble stacking method that aggregates predictions from multiple base classifiers, including decision trees, nearest neighbor algorithms, support vector machines, etc. Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training dataset to mitigate data imbalance, particularly the underrepresentation of severe and moderate malnutrition cases. The research utilizes a dataset comprising 2,789 records of children under five years old collected from community health posts in Indonesia. Data preprocessing included cleaning, normalization, and gender encoding. The model’s performance was evaluated using 5-fold cross-validation and measured by accuracy, precision, recall, and area under the curve metrics. The results show that the proposed model achieved an average accuracy of 99.70% and an area under the curve of 99.99%. An ablation study further demonstrated the significant contribution of each component, feature extraction, fusion mechanism, and ensemble classifier to the final performance. This approach reveals a robust and scalable solution for early nutritional status prediction in healthcare settings.
Enhanced Semarang batik classification using deep learning: a comparative study of CNN architectures Winarno, Edy; Solichan, Achmad; Putra Ramdani, Aditya; Hadikurniawati, Wiwien; Septiarini, Anindita; Hamdani, Hamdani
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9347

Abstract

Batik is an important part of Indonesia’s cultural heritage, with each region producing unique designs. In Central Java, Semarang is known for its distinctive batik patterns that reflect rich local traditions. However, many people are still unfamiliar with these designs, which threatens their preservation. This study develops an automated system to classify Semarang batik patterns, showing how technology can help safeguard cultural heritage. A convolutional neural network (CNN) approach was used to recognize ten batik types, including Asem Arang, Asem Sinom, Asem Warak, Blekok, Blekok Warak, Gambang Semarangan, and Kembang Sepatu. Pre-processing steps—such as image resizing, cropping, flipping, and rotation—improved model performance and reduced complexity. Five CNN architectures (MobileNetV2, ResNet-50, DenseNet-121, VGG-16, and EfficientNetB4) were tested using 224×224 input size, Adam optimizer, ReLU activation, and categorical cross-entropy loss. Results show VGG-16, ResNet-50, and DenseNet-121 achieved perfect accuracy (1.0) on a dataset of 3,000 locally collected images. These findings highlight CNN models’ strong potential for batik pattern recognition, supporting digital preservation of Indonesian culture.
Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation Anwar, Muchamad Taufiq; Hadikurniawati, Wiwien; Winarno, Edy; Supriyanto, Aji
Advance Sustainable Science, Engineering and Technology Vol 1, No 1 (2019): May-October
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v1i1.4876

Abstract

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;
Co-Authors Abriyanto, Oky Ade Iriani Adhi Mulya, Ananda Agung Prihandono Aji Supriyanto Almin, Imam Husni Al Alviani Setya Yuniantika Andiany, Fairuz Eka Andiany, Fairuz Eka Angga Dewandono Anindita Septiarini, Anindita Anindita Septirini Anwar, Muchamad Taufiq Arief Jananto Arthur, Christian Brian Datuesa, Egga Cahyono, Taufik Dwi Danny Manongga Eddy Nurraharjo Edy Winarno Eka Andiany, Fairuz Eka Ardhianto Eko Nur Wahyudi Endang Lestariningsih EngMarkiano Solissa, Everhard Erika Dewi Anggraini Fadhilah, Lailatul Farih, Nia Nailil Fatkhul Amin fatkhurrizqi, Maolana Galih Setiadi Gunawan, Doni Triyoga Gusti Ari Prasetyo Hamdani Hamdani Hamdani Hamdani Heribertus Yulianton Herny Februariyanti Heru Yudia Setyo Anggoro Hindriyanto Dwi Purnomo Huda, Azis Fatchul Imam Husni Al Amin Indriani, Vanyariska Irfak Lahumu Darojat Irwan Sembiring Ivannofick Adha Nugraha Khabib Mustofa Kristoko Dwi Hartomo M Arip Islahudin Mohammad Faisal Fahrul Monica Yoshe Titimeidara Muchamad Maskhur Muji Sukur Putra Ramdani, Aditya Rudi Hariyanto Sari, Lenny Estika Sarita Yuniarti Hanum Setyawan Wibisono Setyawan Wibisono Setyawan Wibisono Sidiq Eko Budiharjo Suci ananda sari Sutarto Wijono Taufiq Dwi Cahyono Tegar Prabowo, Satria Veronica Lusiana Wijayanti, Tri Cicik Zakaria, Franky Zuly Budiarso